On stochastic LQR design and polynomial chaos

Abstract

In this paper we develop a novel theoretical framework for linear quadratic regulator design for linear systems with probabilistic uncertainty in the parameters. The framework is built on the generalized polynomial chaos theory, which can handle Gaussian, uniform, beta and gamma distributions. In this framework, the stochastic dynamics is transformed into deterministic dynamics in higher dimensional state space, and the controller is designed in the expanded state space. The proposed design framework results in a family of controllers, parameterized by the associated random variables. The theoretical results are applied to a controller design problem based on stochastic linear, longitudinal F16 model. The performance of the stochastic design shows excellent consistency with the results obtained from Monte-Carlo based designs, in a statistical sense.

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