摘要
为实现履带机器人在复杂环境下的路径跟踪,提出基于模糊神经网络自适应PID的路径跟踪控制方法。在预瞄控制的基础上,分析位姿偏差耦合关系对路径跟踪的影响,提出使用LQR对耦合关系解耦的方法。算法使用LQR得到最优解耦系数,并使用模糊神经网络PID作为主控制器。既基于所建模型求得最优解耦系数,又不完全依赖于模型,鲁棒性更好。此外,LQR解耦中还有线速度项,可以实现在不同速度下路径跟踪自适应调节。最后,在Matlab/Simulink仿真平台对所设计的算法有效性验证,包括直线和曲线路径跟踪。在线速度为1.5m/s的直线路径跟踪过程中,控制量最大偏差为-0.07rad/s,调节时间为1.3s。在线速度为1m/s的曲线路径跟踪过程中系统最大超调量为20%,调节时间为4s。与传统预瞄控制相比,所设计的基于LQR解耦和模糊神经网络PID的控制方法相较于传统预瞄控制具有调节时间快、稳定性好的特点。
In order to realize the path tracking of crawler robots in complex environments,apath tracking control method based on Fuzzy Neural Network Adaptive PID is proposed.Based on the preview control,the influence of the coupling relationship of pose deviation on path tracking is analyzed,and a method of decoupling the coupling relationship using LQR is proposed.The algorithm uses LQR to get the optimal decoupling coefficient,and uses fuzzy neural network PID as the main controller.It is not only based on the established model to get the optimal decoupling coefficient,but also not completely dependent on the model,so it has better robustness.In addition,there is a linear velocity term in the LQR decoupling,which can realize adaptive adjustment of path tracking at different speeds.Finally,the effectiveness of the designed algorithm is verified on the Matlab/Simulink simulation platform,including straight and curved path tracking.During the linear path tracking with a line speed of 1.5m/s,the maximum deviation of the control amount is-0.07rad/s,and the adjustment time is 1.3s.During curve path tracking with a line speed of 1m/s,the maximum overshoot of the system is 20%,and the adjustment time is 4s.Compared with traditional preview control,the designed control method based on LQR decoupling and fuzzy neural network PID has faster adjustment time and better stability than traditional preview control.
作者
尹奇辉
李捷
梁志鹏
杨磊
Yin Qihui;Li Jie;Liang Zhipeng;Yang Lei(School of Mechanical Engineering,Taiyuan University of Science and Technology,Taiyuan,030024,China)
出处
《中国农机化学报》
北大核心
2020年第5期182-187,共6页
Journal of Chinese Agricultural Mechanization
基金
山西省科技平台项目(201805D121006)。
