A novel adaptive NN control for a class of strict-feedback nonlinear systems

An adaptive neural network control (ANNC) is proposed for a class of strict-feedback uncertain nonlinear systems with unknown system nonlinearities and unknown virtual control gain nonlinearities. Combining the dynamic surface control (DSC) technique with minimal-learning-parameters (MLP) algorithm, a systematic procedure for synthesis of ANNC is developed based on the universal approximation of neural networks. An important feature of the proposed algorithm is that the number of parameters updated on line for each subsystem is reduced only to one, both problems of ldquoexplosion of complexityrdquo and ldquocurse of dimensionrdquo are solved simultaneously, such that the computation load is reduced drastically and it is convenient to implement the controller in applications. It is shown that all closed-loop signals are semi-global uniform ultimate bound (SGUUB) via Lyapunov stability theory. Finally, simulation results are presented to demonstrate the effectiveness of the proposed scheme.