Robust static output feedback based iterative learning control design with a finite‐frequency‐range two‐dimensional ℋ∞ specification for batch processes subject to nonrepetitive disturbances

• Published in: International journal of robust and nonlinear control Vol. 31; no. 12; pp. 5745 - 5761
• Main Authors: Hao, Shoulin, Liu, Tao, Paszke, Wojciech, Gałkowski, Krzysztof, Wang, Qing‐Guo
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.08.2021
• Subjects: batch process; Batch processes; Batch processing; Controllers; Corresponding states; Disturbances; finite‐frequency‐range 2D ℋ∞ specification; Heuristic methods; iterative learning control; Iterative methods; Learning; nonrepetitive disturbances; Output feedback; Robustness; Specifications; State feedback; two‐stage heuristic approach
• ISSN: 1049-8923; 1099-1239
• DOI: 10.1002/rnc.5567

For industrial batch processes subject to time‐varying uncertainties and nonrepetitive disturbances, this article proposes a robust static output feedback (SOF) based iterative learning control (ILC) design with a finite‐frequency‐range two‐dimensional (2D) ℋ∞ performance specification. An important advantage of the proposed design lies in its simple structure and easy implementation, with no need to store the state information of the learning controller as used in the existing 2D dynamic output feedback based ILC methods. Based on a 2D Roesser system setting, sufficient conditions are established in terms of nonlinear matrix inequalities to guarantee robust stability of the resulting closed‐loop ILC systems along both the time and batch directions, by using the matrix dilatation technique. Moreover, a finite‐frequency‐range 2D ℋ∞ performance specification for attenuating nonrepetitive disturbances is introduced for control synthesis. To derive the feasible SOF based ILC controller gains, a two‐stage heuristic approach is developed for iterative computation based on predesigning the corresponding state feedback based ILC gains. An illustrative example is used to demonstrate the effectiveness and merits of the proposed method.