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Tractors are among the most widely used machinery in agriculture. However, low-frequency vibrations during tractor operations pose significant health risks to drivers, such as musculoskeletal disorders, and negatively impact ride comfort. Current approaches rely on offline comfort prediction models, which lack real-time feedback, making them unsuitable for practical field applications. To address this gap, we propose a real-time recommendation system based on Internet of Things (IoT) and Machine Learning (ML) to enhance the driving comfort of agricultural tractors. Our low-cost IoT-enabled solution is compatible with existing tractors, requiring no expensive intelligent upgrades. Using the XGBoost model for ride comfort prediction, we achieved superior performance (R $^{2} =0.96$ , RMSE =0.015) compared to other ML models. Additionally, the Particle Swarm Optimization (PSO) algorithm is employed to recommend optimal operational parameters, reducing the ride comfort value (OVV) by 6.67% in real-time experiments. This study highlights a scalable, data-driven approach for improving tractor comfort and offers a reference for intelligent control strategies in Agriculture 4.0.

ObjectivesIn order to support the testing requirements of the international thermonuclear experimental reactor AC/DC converter system and apply to high-precision control standards for future fusion magnet power supplies, a real-time control system suitable for high-power DC test platforms was developed.MethodsChoosing the QNX real-time operating system as the core platform and combining it with reflective memory technology provided by GE company in the United States, a network architecture for real-time high-speed data exchange was built. The system was designed with multiple operation modes to cope with different testing scenarios, while implementing different levels of safety interlocking mechanisms to ensure equipment safety. This system possessed the capabilities of setting converter operating parameters, fault identification, equipment status monitoring, and millisecond-level real-time control and safety protection functions.ResultsExperimental validation has demonstrated the system’s stability and reliability in high-power environments, achieving precise control of 120 kA steady-state current and 500 kA pulse current. Furthermore, the system not only meets the basic requirements of real-time control but also ensures safe interlocking and continuous stable operation of the equipment during multi-mode operation.ConclusionsThe designed real-time control system for the high-power DC test platform achieves efficient multi-module synchronous management and fully complies with strict millisecond-level control cycle requirements.

The concept of automated control systems for positioning radio-signal transmission and reception devices is discussed in this article. The subjects of this article are methods and means for ensuring precise and stable antenna positioning using web-based controllers with integrated sensors and actuators. This research aimed to develop a model of an automated control system for the positioning of radio signal transmission/reception antennas, including directional antennas with a radiation pattern angle of 60-90 degrees, ensuring the minimization of azimuth positioning error. The objective of this research is to develop automated antenna positioning systems using embedded systems. This article provides an example of the system's operation, where the controller performs antenna positioning tasks with high accuracy for directional antennas, such as Yagi–Uda antennas, ensuring that the azimuth position control error does not exceed 15 degrees. Positioning accuracy is achieved by a calibration procedure and dynamic servomotor adjustment based on the magnetometer data. This system is designed to ensure communication for operating a mobile robotic platform (unmanned vehicles), particularly in the presence of electromagnetic interference. Reliable communication with an unmanned vehicle depends on the positioning of the communication elements. It is a necessary condition for the operation of a mobile robotic platform, which, according to the classification by size groups, belongs to Micro, Mini, and Midi categories of wheeled vehicles and is used in search, rescue, and military operations. The result of the research is the development of the system, as well as its implementation and testing under laboratory conditions, which confirms the operability of the proposed control system model. Conclusions. This article discusses the concept of an automated control system for antenna positioning based on the use of embedded web technologies and their integration with hardware components that ensure precise positioning of radio-signal transmission/reception devices.

The real-time operation of microgrids is crucial due to its ability to enhance energy resilience and efficiency. Continuous monitoring and adaptation to fluctuations in energy supply and demand ensure a stable and reliable electricity supply to local communities, even in the case of grid disturbances. This article presents an efficient and easily implementable real-time energy management and control system based on multi-agent systems for hybrid Low-Voltage Micro-Grids (LVMGs) using energy storage systems and renewable sources. The main objective of the proposed approach is to determine optimal setpoints for all microgrid components to improve overall efficiency and reduce electricity costs while satisfying multiple constraints. The well-defined flexibilities of all microgrid components to adjust their power are exploited to achieve optimal power allocation. The approach is tested and validated by developing a simulation environment using the Java Agent Development (JADE) framework, enabling the implementation of microgrid optimization through MATLAB/Simulink.

Electric machines have existed since the beginning of the 19th century. Although variable-speed drive technology has evolved considerably during the last years, the indirect field-oriented control technique has been in use as a standard control method since the 1960s, using an outer speed loop and an inner current loop. This paper deals with the non-trivial problem of tuning the outer speed controller of modern variable-speed drives, where experimental results are provided to show the need for new tuning methods. The influence of non-modeled effects on the performance of the drive is illustrated considering the dynamical effect of the mechanical speed sensing procedure using optical devices. This effect has not been generally considered. However, it is shown that it has a notable effect on tuning for high performance drives. This is especially true when considering some figures of merits of relevance for drives, such as the torque ripple. A Pareto analysis is proposed to reveal trade-offs between typical figures of merit to establish new tuning methods. To focus our contribution, a five-phase induction drive is considered as the case study, using a finite-state model predictive controller for the stator current control and PI regulators for the outer speed control loop.

This study harnesses EEG signals to enable the real-time control of servo motors, utilizing the OpenBCI Community Dataset to identify and assess brainwave patterns related to motor imagery tasks. Specifically, the dataset includes EEG data from 52 subjects, capturing electrical brain activity while participants imagined executing specific motor tasks. Each participant underwent multiple trials for each motor imagery task, ensuring a diverse and comprehensive dataset for model training and evaluation. A deep neural network model comprising convolutional and bidirectional long short-term memory (LSTM) layers was developed and trained using k-fold cross-validation, achieving a notable accuracy of 98%. The model’s performance was further compared against recurrent neural networks (RNNs), multilayer perceptrons (MLPs), and Τransformer algorithms, demonstrating that the CNN-LSTM model provided the best performance due to its effective capture of both spatial and temporal features. The model was deployed on a Python script interfacing with an Arduino board, enabling communication with two servo motors. The Python script predicts actions from preprocessed EEG data to control the servo motors in real-time. Real-time performance metrics, including classification reports and confusion matrices, demonstrate the seamless integration of the LSTM model with the Arduino board for precise and responsive control. An Arduino program was implemented to receive commands from the Python script via serial communication and control the servo motors, enabling accurate and responsive control based on EEG predictions. Overall, this study presents a comprehensive approach that combines machine learning, real-time implementation, and hardware interfacing to enable the precise and real-time control of servo motors using EEG signals, with potential applications in the human–robot interaction and assistive technology domains.

The study assessed static management, i.e., without planned periodic maintenance of the storage (no sediment dredging) denoted as SC1, or with maintenance denoted as SC2. The second approach was dynamic management based on applying Real-Time Control (RTC) techniques to scenario SC1 and scenario SC2 denoted as RTC-1 and RTC-2, respectively. The dynamic management of the UCT dam with RTC-1 and RTC-2 approaches increase yield by 2.1 and 1.1 ml, respectively. Additionally, RTC-1 and RTC-2 approaches increase volumetric reliability by 5.3 and 2.5%, respectively, while maintaining the required level of service of a stormwater harvesting system. SC1 and SC2 results in water savings of up to 21.15 and 21.45 ZAR/kl, respectively, while RTC-1 and RTC-2 could save up to 19.35 and 19.45 ZAR/kl. Thus, static configurations results in water savings approximately 9% in comparison to RTC. In addition, static configurations harvested stormwater at a relatively lowest unit cost in comparison to RTC configurations. Notwithstanding this finding, the RTC system exhibits a great potential in reshaping the stormwater harvesting system to simultaneously deliver water conservation and stormwater management. HIGHLIGHTS Assessed the extent to which stormwater harvesting could contribute to ‘net zero’, i.e., transitioning the University of Cape Town toward a ‘water-sensitive precinct’.; Impact of planned periodic maintenance in enhancing harvested stormwater volume.; Benefit of Real-Time Control (RTC) in enhancing harvested stormwater volume;

Ohmic heating is a highly efficient method for rapid fluid heating, with applications in fields such as food processing, pharmaceuticals, and chemical engineering. Prior to its industrial application, thorough analysis and modeling are crucial to ensure safe and efficient operations. Therefore, this research focuses on the development and validation of a transfer function-based model for a continuous flow ohmic heater (CFOH). Validation metrics include root mean square error (RMSE) and mean absolute percentage error (MAPE). The developed model achieves an RMSE of ±1.48 and a MAPE of ±2.58% compared to experimental results, demonstrating its accuracy. Furthermore, the research presents the implementation of robust real-time applications of advanced process controllers, including PID, MPC, and AMPC. These controllers were first simulated using the developed model and subsequently deployed in the pilot plant ohmic heater system to achieve precise temperature control and optimised input voltage. The reliability of this procedure was affirmed through a comparison between simulated results and empirical data obtained from the CFOH pilot plant. The study concludes by suggesting potential applications in fault diagnosis, educational training, system identification, and controller design.

Real-time and model-predictive control promises to make urban drainage systems (UDS) adaptive, coordinated, and dynamically optimal. Though early implementations are promising, existing control algorithms have drawbacks in computational expense, trust, system-level coordination, and labor cost. Linear feedback control has distinct advantages in computational expense, interpretation, and coordination. However, current methods for building linear feedback controllers require calibrated software models. Here we present an automated method for generating tunable linear feedback controllers that require only system response data. The controller design consists of three main steps: (1) estimating the network connectivity using tools for causal inference, (2) identifying a linear, time-invariant (LTI) dynamical system which approximates the network, and (3) designing and tuning a feedback controller based on the LTI urban drainage system approximation. The flooding safety, erosion prevention, and water treatment performance of the method are evaluated across 190 design storms on a separated sewer model. Strong results suggest that the system knowledge required for generating effective, safe, and tunable controllers for UDS is surprisingly basic. This method allows near-turnkey synthesis of controllers solely from sensor data or reduction of process-based models. HIGHLIGHTS Connectivity is automatically identified among water level sensors and valves solely using response data.; Inferred connectivity and response data are used to train linear feedback controllers.; Controllers built purely from data prevent flooding and greatly improve water treatment.;

The use of digital twins is a rapidly emerging field for improved real-time control (RTC) and decision support for the operation of collection systems and water resource recovery facilities (WRRFs). Digital twins for collection systems can help minimize the impacts of flow variation due to extreme weather events, attenuate flows to the WRRF, and reduce sewer overflows and the associated effects. Similarly, digital twins for WRRFs can help improve process, energy, and cost efficiency, fully utilise plant volumes, reduce carbon footprint, and support operator training. The current study provides an overview of two digital twin applications for collection systems (Future City Flow) and WRRFs (TwinPlant) and presents a first example of digital twin integration for proactive collection system-WRRF operation under wet-weather conditions. Current applications of the integrated digital twin are described, including (i) proactive implementation of wet-weather operation mode in WRRF based on inflow forecast and (ii) evaluation of the impacts of RTC in collection systems on WRRF performance. Other potential application examples are described together with the challenges related to the use of this solution. Overall, this new approach has a wide potential to support the cooperation within water utilities towards the adoption of integrated wastewater management. HIGHLIGHTS Digital twins can provide a sound base for controlling catchments and water resource recovery facilities.; High-quality online data from the twinned facilities is a key for reliable digital twins.; Digital twins must be robust to gain operator trust.; Connected digital twins provide a basis for system-wide optimization.; Helps to move from siloed to integrated management.;

【Objective】 The aim of this study is to improve the accuracy of water level control, minimize water level oscillations, and improve the convergence efficiency of control algorithms for multi-stage gate-controlled canals operating under significant water level variations. 【Method】 We propose an adaptive predictive control algorithm (APC) which dynamically adjusts the parameters of the identification (ID) model based on real-time canal water level data and modifies the activation frequency of sluices. The performance of the APC algorithm is comprehensively evaluated and compared with other control algorithms under six distinct operational conditions. 【Result】 In comparison to the linear quadratic controller (LQR) and the model predictive controller (MPC) based on the ID model, the APC algorithm shows significant improvements in accuracy and reliability, reducing the regulation duration by 19% to 32% and 8% to 40% respectively, damping the cumulative fluctuation of the canal water level by 47% to 97% and 14% to 93% respectively, lowering the mean absolute deviation of canal water level by 24% to 77% and 5% to 59%, respectively. 【Conclusion】 The APC method can substantially improve the precision of water level control and the convergence for multi-stage gate-controlled canals. It is robust for various canal water level amplitudes, thereby providing a crucial technical support for advancing intelligent management of water conservancy infrastructures.

The emerging challenges facing the City of Gothenburg and surrounding municipalities include the impacts of a variable climate, rapid growth and urbanization, aging infrastructure and changing community values and customer expectations. A digital twin (DT), based on the Future City Flow (FCF) platform, was created for the catchment to address many of these challenges in terms of improved real-time sewerage performance and predictability in the face of storms, improved DT visualization of the regional sewage transport system for operators and greater responsiveness to avoid service interruption. The FCF-based DT provides a sound base for modelling, simulating, forecasting and controlling the catchment. The versatility of the FCF DT makes it useful for gaining insight into catchment dynamics and the simulator can be used to study the effects of different scenarios or changes in the catchment. The predictive control imbedded in the FCF DT recommends setpoints for controlling the catchment that can be used by the operators for decision support or even be directly applied in the control system as model-based predictive control. A DT requires good quality online data from the catchment and, perhaps most importantly, high-quality rain forecasts, especially when prognosing long time horizons. HIGHLIGHTS Digital twins provide a sound base for modelling, simulating, forecasting, and controlling wastewater catchments.; A digital twin needs good-quality online data from the catchment.; High-quality rain forecasts are very important, especially when prognosing over long time horizons.; The model-based predictive control recommends setpoints for controlling the catchment that can be used in an advisory capacity or directly applied.;

Test plants or laboratory prototypes are essential for developing training activities in engineering. In the field of automation and control, simulators or high-fidelity equipment models commonly used in industrial processes are necessary. These tools allow engineering trainees to gain experience working with devices similar to those they will encounter in their professional contexts. This paper presents the design and simulation of a fly-cutting plant for academic use. A 3D model was developed in SketchUp, incorporating features typical of industrial plants. The system’s simulation was carried out in MATLAB R2023b using mathematical modeling. The primary contribution of this work is the design of a low-cost, compact industrial prototype that includes a conveyor belt and a continuous cutting mechanism, enabling the understanding and operation of large-scale industrial processes. Performance tests were conducted using MATLAB, Simulink, and Code Composer Studio. Subsequently, operational and cutting tests were performed using classical control techniques. Additionally, the design features of the fly-cutting plant, which can be easily implemented for process control training activities, are detailed.

This paper presents a synthesis of higher-order control Lyapunov functions (HOCLFs) and higher-order control barrier functions (HOCBFs) capable of controlling nonlinear dynamic systems while maintaining safety. Building on previous Lyapunov and barrier formulations, we first investigate the feasibility of the Lyapunov and barrier function approach in controlling a non-affine dynamic system under certain convexity conditions. Then we propose an HOCLF form that ensures convergence of non-convex dynamics with convex control inputs to target states. We combine the HOCLF with the HOCBF to ensure forward invariance of admissible sets and guarantee safety. This online non-convex optimal control problem is then formulated as a convex Quadratic Program (QP) that can be efficiently solved on board for real-time applications. Lastly, we determine the HOCLBF coefficients using a heuristic approach where the parameters are tuned and automatically decided to ensure the feasibility of the QPs, an inherent major limitation of high-order CBFs. The efficacy of the suggested algorithm is demonstrated on the real-time six-degree-of-freedom powered descent optimal control problem, where simulation results were run efficiently on a standard laptop.

Rising greenhouse gas emissions stemming from road transport have intensified the need for efficient and environmentally friendly propulsion technologies. Hybrid and fuel cell electric vehicles have emerged as a viable solution, integrating internal combustion engines and fuel cells with electric motors to optimize fuel efficiency and reduce emissions. This article reviews and analyzes energy management strategies for the principal powertrain topologies of hybrid electric vehicles, focusing on achieving solution optimality in real-time applications. A thorough and comprehensive overview of rule-based, optimization-based, and learning-based energy management strategies is presented, highlighting their main attributes and providing a comparative analysis in terms of fuel economy improvements, real-time implementation feasibility, and computational complexity, while simultaneously identifying and uncovering areas requiring further research in the field. We found that while rule-based methods offer simplicity and real-time capability, their adaptability remains limited. Optimization-based and learning-based approaches, although often achieving near-optimal solutions, face challenges due to their high computational demands and integration complexities. Our analysis also revealed the importance of leveraging vehicle connectivity and intelligent transportation systems for future energy management developments, which will contribute to broader sustainability goals in the automotive sector.

Conventional solutions for wastewater collection focus on reducing overflow events in the sewage network, which can be achieved by adapting sewer infrastructure or, a more cost-effective alternative, by implementing a non-engineering management solution. The state-of-the-art solution is centered on Real-Time Control (RTC), which is already resulting in a positive impact on the environment by decreasing the volume of wastewater being discharged into receiving waters. Researchers have been continuing efforts towards upgrading RTC solutions for sewage systems and a new approach, although rudimentary, was introduced in 1997, known as Pollution-based RTC (P-RTC), which added water quality (concentration or load) information explicitly within the RTC algorithm. Formally, P-RTC is encompassed of several control methodologies using a measurement or estimation of the concentration (i.e. COD or ammonia) of the sewage throughout the network. The use of P-RTC can result in a better control performance with a reduction in concentration of overflowing wastewater observed associated with an increase of concentration of sewage arriving at the Wastewater Treatment Plant (WWTP). The literature revealed that P-RTC can be differentiated by: (1) implementation method; (2) how water quality is incorporated, and (3) overall control objectives. Additionally, this paper evaluates the hydrological models used for P-RTC. The objective of this paper is to compile relevant research in pollution-based modelling and real-time control of sewage systems, explaining the general concepts within each P-RTC category and their differences.

This paper presents a novel real-time control and electronic instrumentation system (RTC\andEIS) for a prototype plant that uses activated sludge for wastewater treatment. The main contribution in this research was oriented to work on an experimental design with small dimensions and low cost. This design was carried out using two continuous flow storage tanks in which an identification process was carried out with the MATLAB system identification toolbox to know a mathematical model. The mathematical representation obtained is presented as a continuous and a discrete transfer function. To evaluate the performance of the temperature control and instrumentation system, we implemented a simulation of the closed-loop control system and then implemented it using a DAQ, LabVIEW software, electronic devices and the wastewater treatment plant prototype to compare both results. Finally, the secondary treatment effluent was analyzed in a certified water laboratory to have a detailed analysis compared to the initial influent.

Single-inductor 3x multilevel boost (SI-3xMLB) converters, recognized for their potential to supply multiple output voltage levels, are gaining much attention in microgrids due to their capability to diverse sub-systems with changing voltage requirements. However, some loads, such as constant power loads (CPLs), threaten the stability of SI-3xMLB converters due to their negative impedance properties. Thus, advanced regulation mechanisms are necessitated to reduce the destabilization effect of such loads in the microgrids. In response to the issue, in this paper, an adaptive data-driven controller with an observer is designed to mitigate the effect of non-ideal CPLs in dc/dc SI-3xMLB converters. The suggested data-driven controller can stabilize the system outputs using the input-output (I/O). In particular, the multi-agent fuzzy approximate Q-learning (MAFQ-learning) is employed to adjust the coefficients embedded in the data-driven controller dynamically. To do this, a reward function is defined according to the voltage control requirements of the dc/dc SI-3xMLB converter. By interacting the agent of MAFQ-learning with the environment, tunable control coefficients are adaptively adjusted. This process finds optimal policy, allowing the controller to dynamically respond to the effects of CPLs. The real-time responses based on the Arduino platform revealed the superior performance of the suggested controller to stabilize the dc/dc SI-3xMLB boost converter feeding CPL than the discrete time-averaged model predictive control (DTA-MPC), the optimal PI control based on state-action-reward-state-action (SARSA) technique.

The Chicago Area Waterway System (CAWS) is a 133.9 km branching network of navigable waterways controlled by hydraulic structures, in which the majority of the flow is treated wastewater effluent and there are periods of substantial combined sewer overflows. The CAWS comprises a network of effluent dominated streams. More stringent dissolved oxygen (DO) standards and a reduced flow augmentation allowance have been recently applied to the CAWS. Therefore, a carefully calibrated and verified one-dimensional flow and water quality model was applied to the CAWS to determine emission-based real-time control guidelines for the operation of flow augmentation and aeration stations. The goal of these guidelines was to attain DO standards at least 95% of the time. The “optimal” guidelines were tested for representative normal, dry, and wet years. The finally proposed guidelines were found in the simulations to attain the 95% target for nearly all locations in the CAWS for the three test years. The developed operational guidelines have been applied since 2018 and have shown improved attainment of the DO standards throughout the CAWS while at the same time achieving similar energy use at the aeration stations on the Calumet River system, greatly lowered energy use on the Chicago River system, and greatly lowered discretionary diversion from Lake Michigan, meeting the recently enacted lower amount of allowed annual discretionary diversion. This case study indicates that emission-based real-time control developed from a well calibrated model holds potential to help many receiving water bodies achieve high attainment of water quality standards.

In this study, a comparative analysis of three different control methods for precise, real-time control of a complex dynamic double-tank liquid level process system was performed. Since the system in question has a time-delayed structure, feedforward proportional integral (FF-PI) control and cascaded nonlinear feedforward proportional integral delayed (CNPIR) controllers were tested on the process system. While the FF-PI controller improved the response time of the system, it showed limitations in handling external disturbances and nonlinearities. On the other hand, the CNPIR controller showed better improvements in control accuracy and lower overshoot compared to the FF-PI controller. Since the process system has a nonlinear model and is affected by external disturbances, these two controllers were inadequate in this study when compared to the fractional order adaptive proportional integral derivative sliding mode controller (FO-APIDSMC). The FO-APIDSMC controller provided fairly good performance in both tracking accuracy and disturbance rejection control for non-chattering, fast finite-time convergence, increased robustness, and uncertain dynamic processes. Experimental results reveal that the FO-APIDSMC controller achieves superior minimized tracking error and outperforms the FF-PI and CNPIR controllers by effectively handling uncertainties and external disturbances.

The control system of oil-fired boiler units on ships plays a crucial role in reducing the emissions of atmospheric pollutants such as nitrogen oxides (NOx), sulfur dioxides (SO2), and carbon dioxide (CO2). Traditional control methods using conventional measurement sensors face limitations in real-time control due to response delays, which has led to the growing interest in combustion control methods using flame images. To ensure the precision of such combustion control systems, the system model must be thoroughly considered during controller design. However, finding the optimal tuning point is challenging due to the changes in the system model and nonlinearity caused by environmental variations. This study proposes a controller that integrates an internal model control (IMC)-based PID controller with the deep deterministic policy gradient (DDPG) algorithm of deep reinforcement learning to enhance the adaptability of image-based combustion control systems to environmental changes. The proposed controller adjusts the PID parameter values in real-time through the learning of the determination constant lambda (λ) of the IMC internal model. This approach reduces computational resources by shrinking the learning dimensions of the DDPG agent and limits transient responses through constrained learning of control parameters. Experimental results show that the proposed controller exhibited rapid adaptive performance in the learning process for the target oxygen concentration, achieving a reward value of −0.05 within just 105 episodes. Furthermore, when compared to traditional PID tuning methods, the proposed controller demonstrated superior performance, achieving a target value error of 0.0032 and a low overshoot range of 0.0498 to 0.0631, providing the fastest response speed and minimal oscillation. Additionally, experiments conducted on an actual operating ship verified the practical feasibility of this system, highlighting its potential for real-time control and pollutant reduction in marine applications.

Real-time (re-)planning is crucial for autonomous quadrotors to navigate in uncertain environments where obstacles may be detected and trajectory plans must be adjusted on-the-fly to avoid collision. In this paper, we present a control system design for autonomous quadrotors that has real-time re-planning capability, including the hardware pipeline for the hardware–software integration to realize the proposed real-time re-planning algorithm. The framework is based on a modified version of the PX4 Autopilot and a Raspberry Pi 5 companion computer. The planning algorithm utilizes minimum-snap trajectory generation, taking advantage of the differential flatness property of quadrotors, to realize computationally light, real-time re-planning using an onboard computer. We first verify the control system and the planning algorithm through simulation experiments, followed by implementing and demonstrating the system on hardware using a quadcopter.

The frequent occurrence of safety incidents in sewer systems due to the emergency toxicity of hydrogen sulfide (H2S) necessitate timely and efficient prediction, early warning and real-time control. However, various factors influencing H2S generation and emission leads to a substantial computational burden for the existing dynamic sewer process models and fails to timely control the H2S exposure risk. The present study proposed a swift prediction model (SPM) that combined the validated dynamic sewer process model (the biofilm-initiated sewer process model, BISM) with a high-speed machine learning algorithm (MLA), achieving accurately and swiftly predict the dissolved sulfide (DS) concentration and H2S concentration in a specific sewer network. Based on Gradient Boosting Decision Tree-based SPM, the simulated concentrations of DS and H2S are 1.95 mg S/L and 214 ppm, respectively, which are closely to the field-measured values of 1.82 mg S/L and 219 ppm. Notably, SPM achieved a computation time of less than 0.3 s, and a significant improvement over BISM (> 5000 s) for the same task. Moreover, the real-time and dynamic dosing scheme facilitated by SPM outperformed the conventional constant dosing scheme provided by dynamic sewer process model, which significantly improved the H2S control completion rate from 69 % to 100 %, and achieved a significant reduction in chemical dosage. In conclusion, the integration of dynamic sewer process models with MLA addresses the inadequacy of monitoring data for MLA training, and thus pursues swift prediction of H2S generation and emission, and achieving real-time, effective, and economic control of H2S in complex sewer networks.

Steam methane reforming (SMR) is one of the most widely used hydrogen (H2) production processes. In addition to its extensive utilization in industrial sectors, hydrogen is expanding it share as a clean energy carrier, and more sustainable and efficient H2 production methods are continuously being explored and developed. One method replaces conventional fossil fuel-based heating with electrical heating through the flow of electrons across the reformer. At UCLA, an experimental setup was built of an electrically heated steam methane reforming process. This paper describes the system components, explains the digitalization of the experimental setup and introduces methods for building a first-principles-based dynamic process model using parameters estimated via data-driven methods from process experimental data. The modeling approach uses a lumped parameter approximation and employs algebraic equations to solve for gas-phase variables. The reaction parameters are calculated from steady-state experimental data, and the temperature change is modeled with respect to change in electric current using a first-order dynamic model. The overall dynamic process model is then used in a computational model predictive control (MPC) scheme to drive the process to a new H2 production set-point under unperturbed and steam flowrate disturbance cases. The performance and robustness of the proposed MPC scheme are compared to the ones of a classical proportional–integral (PI) controller and are demonstrated to be superior in terms of closed-loop response, robustness, and constraint handling.

This paper presents a non-linear model predictive control approach for offset-free tracking and the rejection of piece-wise constant disturbances. The approach involves augmenting the system’s state vector with the integral of the tracking error, enabling the design of a non-linear model predictive controller for this augmented system. Nominal closed-loop stability is enforced thanks to a terminal equality constraint and proven by a Lyapunov argument. Compared to the existing offset-free approaches in the literature, our method offers greater simplicity, as it does not rely on linear approximations of the system to control. Furthermore, it eliminates the need to estimate disturbances, a task that is especially challenging with non-linear systems. Comprehensive simulations and experimental tests are conducted according to a non-linear, coupled, two-tank laboratory experiment, demonstrating the robustness and effectiveness of the proposed approach.

This study proposes and validates a novel combustion control system for oil-fired boilers aimed at reducing air pollutant emissions through flame image prediction. The proposed system is easily applicable to existing ships. Traditional proportional combustion control systems supply fuel and air at fixed ratios according to the set steam load, without considering the emission of air pollutants. To address this, a stable and immediate control system is proposed, which adjusts the air supply to modify the combustion state. The combustion control system utilizes oxygen concentration predictions from flame images via SEF+SVM as control inputs and applies internal model control (IMC)-based proportional-integral (PI) control for real-time combustion control. Due to the complexity of modeling the image-based system, IMC filter constant tuning through experimentation is essential for achieving effective control performance. Experimental results showed that optimal control performance was achieved when the filter constant λ was set to 1.5. In this scenario, the peak overshoot Mp was reduced to 0.19245, and the Integral of Squared Error (ISE) was minimized to 10.1159, ensuring a stable response with minimal oscillation and maintaining a fast response speed. The results demonstrate the potential of the proposed system to improve combustion efficiency and reduce emissions of air pollutants. This study provides a feasible and effective solution for enhancing the environmental performance of marine oil-fired boilers. Given its ease of application to existing ships, it is expected to contribute to sustainable air pollution reduction across the maritime environment.

Franka Emika robots have gained significant popularity in research and education due to their exceptional versatility and advanced capabilities. This work introduces panda-py — a Python interface and framework designed to empower Franka Emika robotics with accessible and efficient programming. The panda-py interface enhances the usability of Franka Emika robots, enabling researchers and educators to interact with them more effectively. By leveraging Python’s simplicity and readability, users can quickly grasp the necessary programming concepts for robot control and manipulation. Moreover, integrating panda-py with other widely used Python packages in domains such as computer vision and machine learning amplifies the robot’s capabilities. Researchers can seamlessly leverage the vast ecosystem of Python libraries, thereby enabling advanced perception, decision-making, and control functionalities. This compatibility facilitates the efficient development of sophisticated robotic applications, integrating state-of-the-art techniques from diverse domains.

This study describes a method for swinging up and stabilizing a double inverted pendulum (DIP) in real-time utilizing a PID-LQR combined control system. Firstly, a dynamic model of the double inverted pendulum system is made up and the equations of motion are constructed. The pendulum then is moved from its unstable position to a stable one using a PID-LQR controller. A comparison of the PID-LQR controller’s output and suggestions for improving system stability is presented and is suggested combined control system.

Urban stormwater management has become a major issue over the last decades for flood prevention as well as water resource preservation. The development of green infrastructures such as bioretention systems since the 1990s has often been reported as an effective means of runoff mitigation with subsequent conveyed pollutant capture. Nevertheless, climate change involving more frequent extreme weather events as well as the variety of emerging pollutants in urban runoff have put an increasing strain on bioretention processes. Within this context, this mini-review deals with the opportunity of upgrading vegetated bioretention systems with active control technology to enhance their pollutant treatment capacity through proper control of critical bioretention operational variables and relying on improved ecological functioning and resilience. It is envisioned that such nature-based solutions hybridized with real-time control technology would help to improve stormwater reuse for more sustainable urban water management within the nexus of water–energy–food and greenhouse gases in future cities. HIGHLIGHTS Needs to enhance the capacity of soluble pollutant retention by current bioretention cells.; Real-time control of bioretention cells as a promising way to improve biofiltration performances.; Real-time control of soil moisture level to foster ecological functioning, bioremediation and ecosystem resilience of bioretention cells.; Healthier actively controlled bioretention ecosystems as a way to increase stormwater reuse.;

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. HIGHLIGHTS Rainfall nowcast inclusion in control procedures should be preceded by a careful risk analysis.; Nowcast accuracy is highly heterogenous, meaning the applicable for control varies.; Nowcast accuracy can be used to qualify the risk associated with the implementation of control.; Predicting the end of the event was found to be the most reliable predictive ability, with limited performance loss due to uncertainties.;

In tokamak and other fusion devices, magnetic control is the main tool that allows to regulate the plasma current, position and shape; it is in charge of actuating the desired plasma current waveform, steering the plasma position to a given set point and maintain the plasma shape close to a prescribed plasma equilibrium. This work describes the application of several physics concepts and computational tools in order to obtain a novel optimal controller for the plasma centroid position, which has been implemented and tested in the real-time plasma control system at the ISTTOK tokamak. A key point for the development of the new control system was the installation of a recently upgraded hardware, that numerically integrates in real-time the magnetic probes signals.

Additive manufacturing is a promising manufacturing process with diverse applications, but ensuring the quality and reliability of the manufactured products are key challenges. The digital twin has emerged as a technology solution to address these challenge, allowing real-time monitoring and control of the manufacturing process. This paper proposes a digital twin system framework for additive manufacturing that integrates machine learning models, employing Unity, OctoPrint, and Raspberry Pi for real-time control and monitoring. Particularly, the system utilizes machine learning models for defect detection, achieving an Average Precision (AP) score of 92%, with specific performance metrics of 91% for defected objects and 94% for non-defected objects, demonstrating high efficiency. The Unity client user interface is also developed for control and visualization, facilitating easy additive manufacturing process monitoring. This research article presents a detailed description of the proposed digital twin framework and its workflow for implementation, the machine learning models, and the Unity client user interface. It also demonstrates the effectiveness of the integrated system through case studies and experimental results. The main findings show that the proposed digital twin system met its functional requirements and effectively detects defects and provides real-time control and monitoring of the additive manufacturing process. This paper contributes to the growing field of digital twin technology and additive manufacturing, providing a promising solution for enhancing the quality and reliability in the field of additive manufacturing.

We propose to solve a real-time traffic variation of the On-Demand Bus Routing Problem (ODBRP) introduced by Melis and Sörensen (2022). The ODBRP belongs to the category of the Dial-A-Ride Problems (DARP), and features departure and arrival bus station selection. This problem is specifically aimed at planning a fleet of on-demand buses in an urban environment. However, cities are frequently plagued by traffic congestion, which may cause delays and missed time windows for passengers.To deal with this situation, we introduce, study, and solve a variant of the ODBRP in which the travel times are both time-dependent (i.e., the travel time between two nodes depends on the departure time) and updated dynamically.In our approach, congested roads that might cause passenger delays are modeled by frequently updating the travel speed on the road segments that constitute them. The resulting problem is solved by using a K-shortest-path procedure to determine alternative paths between bus stations, as well as a Variable Neighborhood Descent (VND) procedure to repair violated time windows.Our experimental results show the overall efficacy of this real-time control under divergent degrees of flexibility (congestion and the number of buses available). Specifically, the average tardiness, maximum tardiness, and the number of late passengers are significantly reduced under a wide range of congestion scenarios, from slight to severe. In addition, this efficacy holds for various ratios of requests to the number of vehicles.

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.

In order to study the real-time control research of laboratory information system based on nonlinear programming, by analyzing the role played by the laboratory information management system (LIMS) in quality management, and the relationship with quality control, put forward the functions that LIMS should set up and possess in the management of controlled documents, processing of inspection data, etc., an accurate and reliable method to ensure the test results. It has been verified that the system can meet the performance requirements of 350 virtual concurrent users, its memory usage and CPU usage are within acceptable limits, the response time of important operations of each module of the system is within 3 s. The average memory consumption obtained from the test is 2.02 GB, and the average utilization rate of CPU is 66%. The two indicators basically meet the performance requirements of the system. The system can meet the performance requirements of the number of virtual concurrent users 350, both its memory occupancy and CPU utilization rate are in the acceptable range. It not only improves the accuracy of the sample processing results, but also reduces the labor intensity of the technicians, so that the laboratory expands the analysis function from the simple reporting function.

To improve the utilization rate of maize seed fertilizer, this study aimed to propose a precise co-sowing control system for the real-time control of the relative position of seed fertilizer during the co-sowing operation. According to the operating speed of the machine, the longitudinal distance between the seed feeder and the outer groove wheel, the height of the seed and fertilizer falling, and the relative position of the seed and fertilizer falling into the soil, the calculation method for the seed and fertilizer falling into the soil was obtained, the precise co-seeding model of the seed fertilizer was constructed, the control algorithm of the precise co-seeding of the seed fertilizer was designed, and the hardware system and software system were designed. Based on the hardware structure and working principle of the motor drive seeding and fertilization control system, a functional circuit based on the STM32F103ZET6 single-chip microcomputer (Zhengdianyuanzi (Guangzhou) Technology Co., Ltd., Guangzhou, China) was built. When the system is working, the satellite speed measurement module collects the operating speed of the machine, the encoder feeds back the motor speed in real time, a Hall sensor detects the time interval between fertilizer and seed discharge at the point of discharge, and the PID algorithm is applied to make the speed regulation system regulate the motor speed and position and adjust the speed and position of the seed discharge tray and fertilizer on the outer slot wheel in real time. The relative position of seed and fertilizer in the soil can be controlled accurately in the process of sowing fertilizer. The test results showed that when the feed speed was 2, 3, and 4 km·h−1, and the grain spacing was 20, 25, and 30 cm, respectively, the seed fertilizer alignment was better and met the requirements of precise sowing, improving fertilizer utilization rate.

Exoskeleton rehabilitation robots have been widely used in the rehabilitation treatment of stroke patients. Clinical studies confirmed that rehabilitation training with active movement intentions could improve the effectiveness of rehabilitation treatment significantly. This research proposes a real-time control method for an upper limb exoskeleton based on the active torque prediction model. To fulfill the goal of individualized and precise rehabilitation, this method has an adjustable parameter assist ratio that can change the strength of the assist torque under the same conditions. In this study, upper limb muscles’ EMG signals and elbow angle were chosen as the sources of control signals. The active torque prediction model was then trained using a BP neural network after appropriately extracting features. The model exhibited good accuracy on PC and embedded systems, according to the experimental results. In the embedded system, the RMSE of this model was 0.1956 N·m and 94.98%. In addition, the proposed real-time control system also had an extremely low delay of only 40 ms, which would significantly increase the adaptability of human–computer interactions.

Real Time Control (RTC) is widely accepted as a cost-effective way to operate urban drainage systems (UDS) effectively. However, what factors influence RTC efficacy and how this might change in the long term remains largely unknown. This paper reviews the literature to understand what these factors likely are, and how they can be assessed in the future. Despite decades of research, inconsistent definitions of the performance of RTC are used, hindering an objective and quantitative examination of the benefits and drawbacks of different control strategies with regard to their performance and robustness. Furthermore, a discussion on the changes occurring and projected to occur to UDS reveals that the potential impact of these changes on the functioning of RTC systems can be significant and should be considered in the design stage of the RTC strategy. Understanding this ‘best-before’ characteristic of an RTC strategy is the key step to ensure long term optimal functioning of the UDS. Additionally, unexplored potential for RTC systems might exist in the transitions, rehabilitation and construction of drainage systems. The research gaps highlighted here could guide the way for further development of RTC strategies, and enabling more optimal, long term implementation of RTC for urban drainage systems. HIGHLIGHTS This paper critically assesses the current knowledge of Real Time Control for Urban Drainage Systems.; It looks at what changes in the long term are likely to happen to drainage systems and relates this to RTC performance.; It explorers the roles Real Time Control will play in the future.;

This paper describes the novel application of conditional Generative Adversarial Networks (cGANs) for real-time stability region determination (SRD) in power systems. As the network configuration changes during the course of operation, the availability of the stability region would enable the operator to suitably tune the control parameters to maintain stability while maximizing the dynamic performance. Here, the implementation of the cGANs-based SRD is described using transmission and microgrid case studies, where it is demonstrated to adaptively estimate the stability region for different network configurations with high accuracy. It is also shown that the cGANs approach has a significantly shorter execution time as compared to the conventional model-based method, demonstrating its value for real-time use in practical power systems.

The objective of this study was to develop a novel real-time monitoring and control method for ultra-fast laser scribing processes utilizing spectrometer. Adjustment of laser process parameters such as laser power with high precision in real-time is critical in the laser engraving process due to the premium quality and speed requirements of the process. An online monitoring system was established using the Ocean Optics spectrometer, IPG ytterbium pulsed laser, and PXIe-8880 industrial computer. An algorithm for real-time control of the laser scribing process was developed based on the monitoring outcomes using LabVIEWⓇ software. Experimental methods were performed to evaluate the reliability of the developed monitoring system and control algorithm. The sensitivity of the spectrometer was assessed by changing laser power, pulse length, and focal point position. A workpiece consisting of two different metals, including stainless steel SS304L and steel S355, was used to evaluate the performance of the developed algorithm when scribing moved from one material to another. Instant accurate setting of the laser power based on the variations in intensities of metals from 750 AU to 1400 AU validated the reliability of the algorithm.

Controlling an epidemiological model is often performed using optimal control theory techniques for which the solution depends on the equations of the controlled 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 controlled 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 a manner that the number of infected individuals is asymptotically reduced and the number of individuals under PrEP medication remains 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.

The selection of flow control device (FCD) location is an essential step for designing real-time control (RTC) systems in sewer networks. In this paper, existing storage volume-based approaches for location selection are compared with hydraulic optimisation-based methods using genetic algorithm (GA). A new site pre-screening methodology is introduced, enabling the deployment of optimisation-based techniques in large systems using standard computational resources. Methods are evaluated for combined sewer overflow (CSO) volume reduction using the CENTAUR autonomous local RTC system in a case study catchment, considering overflows under both design and selected historic rainfall events as well as a continuous 3-year rainfall time series. The performance of the RTC system was sensitive to the placement methodology, with CSO volume reductions ranging between −6 and 100% for design and lower intensity storm events, and between 15 and 36% under continuous time series. The new methodology provides considerable improvement relative to storage-based design methods, with hydraulic optimisation proving essential in relatively flat systems. In the case study, deploying additional FCDs did not change the optimum locations of earlier FCDs, suggesting that FCDs can be added in stages. Thus, this new method may be useful for the design of adaptive solutions to mitigate consequences of climate change and/or urbanisation. HIGHLIGHTS Genetic algorithm (GA) method gives improved performance over volume approaches.; A screening method is utilised to increase efficiency of the GA.; The new optimisation-based methodology proved suitable for adaptive design.; A local real-time control system is shown to reduce overflow spill volume.;

Urbanization and an increase in precipitation intensities due to climate change, in addition to limited urban drainage systems (UDS) capacity, are the main causes of combined sewer overflows (CSOs) that cause serious water pollution problems in many cities around the world. Model predictive control (MPC) systems offer a new approach to mitigate the impact of CSOs by generating optimal temporally and spatially varied dynamic control strategies of sewer system actuators. This paper presents a novel MPC based on neural networks for predicting flows, a stormwater management model (SWMM) for flow conveyance, and a genetic algorithm for optimizing the operation of sewer systems and defining the best control strategies. The proposed model was tested on the sewer system of the city of Casablanca in Morocco. The results have shown the efficiency of the developed MPC to reduce CSOs while considering short optimization time thanks to parallel computing. HIGHLIGHTS Model predictive control of smart sewer networks.; Artificial Neural Networks and parallel computing enhance the proactivity of the MPC.; Real-Time Control.; Combined sewer overflows reduction.;

With a tremendous number of renewable energy sources (RES) integrated into the distribution system, the inherent uncertainty of RES power generation brings about significant challenges in distribution and power balance within the distribution system. This article proposes an adaptive robust economic dispatch (ARED) model and a real-time control strategy for distribution systems as countermeasures, which make full use of the adjustable capabilities of controllable inverter air-conditioner (IAC) clusters. Firstly, the concept of the adjustable capacity curve (ACC) is developed to accurately quantify the adjustable capacity of an IAC cluster. Afterward, a two-stage adaptive robust optimization is formulated for ARED, which comprehensively takes the adjustable capacity of the IAC cluster and the uncertainty of RES into consideration. Meanwhile, the solution methodology of ARED is also designed based on the column and constraint generation (C&CG) algorithm, where the master problem is quadratic programming with quadratic constraints (QCQP), and the max-min sub-problem is reformulated to a mixed integer linear programming (MILP) form by taking advantage of linear duality theory and big-M method. Finally, a novel real-time decentralized control strategy for IAC clusters is also proposed for purpose of hedging against stochastic RES power fluctuation after every round of ARED decisions. The results of the case study validate the effectiveness of ARED model and real-time control strategy under different uncertainty scenarios of RES power generation.

Accurate and real-time gesture recognition is required for the autonomous operation of prosthetic hand devices. This study employs a convolutional neural network-enhanced channel attention (CNN-ECA) model to provide a unique approach for surface electromyography (sEMG) gesture recognition. The introduction of the ECA module improves the model’s capacity to extract features and focus on critical information in the sEMG data, thus simultaneously equipping the sEMG-controlled prosthetic hand systems with the characteristics of accurate gesture detection and real-time control. Furthermore, we suggest a preprocessing strategy for extracting envelope signals that incorporates Butterworth low-pass filtering and the fast Hilbert transform (FHT), which can successfully reduce noise interference and capture essential physiological information. Finally, the majority voting window technique is adopted to enhance the prediction results, further improving the accuracy and stability of the model. Overall, our multi-layered convolutional neural network model, in conjunction with envelope signal extraction and attention mechanisms, offers a promising and innovative approach for real-time control systems in prosthetic hands, allowing for precise fine motor actions.

This paper provides a new method to nonlinear control theory, which is developed from the eigenvalue assignment method. The main purpose of this method is to locate the pointwise eigenvalues of the linear-like structure built by freezing the nonlinear systems at a given time instant in a desired disk region. Since the control requirements for the transient response characteristics are the major constraints on the selection of the disk centre and radius, two different update algorithms are also developed to reshape the disk region by changing the disk centre and radius at each time step. The effectiveness of the proposed methods is tested in both simulations and experiments. A validated three-DOF laboratory helicopter is used for experiments.

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.;

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.;

An experimental validation of fractional-order PID (FOPID) controllers, which were applied to a two coupled tanks system, is presented in this article. Two FOPID controllers, a continuous FOPID (cFOPID) and a discrete FOPID (dFOPID), were implemented in real-time. The gains tuning process was accomplished by applying genetic algorithms while considering the cost function with respect to the tracking error and control effort. The gains optimization process was performed directly to the two-tanks non-linear model. The real-time implementation used a National Instruments PCIe-6321 card as a data acquisition system; for the interface, we used a Simulink Matlab and Simulink Desktop Real-Time Toolbox. The performance of the fractional controllers was compared with the performance of classical PID controllers.

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.;