Adaptive RBF Neural-Network-Based Design Strategy for Non-Strict-Feedback Nonlinear Systems by Using Integral Lyapunov Functions

This paper develops an adaptive radical basis function neural-network (NN)-based controller design strategy that uses integral Lyapunov functions for a class of non-strict-feedback nonlinear systems subject to perturbations. The design difficulty caused by the non-strict-feedback system structure is handled by using the inherent property of the square of neural network's base vector. The design procedure of the adaptive NN tracking controller is presented by using backstepping technique, which can update the adaptive laws at any time and solve the design problem derived from the correlation degree of the controlled plant. The uniform ultimate boundedness and good tracking performance of the derived closed-loop system are ensured with the design controller. Finally, a comparative simulation example is carried out to prove the effectiveness of the proposed control method.