Adaptive Control of a Class of Nonaffine Systems

An adaptive backstepping neural controller design is presented for a class of nonaffine nonlinear system with mismatched uncertainties. By applying backstepping design strategy and online approaching uncertainties with fully tuned radial basis function (RBF) neural networks (NNs), the adaptive tuning rules are derived from the Lyapunov stability theory. A nonlinear tracking differentiator is introduced to deal with the problem of extremely expanded operation quantity of backstepping method. The developed control scheme guarantees that all the signals of the closed-loop system are uniform ultimate boundedness. Simulation results are provided to show the good tracking performance and effectiveness of the proposed approach.