应用混沌蚁群理论的机械臂控制算法研究

发布时间：2018-12-14 07:25

【摘要】：机械手臂是现代工业代替人做高危和流水作业的重要工具,随着工业机械手臂的大量应用,有效提高了劳动生产率,降低了成本,是一个国家工业化水平的标志。因此,对高性能机械手臂运动控制系统的研究与应用具有重要意义,受到专家学者们的青睐。本文主要是应用群智能仿生算法对机械臂运动学逆解问题的研究。首先,将运动学逆解方程的问题转换成n元函数求最值的问题,即将三维立体函数简化成二维平面函数,再利用所建立的目标函数F与机械手臂运动路径的对应关系,把最优路径规划问题转换成了目标函数F求最大值的问题。并在此基础上,提出了基于混沌蚁群算法的路径寻优方法。在基本蚁群算法与混沌理论结合的具体应用中,针对基本蚁群算法在搜索初期,各路径上的信息素含量相同,因此收敛速度较慢的问题,将混沌理论的混沌初始化引入到基本蚁群算法中,使之对各条路径投放不同量的信息素,从而加快了基本蚁群算法的收敛速度,同时通过引入混沌扰动因子,实时调整信息素的更新,有效地避免了基本蚁群算法在搜索过程中易陷入局部最优的问题。因此,混沌蚁群算法不仅提高了收敛速度、准确性和启发性,同时降低了基本蚁群算法的时间复杂度,更好地解决了最优的路径选择问题。最后,本文以SR165型机械手臂为例,建立基于MATLAB Robotics Toolbox的仿真实验平台,并通过仿真实验以及与基本蚁群算法的仿真实验结果对比,证明了混沌蚁群算法的可行性与优越性。
[Abstract]:Mechanical arm is an important tool for modern industry to replace people to do high risk and flow work. With the extensive application of industrial mechanical arm, the labor productivity has been improved effectively, and the cost has been reduced. It is the symbol of a country's industrialization level. Therefore, the research and application of high performance manipulator motion control system is of great significance and is favored by experts and scholars. In this paper, the inverse kinematics problem of manipulator is studied by using swarm intelligence bionic algorithm. First of all, the inverse kinematics equation is transformed into the problem of finding the maximum value of the n-variable function, that is, the three-dimensional function is simplified into a two-dimensional plane function, and then the corresponding relation between the objective function F and the motion path of the manipulator is used. The optimal path planning problem is transformed into the problem of finding the maximum value of the objective function F. On this basis, a path optimization method based on chaotic ant colony algorithm is proposed. In the concrete application of the combination of basic ant colony algorithm and chaos theory, aiming at the problem that the pheromone content in every path is the same at the initial searching stage of the basic ant colony algorithm, so the convergence speed is slow. The chaos initialization of chaos theory is introduced into the basic ant colony algorithm, which puts different pheromones on different paths, thus speeding up the convergence speed of the basic ant colony algorithm, and at the same time introducing the chaos perturbation factor. The updating of pheromone is adjusted in real time to effectively avoid the problem that the basic ant colony algorithm is prone to local optimization in the search process. Therefore, chaotic ant colony algorithm not only improves the convergence speed, accuracy and enlightenment, but also reduces the time complexity of the basic ant colony algorithm and solves the optimal path selection problem better. Finally, taking the SR165 manipulator as an example, a simulation experiment platform based on MATLAB Robotics Toolbox is established, and the feasibility and superiority of chaotic ant colony algorithm are proved by comparing the simulation results with the basic ant colony algorithm.
【学位授予单位】：辽宁科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TP241

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