Nonlinear MPC-Based Formation Control for Autonomous Ground Vehicles with Dynamic Geometric Patterns

This paper presents a nonlinear model predictive control (NMPC) framework for real-time formation control of autonomous ground vehicles (AGVs) operating under dynamic geometric patterns. The proposed method integrates a nonlinear kinematic bicycle model with a time-varying linearization strategy and constrained quadratic optimization to compute control inputs for each follower agent. Formation references are generated online using geometric transformation functions, enabling flexible spatial configurations such as line, rectangular, half-circle, and V-shaped formations. An exponential convergence model ensures smooth trajectory tracking, while input constraints are enforced at each control step. The controller is decentralized and scalable, with each agent solving its own NMPC problem using leader pose information. Extensive simulations validate the approach across multiple formations, demonstrating accurate tracking, constraint satisfaction, and real-time feasibility. The results confirm that the proposed NMPC architecture provides a unified and modular solution for multi-AGV formation control under nonlinear dynamics.