基于在线辨识的机器人惯量前馈控制仿真研究
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摘要
针对模块化串联协作机器人,当腕部3个关节模块集中在机器人末端时,在机器人运行过程中,本体和负载会对前3个关节产生较大的惯性效应,降低机器人的控制效果.为了消除惯性效应对机器人伺服控制系统的影响,提高协作机器人的跟踪精度,加快动态响应的速度,减少超调量并降低稳态误差,在建立协作机器人动力学模型、计算前馈力矩的基础上,提出了一种基于在线辨识算法的惯量前馈控制技术.由于在机器人运行过程中,各关节电机所带负载的转动惯量不断发生变化,为了实时得到转动惯量实际值,在控制系统中引入变遗忘因子最小二乘辨识算法,对各电机所带负载的转动惯量进行在线辨识.考虑到协作机器人关节中的传动机构多由弹性部件组成,将弹性因素加入到控制系统的设计当中,建立了两刚体弹簧系统被控模型.将转动惯量辨识值输入到前馈通道,实时修正动力学模型中的惯性矩阵,输出前馈电流并叠加在伺服系统电流环的输入端,从而实现前馈控制.最后,在Simulink环境中,基于实验室研制的一种模块化串联协作机器人对该控制技术进行了仿真验证,结果表明:该惯量前馈控制技术能够显著提升机器人控制系统的响应速度与跟踪特性并降低其超调量,验证了该惯量前馈控制技术的可行性与优越性.
For modular tandem cooperative robots,when three joint modules of the wrist are concentrated at the end of the robot,the body and load will have a large inertial effect on the first three joints and the control effect of the robot will be reduced during operation of the robot. In this paper,on the basis of establishing a robot dynamic model and calculating feedforward torque,an inertia feedforward control technology based on online identification algorithm was proposed to eliminate the influence of inertial effect on servo control system of a robot,improve tracking precision,accelerate dynamic response,decrease overshoot,and reduce steady-state error. As the moment of inertia of each joint motor's load constantly changes during the operation of the robot,to obtain the actual moment of inertia in real time,a least square identification algorithm with a variable forgetting factor was introduced into the control system to identify the moment of inertia of each motor's load online. Considering that most of the driving mechanisms in the robot joints are elastic parts,elastic factors were added into the design of the control system,and a control model of two rigid bodies connected by a spring was established. The identified value of the moment of inertia was inputted to the feedforward channel to correct inertia matrix of dynamic model in real time,and the feedforward current was outputted and superimposed on the input of the servo system current loop,and then the feedforward control was realized. Finally,based on a modular serial cooperative robot developed in the laboratory,control technology was simulated and verified in Simulink. The results show that the inertial feedforward control technology significantly improves the response speed and tracking characteristics and reduces the overshoot of the robot control system,which illustrates the feasibility and superiority of this inertial feedforward control technology.
For modular tandem cooperative robots,when three joint modules of the wrist are concentrated at the end of the robot,the body and load will have a large inertial effect on the first three joints and the control effect of the robot will be reduced during operation of the robot. In this paper,on the basis of establishing a robot dynamic model and calculating feedforward torque,an inertia feedforward control technology based on online identification algorithm was proposed to eliminate the influence of inertial effect on servo control system of a robot,improve tracking precision,accelerate dynamic response,decrease overshoot,and reduce steady-state error. As the moment of inertia of each joint motor's load constantly changes during the operation of the robot,to obtain the actual moment of inertia in real time,a least square identification algorithm with a variable forgetting factor was introduced into the control system to identify the moment of inertia of each motor's load online. Considering that most of the driving mechanisms in the robot joints are elastic parts,elastic factors were added into the design of the control system,and a control model of two rigid bodies connected by a spring was established. The identified value of the moment of inertia was inputted to the feedforward channel to correct inertia matrix of dynamic model in real time,and the feedforward current was outputted and superimposed on the input of the servo system current loop,and then the feedforward control was realized. Finally,based on a modular serial cooperative robot developed in the laboratory,control technology was simulated and verified in Simulink. The results show that the inertial feedforward control technology significantly improves the response speed and tracking characteristics and reduces the overshoot of the robot control system,which illustrates the feasibility and superiority of this inertial feedforward control technology.
引文
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[3]Ji Wengang,Lin L,Dai Fengyan,et al.The application of gain scheduling PID and notch filter to AC servo system[C]//2008 International Conference on Electrical Machines and Systems.Wuhan,China,2008:3731-3734.
[4]孙玉阳,孟正大.重载机器人动力学建模及前馈控制方法研究与实现[D].南京:东南大学机械工程学院,2017.Sun Yuyang,Meng Zhengda.Resarch and Implementation of Dynamic Modeling and Feedforward Control for Heavy Duty Robot[D].Nanjing:School of Mechanical Engineering,Southeast University,2017(in Chinese).
[5]Wang Gang,Ren Guoli,Yan Xiang’an.Approach to improvement of robot trajectory accuracy by dynamic compensation[J].Chinese Journal of Mechanical Engineering,2004,17(3):446-449.
[6]Zeeshan Shareef,Pouya Mohammadi,Jochen Steil.Improving the inverse dynamic model of the KUKALWR IV+using idenpendent joint learning[J].IFAC-Papers Online,2016,49(21):507-512.
[7]Rene Felix Reinhart,Zeeshan Shareef,Jochen Jakob Steil.Hybrid analytical and data-driven modeling for feed-forward robot control[J].Sensors,2017,17(2):311
[8]Stückelmaier P,Grotjahn M,Fr?ger C.Identification of the inverse dynamics of a serial robot for robot drive control[C]//IKMT;10 ETG/GMM-Symposium Innovative Small Drives and Micro-Motor Systems.Cologne,Germany,2015:157-164.
[9]Craig J J.Introduction to Robotics:Mechanics and Control[M].New York:Pearson Education,Inc,1989.
[10]Jin Maolin,Lee Jinoh,Tsagarakis Nikolaos G.Modelfree robust adaptive control of humanoid robots with flexible joints[J].IEEE Transactions on Industrial Electronics,2017,64(2):1706-1715.
[11]Ge Lianzheng,Chen Jian,Li Ruifeng.Feedforward control based on fourier series trajectory fitting method for industrial robot[C]//2017 29th Chinese Control and Decision Conference.Chongqing,China,2017:4890-4894.
[12]Qin Jinna,Leonard Francois,Abba Gabriel.Real-time trajectory compensation in robotic friction stir welding using state estimators[J].IEEE Transactions on Control Systems Technology,2016,24(6):2207-2215.
[13]肖聚亮,宋朋宇,洪鹰,等.中空串联机械臂:CN,104191434 A[P].2014-12-10.Xiao Juliang,Song Pengyu,Hong Ying,et al.Hollow Series Manipulator:CN,104191434 A[P].2014-12-10(in Chinese).
[14]肖聚亮,宋朋宇,洪鹰,等.中空串联机械臂用模块化关节:CN,104175329 A[P].2014-12-03.Xiao Juliang,Song Pengyu,Hong Ying,et al.Modular Joints for Hollow Series Manipulators:CN,104175329 A[P].2014-12-03(in Chinese).
[15]Zoubek H,Pacas M.Encoderless identification of twomass-systems utilizing an extended speed adaptive observer structure[J].IEEE Transactions on Industrial Electonics,2017,64(1):595-604.
[16]吴家彪.电机伺服驱动系统及其参数辨识研究[D].杭州:浙江大学电气工程学院,2013.Wu Jiabiao.Research on Motor Servo Drive System and Parameter Identification[D].Hangzhou:School of Electrical Engineering,Zhejiang University,2013(in Chinese).
[17]Niu Li,Xu Dianguo,Yang Ming,et al.On-line inertia identification algorithm for PI parameters optimization in speed loop[J].IEEE Power Electronics Society,2015,30(2):849-859.
[18]常铁原,王月娟.一种具有快速跟踪能力的改进RLS算法研究[J].计算机工程与应用,2011,47(23):147-149.Chang Tieyuan,Wang Yuejuan.An improved RLS algorithm with fast tracking capability[J].Computer Engineering and Applications,2011,47(23):147-149(in Chinese).