期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 56, 期 12, 页码 2803-2818出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2011.2162878
关键词
Constrained control; optimization; robust adaptive control; semidefinite programming; sums-of-squares; uncertain systems
资金
- National Science Foundation [DMI-0556106, EFRI-0735905]
- Air Force Office of Scientific Research [FA9550-06-1-0303]
- Department of Mathematical Sciences, IBM
- T. J. Watson Research Center
In this paper, we propose a new tractable framework for dealing with linear dynamical systems affected by uncertainty, applicable to multistage robust optimization and stochastic programming. We introduce a hierarchy of near-optimal polynomial disturbance-feedback control policies, and show how these can be computed by solving a single semidefinite programming problem. The approach yields a hierarchy parameterized by a single variable (the degree of the polynomial policies), which controls the trade-off between the optimality gap and the computational requirements. We evaluate our framework in the context of three classical applications-two in inventory management, and one in robust regulation of an active suspension system-in which very strong numerical performance is exhibited, at relatively modest computational expense.
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