This paper proposes a decentralized neural adaptive dynamic surface approximated inverse control (DNADSAIC) scheme for a class of large-scale time-delay systems with hysteresis nonlinearities as input. The decentralized control problem under the case only the outputs are measurable is solved by utilizing the radial basis function neural networks approximator and the hysteresis approximated inverse compensator. Also, with the help of finite covering lemma, the traditional Krasovskii functionals are dropped when coping with the delays, leading to the removal of the assumptions on the functions with time-delay states and the acquisition of the arbitrarily small L∞ tracking performance of each hysteretic subsystem with time delays. The analysis of stabilities guarantees all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded. Simulation results illustrate the efficiency of the proposed DNADSAIC scheme.

This paper proposes an adaptive implicit inverse control scheme for a class of discrete-time hysteretic nonlinear systems. The Prandtl-Ishlinskii (PI) model is employed to characterize the hysteresis loop in piezoelectric actuator. The main contributions are: 1) by using the dynamic surface control technique which introduces the digital first-order low-pass filter, the original control system are not required to be transformed into an unknown special form; 2) The hysteresis implicit inverse compensator is constructed to overcome the hysteresis which implies that the hysteresis item coupled with control signal is treated as the temporary control signal from which the method of searching the approximately control signal is designed; 3) by employing the experimental platform of the piezoelectric positioning stage, the experimental verifications of the designed discrete-time adaptive controller are implemented. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SUUB) and the experimental results show the effectiveness of the proposed ADSDMC scheme.