Observer-based iterative learning control for a high relative degree nonlinear system and its application to a vehicular wet-clutch system

Abstract

In this paper, an observer-based iterative learning control with an output feedback control scheme is proposed for a high relative degree nonlinear system with an unmeasurable control state. Multiple differentiation of measurements can make the control result sensitive to measurement noise when controlling such systems. A convergence analysis was established that can use only lower-order differentiation regardless of the highest-order differentiation, based on the robustness condition to measurement noise and initial estimation error. The application to the vehicular wet-clutch system is presented to illustrate the effectiveness of the proposed method and its learning gain selection. These contributions are verified through theoretical analysis and simulation of the wet-clutch system. The simulation results show the effectiveness of the proposed approach for a class of nonlinear systems with a high relative degree.