Decentralized Adaptive Control for a Class of Large Scale Nonlinear Asymmetric Saturation Hysteretic Systems with All States Constrained

An all state constrained decentralized adaptive implicit inversion control scheme is proposed for a class of large scale nonlinear asymmetric saturation hysteretic systems with unknown time delay. Firstly, to improve the transient performance, the asymmetric barrier Lyapunov function is introduced to ensure that the error surface will not exceed within an appropriate range, which is necessary in engineering practice. Secondly, the transmission delay between subsystems is compensated by neural network approximator. According to the finite coverage lemma, only a limited number of previous state values are needed. Finally, the modified implicit inversion is employed to compensate for the adverse effects of the saturation hysteresis. All closed-loop signals are proved to be globally uniformly bounded and the tracking errors converge to a residual set that can be made arbitrarily small. Simulation results on two-machine excitation power systems are presented to illustrate the effectiveness of the proposed scheme.