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This paper presents a new data driven control for a class of discrete-time multiple input and multiple output nonlinear systems which uses the principles of model-free adaptive control and the tensor product. The design of a linearization model using the notion of the tensor product overcomes reset problems in the calculation of the pseudo partial derivative and also improves control performance. The stability of the proposed method is guaranteed by theoretical analysis. Numerical simulation results and an experimental trial are presented to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven model-free sliding mode learning control (MFSMLC) for a class of discrete-time nonlinear systems. In this scheme, the control design does not depend on the mathematical model of the controlled system. The nonlinear system can be transformed into a dynamic linear data system by a novel dynamic linearization method. A recursive learning control algorithm is designed for the nonlinear system that can drive the sliding variable reach and remain on the sliding surface only by using output and input data. Moreover, the chattering is reduced because there is no non-smooth term in MFSMLC. After the strict stability analysis, the effectiveness of MFSMLC is validated by MATLAB simulations. [ABSTRACT FROM AUTHOR]

This paper reviews the development and application of sliding mode predictive control (SMPC) in a tutorial manner. Two core design paradigms are revealed in the combination of sliding mode control (SMC) and model predictive control (MPC). In the first case, MPC is used in the reaching phase to ensure a sliding mode is attained. In the second case, MPC is used to solve the existence problem and define the required performance in the sliding mode. The two approaches are discussed in detail from the perspectives of both theory and application. Finally, some future challenges and opportunities in the area of SMPC are summarized. [ABSTRACT FROM AUTHOR]