Adaptive Neural Control for Nonlinear MIMO Function Constraint Systems

Dear Editor, In this letter, a novel adaptive control design problem for uncertain nonlinear multi-input-multi-output (MIMO) systems with time-varying full state constraints is proposed, where the considered systems consist of various subsystems, and the states of each subsystem are interconnected tightly. It is universally acknowledged that in the existing researches with state constraints, system constraint bounds are always constants or time-varying functions. Different from previous methods, the constraint boundary of this letter is regarded as a special function of not only time but of state variables. In order to handle time-varying full state constraints, the tangent type time-varying barrier Lyapunov functions (tan-TVBLFs) are introduced. By combining neural networks (NNs) and backstepping technique, an intelligent controller is developed. Meanwhile, we introduce an even function to guarantee the feasibility of NN approximation of unknown functions over practical compact sets. The feasibility of the mentioned control strategy is certified through the simulation results.