Abstract
This chapter presents the novel formulation and idea to address the tracking control problem for discrete-time linear systems with randomly varying trial lengths. An ILC scheme with an iteration-average operator is introduced for tracking tasks with nonuniform trial lengths, which thus mitigates the requirement on classic ILC that all trial lengths must be identical. In addition, the identical initialization condition can be absolutely removed. The learning convergence condition of ILC in mathematical expectation is derived through rigorous analysis. As a result, the proposed ILC scheme is applicable to more practical systems. In the end, two illustrative examples are presented to demonstrate the performance and the effectiveness of the averaging ILC scheme for both time-invariant and time-varying linear systems.
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Shen, D., Li, X. (2019). Averaging Techniques for Linear Discrete-Time Systems. In: Iterative Learning Control for Systems with Iteration-Varying Trial Lengths. Springer, Singapore. https://doi.org/10.1007/978-981-13-6136-4_2
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DOI: https://doi.org/10.1007/978-981-13-6136-4_2
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Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6135-7
Online ISBN: 978-981-13-6136-4
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