Prescribed performance based model-free adaptive sliding mode constrained control for a class of nonlinear systems

To tackle the trajectory tracking problem and achieve high control accuracy in many actual nonlinear systems with unknown dynamics, a novel prescribed performance based model-free adaptive sliding mode constrained control (SMCC) strategy is studied which relies on the input/output data of plant rather than the specific model information via a pseudo partial derivative (PPD) parameter. Firstly, two approaches are introduced for PPD estimation, i.e., an observer-based adaptive algorithm, and an algorithm based on the stochastic configuration network approximating the controlled plant model, upon which the realtime control including PPD construction are carried out. Then, the prescribed performance is introduced in the model-free adaptive SMCC scheme as a prescribed range for the output tracking error. Meanwhile, an anti-windup compensation signal is employed to suppress the actuator saturation that commonly exists in many actual nonlinear systems. In addition, the stability analysis of the control system is presented to verify the rationality of the prescribed performance. Finally, simulations are carried out for both a linear induction motor and a battery energy storage system to demonstrate the effectiveness of the proposed control strategy. (C) 2020 Elsevier Inc. All rights reserved.