基于RBF神经网络补偿的一种绳牵引并联机器人 支撑系统的力/位混合控制
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(厦门大学航空航天学院,福建厦门361005)

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E-mail: qilin@xmu.edu.cn.

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TP24

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国家自然科学基金项目(11472234,11072207,11702232).


Force/position hybrid control for a wire-driven parallel robot support system based on RBF neural network compensation
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(School of Aerospace Engineering,Xiamen University,Xiamen361005,China)

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    摘要:

    为了保证用于风洞试验的绳牵引并联机器人支撑系统(wire-driven parallel robot support system, WDPRSS)的末端执行精度,设计一种采用Hamilton-Jacobi Inequality(HJI)定理并基于RBF神经网络补偿的力/位混合控制.通过对WDPRSS的动力学建模分析,选择以位姿作为变量建立WDPRSS的整体动力学方程,将所设计的力/位混合控制代入到整体动力学方程中得到误差闭环系统,对闭环系统进行稳定性分析,结果表明WDPRSS是趋于渐近稳定特性的.对八绳牵引的并联机器人支撑系统进行Matlab/Simulink仿真实验,仿真结果表明所设计的力/位混合控制是正确有效的,满足控制精度要求,并将所设计的力/位混合控制与PD控制进行对比分析.最后,通过样机实验验证所提出控制方案的有效性.

    Abstract:

    In order to guarantee the precision of the end effector of a wire-driven parallel robot support system (WDPRSS) used in a wind tunnel test, a force/position hybrid control is proposed based on the Hamilton-Jacobi Inequality (HJI) theorem and RBF neural network compensation. Through dynamic modeling analysis of the WDPRSS, the whole dynamic equation of the WDPRSS is established using the pose as the variable. The proposed force/position hybrid control is substituted into the dynamic equation to obtain the error closed-loop system. The stability analysis of the closed-loop system shows that the WDPRSS tends to be asymptotic stable. A MATLAB/SIMULINK simulation experiment of the WDPRSS is conducted. The simulation results show that the proposed force/position hybrid control strategy is correct and valid, and satisfies control accuracy requirement. And the designed force/position hybrid control and PD control are compared and analyzed. Finally, the effectiveness of the proposed control scheme is verified by prototype experiments.

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王宇奇,林麒,王晓光,等.基于RBF神经网络补偿的一种绳牵引并联机器人 支撑系统的力/位混合控制[J].控制与决策,2020,35(3):536-546

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  • 在线发布日期: 2020-02-22
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