This paper investigates the use of canonical piecewise affine (PWA) functions for approximation and fast implementation of linear MPC controllers. The control law is approximated in an optimal way over a regular simplicial partition of a given set of states of interest. The stability properties of the resulting closed-loop system are analyzed by constructing a suitable PWA Lyapunov function. The main advantage of the proposed approach to the implementation of MPC controllers is that the resulting stabilizing approximate MPC controller can be implemented on chip with sampling times in the order of tens of nanoseconds.

In this paper, a switched model predictive control strategy is applied for the speed regulation of a powertrain system composed of an electrical motor connected to a rotating load through an elastic shaft and a gear, which introduces backlash. The system is described through a piecewise-affine model and a different control law is designed for each system dynamics, which allows obtaining very simple controllers, suitable for digital implementation in embedded systems. A switched Kalman filter is employed in order to estimate the unmeasurable system states. The proposed control strategy is tested through both simulations and experiments, and is compared against a traditional proportional-integral controller.

This paper is concerned with modeling of smart textiles, aimed at compensating their intrinsic nonlinearities. In particular, a new model is proposed to compensate for hysteresis and relaxation in strain sensors made of Electrolycra. These sensors are increasingly employed in emerging areas such as wearable electronics and soft robotics for their simple transduction mechanism and low cost. However, being intrinsically nonlinear, the signals measured from these devices need some processing, in order to increase their sensing accuracy. Here, we propose a new model for the compensation of the main distortions intrinsic to these soft sensors, which are mainly caused by hysteresis and relaxation, whose combined effect produces rate-dependent hysteresis. The model capabilities are tested on experimental data measured on Electrolycra. The comparisons with the results obtained with two different models witness the good behavior of the proposed model.

Summary: Switch‐mode power supplies (SMPSs) are widely exploited to interface electrical energy sources to motors and other electrical loads. Inductors are usually the biggest and heaviest components in SMPSs, limiting their overall power density. Therefore, there is an increasing interest in designing SMPSs with partially saturating inductors, because this significantly reduces their weight and size, thus increasing power density. This paper provides a review of nonlinear behavioral models (based on easy‐to‐measure quantities) of the inductance, power loss, and temperature rise of inductors working, at least partially, in magnetic saturation. This survey discusses the pros, cons, and ranges of validity of these models, with a glimpse at their application to SMPS simulation, design, monitoring, and control.

This paper proposes a new modeling approach for ferrite-core (FC) power inductors used in switch-mode power supplies (SMPSs). In particular, a novel power-loss-dependent inductance behavioral model is proposed, whose parameters can be identified through a limited set of proper experimental measurements of inductor current and voltage. Unlike conventional thermal modeling approaches, which require information on core temperature, the proposed behavioral model relies only on easily measurable quantities. The model is also suitable for being implemented in circuit simulators. By comparing PSIM simulations and experimental measurements on a buck converter for commercial FC inductors, we show that the model allows a reliable prediction of the steady-state inductor current, under different SMPS working conditions.

This paper proposes a novel methodology for emergency control acting on the mechanical power in a power grid containing steam power generators that experiences a contingency. The control strategy is based on the Model Predictive Control (MPC), a model-based control that is widely adopted for the regulation of linear and nonlinear dynamical systems in various fields of application. The MPC is applied in a decentralized formulation, where only local measurements are necessary. The performances of the proposed control strategy are assessed by means of simulations on a Western Electricity Coordinating Council test case network and on the 39-Bus New England network. The results highlight both the good behavior of the MPC control technique, even in conditions that traditional emergency control would not support, and the robustness of the proposed structure against power system modeling approximations.