Dynamic Feedback Backstepping Control for a Class of MIMO Nonaffine Block Nonlinear Systems

Abstract EN

For a class of MIMO nonaffine block nonlinear systems, a neural network- (NN-) based dynamic feedback backstepping control design method is proposed to solve the tracking problem.

This problem is difficult to be dealt with in the control literature, mainly because the inverse controls of block nonaffine systems are not easy to resolve.

To overcome this difficulty, dynamic feedback, backstepping design, sliding mode-like technique, NN, and feedback linearization techniques are incorporated to deal with this problem, in which the NNs are used to approximate and adaptively cancel the uncertainties.

It is proved that the whole closed-loop system is stable in the sense of Lyapunov.

Finally, simulations verify the effectiveness of the proposed scheme.