Model Predictive Control of Mars Rover Mobile System Based on Second-Order Cone Programming

This paper presents a Model Predictive Control (MPC) method based on Second-Order Cone Programming (SOCP) to address the coordinated control problem of trajectory tracking accuracy and vehicle stability for Mars rovers in complex terrain environments. Firstly, based on the two-degree-of-freedom nonlinear dynamics model of the Mars rover, the system state equations and control inputs are established. Secondly, by transforming the state equations into a second-order cone form, an SOCP optimization problem is constructed to achieve coordinated control of tracking error minimization and vehicle stability optimization. On this basis, the weight matrix of predicted states and output increments is introduced to further optimize the global performance. Simulation results show that the proposed method significantly improves trajectory tracking accuracy while ensuring the stability of the Mars rover, effectively adapting to complex terrain environments.