This paper designs a simple model reference adaptive control based speed controller (MRAC-SC) for a surface-mounted permanent magnet synchronous motor drive. The proposed adaptive scheme is designed to track a reference model that ensures the desired exponential decay of a controlled speed error trajectory. Also, the proposed MRAC method includes an adaptive compensating control term and a stabilizing feedback control term. The former is proposed to compensate for uncertain model parameters (i.e., inertia, friction, and load torque) and the latter is constructed to asymptotically stabilize the error dynamics. The asymptotic stability of the closed loop is guaranteed with both control terms using the Lyapunov approach. The comparative studies between the proposed MRAC-SC, the nonadaptive model reference speed controller, and the conventional proportional-integral SC are performed to justify a fast transient response, a good tracking possibility, and robustness against the parameter uncertainties of the proposed MRAC technique.

This paper proposes a nonlinear disturbance observer (NDO)-based sliding mode speed controller (SMSC) to guarantee the superior control performance in terms of robustness, fast transient response, and small steady-state error for a surface-mounted permanent magnet synchronous motor (SPMSM) drive. Generally, the control performance of the SPMSM drives can be degraded by disturbances, so an NDO with a proper disturbance rejection capability is proposed to appropriately improve the tracking performance of the SMSC designed for the SPMSM drives. Unlike the linear disturbance observers (LDOs), the proposed NDO can efficiently estimate the lumped disturbance such as uncertainties parameters and unmodeled dynamics by using the nonlinear design function. The proposed NDO rejects the complex disturbances as well as self-regulates the observer gains to increase the convergence rate. The feasibility of the proposed NDO-based SMSC is verified by using a MATLAB/Simulink software program and a prototype SPMSM drive system with a TI TMS320F28335 digital signal processor (DSP). The comparative results with a conventional LDO-based SMSC are analyzed under load torque disturbances and model uncertainties to prove the excellent performance of the proposed NDO-based SMSC.

A reinforcement learning-based adaptive optimal fuzzy controller is proposed for maximum power point tracking (MPPT) control of a variable-speed permanent magnet synchronous generator-based wind energy generation system. The algorithm consists of a critic, an adaptive optimal fuzzy controller, and an adaptive optimal fuzzy estimator. The critic is built based on an adaptive neuro-fuzzy inference system (ANFIS) network instead of the neural network as normal to reduce the computation. The error between the system output and the estimator output is used as the input of the critic. In addition, the critic is used to calculate the update law for the parameters of the adaptive optimal fuzzy controller and adaptive optimal fuzzy estimator based on minimizing the input error function. Moreover, the proposed control scheme is output feedback instead of state feedback, which does not require a system model as well as system parameters, so the system is robust to uncertainties and external disturbances. Besides, the stabilization proof is accomplished by using the Lyapunov stability theorem for the closed-loop system and the convergence of the update law. Finally, the effectiveness of the proposed reinforcement learning-based adaptive optimal fuzzy control scheme is verified through simulation with various scenarios such as step wind speed, random wind speed, and system parameter variations. Also, the comparisons with other control schemes in the state-of-art (neural network reinforcement learning based adaptive optimal fuzzy controller, PI controller) are executed to demonstrate the advantages of the proposed control scheme.

This paper proposes an improved iterative learning control (ILC) which significantly enhances the speed tracking performance in the transient and steady-state for a surface-mounted permanent magnet synchronous motor (SPMSM) drive. The proposed ILC encapsulates two control terms: the feedback linearization control terms (FLCTs) force the speed error to approach zero, and the iterative learning control terms (ILCTs) improve the performance of the control inputs based on the stored data such as previous speed error and previous control input. Unlike the conventional feedback linearization control (FLC), the proposed ILC does not require the exact information of the SPMSM parameters, significantly rejects the periodic and non-periodic disturbances, and efficiently provides an enhanced speed tracking performance owing to the included ILCTs. Besides, its stability is proven by showing that the speed tracking error asymptotically goes to zero. In comparative studies, the proposed method offers the better transient performance (e.g., faster transient response and smaller overshoot) and steady-state performance (i.e., smaller steady-state error) than the conventional FLC under load and speed step changes. To prove the practicability of the proposed scheme, the proposed ILC is simulated and implemented on a MATLAB/Simulink software and a prototype SPMSM test-bed using TI TMS320F28335 DSP, respectively.

This paper presents a state‐of‐the‐art comprehensive review of the modern control techniques for the widely recommended two distributed generation systems (DGSs) using permanent magnet synchronous generator (PMSG)‐ and doubly‐fed induction generator (DFIG)‐based wind turbines (WTs) and photovoltaic (PV) arrays in grid‐connected applications. The already published review papers individually study each DGS and also lack a systematic discussion on the control techniques without any suitable classification. This makes it difficult to study the comparatively improved performance of each control technique and comprehensively understand the control requirements of overall DGSs. Unlike existing literature, this paper first distinctively presents the generalized control configurations for both DGSs in the form of control blocks (CBs). Second, the modern control techniques suggested for each DGS are uniquely classified based on the CBs. Third, each CB is studied in detail considering its required control operation for the particular DGS component and recently proposed control techniques for its implementation. Furthermore, a detailed data analysis and comparative graphical illustrations are provided to appropriately study the merits and demerits of every state‐of‐the‐art control technique. Finally, the useful future research directions are presented to realize improved hardware components and control techniques for the grid‐connected WTs and PV arrays DGSs.