Energy-aware leader-follower tracking control for electric-powered multi-agent systems

This paper aims to extend the operation time/range of an electric-powered multi-agent system (MAS) in leader-followertracking tasks, through integrating battery-based energy awareness with distributed tracking control synthesis. While MASs have gained much popularity nowadays, their use and deployment are often restricted by the operation time/range, due to the limited battery capacity. In an effort to overcome such a barrier, this work proposes to leverage a battery's rate capacity effect to extend its runtime, which states that more energy can be drawn from the battery on less aggressive discharging rates. The battery-aware leader-follower tracking control design is then established in a model predictive control (MPC) framework, which strikes a tradeoff between tracking performance and energy consumption rates, accounts for the battery's rate capacity dynamics, and incorporates the energy and power constraints. A distributed optimization method is used to distribute the MPC across the agents of the MAS. leader-follower tracking based on the proposed distributed MPC algorithm is then evaluated through a case study and compared with an existing algorithm in the literature. The simulation results show its effectiveness in extending the operation.