A new online delay estimation-based robust adaptive stabilizer for multi-input neutral systems with unknown actuator nonlinearities. (September 2017) - Tahoun, A.H.
Abstract: This paper studies the problem of actuator-nonlinearities compensation in the multi-input uncertain neutral systems. The neutral systems with different unknown time-varying delays, unmodeled dynamics, nonlinear perturbations and disturbances are considered. A new methodology based on the online estimation of the delays to compensate for the effects generated by dead-zone and saturation, acting in series on a system's input are presented. The online estimations of the unknown delays are accomplished by adaptive laws that guarantee the exponential stabilities of the estimated delays. In the presence of state time delay, derivative state time delay, unmodeled dynamics, nonlinear perturbations, disturbances and both input dead-zone and saturation, the asymptotic stability of the closed-loop system is ensured and the state response converge to the origin. Finally, the practicability and the efficacy of the proposed approach is demonstrated via a numerical example. Highlights: Constraints are common in all practical control systems and can have damaging effects on the system performance unless accounted for in the controller design process. The problem of actuator-nonlinearities compensation in the multi-input uncertain neutral systems is considered. A new approach based on the online estimation of the delays to compensate for the effects generated by unmodeled dynamics, nonlinear perturbations, disturbances and both dead-zone and saturation acting in series on a system's input are presented. The online estimations of the unknown delays are accomplished by novel adaptive laws that guarantee the exponential stabilities of the estimated delays. Under state time delay, derivative state time delay, system uncertainties and both input dead-zone and saturation, the robust stability of the closed-loop system is guaranteed and the state response converge to the origin.