A new subdomain and magnetic circuit hybrid model (SMCHM) is proposed for on-load field prediction in surface-mounted permanent-magnet (SPM) machines. Equivalent current sheets are introduced to represent the nonlinearity effect, whose values are obtained by magnetic circuit and correlated to boundary conditions in subdomain model. The number of reluctances in the magnetic circuit (MC) of proposed model can be selected flexibly according to nonlinearity effect. Instead of sectorial tooth in the conventional subdomain model, parallel tooth is considered in the proposed model to improve the accuracy. The SMCHM can accurately calculate the flux density distributions and electromagnetic performance considering the heavy nonlinearity effect under load conditions with fast computation speed. The finite element (FE) analysis is performed to validate the proposed model, which shows excellent agreement between them. A prototype machine is manufactured to further prove these predictions.

This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method.

The magnetic potential drop in nonlinear iron can be replaced by equivalent current sheets on linear iron edges. Based on this principle, this paper proposes a nonlinear subdomain and magnet circuit hybrid model (SMCHM) for open-circuit field prediction in surface-mounted permanent-magnet (SPM) machines. The values of current sheets are calculated from magnetic circuit (MC) and used as boundary conditions in subdomain model. The finite element (FE) validation is performed, which shows SMCHM can accurately predict the magnetic field distribution and electromagnetic performance as nonlinear FE method does. The accuracy of SMCHM is significantly higher than the conventional subdomain model neglecting nonlinearity effect, and the computation speed of SMCHM is dramatically faster than FE model.

Due to the requirements of industrial drive system for high output power, uniform torque distribution, and accurate synchronous movement, the synchronous control methods of multimotor system have drawn increasing attention. This article refers to a large number of research on synchronous control methods and summarizes and compares different control structures and algorithms. Based on the evaluation criteria of tacking error, synchronization error, and adjustment time, the advantages and disadvantages of three representative control algorithms, including proportional integral derivative (PID) control, model predictive control, and sliding mode control, are studied through experiments. Finally, the development trend of multimotor synchronous control is summarized and prospected.

In this article, a novel robust data-driven model-free predictive control framework based on the I/O data of the controlled plants, which is performed by incorporating the neural predictor-based model-free adaptive control and finite control-set model predictive control, is first proposed. The salient feature of the suggested framework is that the uncertainties, such as unmodeled dynamics and external disturbances, can be explicitly addressed in controlled systems. From a practical standpoint, however, the potential of this proposal is limited by a significantly increased online computational complexity, which makes it difficult to implement. To circumvent this limitation, a supervised imitation learning technique using data labeled is developed to imitate the known suggested controller, which the majority of the online computational burden can be transformed into offline computing by utilizing a trained artificial neural network subject to robustness characteristics. In particular, this development motivates a much simpler robust predictive control solution, which is convenient to implement in applications. Thus, by this proposal, the online implementation of much more complex predictive control strategies is made possible, and it explores a new possibility for future development of the complex control methodology. Finally, extensive simulative and experimental investigations for modular multilevel converter validate the interest and viability of the proposed design methodology.

The focus of this article is to introduce the concept of an online reinforcement learning (RL) solution and to propose a novel finite control-set model predictive control framework subject to system uncertainties, which possesses the excellent applicative potential for power converter systems with unknown perturbations. In this framework, the control task is performed by incorporating an adaptive neural network approximation-based RL and neural predictor-based predictive current control solution. To be more precise, a critic neural network is responsible for learning a strategic utility function online, and an actor network is developed to derive control behaviors by approximating the unknown model dynamics and optimizing the learned utility function obtained from the critic network. Compared to previous works, it not only attenuates the inherent issues of system uncertainties and unknown disturbances, but also provides a flexible framework and allows the enhancement of control property. Furthermore, by deploying the Lyapunov approach, it shows that all signals in the closed-loop system are uniformly ultimately bounded. Finally, numerical simulation and experiments validate our theoretical findings.