RBF Neural Control Design for SISO Nonaffine Nonlinear Systems

In the present paper, an RBF neural control scheme is designed for regulatory control of SISO nonaffine systems facing unknown nonlinearities. Using Taylor series expansion, the nonaffine part of the system is converted into affine form. RBF network is utilized to estimate the equivalent affine system. The parameters of RBF network are updated online based on Lyapunov stability theory. To avoid the requirement of measurement of the states of the system, an observer is designed, which provides the estimated values of the system's states. Using Lyapunov theory, the signals of the system are shown to be asymptotically stable. To validate the effectiveness of the presented scheme, numerical simulation study has been performed.