Abstract
Design an optimized iterative learning control for linear and nonlinear dynamical systems is a challenging task. Norm-optimal iterative learning control (NOILC) is a valuable criterion for these dynamical systems. An iterative learning control algorithm based on optimal control theory is proposed, and the stability and convergence conditions of the proposed control algorithm are analyzed by using the convergence conditions of iterative learning control, and the control design is carried out based on feedforward and feedback control structure. At the same time, by introducing a weighted matrix coefficient to the feedforward control action, the convergence speed of iterative learning control algorithm based on optimal control theory is improved, and it is applied to the Matlab simulation control system. The results show that the convergence effect of the basic optimal control theory and the iterative learning control algorithm based on the weighted matrix coefficient is significant and the performance of the trajectory tracking is improved. The numerical example simulated on MATLAB@2019 and mollified results confirm the validation of the designed algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
