This paper presents a decentralized Voronoi-based linear model predictive control (MPC) technique for the deployment and reconfiguration of a multi-agent system composed of unmanned aerial vehicles (UAVs) in a bounded area. At each time instant, this area is partitioned into non-overlapping timevarying Voronoi cells associated to each UAV agent. The formation deployment objective is to drive the agents into a static configuration based on the Chebyshev center of each Voronoi cell. The proposed MPC-based formation reconfiguration algorithms allow not only faulty/non-cooperating agents to leave the formation, but also recovered/healthy agents to join in the current formation, while avoiding collisions. Simulation results validate the effectiveness of the proposed control algorithms.

This study proposes two control techniques for a buck converter operating in continuous conduction mode at a fixed switching frequency. The non-linear behaviour of this switched system is represented by a discrete piecewise affine (PWA) model. The PWA representation offers a precise approximation of the converter's dynamics in the whole operating domain and also allows the investigation of system's stability and the design of different control laws. The first control approach corresponds to a piecewise linear (PWL) state-feedback controller, designed by using a piecewise quadratic Lyapunov function and by solving a set of linear matrix inequalities. This control method guarantees the stability of the closed-loop system for a wide range of operating points. The second control strategy is a model predictive control. The constrained optimal control problem is formulated and solved using the PWA approximation as a prediction model. The explicit form of the control law is derived off-line as an affine state-feedback controller and stored in a look-up table for implementation. Both PWL and PWA controllers are validated experimentally, showing better performances in comparison with a proportional-integral or constant state-feedback controller.

Background and Aims: Fabry disease (FD) is a rare chronic genetic disorder that presents under a paucity of symptoms. Gastrointestinal (GI) involvement is a common event and can sometimes be debilitating, but relatively often it is overlooked. We aimed to provide a systematic review of main GI symptoms in FD patients and treatment possibilities.Methods: We completed a systematic review of literature, using the MeSH terms: “Fabry disease”, “gastrointestinal”, “gastrointestinal”, “digestive”, “manifestations”, “symptoms”, “clinical”, “treatment”, “therapy” and the supplementary concepts “enzyme replacement”, “chaperone”, “Migalastat”, in different combinations, with defined inclusion and exclusion criteria.Results: From 221 initial studies identified, through our selection process we included a final date base of 51 articles on GI signs and symptoms and their treatment. The primary GI manifestations of the disease consist of abdominal pain, bowel movement disorders or nausea and vomiting. Less frequent manifestations such as diverticular bowel disease, gastroesophageal reflux or achalasia have also been described. Main treatment options in FD are represented by enzyme replacement therapy and chaperone treatment. Patients presenting with GI symptoms unfortunately do not always respond to enzyme replacement, necessitating symptomatic relief.Conclusion: Fabry disease is a rare disease that often involves the GI tract, affecting patients’ quality of life and burdening the healthcare system. Physicians must be aware of the multitude of manifestations in this category of patients, to promptly recognize and treat them.

The inherent advantages such as high robustness, low cost and high starting torque have made switched reluctance machine a strong candidate for electric vehicle applications. However, the serious vibration and acoustic noise are very troublesome. In this paper, a semi-analytical vibration prediction model is developed and an enhanced vibration reduction method is presented via random-varying turn-off angle control that is based on the mechanical property of switched reluctance machine. In this method, a random-variation-frequency sine function is adopted to make the turn-off angle vary with time. At first, a magneto-mechanical coupling model is presented, then the principle of the control strategy is introduced. Next, simulation results are presented under different operating conditions, which validate the effectiveness of the proposed method.

In this study a new concept of functional modelling and simulation is introduced. First, the necessity and the expected outcomes of the new concept are explained. Secondly, the methodology of functional modelling based on a modular concept and the basic elements are presented, with details of OFS (Organico Functional Set). Then, the implementation of the new modelling concept using Sherpa Engineering's PhiSim environment is described in order to perform simulations. Finally, the proposed modelling method is applied to two different applications: a generic parallel hybrid electric vehicle (HEV) and a waste water treatment unit of a building. Simulation results of parallel HEV application are also presented.

This paper presents a new decentralized algorithm for the deployment and reconfiguration of a multi-agent formation in a convex bounded polygonal area when considering several outgoing agents. The system is deployed over a twodimensional convex bounded area, each agent being driven by its own linear model predictive controller. At each time instant, the area is partitioned into Voronoi cells associated with each agent. Due to the movement of the agents, this partition is time-varying. The objective of the proposed algorithm is to drive the agents into a static configuration based on the Chebyshev center of each Voronoi cell. When some This work was supported by the CNRS' LIA on Information, Learning and Control and the Natural Sciences and Engineering Research Council of Canada.

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This paper proposes a control strategy capable of reducing the vibration while keeping a low torque ripple for a switched reluctance machine (SRM). The aim is to minimize the variation of the radial force and to optimize the torque control parameters. At first, the torque ripple reduction method is introduced, for which an off-line optimization of control parameters is adopted. Then, the vibration reduction is achieved by Direct Force Control (DFC), whose purpose is to reduce the variation of the sum of radial forces. However, in order to cope with the drawback of DFC that amplifies the torque ripple, a reference current adapter is proposed based on the limitation of torque and radial force variations. The reference current adapter produces an auto-tuning reference current that achieves a tradeoff between torque ripple and vibration. Finally, simulation results are presented to validate the effectiveness of the proposed control method under different operating points.