Distributed Optimal Cooperative Tracking Control of Multi-Input LTI Systems: An Information Fusion-Based Learning Approach

Abstract

This letter investigates the distributed optimal cooperative tracking control problem for multi-input linear time-invariant (LTI) systems. In this context, the system inputs are generated by a group of agents that communicate with each other over a network, i.e., each control input channel is considered as an agent, which can communicate over a network to transmit information and compute control input. Unlike centralized optimal tracking control, where inputs are designed using global information, each agent in the distributed framework has access only to its own input matrix and communicates solely with its neighbors within the network. This limitation introduces significant challenges in designing the optimal controller. To address this issue, an information fusion method is first proposed, enabling each agent to derive its optimal controller in a distributed manner. For scenarios where the system model is unknown, a fusion-based learning algorithm is further developed. The convergence and optimality of this algorithm are rigorously proved. A simulation example is provided to illustrate the effectiveness of the proposed approach.