Distributed Estimation and Coordinated Tracking Control with Cooperative-Competitive Networks Multi-AUV Based on RMPC

This article investigates the bipartite consensus tracking problem of Autonomous Underwater Vehicles (AUVs) when the matrices of the leader are unavailable for followers. A distributed observer-based robust model predictive control (DO-RMPC) is designed to guarantee the bipartite tracking errors eventually converge to an arbitrarily small compact set. First, a bipartite consensus tracking in AUVs is described based on the Euler-Lagrange equation. Secondly, the distributed observer is designed based on a cooperative-competition network. Then, the DO-based auxiliary control law is con-structed by backstepping, and a collaborative cost function is designed to ensure bipartite consistency between agents. Besides, the designed algorithm includes constraints for controlling the actuator, and the recursive feasibility and stable nature of the bipartite consensus system are proven by Lyapunov theory. Finally, a numerical simulation is given to verify the effectiveness of the proposed method.