Adaptive control design for uncertain switched nonstrict-feedback nonlinear systems to achieve asymptotic tracking performance

In this paper, the adaptive asymptotic tracking control issue is addressed for uncertain switched nonlinear systems with nonstrict-feedback form under the dynamic surface control (DSC) framework. In contrast to most traditional control schemes that can only achieve bounded error tracking performances, the proposed control method in this paper can make the switched systems with unknown nonlinear functions achieve an asymptotic tracking performance. It is completed by introducing nonlinear filters with a compensation term to remove the boundary layer error stemming from using linear filters in the DSC process. Meanwhile, the approximation error caused by the use of radial basis function (RBF) neural networks (NNs) is compensated by an online updated parameter. Furthermore, the nonstrict-feedback form is handled by adopting the inherent properties of RBF NNs. Then, in terms of the backstepping technique and the common Lyapunov function (CLF) approach, the desired control law with only two adaptive laws is set up. Finally, the validity of the developed strategy is verified via simulation results. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper addresses the issue of adaptive asymptotic tracking control for uncertain switched nonlinear systems, achieving asymptotic tracking performance with the introduced control method. By utilizing nonlinear filters, compensation terms, online parameter updates, and inherent properties of RBF NNs, the proposed strategy shows effectiveness in simulation results.