Sampled-data adaptive prescribed performance control of a class of nonlinear systems

This paper investigates the sampled-data adaptive control problem for a class of nonlinear systems with prescribed performance. The radial basis function neural networks are employed to approximate the unknown function in controller design procedure. The sampled-data controller and adaptive laws are designed by backstepping design technique, and an explicit formula for the allowable sampling period is derived. The proposed controller can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the system output satisfies the prescribed performance. Finally, two examples are given to illustrate the effectiveness of the proposed approach.