Collaborative control strategy of optimal trajectory tracking and braking energy recovery for unmanned ground vehicles

To enhance the performance of trajectory tracking control and reduce energy consumption in autonomous vehicles, this study proposes a regenerative braking system (RBS) for distributed drive vehicles, coupled with a collaborative control strategy to optimize both trajectory tracking and braking energy recovery. First, a three-degree-of-freedom (3-DOF) vehicle dynamics model is created. A hierarchical control architecture is then designed to address the collaborative control challenge. At the upper layer, a nonlinear model predictive control (NMPC) trajectory tracking controller generates real-time optimal control inputs for the front steering angle and four-wheel torque on the basis of the reference trajectory. At the lower layer, a rule-based braking force distribution strategy allocates regenerative and mechanical braking forces to maximize energy recovery. Numerical simulations are conducted on the MATLAB-Simulink platform, utilizing the advanced embedded solver acados for NMPC. The computation time for all solvers is maintained below the sampling interval of 20 ms, demonstrating real-time feasibility. The control performance of the proposed NMPC is compared with that of a linear time-varying MPC (LTV-MPC) in a double lane change scenario, while the collaborative control strategy is validated in a race circuit tracking scenario. Results show that the proposed algorithm achieves superior trajectory tracking accuracy and stability, with a lateral error reduction compared with the LTV-MPC. Additionally, the system recovers 41.18 kJ of energy in a single lap (225.09 m) of the race circuit. These findings highlight the potential of the proposed approach to simultaneously enhance driving performance and energy efficiency in autonomous vehicles, paving the way for future applications in energy-aware autonomous driving systems.