Neuro-learning-based adaptive control for state-constrained strict-feedback systems with unknown control direction

A neural networks (NNs)-based learning policy is proposed for strict-feedback nonlinear systems with asymmetric full-state constraints and unknown gain directions. A state-constrained function is introduced such that the proposed adaptive control policy works for systems with constraints or without constraints in a unified structure. Furthermore, the unified state-constrained function can also deal with symmetric and asymmetric constraints without changing adaptive structures, which also avoids discontinuous actions. With Nussbaum gain technique and NNs-based approximation technique, the proposed control method can also effectively deal with the unknown signs of control gains, and matched and mismatched uncertainties are also solved by NN approximation technique. According to the Lyapunov theory, the tracking errors can be proved to be semi-globally uniformly ultimately bounded (SGUUB). Finally the effectiveness of the proposed scheme is validated by numerical simulations. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a neural networks-based learning policy for strict-feedback nonlinear systems with symmetric and asymmetric constraints. By introducing a state-constrained function, systems with and without constraints can be handled in a unified manner, avoiding discontinuous actions. Through the use of Nussbaum gain technique and NNs-based approximation technique, the control method effectively deals with unknown signs of control gains and matched, mismatched uncertainties.