Abstract
This paper presents a linear-extended-state-observer-based repetitive control (RC) method to enhance the disturbance rejection performance and tracking performance for a nonlinear system with aperiodic uncertainties and disturbances. Both design scenarios, i.e., with and without a detailed mathematical model of the plant, are considered, respectively. The proposed method exhibits the following three attractive features. First, instead of tuning the observer gain via the bandwidth idea by trial and error, available information on the plant model is used to design the observer gain and the state-feedback RC gains. As a result, the disturbance rejection ability is elevated. Second, robust stability and learning efficiency are adjusted preferentially through two tuning parameters, which effectively improve both the transient and steady-state performance. Third, the observer gain and state-feedback RC gains are simultaneously optimized through an optimization algorithm, in which an index is introduced to evaluate the overall system performance. The design procedure of the system is presented in detail. Finally, an application to tracking control of a chuck-workpiece system demonstrates the validity of the proposed method. Comparisons with the conventional RC method, the standard extended-state-observer-based RC method, and a linear active-disturbance-rejection-control-based RC method show that the proposed method can achieve the best disturbance rejection performance.
Published Version
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