Adaptive Control for a Class of Nonaffine Systems Based on Fuzzy-Neural Approach

An adaptive control design method is proposed for a class of uncertain single-input single-output (SISO) nonaffine system based on fuzzy-neural approach. To the authors' knowledge, this control problem is firstly considered in this paper. It is considered difficult to be dealt with in the control literature, mainly because that the virtual controls and the final control law of uncertain nonaffine system are not easy to resolve. To overcome this difficulty, the fuzzy-neural approximator cancels the unknown part of the inverse functions adaptively. Then, Inverse design, backstepping design, and feedback linearization techniques are 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. Simulation study was included to demonstrate the effectiveness of the proposed method.