Abstract
The design of control laws for systems subject to complex state constraints still presents a significant challenge. This paper explores a dynamic programming approach to a specific class of such problems, that of reachability under state constraints. The problems are formulated in terms of nonstandard minmax and maxmin cost functionals, and the corresponding value functions are given in terms of Hamilton-Jacobi-Bellman (HJB) equations or variational inequalities. The solution of these relations is complicated in general; however, for linear systems, the value functions may be described also in terms of duality relations of convex analysis and minmax theory. Consequently, solution techniques specific to systems with a linear structure may be designed independently of HJB theory. These techniques are illustrated through two examples.
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The first author was supported by the Russian Foundation for Basic Research, Grant 03-01-00663, by the program Universities of Russia, Grant 03.03.007, and by the program of the Russian Federation President for the support of scientific research in leading scientific schools, Grant NSh-1889.2003.1.
The second author was supported by the National Science and Engineering Research Council of Canada and by ONR MURI Contract 79846-23800-44-NDSAS.
The third and first authors were supported by NSF Grants ECS-0099824 and ECS-0424445.
Communicated by G. Leitmann
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Kurzhanski, A.B., Mitchell, I.M. & Varaiya, P. Optimization Techniques for State-Constrained Control and Obstacle Problems. J Optim Theory Appl 128, 499–521 (2006). https://doi.org/10.1007/s10957-006-9029-4
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DOI: https://doi.org/10.1007/s10957-006-9029-4