引用本文:王乐君,王亚午,赖旭芝,等.基于智能优化算法的Pendubot轨迹规划与控制方法设计[J].控制与决策,2020,35(5):1085-1090
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基于智能优化算法的Pendubot轨迹规划与控制方法设计
王乐君,王亚午,赖旭芝,吴敏
( 中国地质大学(武汉) 自动化学院,武汉430074;复杂系统先进控制与智能自动化湖北省重点实验室,武汉430074)
摘要:
以垂直Pendubot为研究对象,提出一种基于智能优化算法的轨迹规划与控制方法,以解决Pendubot控制过程中难以从摇起区过渡至平衡区的问题.为Pendubot的驱动连杆规划一条从初始角度到中间角度的正向轨迹和一条从中间角度到目标角度的反向轨迹.欠驱动连杆在系统耦合关系作用下进行运动,对应的Pendubot末端点也运动至相应位置.通过遗传算法优化轨迹参数,将正向和反向轨迹拼合为一条由初始角度到目标角度的驱动连杆轨迹的同时,对应的Pendubot末端点轨迹拼合为一条由垂直向下平衡位置到垂直向上平衡位置的完整轨迹,然后设计跟踪控制器跟踪优化后的驱动连杆轨迹至目标角度,由于耦合关系的存在,Pendubot末端点也运动至垂直向上平衡位置.由于Pendubot受重力作用,其末端点很难长时间稳定在垂直向上平衡位置,故设计镇定控制器,实现Pendubot末端点在垂直向上平衡位置的镇定控制.最后通过仿真实验验证所提出方法的有效性,并通过对比说明所提出方法在奇异点规避、控制器设计和控制效果方面的优势.
关键词:  Pendubot  轨迹规划  遗传算法  跟踪控制器  镇定控制器
DOI:10.13195/j.kzyjc.2019.0899
分类号:TP273
基金项目:国家自然科学基金项目(61773353);湖北省自然科学基金创新群体项目(2015CFA010);高等学校学科创新引智计划项目(B17040).
Trajectory planning and control method for Ppendubot based on intelligent optimization algorithm
WANG Le-jun,WANG Ya-wu,LAI Xu-zhi,WU Min
( School of Automation,China University of Geosciences,Wuhan430074,China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan430074,China)
Abstract:
Taking the Pendubot moving in vertical plane motion as the research object, a trajectory planning and control method based on the intelligent optimization algorithm is proposed to solve the problem that it is difficult to transit from swing-up area to balance area in the control process. Firstly, a forward trajectory from the initial angle to the middle angle and a reverse trajectory from the middle angle to the target angle are planned for the Pendubot. The unactuated link moves under the action of system coupling relation, and the corresponding end point of the Pendubot moves to the relevant position. The genetic algorithm is used to optimize the trajectory parameters, so that the forward and reverse trajectories are combined into a driving link trajectory from the initial angle to the target angle, and the corresponding Pendubot terminal trajectory moves from the vertical downward balance position to the vertical upward balance position. Then, a tracking controller is designed to make the driving link move to the target angle along the optimized trajectory of the driving link, the end point of Pendubot also moves to the vertical upward balance position in virtue of the existence of coupling relationship. Due to the influence of gravity, it is difficult for the Pendubot to stabilize the end point at the vertical upward position for a long time. Thus, the stabilization controller is designed to keep the end point stable at the vertical upward balance position. Finally, the effectiveness of the proposed method is proved by simulation experiments, the advantages of this method in singularity avoidance, controller design and control effect are illustrated by comparison.
Key words:  Pendubot  trajectory planning  genetic algorithm  tracking controller  stabilization controller

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