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Urban Drainage Systems can cause ecological and public health issues by releasing untreated contaminated water into the environment. Real-time control (RTC), augmented with rainfall nowcast, can effectively reduce these pollution loads. This research aims to identify key dynamics in the nowcast accuracies and relate those to the performance of nowcast-informed rule-based (RB)-RTC procedures. The developed procedures are tested in the case study of Rotterdam, the Netherlands. Using perfect nowcast data, all developed procedures showed a reduction in combined sewer overflow volumes of up to 14.6%. Considering real nowcast data, it showed a strong ability to predict if no more rain was expected, whilst performing poorly in quantifying rainfall depths. No relation was found in the nowcast accuracy and the consistency of the predicted rainfall using a moving horizon. Using the real nowcast data, all procedures, with the exception of the one predicting the end of the rainfall event, showed a significant risk of operative deterioration (performing worse than the baseline RB-RTC), linked to the relative performance of the nowcast algorithm. Understanding the strengths of a nowcast algorithm can ensure the reliability of the RB-RTC procedure and can negate the need for detailed modelling studies by inferring risks from nowcast data.

This letter proposes a parallelizable algorithm for linear-quadratic model predictive control (MPC) problems with state and input constraints. The algorithm itself is based on a parallel MPC scheme that has originally been designed for systems with input constraints. In this context, one contribution of this letter is the construction of time-varying yet separable constraint margins ensuring recursive feasibility and asymptotic stability of sub-optimal parallel MPC in a general setting, which also includes state constraints. Moreover, it is shown how to tradeoff online run-time guarantees versus the conservatism that is introduced by the tightened state constraints. The corresponding performance of the proposed method as well as the cost of the recursive feasibility guarantees is analyzed in the context of controlling a large-scale mechatronic system. This is illustrated by numerical experiments for a large-scale control system with more than 100 states and 60 control inputs leading to run-times in the millisecond range.

As modern control systems are becoming more complex, and the implemented technology more sophisticated, the importance of the different characteristics of those systems, such as reliability, availability, security, and protection, is increasing. In this article, an automation design for the air conditioning system of classified areas of the Parenteral solutions plant of the eastern pharmaceutical laboratory company of Santiago de Cuba province, Cuba, is proposed because there is a lack of optimal environmental conditions for development and production of pharmaceuticals at a large scale. The proposal is focused on the design of a supervision system and automatic control of the environmental variables that influence the drug production process. The entire algorithm for the design was developed to monitor critical and non-critical variables and control values of temperature, relative humidity, differential pressure, and air velocity according to standards established by pharmaceutical companies. The design of the automation system includes, apart from the control and supervision algorithm for operation efficiency, the necessary instrumentation.

Coupled multiple-tank systems are very important for a wide range of industrial applications due to their unique uses. However, the liquid level control for the coupled two-tank multi-input multi-output (MIMO) system is quite challenging because it has strong nonlinearity and coupling, and it is susceptible to multiple external disturbances. For this process, this paper proposes a novel anti-disturbance control strategy consisting on a nonlinear composite hierarchical anti-disturbance predictive control (CHADPC). First, a model-based explicit nonlinear model predictive controller (ENMPC) is designed assuming that all disturbances are measurable and its global exponential stability is proved. Then, a nonlinear disturbance observer (DO) is designed to estimate the lumped disturbances. The composite controller handling the estimated disturbances is then proposed. Finally, simulation and experimental tracking control tests under perturbations and comparisons with recently reported works have been carried out to highlight the promising performance of the proposed ENMPC and CHADPC schemes.

Dispatching active distribution networks (ADNs) is an energy-intensive application that, if implemented via battery-energy storage systems (BESSs), can require a large capacity of these assets in order to fully balance the uncertainties caused by the stochastic demand and generation. The insufficient capacity of the BESSs often leads to their state-of-charge (SOC) saturation; this results in unreliable dispatch tracking. In this work, we propose and experimentally validate a real-time control scheme that achieves a highly-reliable dispatching of ADNs and ensures that the BESSs’ SOC is not saturated during the daily operation. Our proposed scheme uses a two-layer model predictive control (MPC). The upper-layer MPC, running every 5 minutes, optimizes the BESSs’ SOC trajectories while minimizing the tracking error, considering the prosumption forecast of the whole day. Then, the lower layer MPC, running every 30 seconds, takes the BESSs’ SOC trajectories as constraints while achieving a high-resolution tracking of the dispatch plan over the current 5-minute time horizon. Both layers account for the grid constraints by using the augmented relaxed optimal power-flow (AR-OPF) model; an exact convex relaxation of the original AC-OPF and used in this paper (for the first time in the literature) to solve a real-time constrained control problem for ADNs. Our proposed framework was experimentally validated using a 1.5 MVA/2.5 MWh BESS connected to an actual 24-node medium-voltage (MV) ADN that, in Aigle, Switzerland, hosted an uncontrollable 3.2 MWp distributed photovoltaic generation, 3.4 MVA hydro-power generations, and a 2.8 MW base demand.

This paper deals with real-time control with observer to manipulate desalination plants as well as to monitor system states for smart operations. The controller plays an important role in achieving stabilization of reverse osmosis (RO) systems to guarantee the desired water product and concentration. The super-twisting (STW) sliding mode control (SMC) algorithm guarantees performance while reducing chattering. Supposing that all the state variables are not available by sensors, the observer is implemented to provide state estimation. Since smart operations depend on control algorithm and sensor availability, the proposed strategy provides robustness to ensure the water productivity even under uncertainties or under failure of sensors. The robustness is guaranteed by active controller where 80% of disturbance is eliminated in product water flow and that of product water quality is approximately 95%. As well, the state observer can produce precise predictions of the unmeasured states. Sliding mode control with observer provides the system with stability, while assuring better performances against uncertainties. Finally, the active controller with state estimator can guarantee a robust control strategy and monitoring system to extend the life of the filters and membranes, while ensuring sustainability. This control strategy is highly recommended for smart operations of desalination plants. HIGHLIGHTS Real-time control has been implemented for smart operations of desalination plants against uncertainties.; Super-twisting sliding mode control strategy provides robust performance and stability with state convergences.; Control system utilizes state observer to enhance water treatment performance.; Plant automation can ensure the desired water product and concentration with sustainability.

Complete and efficient detection of unknown targets is the most popular application of UAV swarms. Under most situations, targets have directional characteristics so that they can only be successfully detected within specific angles. In such cases, how to coordinate UAVs and allocate optimal paths for them to efficiently detect all the targets is the primary issue to be solved. In this paper, an intelligent target detection method is proposed for UAV swarms to achieve real-time detection requirements. First, a target-feature-information-based disintegration method is built up to divide the search space into a set of cubes. Theoretically, when the cubes are traversed, all the targets can be detected. Then, a Kuhn-Munkres (KM)-algorithm-based path planning method is proposed for UAVs to traverse the cubes. Finally, to further improve search efficiency, a 3D real-time probability map is established over the search space which estimates the possibility of detecting new targets at each point. This map is adopted to modify the weights in KM algorithm, thereby optimizing the UAVs’ paths during the search process. Simulation results show that with the proposed method, all targets, with detection angle limitations, can be found by UAVs. Moreover, by implementing the 3D probability map, the search efficiency is improved by 23.4%–78.1%.

International journal of computer assisted radiology and surgery (2021). doi:10.1007/s11548-020-02306-9, Published by Springer, Heidelberg [u.a.]

Flooding in coastal cities is increasing due to climate change and sea-level rise, stressing the traditional stormwater systems these communities rely on. Automated real-time control (RTC) of these systems can improve performance, and creating control policies for smart stormwater systems is an active area of study. This research explores reinforcement learning (RL) to create control policies to mitigate flood risk. RL is trained using a model of hypothetical urban catchments with a tidal boundary and two retention ponds with controllable valves. RL's performance is compared to the passive system, a model predictive control (MPC) strategy, and a rule-based control strategy (RBC). RL learns to proactively manage pond levels using current and forecast conditions and reduced flooding by 32% over the passive system. Compared to the MPC approach using a physics-based model and genetic algorithm, RL achieved nearly the same flood reduction, just 3% less than MPC, with a significant 88× speedup in runtime. Compared to RBC, RL was able to quickly learn similar control strategies and reduced flooding by an additional 19%. This research demonstrates that RL can effectively control a simple system and offers a computationally efficient method that could scale to RTC of more complex stormwater systems. HIGHLIGHTS Reinforcement learning (RL) creates policies for real-time coastal stormwater system control.; RL's ability to mitigate flooding and manage ponds is compared to a passive system, model predictive control, and rule-based control.; RL was more efficient than model predictive control using a physics-based model and genetic algorithm.; RL's ability to mitigate flooding exceeded the passive system and rule-based control.

Due to their low concentration and complex nature, the low-cost online and real-time monitoring of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) is a challenge. In this paper, based on a coupling adsorption model of activated carbon and the relationship of PCDD/Fs and flue gas, an online monitoring system with real-time control of final PCDD/F emissions was built for the tracing and control of PCDD/F emissions in municipal solid waste incinerators (MSWIs). According to the online monitoring system, the effects of activated carbon concentration, the specific surface area of activated carbon (AC), operating temperature, contact time, fly ash concentration, residence carbon concentration of fly ash, and the lime concentration of final PCDD/F emissions were discussed. These MSWI operating parameters for reaching standards of 0.1 ng international toxic equivalents (TEQ)/Nm3 and 0.05 ng TEQ/Nm3 were summarized by the predicting model based on several published models.

The scarcity of freshwater resources, combined with deteriorating infrastructure, pushes water utilities to employ optimal operational practices to control water distribution systems (WDSs) based on objectives such as minimizing operational costs or leakages. This paper demonstrates a metaheuristic optimization framework for controlling WDS operations in near real-time by minimizing the total energy consumption, while maintaining sustainable system conditions and operations, such as those of tanks. The proposed framework, at its core, comprises a water demand forecasting model, an optimization-based control model, and a hydraulic continuity model. The hypothesis is that WDS can be controlled more efficiently by forecasting and predicting the near future system conditions based on past and prevailing conditions. Operational time steps of 60, 30, and 15 min are considered, to evaluate the benefits of using shorter operational time steps than the conventional norm. The proposed framework is demonstrated using a small-sized benchmark WDS. The results revealed that real-time control schemes reduce the operational costs of the selected WDS by up to 17.8%, with the shortest time step scheme (15 min) offering the most reduction in operational expenses, at the cost of more computational expensiveness. This study and its findings would help utilities plan more reliable and sustainable operational schemes.

With the growing popularity of microgrids for alternative energy management, there is demand for tools that allow us to study the effect of microgrids in distributed power systems. Popular methods involve software simulation and prototype validation with physical hardware. Simulations often do not capture the complex interactions, and combinations of software simulations with hardware testbeds promise to give a more accurate picture. These testbeds, however, usually aim at the validation of hardware for industrial-scale use, which makes them expensive and not readily accessible. To fill the gap between full-scale hardware and software simulation, we propose a modular lab-scale grid model at a 1:100 power scale over residential single-phase networks with 12 V AC and 60 Hz grid voltage. We present different modules—power sources, inverters, demanders, grid monitors, and grid-to-grid bridges—that can be assembled into distributed grids of almost arbitrary complexity. The model voltage poses no electrical hazards, and microgrids can readily be assembled with an open power line model. Unlike a prior DC-based grid testbed, the proposed AC model allows us to examine additional aspects, such as frequency, phase, active and apparent power, and reactive loads. Grid metrics, including the discretely sampled voltage and current waveforms, can be collected and sent to higher-tier grid management systems. We integrated the modules with Beagle Bone micro-PCs, which in turn connect any such microgrid with an emulation platform built on CORE (Common Open Research Emulator) and the Gridlab-D power simulator, thereby allowing hybrid software/hardware simulations. Our grid modules were shown to fully operate in this environment. Through the CORE system, multitiered control and even remote grid management is possible. However, we also found that the AC waveform poses design challenges that require us to balance accurate emulation (most notably with respect to harmonic distortion) with per-module costs.

Integrating hydrogen fuel cell systems (FCS) remains challenging in the expanding electric vehicle market. One of the levers to meet this challenge is the relevance of energy supervisors. This paper proposes an innovative energy management strategy (EMS) based on the integrated EMS (iEMS) concept. It uses a nested approach combining the best of the three EMS categories (optimization-based (OBS), rules-based (RBS), and learning-based (LBS) strategies) to overcome the real-time operating condition limitations of the fuel cell hybrid electric vehicle (FCHEV). Through a fuel cell/battery hybrid architecture, the purpose is to improve hydrogen consumption and manage the battery state of charge (SOC) under real-time driving conditions. The proposed iEMS approach is based on an OBS with optimal control to make the energy-optimal decision. However, it requires the adaptations of real-time operating conditions and a dynamic SOC horizon management. These requirements are supported by combining an RBS based on expert and fuzzy rules to compute the SOC target on each sliding window and an LBS based on fuzzy C-mean clustering to enhance the cooperative environment data processing and adapt it to the FHCEV topology. Our approach obtained simple and realistic system behaviors while having an acceptable computing time suitable for real time constraint. It was then designed and validated using a 27-h real-time measured database. The results show the effectiveness of the proposed iEMS concept with an excellent performance close to the optimal offline strategy (an under 2% consumption gap).

Reservoirs have a significant role to manage fresh water resources for irrigation, hydropower generation, domestic and industrial use, flood and drought control and navigation. To date, more than 50,000 large dams have been constructed in the world for providing water-related services to our society that support socioeconomic development of many regions. An efficient reservoir operation helps us to maximize benefits and to minimize the negative impacts of existing reservoirs. In practice, reservoir operation is a complex decision-making process involving multi-variables, multiple objectives and constraints, nonlinearity, and uncertainty. A framework of reservoir operation typically involves optimization and simulation procedures in which releases of reservoirs are determined by optimizing the objective functions and the system performance is evaluated using a simulation model. Despite significant developments in reservoir operation have been made in the last 50 years, there is a little progress for operation of a multi-reservoir system concerning real-time control, multi-objective optimization and a basin-wide approach. Therefore, Chapter 2 presents optimization and simulation methods developed in the recent literature and the potential of model predictive control (MPC) for real-time reservoir operation.

Controlling an epidemiological model is often performed using optimal control theory techniques for which the solution depends on the equations of the control system, objective functional and possible state and/or control constraints. In this paper, we propose a model-free control approach based on an algorithm that operates in 'real-time' and drives the state solution according to a direct feedback on the state solution that is aimed to be minimized, and without knowing explicitly the equations of the control system. We consider a concrete epidemic problem of minimizing the number of HIV infected individuals, through the preventive measure pre-exposure prophylaxis (PrEP) given to susceptible individuals. The solutions must satisfy control and mixed state-control constraints that represent the limitations on PrEP implementation. Our model-free based control algorithm allows to close the loop between the number of infected individuals with HIV and the supply of PrEP medication 'in real time', in such manner that the number of infected individuals is asymptotically reduced and the number of individuals under PrEP medication is below a fixed constant value. We prove the efficiency of our approach and compare the model-free control solutions with the ones obtained using a classical optimal control approach via Pontryagin maximum principle. The performed numerical simulations allow us to conclude that the model-free based control strategy highlights new and interesting performances compared with the classical optimal control approach., Comment: This is a preprint of a paper whose final and definite form is published open access by 'Mathematical Biosciences and Engineering' (ISSN: 1551-0018) at [https://www.aimspress.com/mbe/article/5926/special-articles]

Trajectory tracking is a key technology for precisely controlling autonomous vehicles. In this paper, we propose a trajectory-tracking method based on model predictive control. Instead of using the forward Euler integration method, the backward Euler integration method is used to establish the predictive model. To meet the real-time requirement, a constraint is imposed on the control law and the warm-start technique is employed. The MPC-based controller is proved to be stable. The simulation results demonstrate that, at the cost of no or a little increase in computational time, the tracking performance of the controller is much better than that of controllers using the forward Euler method. The maximum lateral errors are reduced by 69.09%, 47.89% and 78.66%. The real-time performance of the MPC controller is good. The calculation time is below 0.0203 s, which is shorter than the control period.

This paper proposes a novel remote monitoring system using a head mounted display (HMD). By using an HMD, a high degree of immersion and the sense of reality is provided to the user. Further, it becomes more convenient to move the camera position while controlling the robot arm on which the camera is mounted through the coordinates obtained from the HMD sensors. The proposed system performs two tasks. Initially, in order to render images in the HMD, the camera captures input images, and the PC connected to the robot arm sends the image to the PC connected to the HMD in real-time after which the image is rendered in the HMD. For the real-time monitoring, we increased the frame per second by reducing the data size. In order to reduce the data size, we define region of interest which is the region a user can see. Then, region of interest of the image is cropped and the resolution of entire image is degraded. Therefore, two images composed of cropped image and degraded image are transmitted and merged into an image. In this way, we can reduce the data size and provide the user with monitoring the image of the original quality. Next, using the inertial measurement unit sensor and base station sensors built into the HMD, the user controls the displacement of the camera during rotation and translation by controlling the robot arm by the user’s own motion. The PC connected to the HMD transmits the motion coordinates of the HMD acquired from the sensor into the PC connected to the robot arm. To control the translation of the camera, we define a coordinate system that represents the y- axis as running from top to bottom, and the x- axis as running from the front to the back in three-dimensional space. Thus, each step motor along each axis, which controls the robot arm, has its movement controlled by supplying appropriate values of angular degrees. Through the above process, the proposed method implements a remote monitoring system with five degrees of freedom. In the experiment, we measured the data transmission delay time and the displacement error due to the robot arm’s motion control to determine whether the system is suitable for a real-time remote monitoring system. Experimental results show that the proposed system can be optimally operated with a frame rate of 15 frames per second and a group of pixel resolutions of $640\times 640$ of the cropped image and $240\times 120$ of the degraded image. In addition, the average error rate of the robot arm’s displacement was 6.45% when the camera position was controlled through the robot arm.

This study fabricated a two-dimensional pneumatic servomechanism-based mask sewing platform. The platform has a simple structure, operates quickly, and is energy-efficient. SOLIDWORKS was used to design the platform, which comprises two rod-less pneumatic cylinders, one auxiliary cylinder, and a fabric-clamping mechanism. These parts are integrated with a sewing machine. The mask sewing platform has two degrees of freedom, as indicated by a mobility analysis of its mechanism. To control the highly nonlinear and time variant pneumatic system, a mathematical model for the pneumatic servo control system was developed and then analyzed in analytical form. An intelligent parameter adjustment feature with a sliding-mode controller was formulated to control the mask sewing platform. An experimental mask sewing platform was then designed and constructed. Finally, a personal computer-based system was developed using MATLAB Simulink for real-time control of the platform. The hybrid and fifth-order paths were successfully implemented in the test rig, and this study's platform was experimentally verified as being capable of automatically producing masks.

International audience; The global output synchronization problem for heterogeneous nonlinear systems having relative degree 2 or higher is studied. The proposed approach consists in two steps. First, a partial projection of individual subsystems into the Brockett oscillators is performed using a sliding-mode control. Second, the network of these oscillators is synchronized using the global synchronization results of a particular second order nonlinear oscillator model from Ahmed et al. (2019). Our approach is based on output feedback and uses a higher order sliding mode observer to estimate the states and perturbations of the synchronized nonlinear systems. Along with numerical simulations, the performance of the proposed synchronization scheme is experimentally verified on a network of Van der Pol oscillators.

In an effort to pursue more advanced missions in space, improved on-board trajectory optimization and path (re)planning capabilities are necessary. Over the past decades, numerous missions have pushed the state of the art in autonomous rendezvous and proximity operations (RPOs). Regardless of the mission, any RPO guidance algorithm must be able to react to a dynamic environment while generating a fuel-efficient trajectory. An adaptive artificial potential function (AAPF) guidance exhibiting these properties has been experimentally evaluated on a spacecraft air-bearing test bed and its performance compared to traditional APF and other real-time guidance methods.

Real-time control of district heating networks in the case of failures requires for accurate and fast strategies able to guarantee thermal comfort to all connected users. In this paper, we demonstrate a control framework that responds to these essential requirements. We minimize a global measure of discomfort based on a smooth maximum approximation. The optimization problem is solved through a gradient-based algorithm that can be naturally integrated with distributed meter readings leading to high accuracy of both forward and sensitivity analysis. Objective function gradients are computed by a discrete adjoint method, which is fast and nearly insensitive to the dimensionality of the optimization problem. The proposed framework is tested with numerical experiments on a reference medium-size distribution network in Turin. Results show that the thermal comfort of most critical users increases quickly, yielding to a nearly homogeneous discomfort distribution at the end of the optimization process. Studying the effect of the inlet pressure head on the optimized system performance reveals that a centralized operation results in increased robustness of the network and allows reducing backup pumping equipment. Furthermore, applying the proposed framework at the distribution network level yields remarkable benefits also in case of failures in the main transportation network.

Supervisory control system for superconducting magnet power supplies in JT-60SA is designed and developed. This system is in charge of many kinds of control; discharge sequence control, real-time magnet current control, data acquisition, machine protection and human safety. In the design, communication interface has to be optimized according to all requirements. The most important feature is real-time control with 4 kHz which is required for equilibrium control of plasma. In order to satisfy this requirement, reflective memory (RFM) network is applied for high speed data sharing with multiple controllers. This paper describes the architecture of the control system and the operation of the power supply system under the supervision.

Possibilities of using mathematical models of the theory of queues for the purpose of control of processes of discrete productions modeled by it in real time are investigated. As a model example, a system for managing the rearrangements of service items in queues was considered. For the analysis the methods of calculation of BMAP-controlled systems and the methods of the theory of recovery on periods of employment for queues with vacancies of requirements were used.As a result of research it is found out that mathematical modeling in the theory of queues can be used for simulation of discrete production for the purpose of design and the production analysis, but cannot be used for management of the modeled processes in real time.

Fusion power is the most significant prospects in the long-term future of energy in the sense that it composes a potentially clean, cheap, and unlimited power source that would substitute the widespread traditional nonrenewable energies, reducing the geographical dependence on their sources as well as avoiding collateral environmental impacts. Although the nuclear fusion research started in the earlier part of 20th century and the fusion reactors have been developed since the 1950s, the fusion reaction processes achieved have not yet obtained net power, since the generated plasma requires more energy to achieve and remain in necessary particular pressure and temperature conditions than the produced profitable energy. For this purpose, the plasma has to be confined inside a vacuum vessel, as it is the case of the Tokamak reactor, which consists of a device that generates magnetic fields within a toroidal chamber, being one of the most promising solutions nowadays. However, the Tokamak reactors still have several issues such as the presence of plasma instabilities that provokes a decay of the fusion reaction and, consequently, a reduction in the pulse duration. In this sense, since long pulse reactions are the key to produce net power, the use of robust and fast controllers arises as a useful tool to deal with the unpredictability and the small time constant of the plasma behavior. In this context, this article focuses on the application of robust control laws to improve the controllability of the plasma current, a crucial parameter during the plasma heating and confinement processes. In particular, a variable structure control scheme based on sliding surfaces, namely, a sliding mode controller (SMC) is presented and applied to the plasma current control problem. In order to test the validity and goodness of the proposed controller, its behavior is compared to that of the traditional PID schemes applied in these systems, using the RZIp model for the Tokamak à Configuration Variable (TCV) reactor. The obtained results are very promising, leading to consider this controller as a strong candidate to enhance the performance of the PID-based controllers usually employed in this kind of systems.

Real-time control (RTC) and sustainable drainage systems (SuDS) can be implemented simultaneously to enhance the performance of existing urban drainage systems (UDS). However, significant challenges arise when choosing the optimal locations due to hydraulic interactions between the different interventions and the high number of possible configurations. This paper presents a novel optimization–simulation framework to optimize the spatial allocation of flow control devices (FCDs) combined with SuDS for combined sewer overflow (CSO) spill mitigation within UDS. Optimal intervention schemes were identified by a genetic algorithm (GA), combining different numbers of FCDs installed in existing manholes with simplified SuDS implemented in different portions of the catchment. The methodology was tested on two case-study catchments with different characteristics to mitigate CSO spills during synthetic storm events. FCD-SuDS configurations were then validated during continuous rainfall series, resulting in CSO spill volumes reduction ranging between 11% and 45% compared to the baseline networks. The results demonstrate how the GA-based method can efficiently identify optimal placement schemes within UDS characterized by different distributions of in-pipe storage potential as well as hydrological responses to rainfall-runoff events, enhancing the combined benefits of the two decentralized solutions for mitigating CSO spills.

Due to the high underground water level, frequent rainfall, and large amounts of infiltration and inflow (I/I) into the sewage system, a city in the plain river network region had to face a series of problems caused by the high water-level operation of the drainage system. Suzhou, a city in the Yangtze River Delta region of China, can be a representative of cities in plain river networks, where this research was carried out. The amount of I/I into the sewage system was evaluated, and the storm water management model (SWMM) was used to further calculate the sewer water storage capacity under dry and wet weather with multi-year average rainfall. Based on the offline model calculation and artificial experiences, the rule-based online regulation and storage real-time control strategy (RTC) is verified, and the online regulation and storage intelligent scheduling demonstration is carried out in the central-city district of Suzhou. The results showed that the infiltration in dry weather accounted for about 20–25% of the total collected wastewater; in wet weather (36 mm precipitation), the extraneous water induced by I/I peaked at 73.64%. The collaborative control of regional multi-stage pumping stations through RTC of the sewage system can effectively avoid the high water-level operation caused by peak sewage flows on dry days. In combination with rainfall forecasting, the coordinated control of plants and pumping stations to pre-empty the sewer pipelines prior to rainfall can, to some extent (up to 35 mm of rainfall in this study), cope with the increase in I/I induced by rainfall.

The database here contains experimental data relevant to an original maximum power point tracking controller for an experimental direct-drive full-variable-speed full-rated converter Type IV Wind Energy Conversion System in standalone operation. The main goal is to maximize power extraction by controlling the duty cycle of a multilevel boost converter, which is responsible for adjusting the angular speed of a permanent magnet synchronous generator coupled to a three-phase induction motor that emulates the wind turbine. Two data acquisition cards with the appropriate signal conditioners were used to obtain measurements of the generator angular speed, output current, and output voltage at the terminals of the multilevel converter. In addition, data related to power coefficient, tip speed ratio, duty cycle, and output power are also included. Two PCs in a Linux real-time platform were used for the emulation, control, and data collection processes. On the other hand, Matlab was used to analyze the data to evaluate the controller's performance to maximize wind power extraction. The database is freely accessible at http://dx.doi.org/10.17632/363d24mcb6.2. This dataset [1] represents a resource for wind power specialists who develop algorithms for wind energy optimization.

The high penetration of electric vehicles (EVs) charging stations (CSs), together with the progressive availability of distributed energy resources, increases the risk of grid overloading and power-quality degradation. Real-time control has been advocated by the recent literature as an alternative to costly grid reinforcement. Performance of most of the existing control methods is assessed via simulations, and real-field validation is rarely performed. We present the experimental validation of a recently proposed real-time control method for EVs CS. This method works at sub-second scale and allows the CS to adapt to the rapidly changing state of the grid caused by highly volatile energy resources, such as photo-voltaic (PV) plants. The method fairly allocates the available power, proportional to the EVs needs, while minimizing the EVs battery wear due to frequent charging power variations. It tracks an aggregated power-setpoint that comes from the main grid controller by solving a mixed-integer optimization problem. Our main goal is to show that the method works in the field, i.e., it can control the charge of commercial EVs that are connected to a real grid through a CS. The field validation has two challenges. The first one is to study the real-time capabilities of the method and by analysing how fast it computes the control power-setpoints. The second one refers to the handling of the non-ideal response of EVs to the control power-setpoints due to implementation and reaction delays and inaccuracies. The experimental results demonstrate the performance of the method and show that it can be deployed in the field.

Many cities face high levels of flooding and pollution from stormwater runoff due to factors such as ongoing urbanization and aging stormwater management infrastructure. As climate change continues to alter precipitation, temperature, and sea levels, existing stormwater systems will be pushed beyond their designed capacity, further increasing flooding and pollution. This dissertation focuses on real-time prediction and control of stormwater related systems as a means to enhance community resilience to these issues. The research advanced the application of emerging deep machine learning techniques to water resources engineering using the coastal city of Norfolk, Virginia as a test-bed for these novel approaches. Norfolk faces recurrent flooding from storm events and ongoing sea level rise, while having to reduce polluted stormwater runoff entering the Chesapeake Bay. The first study uses supervised deep machine learning to create forecasts of groundwater table response to storm events, providing additional information for flood forecasting and stormwater management. The second and third studies explore deep reinforcement learning as a method for real-time control of stormwater systems. In the second study, reinforcement learning is used to create control strategies that mitigate flooding in a simple stormwater system scenario inspired by a watershed in Norfolk. The third study uses reinforcement learning for real-time stormwater system control with the competing objectives of mitigating flooding while also improving water quality by capturing sediment. This was done using a real-world simulation of Norfolk's Hague neighborhood instead of the simplified system from the second study. Key findings from this research are (i) deep machine learning can be used to create real-time hourly forecasts of the groundwater table response to storm events in a coastal city using forecast rainfall and tide conditions as input data with a mean root mean squared error of 0.05 m, (ii) reinforcement learning can learn real-time stormwater system control strategies that reduce flooding compared to conventional, uncontrolled stormwater systems by 32%, (iii) system-level stormwater real-time control with reinforcement learning can reduce flooding by 13% compared to local-scale control rules, and (iv) reinforcement learning can use real-time water quality observations to reduce sediment loads by an average of 52% with only a small increase in flooding (5%) compared to conventional, uncontrolled stormwater systems. While this dissertation has focused on coastal cities, the knowledge and methods developed could be applied to inland stormwater systems as well. These advancements contribute to a growing body of knowledge related to smart stormwater systems, which can aid communities through improved prediction and control of stormwater to reduce flooding and pollution.

The smart rain barrel (SRB) consists of a conventional RB with storage volumes between 200 and 500 L, which is extended by a remotely (and centrally) controllable discharge valve. The SRB is capable of releasing stormwater prior to precipitation events by using high-resolution weather forecasts to increase detention capacity. However, as shown in a previous work, a large-scale implementation combined with a simultaneous opening of discharge valves clearly reduced the effectiveness. The aim of this work was to systematically investigate different control strategies for wet weather by evaluating their impact on sewer performance. For the case study, an alpine municipality was hypothetically retrofitted with SRBs (total additional storage volume of 181 m3). The results showed that combined sewer overflow (CSO) volume and subsequently pollution mass can be reduced by between 7 and 67% depending on rain characteristics (e.g., rain pattern, amount of precipitation) and an applied control strategy. Effectiveness of the SRBs increases with lower CSO volume, whereas more advanced control strategies based on sewer conditions can clearly improve the system's performance compared to simpler control strategies. For higher CSO volume, the SRBs can postpone the start of an CSO event, which is important for a first-flush phenomenon. HIGHLIGHTS An alpine municipality is hypothetically retrofitted with 384 smart rain barrels (SRBs) as an IoT-based solution for micro storages utilised for smart rainwater harvesting.; Control strategies based on sewer conditions show a clear improvement in the system's performance compared with without considering sewer states.; Efficiency is particularly high if overflow volume is in relation to storage volume of the SRBs implemented.

Orientadores: Douglas Eduardo Zampieri, Isabelle Fantoni Coichot Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica Resumo: Esta tese aborda o problema de evitamento de obstáculos para plataformas terrestres semie autônomas em ambientes dinâmicos e desconhecidos. Baseado num sistema monocular, propõe-se um conjunto de ferramentas que monitoram continuamente a estrada a frente do veículo, provendo-o de informações adequadas em tempo real. A partir de um algoritmo robusto de detecção da linha do horizonte é possível investigar dinamicamente somente a porção da estrada a frente do veículo, a fim de determinar a área de navegação, e da deteção de obstáculos. Uma área de navegação livre de obstáculos é então representa a partir de uma imagem multimodal 2D. Esta representação permite que um nível de segurança possa ser selecionado de acordo com o ambiente e o contexto de operação. A fim de reduzir o custo computacional, um método automático para descarte de imagens é proposto. Levando-se em conta a coerência temporal entre consecutivas imagens, uma nova metodologia de gerenciamento de energia (Dynamic Power Management) é aplicada ao sistema de percepção visual a fim de otimizar o consumo de energia. Estas propostas foram testadas em diferentes tipos de ambientes, e incluem a deteção da área de navegação, navegação reativa e estimação do risco de colisão. Uma característica das metodologias apresentadas é a independência em relação ao sistema de aquisição de imagem e do próprio veículo. Este sistema de percepção em tempo real foi avaliado a partir de diferentes bancos de testes e também a partir de dados reais obtidos por diferentes plataformas inteligentes. Em tarefas realizadas com uma plataforma semi-autônoma, testes foram conduzidos em velocidades acima de 100 Km/h. A partir de um sistema em malha aberta, deslocamentos reativos autônomos foram realizados com sucesso Abstract: This thesis addresses the problem of obstacle avoidance for semi- and autonomous terrestrial platforms in dynamic and unknown environments. Based on monocular vision, it proposes a set of tools that continuously monitors the way forward, proving appropriate road informations in real time. A horizon finding algorithm was developed to sky removal. This algorithm generates the region of interest from a dynamic threshold search method, allowing to dynamically investigate only a small portion of the image ahead of the vehicle, in order to road and obstacle detection. A free-navigable area is therefore represented from a multimodal 2D drivability road image. This multimodal result enables that a level of safety can be selected according to the environment and operational context. In order to reduce processing time, this thesis also proposes an automatic image discarding criteria. Taking into account the temporal coherence between consecutive frames, a new Dynamic Power Management methodology is proposed and applied to a robotic visual machine perception, which included a new environment observer method to optimize energy consumption used by a visual machine. This proposal was tested in different types of image texture (road surfaces), which includes free-area detection, reactive navigation and time-to-collision estimation. A remarkable characteristic of these methodologies is its independence of the image acquiring system and of the robot itself. This real-time perception system has been evaluated from different test-banks and also from real data obtained by two intelligent platforms. In semi-autonomous tasks, tests were conducted at speeds above 100 Km/h. Autonomous displacements were also carried out successfully. The algorithms presented here showed an interesting robustness Resumé: Les études développées dans ce projet doctoral ont concerné deux problématiques actuelles dans le domaine des systèmes robotiques pour la mobilité terrestre: premièrement, le problème associé à la navigation autonome et (semi)-autonome des véhicules terrestres dans un environnement inconnu ou partiellement connu. Cela constitue un enjeu qui prend de l'importance sur plusieurs fronts, notamment dans le domaine militaire. Récemment, l'agence DARPA1 aux États-Unis a soutenu plusieurs challenges sur cette problématique robotique; deuxièmement, le développement de systèmes d'assistance à la conduite basés sur la vision par ordinateur. Les acteurs de l'industrie automobile s'intéressent de plus en plus au développement de tels systèmes afin de rendre leurs produits plus sûrs et plus confortables à toutes conditions climatiques ou de terrain. De plus, grâce à l'électronique embarquée et à l'utilisation des systèmes visuels, une interaction avec l'environnement est possible, rendant les routes et les villes plus sûres pour les conducteurs et les piétons. L'objectif principal de ce projet doctoral a été le développement de méthodologies qui permettent à des systèmes mobiles robotisés de naviguer de manière autonome dans un environnement inconnu ou partiellement connu, basées sur la perception visuelle fournie par un système de vision monoculaire embarqué. Un véhicule robotisé qui doit effectuer des tâches précises dans un environnement inconnu, doit avoir la faculté de percevoir son environnement proche et avoir un degré minimum d'interaction avec celui-ci. Nous avons proposé un système de vision embarquée préliminaire, où le temps de traitement de l'information (point critique dans des systèmes de vision utilisés en temps-réel) est optimisé par une méthode d'identification et de rejet d'informations redondantes. Suite à ces résultats, on a proposé une étude innovante par rapport à l'état de l'art en ce qui concerne la gestion énergétique du système de vision embarqué, également pour le calcul du temps de collision à partir d'images monoculaires. Ainsi, nous proposons le développement des travaux en étudiant une méthodologie robuste et efficace (utile en temps-réel) pour la détection de la route et l'extraction de primitives d'intérêts appliquée à la navigation autonome des véhicules terrestres. Nous présentons des résultats dans un environnement réel, dynamique et inconnu. Afin d'évaluer la performance de l'algorithme proposé, nous avons utilisé un banc d'essai urbain et réel. Pour la détection de la route et afin d'éviter les obstacles, les résultats sont présents en utilisant un véhicule réel afin d'évaluer la performance de l'algorithme dans un déplacement autonome. Cette Thèse de Doctorat a été réalisée à partir d'un accord de cotutelle entre l' Université de Campinas (UNICAMP) et l'Université de Technologie de Compiègne (UTC), sous la direction du Professeur Docteur Douglas Eduardo ZAMPIERI, Faculté de Génie Mécanique, UNICAMP, Campinas, Brésil, et Docteur Isabelle FANTONI-COICHOT du Laboratoire HEUDIASYC UTC, Compiègne, France. Cette thèse a été soutenue le 26 août 2011 à la Faculté de Génie Mécanique, UNICAMP, devant un jury composé des Professeurs suivants Doutorado Mecânica dos Sólidos e Projeto Mecânico Doutor em Engenharia Mecânica

Orientadores: Rubens Maciel Filho, Caliane Bastos Borba Costa Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química Resumo: Este trabalho teve como objetivos principais avaliar o comportamento dinâmico de um cristalizador batelada operado por resfriamento, para o caso de estudo da cristalização de ácido adípico, de grande interesse industrial, e propor estruturas de controle para o mesmo. O trabalho é oportuno, uma vez que este tipo de processo é amplamente utilizado industrialmente, porém com baixo grau de automação, sendo utilizado muitas vezes o controle manual. Inicialmente foram realizadas simulações promovendo variações nas principais variáveis do processo, com a finalidade de analisar a dinâmica do mesmo. Posteriormente, foi desenvolvida uma estrutura de controle preditivo com matriz dinâmica (Dynamic Matrix Controller - DMC) com a finalidade de controlar a temperatura do cristalizador, manipulando a temperatura do fluido refrigerante que passa através de uma jaqueta. Esta estrutura de controle apresentou-se eficiente para o controle do cristalizador. Por fim foi desenvolvido um controlador DMC com o objetivo de controlar a supersaturação do sistema. Este controle exige a leitura em tempo real de duas variáveis do processo, temperatura e concentração do soluto em solução. Com essas duas variáveis é possível estimar a supersaturação do sistema em tempo real e compará-la com o setpoint. Para promover o controle da supersaturação é utilizada como variável manipulada a temperatura do fluido refrigerante. Esta estrutura de controle consegue promover maior repetibilidade para o sistema batelada e garante as mesmas condições de operação para todas as bateladas, gerando produtos mais uniformes, o que é bastante vantajoso. Abstract: This study aimed to evaluate the dynamic behavior of a batch cooling crystallizer for the case of adipic acid crystallization, and to propose control structures for the system. This work is appropriate, since this type of process is widely used industrially, but with a low degree of automation, being often used the manual control. Initially simulations were carried out changing the process main variables, in order to analyze the dynamics of the process. In a second stage a control structure with predictive dynamic matrix controller (DMC) was developed in order to control the temperature of the crystallizer, manipulating the temperature of the refrigerant fluid passing through a jacket. This control structure was effective for the control of the crystallizer. Finally it a DMC controller with the objective of controlling the supersaturation of the system was developed. This control requires real-time aquisition of two process variables: temperature and concentration of solute in solution. With the two variables, it is possible to estimate the supersaturation of the system in real time and compare it with the setpoint. To promote the control of supersaturation the temperature of the refrigerant fluid is used as the manipulated variable. This control structure can promote greater repeatability for the batch system and provides the same operating conditions for all batches, producing more uniform products. Mestrado Desenvolvimento de Processos Químicos Mestre em Engenharia Química

Orientador: João Maurício Rosário Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica Resumo: Máquinas-ferramenta CNC são dispositivos complexos que executam movimentos automáticos, precisos e consistentes. Com o propósito de aprimorar o desempenho destes dispositivos face às mudanças bruscas de parâmetros agregados a posição, velocidade e aceleração no decurso de determinada trajetória torna-se imprescindível o estudo de novas arquiteturas de controle. A automação flexível permite que dispositivos robóticos mostrem-se cada vez mais velozes, motivando o desenvolvimento de técnicas que possibilitem de modo eficiente a substituição de controladores clássicos do tipo PID. Este estudo propôs-se a implementar um método alternativo para acionamento de um dispositivo cartesiano CNC de alta precisão com ênfase no desenvolvimento de um ambiente computacional para planejamento de movimentos e síntese de controladores. A geração de movimentos foi realizada por meio de um método de interpolação por splines que garantiu a continuidade e a suavidade dos perfis de trajetória. Para garantir que tais perfis fossem corretamente executados pelo dispositivo cartesiano, dois métodos de controle baseados no modelo do sistema foram implementados de forma a estabelecer comparação entre seus desempenhos. Em seguida, concebeu-se um ambiente de simulação com arquitetura aberta e flexível, o qual permitiu validar as estratégias propostas além de analisar a influência que perturbações externas causavam sobre o comportamento do dispositivo cartesiano. Tendo as estratégias sido validadas, procedeu-se à implementação em dispositivo físico utilizando uma placa FPGA para embarcar os controladores sintetizados. Os resultados mostraram que as estratégias propostas apresentaram bom desempenho quando empregadas junto ao dispositivo cartesiano CNC. Outrossim, o ferramental desenvolvido no estudo é suficientemente genérico para ser estendido a outras aplicações utilizando máquinas-ferramenta CNC Abstract: CNC machine tools are complex devices that execute automatic, precise and consistent movements. Aiming to improve the performance of these devices due to sudden changes on parameters associated to position, speed and acceleration while performing a given trajectory, it becomes indispensable the study of new control architectures. The flexible automation allows faster robotic devices, motivating the development of techniques that efficiently substitute classic PID controllers. This study aimed to implement an alternative method for driving a high-precision CNC cartesian device focusing on the development of a computing environment for movement planning and synthesis of controllers. The movement generation was performed by means of a spline interpolation method that ensured the continuity and smoothness of trajectory profiles. To ensure that these profiles were correctly executed by the cartesian device, two control techniques based on the model of the system were implemented in order to establish comparisons between their performances. Then, a simulation environment with open and flexible architecture was conceived, which allowed the validation of the proposed techniques besides analyzing the influence of external disturbances on the cartesian device behavior. Having the strategies been validated, the implementation in physical device was done using a FPGA board to embed the synthesized controllers. The results showed that the proposed strategies had good performance when applied to the CNC cartesian device. Furthermore, the tools developed in the study are general enough to be extended to other applications using CNC machine tools Mestrado Mecânica dos Sólidos e Projeto Mecânico Mestre em Engenharia Mecânica

With the deepening of planetary exploration, rapid decision making and descent trajectory planning capabilities are needed to cope with uncertain environmental disturbances and possible faults during planetary landings. In this article, a novel decoupling method is adopted, and the analytical three-dimensional constraint equations are derived and solved, ensuring real-time guidance computation. The three-dimensional motion modes and thrust profiles are analyzed and determined based on Pontryagin’s minimum principle, and a supporting semi-analytical reachability judgment method is presented, which can also be used to determine controllability. The algorithm is embedded in the model predictive control (MPC) framework, and several techniques are adopted to enhance stability and robustness, including thrust averaging, thrust correction after ignition, thrust reservation, and open-loop terminal guidance. Numerical simulations demonstrate that the proposed algorithm can guarantee real-time trajectory generation and meanwhile maintain considerable optimality. In addition, the MPC simulation shows that the algorithm can maintain a good accuracy under external disturbances.

In this study, we present a design and a control based on a microcontroller for a multi-axis servo motion chair. The main purpose was to provide the pilot with the tactile sensations required by the human body to increase the immersion of simulation training. With high system control reliability and multi-functionality, the proposed architecture was built with an effective split of the control tasks involved between a master controller and five slave drivers. To assess the effectiveness of the proposed control system, RS485 and RS232 communication transmission interfaces were provided for the control system to obtain related movement information. The performance of the motion chair was assessed through various experimental tests. The results confirmed that the proposed multi-core system-on-chip microcontroller (MCU) with independent communication and real-time control subsystems was feasible to control a multi-axis servo motion chair system.

Nature-based solutions (NBS) as green infrastructures to urban drainage are an effective mitigation strategy both in terms of quantity and quality of runoff. Real-time control (RTC) can complement both flood mitigation and improvement of water quality by controlling elements of the drainage and sewage system. This study assessed the improvement opportunities with RTC of three NBS-related techniques commonly applied in urban drainage with different spatial scales: green roof, bioretention and detention basin and the remaining challenges to integrate both methods. Additionally, our investigations showed that the main difficulties reported involve the planning and monitoring stages of the RTC system. All of the studied devices can benefit from RTC. It is possible to observe that, despite the good results reported in the literature, the application of RTC to NBS studies on urban drainage are very recent. There are several opportunities that can be explored to optimize the performance.

To evaluate the performance of Real Time Control (RTC) of urban drainage systems (UDS) a comparison is made with the pre-RTC situation, making the RTC performance dependent on the functioning of the UDS prior to implementation. To standardise things, a generalised baseline is formulated here as the operation with optimal static settings of the UDS. Two maximum theoretical potential performances are then calculated, one including and one excluding system limitations. These are combined with the generalised baseline to form Realised Potential Indicators (RPIs), objective values which indicate the proximity of the RTC strategy to its maximum potential. The proposed methodology was demonstrated on the case study of Eindhoven, the Netherlands. The results obtained show that using RPIs allows for a more objective assessment and improved understanding of the efficacy of different RTC procedures. Additionally, the RPIs can provide an indication if RTC is sufficient to achieve the desired UDS performance.

This paper presents related literature review on drones or unmanned aerial vehicles that are controlled in real-time. Systems in real-time control create more deterministic response such that tasks are guaranteed to be completed within a specified time. This system characteristic is very much desirable for drones that are now required to perform more sophisticated tasks. The reviewed materials presented were chosen to highlight drones that are controlled in real time, and to include technologies used in different applications of drones. Progress has been made in the development of highly maneuverable drones for applications such as monitoring, aerial mapping, military combat, agriculture, etc. The control of such highly maneuverable vehicles presents challenges such as real-time response, workload management, and complex control. This paper endeavours to discuss real-time aspects of drones control as well as possible implementation of real-time flight control system to enhance drones performance.

The potential real-time control (RTC) has to improve the performance of existing stormwater management systems is a topic of increasing interest as hydraulic and hydrologic modeling capabilities proliferate. The benefits of incorporating precipitation forecast data into a RTC algorithm to allow for prediction-based control of an urban watershed is explored using an EPA SWMM 5.1 watershed model. One reactive and two predictive RTC algorithms are simulated in various configurations across seven dry detention ponds located in the 162 hectare urbanized watershed. The hydraulic benefits they provide at the site and watershed outlet in regards to peak flow and the flow duration curve are compared to conventional, static control. The ponds retrofit with the novel predictive RTC algorithm had lower peak flows during 24-hour design storms more consistently than when retrofit with reactive RTC. The duration of erosive flows at the site level was decreased by the novel predictive RTC in most cases. Improvements at the watershed outlet depended on where RTC was applied as hydrograph compounding was observed during some RTC implementations. Master of Science The consequences of watershed urbanization on nearby waterways has become a more relevant concern as urbanization increases and climate change continues to develop. Conventional stormwater management practices are employed to control peak flows from urbanized drainage areas for certain design storm criteria. Real-time control (RTC) technology has the potential to enable existing stormwater facilities to improve their performance during storm events different from their design conditions. This study compares the performance of several reactive and predictive rule-based RTC algorithms simulated as retrofits on seven dry detention ponds in a 162 hectare urbanized watershed. The results indicate that RTC algorithms that use rainfall forecast data for predictive decision making have the most potential to reduce stream erosion when applied appropriately throughout the watershed.

This research work deals with the implementation of so-called Dynamic Data-Driven Application Systems (DDDAS) in structural mechanics activities. It aims at designing a real-time numerical feedback loop between a physical system of interest and its numerical simulator, so that (i) the simulation model is dynamically updated from sequential and in situ observations on the system; (ii) the system is appropriately driven and controlled in service using predictions given by the simulator. In order to build such a feedback loop and take various uncertainties into account, a suitable stochastic framework is considered for both data assimilation and control, with the propagation of these uncertainties from model updating up to command synthesis by using a specific and attractive sampling technique. Furthermore, reduced order modeling based on the Proper Generalized Decomposition (PGD) technique is used all along the process in order to reach the real-time constraint. This permits fast multi-query evaluations and predictions, by means of the parametrized physics-based model, in the online phase of the feedback loop. The control of a fusion welding process under various scenarios is considered to illustrate the proposed methodology and to assess the performance of the associated numerical architecture.

Pollution caused by combined sewer overflows has become a global threat to the environment. Under this challenge, quality-based real-time control (RTC) is considered as an effective approach to minimize pollution through generating optimal operation strategies for the sewer infrastructure. To suit the fast computation requirement of RTC implementation, simplified quality models are required. However, due to the hydrological complexity, it is not easy to develop simplified quality models which are amenable to be used in real-time computations. Under this context, this paper contributes a preliminary analysis of influencing factors for the quality models of sewer networks in order to give supportive knowledge for both model development and application. Conceptual quality models which were proposed previously by the authors, with total suspended solids (TSS) as quality indicator, are used in this study. A clustering algorithm is used for exploratory analysis. Further analysis about the correlations between different factors and model performance is also carried out. The study and analysis are demonstrated on a real pilot based on the Louis Fargue urban catchment in Bordeaux. Conclusive results about the influencing factors, flow rate, rain intensity and pipe length, as well as their correlations with the TSS models are elaborated., The authors wish to thank the support received by the European Commission research grant of project LIFE EFFIDRAIN (LIFE14 ENV/ES/000860), and the support from Spanish national project DEOCS (DPI2016-76493-C3-3-R). The authors also want to thank Bordeaux Metropole and Aigües de Barcelona for the financial and technical support. This work is also supported by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656), by the Institute of Robotics Industry through the TWINs project.

This paper proposes a robust scheme for optimizing the power flow in a photovoltaic system. The scheme utilizes distributed saddle point dynamics and a decentralized approach to solve the power flow problem. It converts the convex optimization problem of the dynamic system control into the asymptotically stable dynamic systems and employs a linear approximation of power flow equations; specifically, a quadratic programming model is deployed with the aim of minimizing real-power losses to guarantee a globally optimal solution. Then, the photovoltaic inverters and electric networks are analyzed independently in a decentralized manner to exchange injection power among nodes while maintaining their independence to support the plug-and-play feature. A case study and the experimental results show that the proposed scheme achieves higher optimization accuracy and are more economical than the existing state-of-the-art schemes.

In order to improve the robustness and low-speed performance of continuous rotary electro-hydraulic servo system under influences of dynamic uncertainties, parametric perturbation, friction, other non-linear properties, and uncertainties, the robust control strategy was proposed with Adaboost prediction. Firstly, basing on the system mathematic model, the model with structured uncertainty and generalised state equation was established with parametric perturbation and external disturbances, and then the robust controller was developed by adopting [inline-formula] theory. Furthermore, Adaboost algorithm based on radial basis function (RBF) neural network was applied to design the system feedback mechanism, so the multiple weak neural network learners were obtained by using Adaboost algorithm to train system actual output and input. Also, these weak neural network learners constituted a strong learner to predict the electro-hydraulic servo system output and calculate the predictive error so as to adjust the system robust control output, so the real-time control was carried out by the robust controller. Some comparative simulated results are obtained to verify the proposed controller guarantees performances of low speed, tracking accuracy, and ability of anti-interference, which greatly expands the band of frequency response and improve the system robustness.

Peripheral plasma with finite electron density gradients and finite magnetic shear is known to affect polarization of electron cyclotron (EC) waves. Calculation of the ratio between the ordinary (O) mode and the extraordinary (X) mode, integrated in the ray-tracing code developed for EC heated plasmas in the Large Helical Device (LHD), enables the search for the optimum EC wave polarization in order to excite the pure O/X mode at the EC resonance layer. The real-time control system of the incident polarization was developed for maximum single-pass absorption of EC waves, based on the dependence of optimum EC wave polarization on peripheral density profiles. The polarization control system is equipped with a fast field programmable gate array, which processes in real time the calculation of the peripheral electron density profile and the optimum EC wave polarization for motion control of the polarization rotator and the elliptical polarizer on the transmission line. The real-time control in LHD experiments functioned properly in maintaining the absorbed power of the EC wave higher than that without the control, demonstrating that the purer heating mode was successfully excited.

This paper presents recent progress in studies of neoclassical tearing modes (NTMs) on TCV, concerning the new physics learned and how this physics contributes to a better real-time (RT) control of NTMs. A simple technique that adds a small (sinusoidal) sweeping to the target electron cyclotron (EC) beam deposition location has proven effective both for the stabilization and prevention of 2 / 1 NTMs. This relaxes the strict requirement on beam-mode alignment for NTM control, which is difficult to ensure in RT. In terms of the EC power for NTM stabilization, a control scheme making use of RT island width measurements has been tested on TCV. NTM seeding through sawtooth (ST) crashes or unstable current density profiles (triggerless NTMs) has been studied in detail. A new NTM prevention strategy utilizing only transient EC beams near the relevant rational surface has been developed and proven effective for preventing ST-seeded NTMs. With a comprehensive modified Rutherford equation (co-MRE) that considers the classical stability both at zero and finite island width, the prevention of triggerless NTMs with EC beams has been simulated for the first time. The prevention effects are found to result from the local effects of the EC beams (as opposed to global current profile changes), as observed in a group of TCV experiments scanning the deposition location of the preemptive EC beam. The co-MRE has also proven able to reproduce well the island width evolution in distinct plasma scenarios on TCV, ASDEX Upgrade and MAST, with very similar constant coefficients. The co-MRE has the potential to be applied in RT to provide valuable information, such as the EC power required for NTM control with RT-adapted coefficients, contributing to both NTM control and integrated control with a limited set of actuators.

The practice of rainwater harvesting (RWH) has been studied extensively in recent years, as it has the potential to alleviate some of the increasing stress on urban water distribution systems and drainage networks. Within the field, an approach of real-time control of rainwater storage is emerging as a method to improve the ability of RWH systems to reduce runoff and urban drainage flows. As applying real-time control on RWH tanks means releasing water that could be used for supply, applying controlled-release policies often hinders the RWH system’s ability to supply water. The suggested study presents an approach that has the potential to improve the capability of a distributed network of RWH systems to mitigate peak drainage flows while substantially reducing the impact on harvested rainwater availability. The suggested method uses a genetic algorithm to generate release policies, which are tailored for any given rain event and initial conditions. The algorithm utilizes the modeled drainage system’s response to a given rainfall pattern and manages to substantially reduce peak drainage flows with little impact on available rainwater when compared to the conventional no-release alternative and other active release methods.

The real-time model-based control of polymer electrolyte membrane (PEM) fuel cells requires a computationally efficient and sufficiently accurate model to predict the transient and long-term performance under various operational conditions, involving the pressure, temperature, humidity, and stoichiometry ratio. In this article, recent progress on the development of PEM fuel cell models that can be used for real-time control is reviewed. The major operational principles of PEM fuel cells and the associated mathematical description of the transport and electrochemical phenomena are described. The reduced-dimensional physics-based models (pseudo-two-dimensional, one-dimensional numerical and zero dimensional analytical models) and the non-physics-based models (zero-dimensional empirical and data-driven models) have been systematically examined, and the comparison of these models has been performed. It is found that the current trends for the real-time control models are (i) to couple the single cell model with balance of plants to investigate the system performance, (ii) to incorporate aging effects to enable long-term performance prediction, (iii) to increase the computational speed (especially for one-dimensional numerical models), and (iv) to develop data-driven models with artificial intelligence/machine learning algorithms. This review will be beneficial for the development of physics or non-physics based models with sufficient accuracy and computational speed to ensure the real-time control of PEM fuel cells.

This paper provides an insight into behavior and effects of pressure reducing valves (PRVs) and of valves controlled in real time (RTC) during hydrant activation scenarios, which determine high-flow conditions in water distribution networks (WDNs). The analysis is carried out through the pressure-driven unsteady flow modeling of an Italian WDN, in which a pressure control device is assumed to be installed in the pipe outgoing from the source. Scenarios are constructed considering alternatively a PRV or an RTC valve, as pressure control device, and two different sites for hydrant activation. The results highlight that, thanks to its capability of controlling the service pressure at the critical node, the RTC valve performs better than the PRV, which is regulated based on its outlet pressure. In fact, the adoption of the RTC valve attenuates pressure deficits in the network, above all when the control time step is suitably small to attenuate pressure head oscillations at the controlled node. An addi...

International audience; This paper proposes a parameterized NMPC scheme for control of semi-active suspension system for a quarter car vehicle model. The proposed controller is compared against a linearization based MPC to verify its performance under real-time (RT) embedded conditions. The linearization based MPC method linearizes the non-linear dynamical system/constraints at the current operating point and a linear MPC problem is solved by means of a quadratic program (QP) solver to obtain the optimal control input. The proposed parameterized NMPC method finitely parameterizes the control input and for each parameterized input, the non-linear dynamical system is numerically simulated and the optimal input is elicited from the simulations which minimizes the objective function and satisfies the system constraints. The methods were considered on the basis of RT implementable scenario and practical viability for the target system. The methods were successfully tested by means of hardware in the loop (HIL) simulations under RT conditions for given control objective on dSPACE MicroAutoBox II hardware. The results from HIL simulations exudes better performance of parameterized NMPC against linearization based MPC in terms of computational efficiency and RT feasibility of the controller.