Adaptive control of non-affine nonlinear systems using radial basis function neural network

This paper proposes an adaptive controller with Gaussian radial base function neural network (RBFN). The controller is for a class of non-affine nonlinear systems with ill-defined mathematical model. It can work in conjunction with another continuous controller such as a PID controller to improve the performance. Based on Lyapunov´s stability theorem, the adaptation laws are conceived for the parameters of the RBFN, including the output weights, the centers, and the variances of the Gaussian radial functions. A bounding control is also developed to help for stability. The effectiveness of the controller is illustrated on a simulation example of a continuously stirred tank reactor (CSTR).