Optimal Control Design under Limited Model Information for Discrete-Time Linear Systems with Stochastically-Varying Parameters
For control engineers designing decentralized controllers in large-scale systems, this work provides a theoretical framework to quantify the value of model information, though the results are incremental and specific to the assumed system structure.
This paper designs optimal state-feedback controllers for interconnected discrete-time linear systems with stochastically-varying parameters, using only local plant model information and statistical beliefs about the rest of the system. An upper bound for the performance degradation ratio is derived for fully-actuated subsystems, showing limited information can achieve near-optimal performance.
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.