A multistage suboptimal dual controller using optimal predictors

This work concerns the control of stochastic systems with unknown and randomly time-varying parameters. Cost functions which consider the sum of output variances up to M steps ahead in time are adopted in the optimization of the control performance. Optimal predictors are used to replace the future...

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Bibliographic Details
Published in:IEEE transactions on automatic control 1999-05, Vol.44 (5), p.1002-1008
Main Authors: Maitelli, A.L., Yoneyama, T.
Format: Article
Language:English
Subjects:
Applied sciences
Automatic control
Computer science
control theory
systems
Computer simulation
Control system synthesis
Control systems
Control theory. Systems
Design methodology
Differential equations
Exact sciences and technology
Feedback
Linear systems
Mathematical models
Multistage
Nonlinear equations
Nonlinear systems
Optimal control
Optimization
Polynomials
Simplification
Stability
Stochastic systems
Time varying systems
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Summary:This work concerns the control of stochastic systems with unknown and randomly time-varying parameters. Cost functions which consider the sum of output variances up to M steps ahead in time are adopted in the optimization of the control performance. Optimal predictors are used to replace the future outputs which are needed in the solution of the optimization problem. The consequences of this simplification are investigated. A formula is obtained for the computation of the control signal in the case of M-steps-ahead optimization. The relationships between the controller presented here and other classical suboptimal dual controllers are analyzed. Simulation results illustrate the actual performance of the new controller.
ISSN:0018-9286
1558-2523
DOI:10.1109/9.763217