Study on robust model predictive control for dynamical systems with constraints of logic and state switchings
| Project/Area Number |
15560373
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Control engineering
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| Research Institution | University of Tsukuba |
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Principal Investigator |
KAWABE Tohru University of Tsukuba, Graduate School of Systems and Information Engineering, Associate Professor, 大学院・システム情報工学研究科, 助教授 (40224844)
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| Project Period (FY) |
2003 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥3,700,000 (Direct Cost: ¥3,700,000)
Fiscal Year 2005: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 2004: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2003: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | Piecewise linear system / Finite-time receding horizon control / Mixed logical dynamical system / Minimax robust control design / Partial external force control / Mixed Logical Dynamicalシステム / 相補性条件 / Explicitモデル予測制御法 / Minimaxロバスト設計 |
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| Research Abstract |
A modeling method for a kind of hybrid systems with two types constraints of discontinuous changes or switching of states and of logics has been developed based on the analysis results of suitability of extended application of the mixed logical dynamical system model and the piecewise linear or piecewise affine model.
By using this model, the finite-time receding horizon control design method has been developed based on the minimax control design approach. The developed design method can get rid of drawbacks of pre-existing explicit model predictive control. It's difficult to apply the fast moving systems, such as mechanical systems, mechatronics systems, and so on, due to the computation time. The partial external force control method has also been proposed for the systems with strong nonlinear properties. Furthermore, the integrated control design method based on the model predictive control and adaptive DA (Discrete-Analog) converter has been developed for sampled-data dynamical systems, which is a kind of hybrid systems, with constraints.
Then, some optimization methods based on the neural networks with chaotic noise, genetic algorithm and evolutionary computing have been developed as effective optimization tools for the proposed design methods.
From various simulations and experiments, the effectiveness of the proposed design methods have been confirmed.
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Report
(4 results)
Research Products
(47 results)
