Generalized pole-placement self-tuning controller Part 2, Application to robot manipulator control

摘要

Abstract A practical application of self-tuning generalized pole placement (GPP) is discussed. The application, which involves the control of a five-axis, electrically actuated robot manipulator, is presented for two reasons. First, it illustrates the performance of a novel neo-classical multi-step predictive self-tuner in an important area of applied research—namely, robot control. Second, since the manipulator in question is electrically driven through a harmonic gearbox, the investigation has a general relevance to the area of self-tuning electromechanical servomechanisms. Two forms of GPP algorithms are compared, one based upon a controlled autoregressive integrated moving average model and the other upon a controlled autoregressive moving average model. The relative merits are discussed in the context of (i) single-input single-output and multiloop robot joint control, (ii) programmed setpoint control, and (iii) the use of the performance tuning aids with which the GPP algorithm is equipped. Additional informationNotes on contributorsM. A. LELIĆ On leave from the Faculty of Electrical Engineering, University of Tuzla, Ozrenskog Odreda 2, 75000 Tuzla, Yugoslavia.