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Considering the limitations of the PID, many scholars have carried out research on path tracking control based on MPC theory. As a popular feedback control algorithm, MPC is widely used in the industrial production control process [18]. The MPC algorithm is a control method that considers the inputs, outputs and future states of the system and compares them with future reference signals when calculating the optimal control inputs of the system. Given the control signal delay problem existing in PID, MPC adds a prediction model to the control system to predict the future state of the robot, thus avoiding the phenomenon of control lag in PID [19]. Gong and Sun used an integral front-wheel steering vehicle as a research object, and applied kinematic and dynamic models to build a prediction model and performed a quadratic programming solution to improve the accuracy of UGV path tracking on good road surfaces [20,21]. Zhao [22] proposed a preview time adaptive control method for path tracking based on the MPC algorithm and vehicle dynamics model, and used a differential torque control method based on reference heading angle to improve the reliability and accuracy of path tracking under constant torque demand. Wang [23] designed a combined steering and braking path tracking controller based on the MPC algorithm. By linearizing the nonlinear vehicle and tire models, a linear time-varying MPC was designed to improve the real-time performance of the system. Simulation results show that the overall control method had better path tracking performance, less interference between steering and braking, and less influence on the longitudinal motion. Jiang [24,25] proposed a model-free predictive control strategy based on particle swarm optimization (PSO). First, the MFAPC control scheme was improved by integrating vehicle state parameters; then, the main parameters of the improved control scheme were optimized based on the PSO algorithm; finally, the effectiveness of the method under different operating conditions was verified by simulation. Kang [26] designed a six-wheel distributed unmanned vehicle drive control algorithm based on slip steering to determine the torque command on each wheel by determining the optimal longitudinal tire force to keep the slip rate value below the limit and to track the desired tire force. Simulations and tests show that the control method has adequate control performance. Xu [27] proposed an adaptive trajectory tracking control method based on pre-targeting time, which introduces a weighting factor method for differential steering and autonomous steering to achieve coordinated trajectory tracking control for distributed unmanned vehicles with differential and autonomous steering. 2ff7e9595c
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