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A hybrid intelligent active force controller for robot arms using evolutionary neural networksud

机译:基于进化神经网络的机器人手臂混合智能主动力控制器 ud

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摘要

In this paper, we propose a hybrid intelligent parameter estimator for the active force control (AFC) scheme which utilizes evolutionary computation (EC) and artificial neural networks (ANN) to control a rigid robot arm. The EC part of the algorithm composes of a hybrid genetic algorithm (GA) and an evolutionary program (EP). The development of the controller is divided into two stages. In the first stage, which is performed off-line, the proposed EC algorithm is employed to evolve a pool of ANN structures until they converge to an optimum structure. The population is divided into different groups according to their fitness. The elitist group will not undergo any operation, while the second group, i.e. stronger group, undergoes the EP operation. Hence, the behavioral link between the parent and their offspring can be maintained. The weaker group undergoes a GA operation since their behaviors need to be changed more effectively in order to produce better offspring. In the second stage, the evolved ANN obtained from the first stage, which represent the optimum ANN structural design, is used to design the on-line intelligent parameter estimator to estimate the inertia matrix of the robot arm for the AFC controller. In this on-line stage, the ANN parameters, i.e. the weights and biases, are further trained using live data and back-propagation until a satisfactory result is obtained. The effectiveness of the proposed scheme is demonstrated through a simulation study performed on a two link planar manipulator operating in a horizontal plane. An external load is introduced to the manipulator to study the effectiveness of the proposed scheme ud ud
机译:在本文中,我们提出了一种用于主动力控制(AFC)方案的混合智能参数估计器,该方案利用进化计算(EC)和人工神经网络(ANN)来控制刚性机器人手臂。该算法的EC部分由混合遗传算法(GA)和进化程序(EP)组成。控制器的开发分为两个阶段。在离线执行的第一阶段,所提出的EC算法用于演化ANN结构库,直到它们收敛到最佳结构为止。人口根据其适合度分为不同的组。精英团体将不会进行任何手术,而第二组,即更强的团体会进行EP手术。因此,可以保持父母与其后代之间的行为联系。较弱的群体要接受GA手术,因为他们的行为需要更有效地改变才能产生更好的后代。在第二阶段中,从第一阶段获得的演化的ANN代表了最佳的ANN结构设计,用于设计在线智能参数估计器,以估计AFC控制器的机械手惯性矩阵。在此在线阶段,使用实时数据和反向传播对ANN参数(即权重和偏差)进行进一步训练,直到获得满意的结果。通过对在水平面内工作的两连杆平面操纵器进行的仿真研究证明了所提出方案的有效性。将外部负载引入机械手,以研究所提出方案的有效性

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