Neural network based adaptive prescribed performance control for a class of switched nonlinear systems

In this paper, an adaptive prescribed performance control problem is studied for a class of switched uncertain nonlinear systems under arbitrary switching signals. Radial basis function neural networks (RBFNNs) are employed to approximate the unknown terms. By introducing the transformation errors, the original system with prescribed performance constraints are transformed into a class of new systems without constraints. Then, we design controllers to stabilize the transformed system. In this way, the original system is stabilizable and the prescribed performance is guaranteed. Update laws are designed such that the parameter estimation is fixed until its corresponding subsystem is active. Compared with the existing results, the main contributions of this paper are characterized as follows: (1) a PPC approach is proposed for a class of switched uncertain nonlinear systems in a general form and (2) RBFNNs are used to approximate the unknown terms of each subsystem. In the end, a simulation example is developed to illustrate the effectiveness of the present approach.