Advanced Leader-Follower Control Strategies: Integrating Adaptation Laws with Model Predictive Control

This article investigates the intricate dynamics of the leader-follower problem within the framework of model predictive control (MPC). The study focuses on a scenario where a leader, characterized by a differential dynamic model, is diligently followed by a follower vehicle with a distinct differential dynamic model. The follower has full access to the leader's state information, facilitating real-time informed decision-making. A novel adaptation law is introduced to adjust the weighting matrix of the MPC controller, ensuring the follower approaches the leader in the tangent plane manifold by prioritizing the heading angle error. The control strategy is designed to synchronize the follower's trajectory with that of the leader, which performs various maneuvers such as lane changes, abrupt heading angle alterations, and sudden shifts in linear velocity. The leader-follower formation control problem is thoroughly investigated across diverse scenarios, including straight-line movements, circular trajectories, and intricate S-shaped paths. Comprehensive analysis demonstrates the effectiveness of MPC and the proposed adaptation law in achieving precise and adaptable formation control, significantly enhancing the understanding of leader-follower dynamics under varying conditions. This research contributes to the field by offering a robust solution for precise and reliable formation control in dynamic environments, showcasing the potential of MPC in autonomous systems.