Adaptive neural networks control for MIMO nonlinear systems with unmeasured states and unmodeled dynamics

Highlights：

• This paper presents an adaptive backstepping control scheme based NNs technique for a class of un- certain nonlinear MIMO systems with unmeasured states and unmodeled dynamics. The contributions of this paper are listed as follows:

• The unmodeled dynamics of the system are processed by a dynamic signal. Then, the combinational uncertainties caused by unmodeled dynamics, unknown nonlinear functions and dynamic disturbances are approximated by neural networks.

• An observer is established to estimate the unmeasured states in the system. It is proved that the observer error vectors can be made arbitrarily small by selecting appropriate parameters.

摘要

This paper presents an adaptive backstepping control scheme based NNs technique for a class of un- certain nonlinear MIMO systems with unmeasured states and unmodeled dynamics. The contributions of this paper are listed as follows:•The unmodeled dynamics of the system are processed by a dynamic signal. Then, the combinational uncertainties caused by unmodeled dynamics, unknown nonlinear functions and dynamic disturbances are approximated by neural networks.•An observer is established to estimate the unmeasured states in the system. It is proved that the observer error vectors can be made arbitrarily small by selecting appropriate parameters.

论文关键词：Adaptive neural networks control,Multi input and multi output uncertain nonlinear systems,Unmeasured states,Unmodeled dynamics

论文评审过程：Received 14 January 2021, Revised 16 March 2021, Accepted 12 May 2021, Available online 30 May 2021, Version of Record 30 May 2021.