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This paper presents a novel scheme based on distributed model-based predictive control for the secondary level control of hybrid AC/DC microgrids. Prediction models based on droop control and power transfer equations are proposed to characterize the generators in both the AC and DC sub-microgrids, whereas power balance constraints are used to predict the behavior of interlinking converters. The operational constraints (such as powers and control action limits) are included in all the formulations. Experimental results validate the proposed scheme for the following cases: (i) load changes, working within operating constraints, (ii) managing frequency regulation in the AC sub-microgrid, voltage regulation in the DC sub-microgrid and global power consensus in the whole hybrid microgrid, and (iii) maintaining the microgrid performance in the presence of communication malfunction while ensuring that plug-and-play capability is preserved.

The COVID-19 pandemic has challenged health services and governments in Canada and around the world. Our research aims to evaluate the effect of domestic and international air travel patterns on the COVID-19 pandemic in Canadian provinces and territories.Air travel data were obtained through licensed access to the 'BlueDot Intelligence Platform', BlueDot Inc. Daily provincial and territorial COVID-19 cases for Canada and global figures, including mortality, cases recovered, and population data were downloaded from public datasets. The effects of domestic and international air travel and passenger volume on the number of local and non-local infected people in each Canadian province and territory were evaluated with a semi-Markov model. Provinces and territories are grouped into large (100 000 confirmed COVID-19 cases and 1 000 000 inhabitants) and small jurisdictions (≤100 000 confirmed COVID-19 cases and ≤ 1 000 000 inhabitants).Our results show a clear decline in passenger volumes from March 2020 due to public health policies, interventions, and other measures taken to limit or control the spread of COVID-19. As the measures were eased, some provinces and territories saw small increases in passenger volumes, although travel remained below pre-pandemic levels. During the early phase of disease introduction, the burden of illness is determined by the connectivity of jurisdictions. In provinces with a larger population and greater connectivity, the burden of illness is driven by case importation, although local transmission rapidly replaces imported cases as the most important driver of increasing new infections. In smaller jurisdictions, a steep increase in cases is seen after importation, leading to outbreaks within the community.Historical travel volumes, combined with data on an emerging infection, are useful to understand the behaviour of an infectious agent in regions of Canada with different connectivity and population size. Historical travel information is important for public health planning and pandemic resource allocation.

1008 Background: Treatment for metastatic breast cancer has advanced since 2000, but we do not know if those advances have reduced mortality in the general population. Methods: Four Cancer Intervention and Surveillance Network (CISNET) models simulated US breast cancer mortality from 2000 to 2017 using national data on mammography use and performance, efficacy and dissemination of estrogen receptor (ER) and HER2-specific treatments of early-stage (stages I-III) and metastatic (stage IV or distant recurrence) disease, and competing mortality. Models compared overall and ER/HER2-specific breast cancer mortality rates from 2000 to 2017 relative to estimated rates with no screening or treatment, and attributed mortality reductions to screening, early-stage or metastatic treatment. Results of an exemplar model are shown. Results: The mortality reduction attributable to early-stage treatment increased from 35.8% in 2000 to 48.2% in 2017, while the proportion attributable to metastatic treatment decreased slightly from 23.9% to 20.6%. The increasing contribution of early-stage treatment reflects the transition of effective metastatic treatments to early-stage disease: accordingly, ten-year distant recurrence-free survival improved (82.5% in 2000, 87.3% in 2017; for ER+HER2+, 78.2% to 90.9%). Survival time after metastatic diagnosis also increased, doubling from 1.48 years in 2000 to 2.80 years in 2017, with the best survival for women with ER+HER2+ cancers (4.08 years) and worst for ER-HER2- (1.22 years). Conclusions: Advances in metastatic breast cancer treatment are reflected in lower population mortality, both through transition to early-stage treatment and gains for women with metastatic disease. These results may inform patient/physician discussions about breast cancer prognosis and expected benefits of treatment. [Table: see text]

Takagi–Sugeno fuzzy models are cataloged as universal approximators and have been proven to be a powerful tool for the prediction of systems. However, in certain cases they may fail to inherit the main properties of a system which may cause problems for control design. In particular, a non-suitable model can generate a loss of closed-loop performance or stability, especially if that model is not controllable. Therefore, ensuring the controllability of a model to enable the computation of appropriate control laws to bring the system to the desired operating conditions. Therefore, a new method for identification of fuzzy models with controllability constraints is proposed in this paper. The method is based on the inclusion of a penalty component in the objective function used for consequence parameter estimation, which allows one to impose controllability constraints on the linearized models at each point of the training data. The benefits of the proposed scheme are shown by a simulation-based study of a benchmark system and a continuous stirred tank: the stability and the closed-loop performances of predictive controllers using the models obtained with the proposed method are better than those using models found by classical and local fuzzy identification schemes.

For all the spine surgeons, thoracic disc herniations (TDHs) entrust a real challenge in terms of patient diagnosis, proper selection, surgical technique, and potential adverse events. TDHs are relatively uncommon compared to the lumbar and cervical levels. Literature reports a variable prevalence of TDHs around 6% to 40%, but less than 1% of all disk herniations are symptomatic TDHs, evidencing as a relatively unusual condition. Nowadays, transforaminal endoscopic thoracic discectomy (TETD) has been implemented as an alternative to classic open procedures with results that are as good as and, in some situations, better than those in traditional discectomy. However, the surgeon must be familiar with endoscopic lumbar spine surgery before opting to perform a TETD, considering that the learning curve is much harder. We describe all the steps and safety considerations during TETD based on the anatomic differences compared to lumbar endoscopic procedures. TETD is an effective and safe method that yields more benefits, provides a direct route to the lesion with less morbidity, and is performed in a minimally invasive way. Many severe complications related to the thoracic region could be avoided having the proper knowledge, adequate technique, and safety routes and considerations.

This paper presents a new methodology for Prediction Interval (PI) construction based on a modified Takagi-Sugeno fuzzy system trained with a joint Supervision loss function. Given a desired coverage level, this model is capable of providing predictions of the expected value of the system along with the interval bounds. This methodology is tested by simulation experiments using a dataset containing real temperature data from a rural community in southern Chile. The proposed model was compared with a state-of-the-art Takagi-Sugeno Fuzzy Numbers model. It was shown that the Joint Supervision method manages to obtain slightly superior results to the Fuzzy Numbers approach while greatly reducing the complexity of the training loss function. Additionally, since the proposed model was trained using Particle Swarm Optimization, further performance improvements could be made by employing gradient-based optimization algorithms, since they are compatible with the Joint Supervision loss function.

A model predictive control strategy (MPC) based on fuzzy optimization is proposed in this work for a multi-room heating, ventilation and air conditioning (HVAC) system. The proposed strategy aims to minimize energy consumption, while requiring different thermal conditions for each room. For this system, the combination of MPC and fuzzy optimization arises as a suitable control strategy, due to the benefits given by the use of fuzzy constraints for the managment of thermal comfort.The soft constraint scheme provided by the fuzzy optimization allows to reduce the power consumption of HVAC systems. This is achieved by allowing some constraint violations in specific cases where the proper operation of the system is not compromised. In this context, the main contribution of this work is the introduction of a new framework where the thermal requirements of several rooms can be managed by fuzzy constraints, which are handled as additional terms in the objective function of the MPC. The optimization problem of the MPC is nonlinear, and it is solved with a suitable particle swarm optimization (PSO) method. Simulations results show the effectiveness of the proposed controller to reduce the energy consumption compared with a classical MPC implementation, while maintaining constraint satisfaction in appropriate levels.

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Most stochastic Model Predictive Control (MPC) formulations allow constraint violations via the use of chance constraints, thus increasing control authority and improving performance when compared to their robust MPC counterparts. However, common stochastic MPC methods handle chance constraints conservatively: constraint violations are often smaller than allowed by design, thus limiting the potential improvements in control performance. This is a consequence of enforcing chance constraints overlooking the past behavior of the system and/or of an over tightening of the constraints on the predicted sequences. This work presents a stochastic MPC strategy that uses the observed amount of constraint violations to adaptively scale the tightening parameters, thus eliminating the aforementioned conservativeness. It is proven using Stochastic Approximation that, under suitable conditions, the amount of constraint violations converges in probability when using the proposed method. The effectiveness and benefits of the approach are illustrated by a simulation example.

The adoption of Electric Vehicles (EVs) has substantially increased during the last decade, creating the need for customized EV-oriented routing strategies capable of using the enormous amount of historical, and real-time, traffic data that is collected through Intelligent Transport Systems (ITSs). Existing EV routing algorithms, however, concentrate mostly on the usage historical data to compute offline optimal paths, whereas the use of real-time traffic information to compute en-route path updates is still an almost unexplored topic; mainly due to its inherent computational challenges. This research effort proposes a Prognostic Decision Making (PDM) strategy to solve in real-time the Electric Vehicle Dynamic Stochastic Shortest Path Problem (EV-DSSPP); aiming at the simultaneous utilization of historical and real-time traffic data. Factors such as recurring and non-recurring traffic congestion, elevation, velocity and EV’s parameters are incorporated into the decision-making process. The proposed strategy has two hierarchical functional layers. The lower layer consists of a fast-computing routing algorithm that, by construction, guarantees a real-time execution. The higher layer organizes the periodic en-route execution of the first layer to compute en-route path updates during a trip. This strategy can hence serve as an expert router that works jointly with an ITS to assist EV drivers on route. The proposal is validated through a simulation study based on real-world traffic data collected in Santiago, Chile. The results show that periodic en-route path updates can generate a reduction in both travel time and energy consumption, which evidences the benefits of incorporating real-time traffic information into the EV-routing problems.

The stability of stochastic Model Predictive Control (MPC) subject to additive disturbances is often demonstrated in the literature by constructing Lyapunov-like inequalities that guarantee closed-loop performance bounds and boundedness of the state, but convergence to a terminal control law is typically not shown. In this work we use results on general state space Markov chains to find conditions that guarantee convergence of disturbed nonlinear systems to terminal modes of operation, so that they converge in probability to a priori known terminal linear feedback laws and achieve time-average performance equal to that of the terminal control law. We discuss implications for the convergence of control laws in stochastic MPC formulations, in particular we prove convergence for two formulations of stochastic MPC.

The stability of stochastic model-predictive control (MPC) subject to additive disturbances is often demonstrated in the literature by constructing Lyapunov-like inequalities that ensure closed-loop performance bounds and boundedness of the state, but tight ultimate bounds for the state and nonconservative performance bounds are typically not determined. In this article, we use an input-to-state stability property to find conditions that imply convergence with probability 1 of a disturbed nonlinear system to a minimal robust positively invariant set. We discuss implications for the convergence of the state and control laws of stochastic MPC formulations, and we prove convergence results for several existing stochastic MPC formulations for linear and nonlinear systems.

Optimal robust MPC for constrained linear systems that are subject to additive uncertainty requires a closed loop optimization with computation that rises exponentially with the prediction horizon, N. This was overcome by parameterized tube MPC (PTMPC) that used a triangular separable prediction strategy in which predictions are decomposed into a nominal and N – 1 partial prediction sequences, each concerned with the compensation of a particular future disturbance. Beyond the first N steps, the PTMPC control law can be taken to be a static state feedback. Use of N – 1 partial prediction sequences causes the number of free variables and constraints to rise quadratically with N. To reduce this to linear, this paper proposes a modification of PTMPC that uses a single partial prediction sequence that is used in connection with all particular future disturbances but allows the compensatory effect of the partial prediction sequences to extend beyond the first N predictions. The prediction structure is of a triangular striped nature and extends over an infinite horizon. As illustrated by a simulation example, the resulting scheme can outperform PTMPC in terms of the size of the domain of attraction and affords a reduction in computation which allows for the use of longer horizon (for the same computational demand).

As CO2 emission regulations increase, fleet owners increasingly consider the adoption of Electric Vehicle (EV) fleets in their business. The conventional Vehicle Routing Problem (VRP) aims to find a set of routes to reduce operational costs. However, route planning of EVs poses different challenges than that of Internal Combustion Engine Vehicles (ICEV). The Electric Vehicle Routing Problem (E-VRP) must take into consideration EV limitations such as short driving range, high charging time, poor charging infrastructure, and battery degradation. In this work, the E-VRP is formulated as a Prognostic Decision-Making problem. It considers customer time windows, partial midtour recharging operations, non-linear charging functions, and limited Charge Station (CS) capacities. Besides, battery State of Health (SOH) policies are included in the E-VRP to prevent early degradation of EV batteries. An optimization problem is formulated with the above considerations, when each EV has a set of costumers assigned, which is solved by a Genetic Algorithm (GA) approach. This GA has been suitably designed to decide the order of customers to visit, when and how much to recharge, and when to begin the operation. A simulation study is conducted to test GA performance with fleets and networks of different sizes. Results show that E-VRP effectively enables operation of the fleet, satisfying all operational constraints.

This paper presents a software architecture for microcontrollers running on data capture cards for the Internet of Things (IoT). The Software Architecture describes the structure, operation and interaction of the different components from a global perspective. This paper describes various components and subcomponents of the architecture and its communication. A case study is developed based on the proposed architecture for a cilantro crop in Colombia.

A Robust MPC strategy for Heating, Ventilation and Air Conditioning Systems (HVAC) is proposed in this work. The typical control objective of minimizing energy consumption while maintaining user comfort is considered in this work. Robust MPC is naturally suited for HVAC systems with the aforementioned control goal because it is a control strategy that considers process constraints and the optimization of a performance index, and explicitly handles uncertainty. In this system, the uncertainty comes from the ambient temperature and internal loads predictions, which are the main factors driving the thermal dynamics. Thus, effectively predicting their future behaviour and uncertainty aids for the quality of the control system. In this context, the main contribution of this work is the introduction of a new framework that uses fuzzy interval models for predicting bounds of uncertain variables in a Robust MPC formulation. These bounds are constructed so that the future values of the relevant variables are within them with a predefined probability. In this work, fuzzy interval models are trained and used to predict the future system disturbances, and these are in turn used to provide bounds for the predictions of room temperatures of the HVAC system. Simulation results show the effectiveness of the proposed strategy, in terms of yielding higher percentage of constraints satisfaction when compared to a classical MPC method. Additionally, it is shown that an appropriate compromise between the system performance and the rate of constraint satisfaction can be achieved by varying the coverage probability of the fuzzy interval models.

Polytopic dynamics have been introduced into the predictions in Model Predictive Control (MPC) and have been optimized so as to maximize invariant ellipsoids for systems subject to multiplicative uncertainty. This work was recently extended to the case of mixed additive and multiplicative uncertainty, and sufficient conditions were provided to guarantee the invariance of an ellipsoid. Additionally, under the assumption that the multiplicative uncertainty is known during a prediction horizon N , this extension was used as the terminal control law of an overall robust MPC strategy that deployed an affine-in-the-disturbance policy. This assumption was needed to enable the handling of constraints in the overall MPC strategy. The aims of this paper are to establish the necessity and sufficiency of the relevant invariance conditions of the polytopic dynamics, and to propose a robust MPC strategy that uses polytopic sets to describe the evolution of predicted variables and therefore does not make an assumption that future multiplicative uncertainty is known. The number of constraints and free variables of the resulting online optimization grows linearly with N . The benefits of the strategy are illustrated by numerical simulations.

This paper presents a methodology based on generic evolutionary algorithms to solve a dynamic pickup and delivery problem formulated under a hybrid predictive control approach. The solution scheme is designed to support the dispatcher of a dial-a-ride service, where quick and efficient real-time solutions are needed. The scheme considers different configurations of particle swarm optimization and genetic algorithms within a proposed ad-hoc methodology to solve in real time the nonlinear mixed-integer optimization problem related with the hybrid predictive control approach. These consist of different techniques to handle the operational constraints (penalization, Baldwinian, and Lamarckian repair) and encodings (continuous and integer). For parameter tuning, a new approach based on multiobjective optimization is proposed and used to select and study some of the evolutionary algorithms. The multiobjective feature arises when deciding the parameters with the best trade-off between performance and computational effort. Simulation results are presented to compare the different schemes proposed and to advise conditions for the application of the method in real instances.

The optimisation of predicted control policies in model predictive control (MPC) enables the use of information on uncertainty that, though not available at current time, will be so at a future point on the prediction horizon. Optimisation over feedback laws is however prohibitively computationally expensive. The so-called affine-in-the-disturbance strategies provide a compromise and this article considers the use of disturbance compensation in the context of stochastic MPC. Unlike the earlier approaches, compensation here is applied over the entire prediction horizon (extending to infinity) thereby leading to a significant constraint relaxation which makes more control authority available for the optimisation of performance. In addition, our compensation has a striped lower triangular dependence on the uncertainty on account of which the relevant gains can be obtained sequentially, thereby reducing computational complexity. Further reduction in computation is achieved by performing this computation offline. Simulation results show that this reduction can be gained at a negligible cost in terms of closed-loop performance. © 2013 Taylor and Francis Group, LLC.