The efficient control of nonlinear processes is generally considered to be challenging. The development of digital computers promotes the study of nonlinear process control technology. Due to the discrete sampling of digital computer, it is necessary to develop the corresponding control algorithms for nonlinear processes. In this paper, a new equivalent control-based discrete-time sliding mode control is proposed for a class of nonlinear process with uncertainty and external disturbance. An adaptive law and a disturbance observer are designed to estimate the uncertainty and the disturbance, respectively. By combining with them, the new discrete-time sliding mode control is developed with good performance. The corresponding theoretical analysis is well verified by using Lyapunov function. Finally, the proposed approach is demonstrated by case studies in light of MATLAB.
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.
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