采用混合算法优化的并联机械臂能量消耗与误差仿真研究
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摘要
针对并联机械臂存在能量消耗严重、运动轨迹跟踪精度较低等问题,采用了混合算法优化并联机械臂运动机构,并对优化结果进行仿真验证.建立并联机械臂运动机构简图模型,推导齐次变换矩阵运动方程式,给出并联机械臂8种运动模式.确定并联机械臂运动的设计变量,构造能量消耗优化目标函数,在约束条件下,采用混合算法优化目标函数.用PID控制方法,在运动模式1状态下,通过Matlab软件将优化结果进行仿真验证.同时,与优化前的仿真结果进行对比和分析.仿真结果表明,优化后主动连杆消耗的能量较少,被动连杆运动轨迹跟踪所产生的误差较小.并联机械臂采用混合算法优化后,能够减少并联机械臂运动机构能量消耗,提高运动轨迹的追踪精度.
Aiming at the problem of serious energy consumption and low tracking accuracy of parallel manipulators,a hybrid algorithm is used to optimize the motion mechanism of parallel manipulator,and the optimization results are simulated and verified.The kinematic mechanism of parallel manipulator is set up,and the equation of the homogeneous transformation matrix is derived,and eight kinds of parallel manipulator motion modes are given.The design variables of the parallel manipulator motion are determined,and the target function of energy consumption optimization is constructed.Under the constraint conditions,the hybrid algorithm is used to optimize the objective function.The PID control method is used to verify the optimization results by Matlab software under the one state of motion mode.At the same time,the simulation results before optimization are compared and analyzed.The simulation results show that,after the optimization,the energy consumption of the active connecting rod is less,and the error caused by the track tracking of the passive connecting rod is less.When the parallel manipulator is optimized by the hybrid algorithm,the energy consumption of the parallel manipulator can be reduced and the tracking precision of the motion trajectory can be improved.
Aiming at the problem of serious energy consumption and low tracking accuracy of parallel manipulators,a hybrid algorithm is used to optimize the motion mechanism of parallel manipulator,and the optimization results are simulated and verified.The kinematic mechanism of parallel manipulator is set up,and the equation of the homogeneous transformation matrix is derived,and eight kinds of parallel manipulator motion modes are given.The design variables of the parallel manipulator motion are determined,and the target function of energy consumption optimization is constructed.Under the constraint conditions,the hybrid algorithm is used to optimize the objective function.The PID control method is used to verify the optimization results by Matlab software under the one state of motion mode.At the same time,the simulation results before optimization are compared and analyzed.The simulation results show that,after the optimization,the energy consumption of the active connecting rod is less,and the error caused by the track tracking of the passive connecting rod is less.When the parallel manipulator is optimized by the hybrid algorithm,the energy consumption of the parallel manipulator can be reduced and the tracking precision of the motion trajectory can be improved.
引文
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[2]吴孟丽,王立文.一种新型并联机器人的静力学优化设计[J].中国机械工程,2010,21(19):2308-2312.WU M L,WANG L W.Statics optimum design of a new parallel robot[J].China Mechanical Engineering,2010,21(19):2308-2312.
[3]陈川雄,孔建.煤炭采样机械臂传动系统优化设计及分析[J].煤炭技术,2016,35(2):259-262.CHEN C X,KONG J.Optimization design and analysis on transmission system of coal sample manipulator[J].Coal Technology,2016,35(2):259-262.
[4]肖凡,李光,周鑫林.多连杆机械臂GA-RBF神经网络轨迹跟踪控制[J].机械科学与技术,2017,29(7):17-22.XIAO F,LI G,ZHOU X L.GA-RBF neural network control for trajectory tracking of multilink robot arm[J].Mechanical Science and Technology for Aerospace Engineering,2017,29(7):17-22.
[5]HUANG H C,CHIANG C H.An evolutionary radial basis function neural network with robust genetic based immunecomputing for online tracking control of autonomous robots[J].Neural Processing Letters,2016,44(1):20-33.
[6]SINGH A K,BARANWAL N,NANDI G C.Development of a self reliant humanoid robot for sketch drawing[J].Multimedia Tools and Applications,2017,76(18):18849-18868.
[7]HERMAN P,ADAMSKI W.Nonlinear trajectory tracking controller for a class of robotic vehicles[J].Journal of the Franklin Institute,2017,354(13):5145-5161.
[8]MOBAYEN S,TCHIER F,RAGOUB L.Design of an adaptive tracker for n-link rigid robotic manipulators based on supertwisting global nonlinear sliding mode control[J].International Journal of Systems Science,2017,48(9):1991-2001.
[9]陈岩,王宗宪.基于多准则寻优策略的差分进化算法[J].山东科技大学学报,2016,35(1):102-107.CHEN Y,WANG Z X.Differential evolution algorithm based on multi-criteria optimization strategy[J].Journal of Shandong University of Science and Technology,2016,35(1):102-107.
[10]杨博宇.多群协同多目标粒子群优化算法及其应用研究[D].沈阳:东北大学,2013.YANG B Y.Research on multi-population co-evolutionary multi-objective particle swarm optimization and its application[D].Shenyang:Northeastern University,2013.
[11]MOHAMMAD A.Optimal design of fractional-order PID controller for five bar linkage robot using a new particle swarm optimization algorithm[J].Soft Computing,2016,20(10):4056-4065.