DocumentCode :
4817
Title :
Optimal Codesign of Nonlinear Control Systems Based on a Modified Policy Iteration Method
Author :
Yu Jiang ; Yebin Wang ; Bortoff, Scott A. ; Zhong-Ping Jiang
Author_Institution :
Eng. Dev. Group, MathWorks, Inc., Natick, MA, USA
Volume :
26
Issue :
2
fYear :
2015
fDate :
Feb. 2015
Firstpage :
409
Lastpage :
414
Abstract :
This brief studies the optimal codesign of nonlinear control systems: simultaneous design of physical plants and related optimal control policies. Nonlinearity of the optimal codesign problem could come from either a nonquadratic cost function or the plant. After formulating the optimal codesign into a nonconvex optimization problem, an iterative scheme is proposed in this brief by adding an additional step of system-equivalence-based policy improvement to the conventional policy iteration. We have proved rigorously that the closed-loop system performance can be improved after each step of the proposed policy iteration scheme, and the convergence to a suboptimal solution is guaranteed. It is also shown that under certain conditions, this additional policy improvement step can be conducted by solving a quadratic programming problem. The linear version of the proposed methodology is addressed in the context of linear quadratic regulator. Finally, the effectiveness of the proposed methodology is illustrated through the optimal codesign of a load-positioning system.
Keywords :
closed loop systems; concave programming; control system synthesis; iterative methods; linear quadratic control; nonlinear control systems; quadratic programming; closed-loop system; linear quadratic regulator; load-positioning system; nonconvex optimization problem; nonlinear control systems; optimal codesign problem; policy iteration method; quadratic programming problem; system-equivalence-based policy improvement; Approximation methods; Control systems; Convergence; Equations; Learning systems; Optimization; Vectors; Codesign; nonlinear systems; optimal control; policy iteration; policy iteration.;
fLanguage :
English
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
2162-237X
Type :
jour
DOI :
10.1109/TNNLS.2014.2382338
Filename :
7001716
Link To Document :
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