The wireless communication used by vehicles in collaborative vehicle platoons is vulnerable to cyber-attacks, which threaten their safe operation. To address this issue we propose a topology-switching coalitional model predictive control (MPC) method based on a reduced order unknown input observer which detects and isolates the cyber-attacks, so that the attacked communication links can be disabled by means of a topology switch. Also, the MPC controller is designed to guarantee robustness against undetected attacks and the increase of uncertainty derived from disabling communication links. The proposed control method also conforms to a relaxed string stability condition and is guaranteed to be safe against crashes.

This paper presents a stochastic model predictive control (SMPC) strategy to maximize the economic profit of a greenhouse crop production. The strategy consists of an optimization problem that is solved with a multi-scenario MPC formulation (MS-MPC). It considers the uncertainty of market price by using its historical evolution per year as multiple price scenarios in the cost function. MS-MPC calculates a single set of dates to harvest and sell the crop production that optimizes profits for all the considered scenarios. In addition, MS-MPC determines the optimal temperature references that should be achieved inside the greenhouse for the growth of the crop. A case study for a Mediterranean tomato crop is simulated to analyze the performance of the developed MS-MPC strategy using a hierarchical control architecture with two layers. In the upper layer, MS-MPC calculations are executed following a receding horizon implementation. In the lower layer, regulatory control techniques are applied to reach the optimal temperature references by using natural ventilation and a heating system. Results show that MS-MPC can improve economic profits compared to the use of an average price scenario for the MPC calculations.Note to Practitioners—This paper was motivated by the need of greenhouse farmers for strategies that maximize profit considering market prices and crop production dynamics. It is not easy for them to make decisions to achieve such long-term objectives while minimizing economic risks, because market prices are very difficult to predict. As a solution, this work presents a novel control strategy to maximize profits by means of automatic selection of the best possible dates to harvest and sell the crop production. This selection is made thanks to considering the uncertainty of market prices in the control strategy by evaluating different scenarios, which are recorded evolutions of the prices from previous years. Although the strategy was tested in simulation, results suggest that using multiple scenarios of historical prices is better for the optimization of profits than just considering an average yearly trend of prices. The more scenarios are considered, the more protection against possible evolution of market prices is obtained. In future research, this control strategy could be extended to include other uncertainties of factors affecting decision making, such as weather forecasts.

Tumor growth models can help predict the response of tumors to different treatments. This work presents a generic mathematical model that combines tumor growth, the pharmacokinetics of the drugs administered, and the evolution of the possible side effects of the treatment. Tumors are complex systems, and they can exhibit different growth patterns under the same initial conditions. To deal with these uncertainties, a stochastic model predictive strategy has been developed to reduce the tumor size while minimizing side effects. Thus, probabilistic constraints have been incorporated into the optimization problem, giving rise to a chance-constrained model predictive approach. A one-year treatment simulation assessment is performed to compare the stochastic model predictive controller with the standard implementation. The results demonstrate that the proposed approach improves the performance of the controller, satisfying the marked objectives. Overall, while tumor growth modeling can provide valuable insights into the behavior of tumors, it is important to incorporate sources of uncertainty to ensure that the models accurately capture the behavior of all tumors.

This article proposes a real-time implementation of distributed model predictive controllers to maximize the thermal energy generated by parabolic trough collector fields. For this control strategy, we consider that each loop of the solar collector field is individually managed by a controller, which can form coalition with other controllers to attain its local goals while contributing to the overall objective. The formation of coalitions is based on a market-based mechanism in which the heat transfer fluid is traded. To relieve the computational burden online, we propose a learning-based approach that approximates optimization problems so that the controller can be applied in real time. Finally, simulations in a 100-loop solar collector field are used to assess the coalitional strategy based on neural networks in comparison with the coalitional model predictive control. The results show that the coalitional strategy based on neural networks provides a reduction in computing time of up to 99.74% and a minimal reduction in performance compared to the coalitional model predictive controller used as the baseline.

We propose a model-predictive control (MPC)-based approach to solve a human-in-the-loop control problem for a network system lacking sensors and actuators to allow for a fully automatic operation. The humans in the loop are, therefore, essential; they travel between the network nodes to provide the remote controller with measurements and to actuate the system according to the controller’s commands. Time instant optimization MPC is utilized to compute when the measurement and actuation actions are to take place to coordinate them with the network dynamics. The time instants also minimize the burden of human operators by tracking their energy levels and scheduling the necessary breaks. Fuel consumption related to the operators’ travel is also minimized. The results in a digital twin of the Dez Main Canal illustrate that the new algorithm outperforms previous methods in terms of meeting operational objectives and taking care of human well-being, but at the cost of higher computational requirements.

Acquiring vast and reliable data of physicochemical parameters is critical to environment monitoring. In the context of water quality analysis, data collection solutions have to overcome challenges related to the scale of environments to be explored. Sites to monitor can be large or remote. These challenges can be approached by the use of Unmanned Vehicles (UVs). Robots provide both flexibility on intervention plans and technological methods for real-time data acquisition. Being autonomous, UVs can explore areas difficult to access or far from the shore. This paper presents a nonlinear Model Predictive Control (MPC) for UV-based exploration. The strategy aims to improve the data collection of physicochemical parameters with the use of an Unmanned Surface Vehicle (USV) targeting water quality analysis. We have performed simulations based on real field experiments with a SPYBOAT® on the Heron Lake in Villeneuve d’Ascq, France. Numerical results suggest that the proposed strategy outperforms the schedule of mission planning and exploration for large areas.

This paper presents experimental results from the application of a data-based model predictive decision support system to drug inventory management in the pharmacy of a mid-size hospital in Spain. The underlying objective is to improve the efficiency of their inventory policy by exploiting pharmacy historical data. To this end, the pharmacy staff was aided by a decision support system that provided them with quantities needed for the satisfaction of clinical needs and the risk of stockout in case no order is placed for different time horizons. With this information in mind, the pharmacy service takes the final order decisions. The results obtained during a test period of four months are provided and compared with those of a previous model predictive control approach, which was implemented in the same hospital in the past, and with the usual policy of the pharmacy department.

This paper discusses the application of coalitional model predictive control (MPC) to freeways traffic networks, where the goal is reducing the time spent by the drivers through a dynamic setting of variable speed limits (VSL) and ramp metering. The prediction model METANET is used to represent the traffic flows evolution. The system behavior and objective function lead to a non-convex and non-linear optimization problem, which can only be solved in a centralized fashion for small networks. The underlying motivation of this paper is the continued advance of clustering methods in the control of large-scale and spatially distributed systems. The global freeway system is partitioned into a set of coupled sub-stretches, which in turn are assigned to the different agents involved in the control problem. These local controllers can dynamically assemble into coalitions to take coordinated measures. In this work, a top-down approach is considered: the bottom layer consists of the set of controllers that compute the VSL and ramp-metering across time; and the supervisory layer changes periodically the information exchange structure to promote coalitions of those controllers that bring greater performance to the global system. In this way, a balance is sought between optimality and efficiency. Finally, the coalitional approach is simulated on a stretch of traffic freeway where cooperation with adjacent sub-stretches is allowed.

International audience

In this paper, we focus on reducing the on-grid energy consumption in heterogeneous radio access networks (HetNets) supplied with hybrid power sources (grid and renewables). The energy efficiency problem is analyzed over both short- and long-timescales by means of reactive and proactive management strategies. For short-timescale case, a renewable-energy (RE) aware user equipment-base station association is proposed and analyzed for the cases when no storage infrastructure is available. For long-timescale case, a traffic flow method is proposed for load balancing in RE base stations (BSs), which is combined with a model predictive controller (MPC) to include forecast capabilities of the RE source behavior in order to better exploit a Green HetNet with storage support. The mechanisms are evaluated with data of solar measurements from the region of Valle de Aburrá, Medellín, Colombia and wind estimations from the Moscow region, Russian Federation. Results show how the green association proposal can reduce on-grid energy consumption in a HetNet by up to 34%, while is able to exceed the savings obtained by other methods, including the best-signal level policy by up to 15%, additionally providing high network efficiency and low computational complexity. For the long-timescale case, MPC attainable savings can be up to 22% with respect to the on-grid only Macro-BS approach. Finally, an analysis of our proposals in a common scenario is included, which highlights the relevance of storage management, although emphasizing the importance of combining reactive and proactive methods in a common framework to exploit the best of each approach.

In this paper, the user-base station association problem is addressed to reduce grid consumption in heterogeneous cellular networks powered by hybrid energy sources (grid and renewable energy). This paper proposes a novel distributed control scheme inspired by population games and designed considering both atomicity and non-anonymity, i.e., describing the individual decisions of each agent. The controller performance is considered from an energy-efficiency perspective, which requires the guarantee of appropriate quality-of-service levels according to renewable energy availability. The efficiency of the proposed scheme is compared with other heuristic and optimal alternatives in two simulation scenarios. Simulation results show that the proposed approach inspired by population games reduces grid consumption by 12% when compared to the traditional best-signal-level association policy.

Traditionally, the problem of hospital pharmacy management of inventories has been paid little attention. This problem is treated in this paper as a work-in-progress. The main objective is to estimate the demand of stock in different services of the hospital pharmacy, obtaining a reliable method for future planning and management. A better demand estimation implies a better management and therefore, significant savings in inventory costs. Some methods will be described an applied to series of data from the Hospital Universitario Reina Sofia (Córdoba). The results will be compared in order to establish the best strategy to implement.

Inventory management is one of the main tasks that the pharmacy department has to carry out in a hospital. It is a complex problem that requires to establish a tradeoff between different and contradictory optimization criteria. The complexity of the problem is increased due to the constraints that naturally arise in this type of applications. In this paper, which corresponds to preliminary works performed to implement robust and advanced control techniques for pharmacy management in two Spanish hospitals, we propose, assess and compare three robust model predictive control(Chance-Constraints, Multi-Scenarios approach and Tree-Based Methods) as a mean to relieve this issue.