Distributed Estimation and Control for Discrete Time-Varying Interconnected Systems

This article is concerned with the distributed estimation and control problem for time-varying large-scale interconnected systems (LISs). A novel decoupling strategy with sequential-structure is developed to deal with the interconnected terms in large-scale systems. Then, by using the idea of bounded recursive optimization, the local estimator gain for each subsystem is designed by solving self-relative convex optimization problem that is constructed based on each subsystem's own information and its neighboring information. In this case, such design scheme of each local estimator can realize fully distributed estimation. Based on the distributed estimator, fully distributed estimator-based control method is also designed by constructing self-relative convex optimization problems. Notice that the solutions to the above-constructed convex optimization problems can be easily obtained by the standard software packages, and the computational complexity of each optimization problem is low even though the scale of interconnected systems is large. Furthermore, stability conditions are derived such that the designed distributed estimator and controller for time-varying LISs are asymptotically bounded. Finally, two illustrative examples are employed to show the effectiveness of the proposed methods.

Automated summary

This article discusses the problem of distributed estimation and control for time-varying large-scale interconnected systems (LISs). A novel decoupling strategy and bounded recursive optimization are utilized to design local estimator gain and control methods for fully distributed estimation and control. Stability conditions are also derived to ensure the boundedness of the designed time-varying LISs.