State-Output Risk-Constrained Quadratic Control of Partially Observed Linear Systems

TL;DR

This paper introduces a risk-averse quadratic control method for partially observed linear systems, balancing regulation performance with stochastic disturbance variability using risk constraints, and providing a pre-computable, stable policy.

Contribution

It develops a novel risk-averse control framework for partially observed LTI systems that incorporates statistical variability constraints and maintains classical LQ control benefits.

Findings

Optimal policy has affine structure with respect to MMSE estimates.

Policy adjusts regulation more strictly in riskier directions.

Controller is always internally stable regardless of parameters.

Abstract

We propose a methodology for performing risk-averse quadratic regulation of partially observed Linear Time-Invariant (LTI) systems disturbed by process and output noise. To compensate against the induced variability due to both types of noises, state regulation is subject to two risk constraints. The latter renders the resulting controller cautious of stochastic disturbances, by restricting the statistical variability, namely, a simplified version of the cumulative expected predictive variance of both the state and the output. Our proposed formulation results in an optimal risk-averse policy that preserves favorable characteristics of the classical Linear Quadratic (LQ) control. In particular, the optimal policy has an affine structure with respect to the minimum mean square error (mmse) estimates. The linear component of the policy regulates the state more strictly in riskier directions, where the process and output noise covariance, cross-covariance, and the corresponding penalties are simultaneously large. This is achieved by "inflating" the state penalty in a systematic way. The additional affine terms force the state against pure and cross third-order statistics of the process and output disturbances. Another favorable characteristic of our optimal policy is that it can be pre-computed off-line, thus, avoiding limitations of prior work. Stability analysis shows that the derived controller is always internally stable regardless of parameter tuning. The functionality of the proposed risk-averse policy is illustrated through a working example via extensive numerical simulations.