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

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.