Optimal Control Design under Limited Model Information for Discrete-Time Linear Systems with Stochastically-Varying Parameters

TL;DR

This paper develops optimal control strategies for interconnected discrete-time linear systems with stochastically-varying parameters, using limited local model information and statistical beliefs, and analyzes the impact on control performance.

Contribution

It introduces a method for designing optimal controllers with limited model information and derives bounds on performance degradation compared to full information scenarios.

Findings

Optimal controllers are linear in state and depend on parameter statistics.

An upper bound on performance degradation ratio is established for fully-actuated subsystems.

Comparisons show the benefits of using statistical and full model information in control design.

Abstract

The value of plant model information available in the control design process is discussed. We design optimal state-feedback controllers for interconnected discrete-time linear systems with stochastically-varying parameters. The parameters are assumed to be independently and identically distributed random variables in time. The design of each controller relies only on (i) exact local plant model information and (ii) statistical beliefs about the model of the rest of the system. We consider both finite-horizon and infinite-horizon quadratic cost functions. The optimal state-feedback controller is derived in both cases. The optimal controller is shown to be linear in the state and to depend on the model parameters and their statistics in a particular way. Furthermore, we study the value of model information in optimal control design using the performance degradation ratio which is defined as the supremum (over all possible initial conditions) of the ratio of the cost of the optimal controller with limited model information scaled by the cost of the optimal controller with full model information. An upper bound for the performance degradation ratio is presented for the case of fully-actuated subsystems. Comparisons are made between designs based on limited, statistical, and full model information. Throughout the paper, we use a power network example to illustrate concepts and results.