2005 Fiscal Year Final Research Report Summary
STUDY ON RECONSTRUCTION OF INTELLIGENT CONTROL SYSTEMS BY UTIUZING NONLINEAR H-INFINITY CONTROL AND COPUTATIONAL STATISTICS
| Project/Area Number |
14550457
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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 | THE INSTITUTE OF STATISTICAL MATHEMATICS |
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Principal Investigator |
MIYASATO Yoshihiko THE INSTITUTE OF STATISTICAL MATHEMATICS, ASSOCIATE PROFESSOR, 数理・推論研究系, 助教授 (30174155)
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| Project Period (FY) |
2002 – 2005
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| Keywords | ADAPTIVE CONTROL / ROBUST CONTROL / H-INFINITY CONTROL / COMPUTATIONAL STATISTICS / NONLINEAR CONTROL / NEURAL NETWORK / GAIN-SCHEDULING |
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| Research Abstract |
1.New class of adaptive controllers which are optimal or sub-optimal to certain meaningful cost functionals, were derived. The adaptive H2 or H-infinity optimal (or sub-optimal) control systems are constructed for genoral daises of adaptive control problems.
2.By extending the study result of 1, the nonlinear adaptive H-infinity control schemes are developed for nonlinear time-varying processes. The resulting control strategy is derived as a solution for certain class of H-infinity control problems, where estimation errors of tuning parameters and time-varying elements of system parameters are regarded as external disturbances.
3.By extending the study result of 2, the nonlinear adaptive H-infinity control schemes are developed for nonlinear parametric models including three-layered neural networks. The resulting control strategy is derived as a solution for certain class of H-infinity control problems, where approximation errors and algorithmic errors in the estimation structures of non
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linear parametric models, are reqarded as external disturbances.
4.The adaptive gain-scheduled H-infinity control strategy is developed for linear parameter-varying (LPV) systems. In the proposed control schemes, estimates of unknown scheduled parameters are obtained recursively, and those are fed to the controllers to stabilize plants and attain H-infinity control performance adaptively. Stability analysis is carried out via Lyapunov approaches based on linear matrix inequalities (LMI) in "Bounded Real Lemma".
5.By applying the study results of 2 and 3 into 4, the robust control version of the adaptive gain-scheduled H-infinity control scheme is developed for LPV systems. Stabilizing control signals are added to regulate the effect of time-varying scheduling parameters, and those are derived as a solution of certain H-infinity control problems. The same control schemes are applied to the gain-scheduled control for LPV systems with nonlinear parametric models and time-delayed elements.
6.The iterative learning control schemes by utilizing hybrid adaptation laws are developed for motion control of robotic manipulators. The gradient and least squares hybrid adaptation laws are proposed, and stability analysis of overall systems is carried out. Additionally, by extending hybrid adaptation schemes, two-dimensional adaptive control procedures are proposed. The two-dimensional adaptice control structures contain 2 types of adaptation processes, off-line tuning and on-line tuning, simultaneously, and provide more skillful learning properties where adaptive processes themselves are improved adaptively. Less
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Research Products
(36 results)
