Permanent magnet machines with segmented stator cores are affected by additional harmonic components of the cogging torque which cannot be minimized by conventional methods adopted for one-piece stator machines. In this study, a novel approach is proposed to minimize the cogging torque of such machines. This approach is based on the design of multiple independent shapes of the tooth tips through a topological optimization. Theoretical studies define a design formula that allows to choose the number of independent shapes to be designed, based on the number of stator core segments. Moreover, a computationally-efficient heuristic approach based on genetic algorithms and artificial neural network-based surrogate models solves the topological optimization and finds the optimal tooth tips shapes. Simulation studies with the finite element method validates the design formula and the effectiveness of the proposed method in suppressing the additional harmonic components. Moreover, a comparison with a conventional heuristic approach based on a genetic algorithm directly coupled to finite element analysis assesses the superiority of the proposed approach. Finally, a sensitivity analysis on assembling and manufacturing tolerances proves the robustness of the proposed design method.

This paper proposes a nonintrusive parameter identification procedure suitable for Internet-of-Things (IoT)-embedded isotropic permanent magnet synchronous machines (PMSM). The method is designed for scenarios where only measurements collected without additional sensors, dedicated tests or signal injection from in-service off-the-shelves motor drives are available. After the automatic detection of the steady state operating conditions defined by the triplet current-speed-temperature, the rotor flux linkage, the stator resistance, the inductance, and the inverter distorted voltage term are estimated using two operating points. Particular emphasis is placed in defining the criteria of selecting these two optimal operating conditions to minimize the estimation errors. The latter are due to the inevitable difference between the parameters in different operating points. As a vessel to investigate the effectiveness of the proposed parameter identification, experimental and simulation tests carried out on a high-speed PMSM drive have been used for validation purpose. The proposed method is also compared with existing methods from the literature to demonstrate its superiority in the considered scenario.

Parameter identification of permanent magnet synchronous machines (PMSMs) represents a well-established research area. However, parameter estimation of multiple running machines in large-scale applications has not yet been investigated. In this context, a flexible and automated approach is required to minimize complexity, costs, and human interventions without requiring machine information. This paper proposes a novel identification strategy for surface PMSMs (SPMSMs), highly suitable for large-scale systems. A novel multistep approach using measurement data at different operating conditions of the SPMSM is proposed to perform the parameter identification without requiring signal injection, extra sensors, machine information, and human interventions. Thus, the proposed method overcomes numerous issues of the existing parameter identification schemes. An IoT/cloud architecture is designed to implement the proposed multistep procedure and massively perform SPMSM parameter identifications. Finally, hardware-in-the-loop results show the effectiveness of the proposed approach.

Dielectric elastomer actuators (DEAs) require high driving voltages (typically in the order of kV) to generate meaningful displacements. As DEA technology becomes more spread, the availability of small, inexpensive, lightweight, and low-power driving electronic circuits is essential. Moreover, to achieve fast and accurate control of the voltage delivered to the DEA, the development of dynamic models capable of accurately describing the response of the driving circuit is of fundamental importance. This work describes a control-oriented model for a novel high-voltage driving circuit for DEAs, consisting of a combination of a charging stage (i.e., a cascade of a resonant converter and a rectifier Greinacher circuit) and a discharging stage. The proposed circuit is small, lightweight, and low cost. It is capable of driving capacitive loads on the order of 600 pF by amplifying a 0–6 V input signal up to 0–3 kV at its output. After presenting the circuit, a lumped-parameter dynamic model is derived to describe the input–output relationship. The prediction performances of the developed model are assessed through extensive experiments.

This article deals with the closed-loop optimal control of mechatronic devices based on dielectric elastomer membranes. The goal is to minimize the input electrical energy required to achieve a given position regulation task. The actuator is modeled based on a free-energy framework, which provides a thermodynamically consistent characterization of the losses that occur during actuation. Due to the strongly nonlinear behavior of both system model and dissipation function, traditional techniques based on the analytical solution of the Hamilton-Jacobi-Bellman (HJB) equation cannot be applied. Therefore, a reinforcement learning-based algorithm is here proposed as a tool to solve, offline, the HJB equation related to the energy minimization problem. After discussing the theory, experimental results are presented to validate the effectiveness of the proposed approach for different positioning tasks.

A power buffer is a power electronics converter with a large capacitor that shields a weak DC grid from abrupt load changes. Distributed control solutions have been shown to be superior to the decentralized ones; however, the effects of the communication network topology on the control performance of these buffers have not yet been studied. This article offers a data-driven optimal solution to reduce the interactions between different control loops of power buffers while minimizing a closed-loop performance function. Reinforcement learning methods deal with the optimal control of nonlinear systems, and a Tabu Search method addresses the resulting combinatorial problem. The proposed solutions are validated for a DC microgrid in a controller/hardware-in-the-loop environment.