Optimal Inversion-Based Iterative Learning Control for Overactuated Systems
| Autor: | Deokkyun Yoon, Chinedum E. Okwudire, Xinyi Ge |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science 020208 electrical & electronic engineering Iterative learning control Monotonic function Inversion (meteorology) 02 engineering and technology Optimal control 020901 industrial engineering & automation Constrained optimization problem Rate of convergence Control and Systems Engineering Control theory Robustness (computer science) Frequency domain 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering |
| Zdroj: | IEEE Transactions on Control Systems Technology. 28:1948-1955 |
| ISSN: | 2374-0159 1063-6536 |
| DOI: | 10.1109/tcst.2019.2917682 |
| Popis: | An optimal inversion-based iterative learning control (Opt-In ILC) approach for overactuated systems is proposed. The Opt-In ILC update law is formulated as a constrained optimization problem using the plant model. Specifically, the ILC update law is designed to minimize control effort subject to a user-specified error convergence rate. To achieve robust monotonic convergence (MC), a frequency-domain optimization framework is adopted to determine the best plant model and robustness filter for Opt-In ILC. Simulations and experiments on an overactuated coarse–fine stage are used to demonstrate optimal control effort allocation and optimal selection of plant model and robustness filter via the proposed Opt-In ILC approach. |
| Databáze: | OpenAIRE |
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