Nontracking type iterative learning control based on economic model predictive control

Nontracking type iterative learning control based on economic model predictive control
Title:
Nontracking type iterative learning control based on economic model predictive control
Other Titles:
International Journal of Robust and Nonlinear Control
Publication Date:
08 October 2020
Citation:
Long, Y, Xie, L, Liu, S. Nontracking type iterative learning control based on economic model predictive control. Int J Robust Nonlinear Control. 2020; 30: 8564– 8582. https://doi.org/10.1002/rnc.5261
Abstract:
Most existing iterative learning control algorithms are designed to improve tracking performance with respect to a given trajectory over a fixed time period. In this article, we design two iterative learning-based economic model predictive controllers for repetitive tasks where no target trajectory is available. The controller is able to search for suboptimal trajectories with good performance by exploiting information from previous experience. Compared with existing works, the objective function is not assumed to be positive definite so it is not limited to the tracking problem but can represent more general economic performance index. The controller can learn from the previous closed-loop trajectory, resulting in a performance which is guaranteed to be no worse than the previous one. Under some standard assumptions in model predictive control, the recursive feasibility of the algorithms is ensured. We show that the fixed operation time algorithm can guarantee that the performance is no worse than the previous iteration even without the dissipative assumption. By allowing the operation time to vary, the flexible operation time algorithm can balance the operation time and the system performance if dissipative assumption is satisfied. For both algorithms, each iteration is guaranteed to be completed within a uniformly bounded time duration.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Natural Science Foundation of China - -
Grant Reference no. : 61633014 and 61733010

This research / project is supported by the Natural Science Foundation of Shandong Province - -
Grant Reference no. : ZR2018MF021

This research / project is supported by the National Research Foundation, Singapore - Singapore Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program
Grant Reference no. : N.A.
Description:
This is the peer reviewed version of the following article: Long, Y, Xie, L, Liu, S. Nontracking type iterative learning control based on economic model predictive control. Int J Robust Nonlinear Control. 2020; 30: 8564– 8582. https://doi.org/10.1002/rnc.5261, which has been published in final form at https://doi.org/10.1002/rnc.5261. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
ISSN:
1049-8923
1099-1239
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