TR2015-117

An Iterative Approach to the Optimal Co-Design of Linear Control Systems


    •  Jiang, Y., Wang, Y., Bortoff, S.A., Jiang, Z.-P., "An Iterative Approach to the Optimal Co-Design of Linear Control Systems", International Journal of Control, DOI: 10.1080/​00207179.2015.1091510, Vol. 89, No. 4, pp. 680-690, October 2015.
      BibTeX TR2015-117 PDF
      • @article{Jiang2015oct,
      • author = {Jiang, Y. and Wang, Y. and Bortoff, S.A. and Jiang, Z.-P.},
      • title = {An Iterative Approach to the Optimal Co-Design of Linear Control Systems},
      • booktitle = {International Journal of Control},
      • journal = {International Journal of Control},
      • year = 2015,
      • volume = 89,
      • number = 4,
      • pages = {680--690},
      • month = oct,
      • doi = {10.1080/00207179.2015.1091510},
      • url = {https://www.merl.com/publications/TR2015-117}
      • }
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  • Research Areas:

    Control, Optimization, Dynamical Systems

Abstract:

This paper investigates the optimal co-design of both physical plants and control policies for a class of continuous time linear control systems. The optimal co-design of a specific linear control system is commonly formulated as a nonlinear non-convex optimization problem (NNOP), and solved by using iterative techniques, where the plant parameters and the control policy are updated iteratively and alternately. This paper proposes a novel iterative approach to solve the NNOP, where the plant parameters are updated by solving a standard semi-definite programming problem, with non-convexity no longer involved. The proposed system design is generally less conservative in terms of the system performance compared to the conventional system-equivalence-based design, albeit the range of applicability is slightly reduced. A practical optimization algorithm is proposed to compute a sub-optimal solution ensuring the system stability, and the convergence of the algorithm is established. The effectiveness of the proposed algorithm is illustrated by its application to the optimal co-design of a physical load positioning system.