基于PMAC引线键合轨迹规划研究
发布时间：2018-07-21 11:35
【摘要】：随着工业机器人、汽车、机械臂行业的快速发展和人们对其精度、稳定性要求的提高,针对它们的运动轨迹研究成为重要的研究课题。学者们通过不同的方法来研究轨迹规划,有的学者通过提出运动轨迹规划算法来规划轨迹；有的学者通过将其他领域的控制方法、算法应用到轨迹规划上；有的学者从轨迹的基本元素——速度、加速度、位移——的连续性出发,根据不同轨迹的工艺要求来拟合；也有的学者将运动分段,用不同的插值方法来拟合等等。本文以平面LED全自动超声波金丝球焊线机WB2001为试验平台,以焊线机劈刀为研究对象,以引线键合轨迹为优化目标,利用PMAC运动控制系统中自带的运动轨迹规划模式来规划轨迹并结合其PID控制系统进一步优化、规划轨迹。本文从混合模式轨迹规划、整段控制参数和混合模式轨迹优化、分段控制参数和混合模式轨迹规划三个角度来规划引线键合轨迹。混合模式轨迹规划将轨迹进行分段,在保证位置、速度、加速度连续基础上,以跟随误差和重复误差为试验指标,以不同控制模式为试验变量,来规划轨迹。在规化过程中,把不同的控制模式数值化,并结合正交试验建立不同控制模式与试验指标的数学模型,用多目标遗传算法获得规化目标的Pareto最优解,从而得到最优轨迹规划方式,实现引线键后轨迹规划。整段控制参数与混合模式轨迹规划是通过试验设计和响应面模型方法来获取试验数据,并建模分析来实现参数整定。用整段控制参数设计正交试验,用Design-expert软件和MATLAB软件分析计算试验数据来优化轨迹,和智能方法整定相比,简单易获得而且对理论要求较低。分段控制参数与混合模式轨迹规划是利用分段控制参数与混合模式一起来规划轨迹。先把整段运动分段处理,对每一段运动不同模式、不同控制参数进行试验,并建模分析,先找到每段各模式的最优控制参数,对比每段中各模式的最优控制参数获得最优控制模式和控制参数组合,就这样按顺序找出每段最优组合,一步一步找出整段的最优组合,其中所获得后段运动的控制参数以前部分运动为基础。轨迹规划过程中,将理论方法得到的结果代入试验平台进行验证,结果验证了所采用的三种轨迹规划方法的正确性、可靠性。
[Abstract]:With the rapid development of industrial robot, automobile and manipulator industry and the improvement of its precision and stability, the research on their motion trajectory has become an important research topic. Some scholars put forward motion trajectory planning algorithm to plan trajectory, some scholars apply the algorithm to trajectory planning by using control methods in other fields, and some scholars study trajectory planning by different methods, some scholars put forward motion trajectory planning algorithm to plan trajectory, and some scholars apply other control methods to trajectory planning. Some scholars start from the continuity of the basic elements of the trajectory-velocity, acceleration, displacement-and fit them according to the technological requirements of different trajectories; others fit the motion segments with different interpolation methods and so on. This paper takes WB2001, an automatic planar LED ultrasonic gold wire ball welding machine, as the test platform, takes the splitter of the wire welding machine as the research object, and takes the lead bond trajectory as the optimization goal. The trajectory planning model of PMAC motion control system is used to plan the trajectory and its pid control system is further optimized to plan the trajectory. In this paper, the lead bond trajectory is planned from three angles: hybrid mode trajectory planning, whole control parameter and mixed mode trajectory optimization, piecewise control parameter and mixed mode trajectory planning. The trajectory of mixed mode trajectory planning is divided into sections. On the basis of continuous position, velocity and acceleration, tracking error and repetition error are taken as test indexes, and different control modes are used as test variables to plan the trajectory. In the process of planning, different control modes are numerically simulated, and mathematical models of different control modes and test indexes are established by orthogonal test. The Pareto optimal solution of the planning target is obtained by using multi-objective genetic algorithm. The optimal trajectory planning method is obtained and the post-lead trajectory planning is realized. The whole control parameter and mixed mode trajectory planning are used to obtain the test data by experimental design and response surface model method, and to realize parameter tuning by modeling and analysis. The orthogonal test is designed with the whole control parameters, and the test data are analyzed and calculated by Design-expert software and MATLAB software to optimize the trajectory. Compared with the intelligent tuning method, it is simple and easy to obtain, and the theoretical requirements are lower. Piecewise control parameters and mixed mode trajectory planning are designed by using piecewise control parameters and mixed modes. First, the whole segment motion is divided into sections, and the different modes and control parameters of each segment are tested, and the optimal control parameters of each model are found out first by modeling and analyzing. By comparing the optimal control parameters of each mode in each section, the optimal control mode and the combination of control parameters are obtained, and the optimal combination of each segment is found in sequence, and the optimal combination of the whole section is found step by step. The obtained control parameters of the posterior motion are based on the previous partial motion. In the course of trajectory planning, the results obtained by the theoretical method are put into the experimental platform to verify the correctness and reliability of the three trajectory planning methods.
【学位授予单位】：广东工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN312.8
[Abstract]:With the rapid development of industrial robot, automobile and manipulator industry and the improvement of its precision and stability, the research on their motion trajectory has become an important research topic. Some scholars put forward motion trajectory planning algorithm to plan trajectory, some scholars apply the algorithm to trajectory planning by using control methods in other fields, and some scholars study trajectory planning by different methods, some scholars put forward motion trajectory planning algorithm to plan trajectory, and some scholars apply other control methods to trajectory planning. Some scholars start from the continuity of the basic elements of the trajectory-velocity, acceleration, displacement-and fit them according to the technological requirements of different trajectories; others fit the motion segments with different interpolation methods and so on. This paper takes WB2001, an automatic planar LED ultrasonic gold wire ball welding machine, as the test platform, takes the splitter of the wire welding machine as the research object, and takes the lead bond trajectory as the optimization goal. The trajectory planning model of PMAC motion control system is used to plan the trajectory and its pid control system is further optimized to plan the trajectory. In this paper, the lead bond trajectory is planned from three angles: hybrid mode trajectory planning, whole control parameter and mixed mode trajectory optimization, piecewise control parameter and mixed mode trajectory planning. The trajectory of mixed mode trajectory planning is divided into sections. On the basis of continuous position, velocity and acceleration, tracking error and repetition error are taken as test indexes, and different control modes are used as test variables to plan the trajectory. In the process of planning, different control modes are numerically simulated, and mathematical models of different control modes and test indexes are established by orthogonal test. The Pareto optimal solution of the planning target is obtained by using multi-objective genetic algorithm. The optimal trajectory planning method is obtained and the post-lead trajectory planning is realized. The whole control parameter and mixed mode trajectory planning are used to obtain the test data by experimental design and response surface model method, and to realize parameter tuning by modeling and analysis. The orthogonal test is designed with the whole control parameters, and the test data are analyzed and calculated by Design-expert software and MATLAB software to optimize the trajectory. Compared with the intelligent tuning method, it is simple and easy to obtain, and the theoretical requirements are lower. Piecewise control parameters and mixed mode trajectory planning are designed by using piecewise control parameters and mixed modes. First, the whole segment motion is divided into sections, and the different modes and control parameters of each segment are tested, and the optimal control parameters of each model are found out first by modeling and analyzing. By comparing the optimal control parameters of each mode in each section, the optimal control mode and the combination of control parameters are obtained, and the optimal combination of each segment is found in sequence, and the optimal combination of the whole section is found step by step. The obtained control parameters of the posterior motion are based on the previous partial motion. In the course of trajectory planning, the results obtained by the theoretical method are put into the experimental platform to verify the correctness and reliability of the three trajectory planning methods.
【学位授予单位】：广东工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN312.8
【相似文献】
相关期刊论文 前10条
1 胡贯朋;高雪官;马培荪;;一种类人型机器人踢球时腿部轨迹规划实验方案[J];实验室研究与探索;2006年01期
2 穆海华;周云飞;严思杰;杨华;;超精密点对点运动3阶轨迹规划算法研究[J];机械科学与技术;2008年02期
3 吕俊杰;李建华;;被动动力式机器人行走极限环收敛轨迹规划[J];机械与电子;2009年03期
4 姜倩倩;李素玲;陈大伟;;板簧搬运机械手轨迹规划研究[J];山东理工大学学报(自然科学版);2012年02期
5 徐达;夏祥;李华;肖自强;李闯;;基于组合函数的弹药装填机器人轨迹规划[J];装甲兵工程学院学报;2013年02期
6 丁汉;机器人冗余控制和离线轨迹规划的研究[J];机器人;1990年04期
7 邓建一,张宗明;机器人轨迹规划的一个新方法[J];上海交通大学学报;1991年03期
8 叶辰雷;刘晓平;，
本文编号：2135394
本文链接：https://www.wllwen.com/kejilunwen/dianzigongchenglunwen/2135394.html
