Adaptive Neural Neworks Control for Uncertain Nonlinear State Constrained Systems With Input Delay

In this paper, an adaptive controller is investigated for a class of strict feedback nonlinear state constrained systems with both input delay and unknown control gains. To handle the unknown control gains and prevent constraint violation, we employ the integral Barrier Lyapunov Functions (iBLFs), which reduce some of the conservatism of transforming original state constraints into transformed error constraints. Pade approximation and an intermediate variable are applied to compensate the effect of the input delay. The unknown functions are approximated by Neural networks (NNs). It is proved that tracking error can converge to a compact set of the origin without violating the state constraint, and all closed loop signals remain bounded. The proposed controller can be verified by a numerical example.