Adaptive robust motion control of linear motors for precision manufacturing

• Published in: Mechatronics (Oxford) Vol. 12; no. 4; pp. 595 - 616
• Main Authors: Yao, Bin, Xu, Li
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.05.2002; Elsevier Science
• Subjects: Adaptive control; Adaptive control systems; Applied sciences; Computer simulation; Control nonlinearities; Exact sciences and technology; Linear motor; Mechanical engineering. Machine design; Motion control; Precision engineering, watch making; Precision motion control; Robust control; Robustness (control systems); Servo control; Servomechanisms; Robust control; Linear motor; Servo control; Precision motion control; Adaptive control; Positioning; Non linear model; Precision engineering; Linear control; Motion control; Modeling
• ISSN: 0957-4158; 1873-4006
• DOI: 10.1016/S0957-4158(01)00008-3

Linear motors offer several advantages over their rotary counterparts in many precision manufacturing applications requiring linear motion; linear motors can achieve a much higher speed and have the potential of gaining a higher load positioning accuracy due to the elimination of mechanical transmission mechanisms. However, these advantages are obtained at the expense of added difficulties in controlling such a system. Specifically, linear motors are more sensitive to disturbances and parameter variations. Furthermore, certain types of linear motors such as the iron core are subject to significant nonlinear effects due to periodic cogging force and force ripple. To address all these issues, the recently proposed adaptive robust control (ARC) strategy is applied and a discontinuous projection-based ARC controller is constructed. In particular, based on the special structures of various periodic nonlinear forces, design models consisting of known basis functions with unknown weights are used to approximate those unknown nonlinear forces. On-line parameter adaptation is then utilized to reduce the effect of various parametric uncertainties such as unknown weights, inertia, and motor parameters while certain robust control laws are used to handle the uncompensated uncertain nonlinearities effectively for high performance. The resulting ARC controller achieves a guaranteed transient performance and a guaranteed final tracking accuracy in the presence of both parametric uncertainties and uncertain nonlinearities. In addition, in the presence of parametric uncertainties, the controller achieves asymptotic output tracking. Extensive simulation results are shown to illustrate the effectiveness of the proposed algorithm.