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基于免疫遗传算法的移动机器人轨迹跟踪 被引量:8

Trajectory Tracking of Mobile Robot Based on Immune Genetic Algorithm
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摘要 为实现移动机器人轨迹的准确跟踪,提出一种基于免疫遗传算法的移动机器人控制器参数整定方法.首先建立移动机器人的结构模型、运动学模型、电机控制系统模型,然后针对所建立的模型,采用一种改进的免疫遗传算法对电机控制系统模型参数进行了整定.该算法在选择下一代的过程中,既考虑了抗原的适应度函数,强调抗体的个人竞争,保证了算法的收敛速度,又引入了抗体浓度概念来体现抗体间的相互交流,从而强化了算法的全局搜索能力,弥补了传统遗传算法搜索能力不足的缺陷.文中最后利用移动机器人的实验平台,进行了带负载下驱动电机的阶跃信号响应和移动机器人运动轨迹的跟踪实验,电机的阶跃信号响应良好,移动机器人也能较好地复现所规划的轨迹.仿真与实验结果表明,所提出的基于免疫遗传算法的机器人轨迹跟踪控制算法有较高的可行性与优越性. In order to accurately track the trajectory of mobile robot, a parameter tuning method based on the im- mune genetic algorithm is proposed. In the investigation, first, the structure model, the kinematics model and the motor control system model of mobile robot are constructed. Next, an improved immune genetic algorithm is em- ployed to optimize the parameters of the motor control system model. This algorithm, on one hand, not only consi- ders the fitness function of antibodies but also strengthens the competition among antibodies during the choice of the next generation, thus guaranteeing the convergence rate. On the other hand, it takes the antibody concentration into consideration to reflect the communication between two antibodies, thus improving the global searching ability and making up for the lack of searching ability of conventional genetic algorithms. The step signal response of loaded motor and the trajectory tracking of mobile robot are then dealt with on an experimental platform, with good res- ponse and accurate tracking ability for planned trajectories being obtained. It is concluded from the simulated and the experimental results that the trajectory tracking based on the immune genetic algorithm is of high feasibility and many advantages.
出处 《华南理工大学学报(自然科学版)》 EI CAS CSCD 北大核心 2013年第7期13-18,25,共7页 Journal of South China University of Technology(Natural Science Edition)
基金 广东省科技计划项目(2012B010900076) 广东省教育部产学研结合项目(2012B090400150) 广东省战略性新兴产业项目(2011A0199010010) 中山市科技计划项目(2011CXY007)
关键词 移动机器人 轨迹跟踪 控制器参数整定 免疫遗传算法 mobile robot trajectory tracking controller parameter tuning immune genetic algorithm
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参考文献16

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