Adaptive Neural Fault-Tolerant Control for Nonlinear Fractional-Order Systems with Positive Odd Rational Powers

Abstract

In this paper, the problem of adaptive neural fault-tolerant control (FTC) for the fractional-order nonlinear systems (FNSs) with positive odd rational powers (PORPs) is considered. By using the radial basis function neural networks (RBF NNs), the unknown nonlinear functions from the controlled system can be approximated. With the help of an adaptive control ideology, the unknown control rate of the actuator fault can be handled. In particular, the FNSs subject to high-order terms are studied for the first time. In addition, the designed controller can ensure the boundedness of all the signals of the closed-loop control system, and the tracking error can tend to a small neighborhood of zero in the end. Finally, the illustrative examples are shown to validate the effectiveness of the developed method.