Adaptive Integral Sliding Mode Control for a Class of Nonlinear Systems

This paper presents an adaptive control method for a class of nonlinear systems with matched uncertainties. Firstly, radial basis function neural networks is adopted to approximate the unknown system perturbance, then an robust adaptive control law is developed to stabilize the system based on the so-called integral sliding mode design approach. The reachability of the sliding surface and the convergence of the weight of the neural networks are showed by Lyapunov theory. Finally, some simulation studies are included to illustrate the effectiveness of the proposed method.