Abstract
This paper gives an applied view of linear-quadratic worst-case control and relates it to linear-quadraticGaussian smoothing. It extends the worst-case control problem formulation to include tracking of desired output histories and nonzero terminal constraintsand separatestheproblem into future, past, and present problems, each of which must satisfy a conjugate-point condition. It includes a e nite time horizon example, namely, a helicopter hover-position change in which the disturbance is horizontal wind velocity. Linear-quadratic best-case controllers can be obtained by using positive instead of negative weights in the quadratic performance index.
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