State-constrained Multi-agent Cooperative Adaptive Control and its Application in Multi-train System

This paper investigates the multi-agent system leader following consensus problem and its application in the intelligent transportation systems (ITS) field. The leader agent provides the desired reference trajectory, and the other follower agents operate cooperatively with the leader under the predefined motion state constraints. The considered agents are second-order nonlinear systems with parameter uncertainties and unknown disturbances. To achieve cooperative operation of the system, a state-constraints multi-agent cooperative adaptive control (SMCAC) method is given for the follower agent. The barrier Lyapunov function (BLF) is constructed to analyze the performance of the method in terms of error convergence and state constraints. The proposed method is then applied to the control of a multi-train system under the train-to-train communication topology. Numerical simulations on a five-train system are given to demonstrate the theoretical analysis.

I. Introduction

Cooperative control of multi-agent systems has garnered widespread attention across various domains, such as vehicle platooning [1], UAV swarm coordination [2], and robot collaboration [3]. These systems can be categorized into leaderless consensus and leader following consensus problems. In the latter case, one agent assumes the leader role and provides the desired reference trajectory. The objective for follower agents is to achieve consensus with the leader’s trajectory. In different fields, this problem is referred to as cooperative optimal control [4], cooperative tracking [5] or model reference consensus [6].