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

A hydraulic omnidirectional leveling system was designed based on “three-layer frame” and a composite Q learning-BP neural network-PID (QBP-PID) omnidirectional leveling control algorithm was proposed, and the crawler machine was taken as the research object. Firstly, the structural scheme and working principle of the whole omnidirectional leveling machine were given. On this basis, a dynamic model of the entire crawler machine, including the omnidirectional leveling system, was established. Then aiming at the problem that PID control parameters were difficult to be adjusted, an omnidirectional leveling compound QBP-PID controller was established. The PID control parameters were updated in real time through the BP neural network, and the Q-learning algorithm was introduced to update the neural network connection weights online. Results of simulation showed that the leveling time under QBP-PID control was 2.8s for 20° transverse leveling and 3.2s for 25° longitudinal leveling. Compared with PID and BP-PID control, the leveling time was reduced, and no overshoot occurred. In the end, the complete machine test on transverse slope road and longitudinal slope road was carried out. Compared with the simulation results, the errors of transverse and longitudinal leveling time were 0.6s and 0.4s. And the body inclination angle on the horizontal ground was less than 1.5°, which satisfied the leveling performance demand of hilly mountainous agricultural machinery.