Abstract

Abstract In this paper, an adaptive iterative learning control (AILC) scheme is designed for discrete-time nonlinear systems with random initial condition and time-iteration-varying parameter. The time-iteration-varying parameter is generated by a general iteration-varying high-order internal model (HOIM) with iteration-varying order and coefficients, and the parameter updating law is designed based on least square method. Compared with the existing works based on iteration-invariant HOIM with fixed order and coefficients, our work significantly extends the application scope of HOIM-based ILC. Using the designed HOIM based iterative learning controller, the learning convergence in the iteration domain is guaranteed through rigorous theoretical analysis under Lyapunov theory. Moreover, an illustrative example is given to demonstrate the effectiveness of the proposed method.

Full Text
Paper version not known

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 call

Disclaimer: 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.