Data-driven model-free sliding mode learning control for a class of discrete-time nonlinear systems

Abstract

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.

Dynamic data model and robust controller with high‐frequency component reduction for unknown time‐varying discrete‐time systems

“…7 Furthermore, the evolution of learning algorithms has been guided by the necessity for parameters to change slowly relative to the sampling frequency of discrete-time systems. 8,9 The surge in popularity of electric vehicles has heightened focus on control systems for brushless DC motors (BLDC). 10 However, controlling BLDC systems presents a significant challenge, given their classification as multivariable nonlinear systems, making it exceptionally difficult to derive precise mathematical models.…”

“…In simpler terms, DLDM is not applicable in scenarios where the input remains constant 7 . Furthermore, the evolution of learning algorithms has been guided by the necessity for parameters to change slowly relative to the sampling frequency of discrete‐time systems 8,9 …”

Friction Compensation Control of Electromechanical Actuator Based on Neural Network Adaptive Sliding Mode

“…In order to get a better vibration isolation capability than other methods, in [ 23 ] a combination of skyhook and groundhook control−based magneto rheological lookup table technique called hybrid control for a quarter car was developed. In [ 24 ], a data−driven model−free sliding mode learning control (MFSMLC) for a class of discrete−time nonlinear systems was proposed. This method does not require a specific mathematical model, and in addition the chattering is reduced because there is no non−smooth term in the controller.…”