Cooperative Tuning of Multi-Agent Optimal Control Systems

TL;DR

This paper presents a novel framework for cooperative tuning of multi-agent optimal control systems, enabling agents to coordinate parameter adjustments in dynamics and objectives to minimize team loss, validated through theoretical analysis and simulations.

Contribution

It introduces a new consensus-based distributed optimization approach for multi-agent control tuning, incorporating parameters in both dynamics and objectives, which was not addressed in prior work.

Findings

The proposed method achieves consensus on optimal parameters among agents.

The framework effectively minimizes team loss in multi-agent rendezvous scenarios.

Theoretical analysis confirms convergence and stability of the tuning algorithm.

Abstract

This paper investigates the problem of cooperative tuning of multi-agent optimal control systems, where a network of agents (i.e. multiple coupled optimal control systems) adjusts parameters in their dynamics, objective functions, or controllers in a coordinated way to minimize the sum of their loss functions. Different from classical techniques for tuning parameters in a controller, we allow tunable parameters appearing in both the system dynamics and the objective functions of each agent. A framework is developed to allow all agents to reach a consensus on the tunable parameter, which minimizes team loss. The key idea of the proposed algorithm rests on the integration of consensus-based distributed optimization for a multi-agent system and a gradient generator capturing the optimal performance as a function of the parameter in the feedback loop tuning the parameter for each agent. Both theoretical results and simulations for a synchronous multi-agent rendezvous problem are provided to validate the proposed method for cooperative tuning of multi-agent optimal control.