Optimal Structured Static State-Feedback Control Design with Limited Model Information for Fully-Actuated Systems
This work provides a theoretical framework for control design under constrained model access, relevant for distributed or networked control systems where global model information is unavailable.
The paper introduces limited model information control design methods for fully-actuated systems, computing optimal structured static state-feedback controllers under separable quadratic cost. It quantifies the trade-off between model information usage and closed-loop performance using competitive ratio metrics.
We introduce the family of limited model information control design methods, which construct controllers by accessing the plant's model in a constrained way, according to a given design graph. We investigate the closed-loop performance achievable by such control design methods for fully-actuated discrete-time linear time-invariant systems, under a separable quadratic cost. We restrict our study to control design methods which produce structured static state feedback controllers, where each subcontroller can at least access the state measurements of those subsystems that affect its corresponding subsystem. We compute the optimal control design strategy (in terms of the competitive ratio and domination metrics) when the control designer has access to the local model information and the global interconnection structure of the plant-to-be-controlled. Lastly, we study the trade-off between the amount of model information exploited by a control design method and the best closed-loop performance (in terms of the competitive ratio) of controllers it can produce.