基于改进差分进化算法的在线轨迹优化  

Trajectory optimization based on improved differential evolution algorithm

作　　者：韩敏[1] 王明慧[1] 范剑超[1] 

机构地区：[1]大连理工大学电子信息与电气工程学院,辽宁大连116024

出　　处：《控制与决策》2012年第2期247-251,共5页Control and Decision

基　　金：国家自然科学基金项目(61074096);国家863计划项目(2007AA04Z158);国家科技支撑计划项目(2006BAB14B05)

摘　　要：首先提出一种基于混沌映射的差分进化算法,通过引入混沌映射的概念,在群体初始化和子代重构两个方面对经典差分进化算法进行改进,提高其寻优精度及稳定性,并通过对几个典型的Benchmark函数进行对比测试,验证该算法的全局收敛能力与稳定性.然后将该改进算法应用于在线轨迹优化,利用其快速寻优、不依赖梯度信息等特点,结合滚动窗口的思想,提出局部极值逃逸方法,实现了轨迹的在线优化.最后在板球系统上通过仿真实验,验证了所提出方法的有效性.Firstly,the paper proposes a chaotic mapping based differential evolution algorithm.By introducing the concept of chaotic map,both the group initialization and reconstruction of offspring of classical differential evolution algorithm are modified to improve accuracy and stability.And several contrast tests on the typical Benchmark functions verify the global convergence and stability of the algorithm.Then,the improved algorithm is applied to online trajectory optimization.The best trajectory online is realized by using the characteristics of its fast searching and doing not rely on gradient information.By combining the ideas of rolling window,the method of local minimum escape is proposed.Finally,the simulation result on ball and plate system shows the effectiveness of the proposed method.

关 键 词：差分进化 混沌映射 轨迹优化 在线优化 板球系统 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]