Full-state-constrained intelligent adaptive control for nonlinear systems with unmodeled dynamics and mismatched disturbances

This paper develops a novel full-state-constrained intelligent adaptive control (FIAC) scheme for a class of uncer-tain nonlinear systems under full state constraints, unmodeled dynamics and external disturbances. The key pointof the proposed scheme is to appropriately suppress and compensate for unmodeled dynamics that are coupled withother states of the system under the conditions of various disturbances and full state constraints. Firstly, to guaranteethat the time-varying asymmetric full state constraints are obeyed, a simple and valid nonlinear error transforma-tion method has been proposed, which can simplify the constrained control problem of the system states into abounded control problem of the transformed states. Secondly, considering the coupling relationship between theunmodeled dynamics and other states of the controlled system such as system states and control inputs, a decouplingapproach for coupling uncertainties is introduced. Thereafter, owing to the employed dynamic signal and bias radialbasis function neural network (BIAS-RBFNN) improved on traditional RBFNN, the adverse effects of unmodeleddynamics on the controlled system can be suppressed appropriately. Furthermore, the matched and mismatcheddisturbances are reasonably estimated and circumvented by a mathematical inequality and a disturbance observer, respectively. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed FIACstrategy.