Neural-adaptive Control for a Class of Uncertain Block Nonaffine System

Based on neural networks, an adaptive control design method was proposed for a class of uncertain block nonaffine system. This problem is consider difficult to be dealt with in the control literature, mainly because that the virtual controls of block nonaffine system are not easy to resolve. To overcome this difficulty, the RBF neural network (NN) was used to approximate and adaptively cancel the unknown part of the inverse functions. Then, Inverse design, backstepping design, and feedback linearization techniques were incorporated to deal with this problem. It is proved that the whole closed-loop system is stable in the sense of Lyapunov. The control performance is guaranteed by suitably choosing the design parameters. Finally, numerical simulation study was presented to demonstrate the effectiveness of the proposed method.