TR2014-130

Nonlinear Optimal Co-Design Based on A Modified Policy Iteration Method


    •  Jiang, Y.; Wang, Y.; Bortoff, S.A.; Jiang, Z.-P., "Nonlinear Optimal Co-Design Based on A Modified Policy Iteration Method", IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2014.2382338, ISSN: 2162-237X, Vol. 26, No. 2, pp. 409-414, February 2015.
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      • @article{Jiang2015jan,
      • author = {Jiang, Y. and Wang, Y. and Bortoff, S.A. and Jiang, Z.-P.},
      • title = {Nonlinear Optimal Co-Design Based on A Modified Policy Iteration Method},
      • journal = {IEEE Transactions on Neural Networks and Learning Systems},
      • year = 2015,
      • volume = 26,
      • number = 2,
      • pages = {409--414},
      • month = jan,
      • publisher = {Neural Networks and Learning Systems, IEEE Transactions on},
      • doi = {10.1109/TNNLS.2014.2382338},
      • issn = {2162-237X},
      • url = {http://www.merl.com/publications/TR2014-130}
      • }
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  • Research Areas:

    Control, Optimization, Dynamical Systems


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.