Iterative learning control for intermittently sampled data: Monotonic convergence, design, and applications. (May 2022)

• Record Type: Journal Article
• Authors: Strijbosch, Nard; Oomen, Tom
• Is Part Of: Automatica. Volume 139(2022)
• Publication Date: 2022-05
• Subjects: Iterative learning control -- Intermittent data -- Incremental encoders; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2022.110171
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: The standard assumption that a measurement signal is available at each sample in iterative learning control (ILC) is not always justified, e.g., when exploiting time-stamped data from incremental encoders or in systems with data dropouts. The aim of this paper is to develop a computationally tractable ILC framework that is capable of exploiting intermittent data while maintaining favourable properties, including monotonic convergence. A controllability and observability analysis of the intermittent ILC framework leads to appropriate monotonic convergence conditions which allow for missing data. These conditions lead to a new explicit ILC controller design independent of the sampling instances, which is reminiscent of gradient-descent ILC. The approach is demonstrated on both an intuitive example and a practically relevant example which exploits time-varying timestamped data from an incremental encoder.