Abstract
This paper considers the linear quadratic regulator (LQR) optimal control problem of multi-agent unmanned vehicle systems under communication constraints with packet drops. The problem is formulated into a distributed optimization problem of minimizing a global cost function through the sum of local cost functions by using local information exchange. By utilizing a newly developed optimization technique, we propose a novel algorithm to solve the distributed LQR problem in a first order (gradient descent based) manner. Moreover, we adopt the key idea of virtualizing an extra node for each agent to store information from the previous step and create a fully distributed optimization algorithm. Extensive simulations demonstrate the efficacy and robustness of the proposed solution.
Shengjun(Daniel) Zhang
Ph.D. in Electrical Engineering
My research interests include distributed optimization, statistical learning and control theory.