Optimization Algorithms for Kinematically Optimal Design of Parallel Manipulators
Optimization Algorithms for Kinematically Optimal Design of Parallel Manipulators
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DOI：
10.1109/tase.2013.2259817
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发表时间：
2014-04
影响因子：
5.6
通讯作者：
Y. Lou;Yongsheng Zhang;Ruining Huang;Xin Chen;Zexiang Li
中科院分区：
文献类型：
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作者：
Y. Lou;Yongsheng Zhang;Ruining Huang;Xin Chen;Zexiang Li
优化设计是并联机器人机构设计中必不可少的环节。公式化的优化设计问题通常是约束、非线性、多峰的，甚至没有封闭形式的解析表达式。数值优化算法，从而应用到解决问题。然而，优化算法通常是任意选择的。本文旨在为优化设计问题的算法选择提供指导。以Delta机器人和Gough-Stewart平台为例，详细研究了多初始点序列二次规划（SQP）、受控随机搜索（CRS）、遗传算法（GA）、差分进化（DE）和粒子群优化（PSO）等典型算法的收敛性能.结果表明，多初始点SQP可以有效地用于简单的设计问题，而DE和PSO执行有效和稳定的所有设计问题。CRS可以用来产生良好的初始点，因为它表现出良好的收敛性在开始阶段的演变。
Optimal design is an inevitable step for parallel manipulators. The formulated optimal design problems are generally constrained, nonlinear, multimodal, and even without closed-form analytical expressions. Numerical optimization algorithms are thus applied to solve the problems. However, the optimization algorithms are usually chosen ad arbitrium. This paper aims to provide a guideline to choose algorithms for optimal design problems. Typical algorithms, the sequential quadratic programming (SQP) with multiple initial points, the controlled random search (CRS), the genetic algorithm (GA), the differential evolution (DE), and the particle swarm optimization (PSO), are investigated in detail for their convergence performances by using two canonical design examples, the Delta robot and the Gough-Stewart platform. It is shown that SQP with multiple initial points can be efficient for simple design problems, while DE and PSO perform effectively and steadily for all design problems. CRS can be used to generate good initial points since it exhibits excellent convergence evolution in the starting period.