基于双参数自适应优化的无人履带车辆轨迹跟踪控制

To improve the poor adaptability of trajectory tracking controllers with fixed parameters, an optimized adaptive dual-parameter trajectory tracking algorithm for unmanned tracked vehicles based on the improved Particle Swarm Optimization (IPSO) and Multi-Layer Perceptron (MLP) algorithms is proposed. In the offline state, based on the collected actual vehicle data, the IPSO algorithm is used to construct the optimal parameter data set under different motion primitives, aiming for high accuracy, high stability, and low time cost of trajectory tracking. With the motion primitive type and vehicle speed as feature vectors, control time domain length and control time step length as labels, adaptive learning rate optimization algorithm is used to complete the training of the MLP neural network model. In the online state, according to the trajectory information and vehicle state feedback information provided by the planning layer, the MLP neural network outputs the predicted optimal control time domain length and control time step. These parameters are then input to the model predictive controller as dual parameters, enabling the adaptive trajectory tracking control. ROS-VREP co-simulation test and actual vehicle test based on a bilateral electric drive platform are carried out. Vehicle test results show that under various working conditions including large curvature steering, the proposed controller achieves a 30. 5% reduction in average lateral error, a 17. 2% decrease in average heading error, and a 7. 8% reduction in average change rate of rotation angle, compared with the fixed-parameter trajectory tracking control method with the same calculation time cost. The results verify the feasibility and effectiveness of the new algorithm.