A High Performance Tracking Control Method Based on a Disturbance Observer With Parameter Adaptation

Abstract

A disturbance observer (DOB) is a useful control algorithm for systems with uncertain dynamics, such as nonlinearity and time-varying dynamics. The DOB, however, is designed based on a nominal model, and its stability is sensitive to the magnitude of discrepancy between a controlled system and its nominal model. Therefore, to increase the stability margin of the DOB, it requires an accurate model identification, which is often difficult for nonlinear or uncertain systems. In this paper, the parameters of the nominal model are continuously updated by a parameter adaptation algorithm (PAA) to keep the model discrepancy small, such that the DOB is able to show its desired performance without losing stability robustness even in the presence of nonlinearity and/or time-varying dynamics. In the integration of the DOB and the PAA, however, there exists a complicated signal interaction. In this paper, such interaction problem is solved from a practical point of view; signal filtering. The proposed method shows improved performance for an electric motor system, and is verified by experimental results in this paper.