Feedforward coefficient identification and nonlinear composite feedback control with applications to 3-DOF planar motor
- 435 Downloads
Due to modeling errors, accurate feedforward coefficient of the controller cannot be obtained with the standard method on the basis of the nominal model. Meanwhile, the system is uncertain in practice. Consequently, the MIMO (multi-input multi-output) system of the planar motor cannot be completely decoupled by feedback linearization, and the convergence of the tracking errors is no longer guaranteed. In order to improve the robustness and the tracking ability of the planar motor, a feedforward coefficient identification method and nonlinear composite feedback controller are proposed, thus guaranteeing stability by Lyapunov theory, wherein the feedforward coefficient can be obtained by the PD control experiment. The results of two different trajectory tracking experiments show that it is more accurate than the standard method. Moreover, this coefficient is suitable for different trajectories, so it avoids the drawback of ILC (iterative learning control) method, by which the feedforward term obtained cannot be reused if the length of the trajectory changes. The nonlinear composite feedback controller consists of u 1 and u 2 terms. u 1 is designed to compensate for modeling errors, therefore the robustness is improved and the coupling effects among multi-DOF (degrees of freedom) are reduced. In balancing the trade-off between disturbance rejection and noise sensitivity, an amplitude-based variable-gain function is applied in u 2. The trajectory tracking experimental results show that the overall controller is an attractive approach for the uncertain multi-DOF systems.
KeywordsPlanar motor Modeling errors Feedforward coefficient identification Nonlinear composite feedback controller Trajectory tracking
Unable to display preview. Download preview PDF.
- M. M. Cornelis, Magnetically levitated planar actuator with moving magnets: Dynamics, commutation and control design, Einhoven: Technische Universiteit Einhoven (2008).Google Scholar
- J. W. Jansen, Magnetically levitated planar actuator with moving magnets: Electromechanical analysis and design, Einhoven: Technische Universiteit Einhoven (2007).Google Scholar
- Jay H. Lee and Kwang S. Lee, Iterative learning control applied to batch processes: An overview, 15(10) (2007) 1306–1318.Google Scholar
- J. K. Tar, J. F. Bito and I. J. Ruda, An SVD based modification of the adaptive inverse dynamics controller. Applied Computational Intelligence and Informatics (2009) 193–198.Google Scholar
- J. H. Park, D. H. Kim and Y. J. Kim, Anti-lock brake system control for buses based on fuzzy logic and a slidingmode observer, Journal of Mechanical Science and Technology, 15(10) (2001) 1398–1407.Google Scholar
- J. Freudenberg, J. Middleton and Stefanopoulou, A survey of inherent design limitations, Proc. American Control Conference, Chicago (2000) 2987–3001.Google Scholar
- M. Iwasaki, K. Sakai and N. Matsui, High-speed and highprecision table position system by using mode switching control, Industrial Electronics Society, Proceedings of the 24th Annual Conference of the IEEE, 3 (1998) 1727–1732.Google Scholar