Data-driven terminal iterative learning control with high-order learning law for a class of non-linear discrete-time multiple-input–multiple output systems

• Published in: IET control theory & applications 2015-04, Vol.9 (7), p.1075-1082
• Main Authors: Chi, Ronghu, Liu, Yu, Hou, Zhongsheng, Jin, Shangtai
• Format: Article
• Language: English
• Subjects: control performance improvement; control plant; control system synthesis; Control systems; data‐driven terminal iterative learning control; Design engineering; discrete time systems; dynamical linearisation; Dynamical systems; high‐order learning law; Iterative methods; I‐O data; Learning; learning systems; MIMO systems; nonlinear control systems; nonlinear discrete‐time multiple‐input‐multiple output systems; Nonlinear dynamics; Nonlinearity; non‐affine systems; parameter updating law design; Special Issue: Data-Based Control and Process Monitoring with Industrial Applications; Terminals; time‐varying system; time‐varying systems
• ISSN: 1751-8644; 1751-8652; 1751-8652
• DOI: 10.1049/iet-cta.2014.0754

In this study, a novel data-driven terminal iterative learning control with high-order learning law is proposed for a class of non-linear non-affine discrete-time multiple-input–multiple output systems, where only the system state or output at the endpoint is measurable and the control input is time-varying. A new data-driven dynamical linearisation is proposed in the iteration domain and the linearisation data model can be updated by a designed parameter updating law iteratively. The high-order learning control law makes it possible to utilise more control knowledge of previous runs to improve control performance. The design and analysis of the proposed approach only depends on the I/O data of the control plant without requiring any explicit model information. Both theoretical analysis and extensive simulations are provided to confirm the effectiveness and applicability of this novel approach.