Adaptive Backstepping Control for a Class of Nonaffine Nonlinear Systems Based Neural Networks

Aiming at a class of nonaffine nonlinear system with uncertainties, an adaptive backstepping neural controller design is presented. By applying backstepping design strategy and online approaching nonlinearity with fully tuned radial basis function (RBF) neural networks, 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 uniformly ultimately bounded. The effectiveness of the proposed controller is illustrated through a simulation example.