Adaptive Inverse Compensation for Unknown Input and Output Hysteresis Using Output Feedback Neural Control

The search for new approaches for output feedback control of uncertain nonlinear systems with unknown input and output hysteresis is an interesting problem in control theory. One challenging issue obstructs the development of output feedback control design is that both the genuine system input and output are unknown signals and unable to be employed in the observer and controller design. To obviate such obstruction, a new control design framework for adaptively compensating the input and output hysteresis is proposed with two adaptive hysteresis inverse operators, which are also utilized to develop a novel adaptive hysteresis operator-based filter. It is proved that with the proposed control scheme, all the closed-loop signals are bounded and the tracking error ultimately converges to a tunable residual around zero. Simulation studies demonstrate the methods developed.