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作 者:禹鑫燚[1] 罗惠珍 史栓武 魏岩 欧林林[1] YU Xinyi;LUO Huizhen;SHI Shuanwu;WEI Yan;OU Linlin(College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023)
出 处:《高技术通讯》2023年第11期1181-1191,共11页Chinese High Technology Letters
基 金:国家自然科学基金(62373329);浙江省自然科学基金(LY21F030018)资助项目。
摘 要:为了提高人机协作的协调性,本文设计了基于非零和博弈的自适应人机协作系统,系统由互相解耦的内外环构成。在外环中,通过引入非零和博弈的方法设计人机协作策略,构建关于人力和机器人控制输入的能量函数,通过求解博弈中的纳什均衡达到最优控制。针对能量函数中的不确定参数,采用神经网络估计器进行更新,以估计人和机器人的力。并且通过设计神经网络函数的中心值,获得机器人控制力与跟踪误差的关系,保证控制方法的跟踪性。在更新过程中自适应调整刚度系数,实现人机柔顺协调。另外,在内环中设计了神经网络控制器,采用径向基神经网络,基于实时采集的机器人系统输入输出数据逼近控制器中未知非线性的机器人动力学模型,提高了系统跟踪精度。仿真结果验证了本文方法的有效性。In order to improve the coordination of human-robot cooperation,this paper proposes an adaptive system of human-robot interaction based on non-zero-sum game.The system consists of inner loop and outer loop which are decoupled.In the outer loop,the human-robot cooperation control is designed by introducing a non-zero-sum game method,and an energy function about human and robot force is constructed,and the optimal control is achieved by solving the Nash equilibrium.The neural network estimator is employed to update the uncertain parameters in the energy function and estimate the output force of human and robot.By designing the central value of the neural network function,the relationship between the robot force and the tracking error is obtained to ensure the tracking performance of the method.During the update process,the stiffness coefficient is adaptively adjusted when external force exists,so as to realize the compliance and coordination of human and robot.In addition,a neural network controller is designed in the inner loop,and the radial basis neural network is applied to approximate the unknown robot dynamics model using the collected input and output data of robot system,which improves the tracking accuracy of the system.Simulation results verify the effectiveness of this method.
分 类 号:TP242[自动化与计算机技术—检测技术与自动化装置]
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