摘要
介绍用于MotomamV3X机器人上的新型多维腕力传感器,比较遗传算法与人工神经网络的特点,将遗传算法的交叉和变异操作进行改进,提出一种融合改进遗传算法(Genetic algorithm,GA)的函数连接型人工神经网络(Functional link artificial neural network FLANN),并将其用于所介绍的新型机器人腕力传感器动态建模与动态性能补偿中。介绍动态建模与动态补偿原理及改进遗传神经网络算法,给出该传感器的动态模型和动态补偿模型。该方法利用腕力传感器的动态标定数据,采用改进遗传神经网络搜索和优化模型参数,保留了遗传算法的全局搜索能力和FLANN结构简单,鲁棒性好,且具备自学习能力的特点,克服了FLANN容易陷入局部极小的缺陷,具有快的网络训练速度及高的动态建模精度。理论分析和试验结果都证实了所提出的动态建模与动态补偿方法的有效性。
A new kind of multi-dimensional wrist force sensor applied to MotomamV3X robot is introduced. The characteristics of genetic algorithm （GA） and artificial neural networks （ANN） are compared. The operator of crossover and mutation for GA is improved. A kind of new dynamic modeling and compensation method is presented based on improved genetic algorithm for the proposed sensor. The dynamic modeling and compensation principle and the algorithms of improved genetic neural networks （IGNN） are introduced and the dynamic model and compensation model are given for the proposed robot wrist force sensor. In this method, the dynamic model and compensation model of wrist force sensor can be set up according to measurement data of the dynamic calibration, where the dynamic model and compensation model parameters are trained by improved genetic neural network. So the method remains the global searching ability of GA and the simple structure and good robustness and self-learning ability of FLANN and can overcome FLANN＇s shortcoming of easy convergence to the local minimum points and has fast network training speed and high modeling precision. Their effectiveness is verified by experirnents and theoretical analysis.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2006年第12期239-244,共6页
Journal of Mechanical Engineering
基金
江苏省高等学校自然科学基金(04KJD140033)。
关键词
机器人腕力传感器
动态建模
动态补偿
函数连接型人工神经网络
遗传算法
Robot wrist force sensor Dynamic modeling Dynamic compensation Function link artificial neural networks Genetic algorithm
