Adaptive neural network tracking control for a class of output-constrained nonlinear switched systems

Abstract

In this paper, an adaptive neural network (NN) tracking control is proposed for a class of uncertain switched nonlinear systems with an output constraint. By employing a barrier Lyapunov function (BLF), the constrained system is transformed into an unconstrained one, which means the control objectives of the both systems are equivalent. In the controller design process, command filter technique is applied to avoid the so-called explosion of complexity in traditional backstepping design procedure, and radial basis function NNs are directly utilized to model the unknown nonlinear functions. The designed controller guarantees that all closed-loop signals are semi-global uniform ultimate boundedness (SGUUB), while the output constraint is not violated and the output tracking error converges to a small neighborhood of the origin. The effectiveness of the proposed approaches is illustrated by a numerical example.