Partial model‐free sliding mode control design for a class of disturbed systems via computational learning algorithm

Abstract

In this article, a partial model‐free sliding mode control (SMC) strategy is proposed for a class of disturbed systems. A partial model‐free SMC law is designed to attenuate the matched external disturbances by just employing partial dynamics information. A complete model‐free policy iteration algorithm is integrated to the designed SMC scheme such that the optimal control performance of the disturbed system is achieved. The implementation of the proposed partial model‐free SMC strategy is based on a computational learning algorithm, which involves date collection, policy iteration, and optimal control phases. The feasibility of the proposed SMC strategy in data collection phase and optimal control phase are analyzed, respectively. Finally, a numerical example is employed to verify the effectiveness of the proposed SMC strategy.

Data‐based learning control for optimization of nonlinear systems

“…A novel Nash game‐theoretical optimal adaptive robust control design approach is proposed to address the constraint‐following control problem for the uncertain underactuated mechanical systems with fuzzy evidence theory 13 . A partial model‐free sliding mode control strategy is proposed for a class of disturbed systems 14 . A new data‐based adaptive dynamic programming algorithm is proposed to solve the optimal control policy for discrete‐time systems with uncertainties 15 .…”

“…11 A novel optimal constraint-following controller is proposed for uncertain mechanical systems. 12 The third group of papers [13][14][15][16][17][18][19] focuses on robustness on data-based optimal learning control. A novel Nash game-theoretical optimal adaptive robust control design approach is proposed to address the constraint-following control problem for the uncertain underactuated mechanical systems with fuzzy evidence theory.…”