Lunar Rover's Path Tracking Control Strategy Using Zero-Order Optimization Method

The development of lunar rovers is critical for human exploration of the lunar surface. These rovers will need to travel longer distances at higher speeds, and the efficient control of their paths is essential for mission success. Model Predictive Control (MPC) is a path tracking method with foresight and robustness, capable of predicting vehicle movement and solving optimization problems to minimize cost functions. However, the complexity of lunar environments necessitates simplified models, such as kinematic models, to reduce computational cost. The study proposes a special MPC method that uses zeroth-order gradient optimization to directly solve the nonlinear model and generate precise control commands, significantly reducing computational burden and meeting the computational cost requirements of lunar rover control.