ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

Aiming to enhance the path tracking capability of agricultural machinery in complex environments, an adaptive predictive control method was proposed-based on multi-objective optimization. The goal was to reduce external disturbances and improve path smoothness. Firstly, a kinematic model and error model of the machinery were developed, and its dynamic behavior under working conditions was analyzed. The arctic parrot algorithm was introduced, with a comprehensive error objective function designed for path tracking. By combining real-time feedback, AP adjusted model predictive control (MPC) parameters for better accuracy. Next, a multi-objective optimization algorithm was integrated with the MPC cost function to improve tracking accuracy, smoothness, and stability. To address delays caused by increased controller dimensionality, Latin hypercube sampling was used for efficient population initialization, reducing computational load. An early stopping mechanism and fitness memory were applied to accelerate the optimization process by halting iterations once a fitness threshold reached. Additionally, a warm start technique-based on historical data was introduced to shorten optimization time, enabling faster application to new tasks. Simulation results at 1.0 m/s showed a lateral maximum absolute error of only 0.06 m, with an average error of 0.02 m, while running time remained comparable to traditional MPC algorithms. Path smoothness was improved by 83%, indicating enhanced stability. In field tests, the algorithm outperformed traditional MPC with error reductions of 33%, 35%, and 38% at speeds of 0.5 m/s, 1.0 m/s, and 1.5 m/s, respectively. Path smoothness was increased by 40%, 51%, and 10%. These results validated the effectiveness of this method in practical applications, ensuring stable performance across complex scenarios and reducing path deviations due to external factors.