Design of a chattering‐free integral terminal sliding mode approach for DFIG‐based wind energy systems

Abstract

Summary This article proposes a Fuzzy Second Order Integral Terminal Sliding Mode (FSOITSM) control approach for DFIG‐based wind turbines subject to grid faults and parameter variations. Since traditional terminal sliding mode control (SMC) suffers from singularity, a novel integral terminal sliding manifold is proposed to eliminate chattering and improve the wind turbine's performance in the presence of faults and disturbances. A fuzzy system is proposed to auto‐tune the controllers' gains and ensures the invariance of the sliding surfaces even under heavy uncertainties, thus further improving the reliability and performance of the proposed controller. The performance of the proposed approach was assessed under various operating conditions. A comparison analysis with a standard SMC approach as well as the state of the art in voltage sag mitigation was also carried over. Reliability, robustness, and power availability under faulty grid conditions are among the main features of the proposed approach. In addition, the proposed approach exhibited chattering free dynamics and enabled the finite time convergence of the sliding manifold and overcame the singularity problem associated with standard TSMC.

Fixed‐time linear quadratic adaptive sliding mode control for a class of cart‐pendulum robots

“…In order to verify efficiency of the proposed fixed‐time sliding mode method, comparisons of sliding mode variable $$$ \left\Vert \sigma \right\Vert $$$ with three kinds of sliding mode methods, that is, integral terminal sliding mode method in Reference 2, fixed‐time sliding mode method in Reference 27, and nonsingular terminal sliding mode method 21 are illustrated in Figure 11. It is indicated by Figure 11 that the dynamic performance of the cart‐pendulum robotic system by using the fixed‐time sidling mode methods are better than that of by using the methods proposed in References 2 and 21. Besides, it can be seen from the Figure 11 that the convergence time is similar with the fixed‐time sidling mode methods in this paper and Reference 27.…”

“…Comparisons of the sliding mode variable $$$ \left\Vert \sigma \right\Vert $$$ by using the proposed sliding mode method and the existing sliding mode methods in References 2,21 and 27…”

“…Remark Note that there are some advantages of the proposed sliding mode surface in () as follows. Compared with traditional sliding mode surface proposed in References 1‐3,18‐21, the sliding mode surface developed in this paper can deal with the fixed‐time stability problem of systems. Moreover, compared with fixed‐time sliding mode surface proposed in References 4,27,28, the proposed sliding mode surface reduce the usage of signum function which means the proposed method may save the computational resource and alleviate the chattering phenomenon of control inputs.…”

“…Corollary 1. Consider the cart-pendulum robot in (2) and construct the sliding mode surface in (12). If the adaptive RBFNN-based sliding-mode control scheme is developed based on (15) with 𝜏 1 in ( 16) and solvable (18), 𝜏 2 is further designed by…”

“…In recent years, many kinds of advanced control methods have been developed to compensate for the effects of the imperfections. In terms of a large number of relevant stability and tracking results for various systems, numerous efficient control methods are developed based on the techniques of sliding-mode control, [1][2][3][4] adaptive control, [5][6][7] robust control, [8][9][10] observer-based control, 11,12 intelligent control, [13][14][15] model predictive control 16,17 and so forth.…”