基于现场总线的码垛机器人控制系统研究
|
摘要
机器人是20世纪人类最伟大的发明之一,而码垛机器人则是其中广泛应用的代表。码垛机器人作为现代码垛系统中最重要的设备,它的操作范围、码垛速度、稳定性和可靠性等工作能力决定了整个码垛系统设计的成败。而其控制系统性能在很大程度上决定了码垛机器人的性能。一个良好的控制系统要有灵活方便的操作方式,多种形式的运动控制方式和安全可靠性,并且控制性能要能够满足工作要求。本文从码垛机器人的这些特性出发,在深入研究了现场总线技术、机器人控制技术及工业控制网络技术的基础上,完整的设计了这个基于现场总线的码垛机器人的控制系统。
首先,本文考察了当前国内外码垛机器人的研究现状和发展趋势,说明了研究码垛机器人控制系统的目的和意义。现场总线作为一种工业数据总线,主要解决工业现场的智能化仪器仪表、控制器、执行机构等现场设备间的数字通信以及这些现场控制设备和高级控制系统之间的信息传递问题。文中概要介绍了常用现场总线的特点、发展现状和研究方向,详细阐述了本系统中所使用的ETHERNETPowerlink实时以太网的通信协议、工作原理及通信的实现方法。
其次,根据系统的结构方案以及技术要求完成了码垛机器人控制系统的总体设计。控制方案依据当今最新控制理念,采用贝加莱高性能的PCC(可编程计算机控制器)作为主控制单元。本文详细的叙述了PCC系统和伺服驱动系统的硬件设计方法,并对系统的控制功能进行了说明。控制程序在B&R Automation Studio的环境下进行开发,程序主体采用ANSI C语言编写,软件设计采用结构化的设计方法,自上而下逐步的细化技术并分解步骤,将待开发的软件系统划分为若干个相互独立的模块,并且每个模块根据控制的要求设置优先级,充分体现“分时多任务”的优越性,同时还能保证良好的维护性和清晰的结构。
最后,借助于智能控制理论,对码垛机器人手臂的运行轨迹进行了智能优化和跟踪控制,仿真结果表明,码垛机器人运行周期的时间明显缩短,其关节都能很好的跟踪期望轨迹,并具有很好的抗干扰性能。
码垛机器人控制系统的设计已经全部完成,并且实现了系统的运动控制。此系统已经成功应用于某啤酒厂的生产线上,运行状态良好、性能稳定、易于操作、维护方便,受到厂家的广泛好评。
Robot is one of the greatest inventions in the 20th century, and palletizing robot is widely used. As the most important equipment in the modern palletizing system, Palletizing robot's operation scope, stacking velocity, stability and reliability determines the success or failure of the design of the entire palletizing system. And its control system performance largely determines the performance of the palletizing robot. A good control system is convenient to operate, various forms of exercise control mode and the safety and reliability, and its control performance must attain the job requirements. In this paper, from the characteristics of palletizing robot, on the basis of the deep research in the fieldbus technology. robot control technology and industrial control network technology, it completely design the control system of the palletizing robot based on fieldbus .
Firstly, it introduces the current research status and the development trend of palletizing robot, and illustrates the purpose and significance of the control system's research. As an industrial data bus, The fieldbus mainly solve the digital communication between the site equipments such as the intelligent instruments, controller and executing agency. And it also solve the information transfer between the site equipment and the advanced control system. This paper briefly introduces the characteristics, development status and research direction of common fieldbus. and detailedly expounds the communication protocol, working principle and realization method of the ETHERNET Powerlink used in this system.
Secondly, it completes the overall design of the palletizing robot's control system according to the structure of the system and technical requirements. According to the modern control methods, The control scheme uses the B&R PCC (Programmable Computer Controller) as the main control unit. In this paper, it detailedly describes the hardware design methods of the PCC system and the servo drive system, and introduces the control function of system. The control program is developed in the B&R Automation Studio environment, using the ANSI C language as the main program language. Using the structural design method, software design adopts the refining technique and top-down gradually decompose steps. Then software system is divided into several independent modules, and set priority of each module according to control requirements. So it fully reflects the superiority of time-sharing multitasking, at the same time, ensure the good maintainability and clear structure.
Finally, using the intelligent control theory, it makes the intelligent optimization and tracking control for the movement trajectory of the palletizing robot arm. Simulation results indicate that the cycle time is obviously shortened, and the joints can accurately track desired trajectory, and have good anti-disturbance performance.
It has completed the design of palletizing robot control system. And the control system has realized motion control of system. This system has been successfully applied to the production line of a brewery. And it has good operating condition, stable performance, easy operation, convenient maintenance, and is widely praised by the manufacturer.
首先,本文考察了当前国内外码垛机器人的研究现状和发展趋势,说明了研究码垛机器人控制系统的目的和意义。现场总线作为一种工业数据总线,主要解决工业现场的智能化仪器仪表、控制器、执行机构等现场设备间的数字通信以及这些现场控制设备和高级控制系统之间的信息传递问题。文中概要介绍了常用现场总线的特点、发展现状和研究方向,详细阐述了本系统中所使用的ETHERNETPowerlink实时以太网的通信协议、工作原理及通信的实现方法。
其次,根据系统的结构方案以及技术要求完成了码垛机器人控制系统的总体设计。控制方案依据当今最新控制理念,采用贝加莱高性能的PCC(可编程计算机控制器)作为主控制单元。本文详细的叙述了PCC系统和伺服驱动系统的硬件设计方法,并对系统的控制功能进行了说明。控制程序在B&R Automation Studio的环境下进行开发,程序主体采用ANSI C语言编写,软件设计采用结构化的设计方法,自上而下逐步的细化技术并分解步骤,将待开发的软件系统划分为若干个相互独立的模块,并且每个模块根据控制的要求设置优先级,充分体现“分时多任务”的优越性,同时还能保证良好的维护性和清晰的结构。
最后,借助于智能控制理论,对码垛机器人手臂的运行轨迹进行了智能优化和跟踪控制,仿真结果表明,码垛机器人运行周期的时间明显缩短,其关节都能很好的跟踪期望轨迹,并具有很好的抗干扰性能。
码垛机器人控制系统的设计已经全部完成,并且实现了系统的运动控制。此系统已经成功应用于某啤酒厂的生产线上,运行状态良好、性能稳定、易于操作、维护方便,受到厂家的广泛好评。
Robot is one of the greatest inventions in the 20th century, and palletizing robot is widely used. As the most important equipment in the modern palletizing system, Palletizing robot's operation scope, stacking velocity, stability and reliability determines the success or failure of the design of the entire palletizing system. And its control system performance largely determines the performance of the palletizing robot. A good control system is convenient to operate, various forms of exercise control mode and the safety and reliability, and its control performance must attain the job requirements. In this paper, from the characteristics of palletizing robot, on the basis of the deep research in the fieldbus technology. robot control technology and industrial control network technology, it completely design the control system of the palletizing robot based on fieldbus .
Firstly, it introduces the current research status and the development trend of palletizing robot, and illustrates the purpose and significance of the control system's research. As an industrial data bus, The fieldbus mainly solve the digital communication between the site equipments such as the intelligent instruments, controller and executing agency. And it also solve the information transfer between the site equipment and the advanced control system. This paper briefly introduces the characteristics, development status and research direction of common fieldbus. and detailedly expounds the communication protocol, working principle and realization method of the ETHERNET Powerlink used in this system.
Secondly, it completes the overall design of the palletizing robot's control system according to the structure of the system and technical requirements. According to the modern control methods, The control scheme uses the B&R PCC (Programmable Computer Controller) as the main control unit. In this paper, it detailedly describes the hardware design methods of the PCC system and the servo drive system, and introduces the control function of system. The control program is developed in the B&R Automation Studio environment, using the ANSI C language as the main program language. Using the structural design method, software design adopts the refining technique and top-down gradually decompose steps. Then software system is divided into several independent modules, and set priority of each module according to control requirements. So it fully reflects the superiority of time-sharing multitasking, at the same time, ensure the good maintainability and clear structure.
Finally, using the intelligent control theory, it makes the intelligent optimization and tracking control for the movement trajectory of the palletizing robot arm. Simulation results indicate that the cycle time is obviously shortened, and the joints can accurately track desired trajectory, and have good anti-disturbance performance.
It has completed the design of palletizing robot control system. And the control system has realized motion control of system. This system has been successfully applied to the production line of a brewery. And it has good operating condition, stable performance, easy operation, convenient maintenance, and is widely praised by the manufacturer.
引文
[1]丁学恭.机器人控制研究.浙江:浙江大学出版社,2006.9:1-6
[2]蔡自兴.机器人学.北京:清华大学出版社,2000.9
[3]刘极峰.机器人技术基础.北京:高等教育出版社,2006.
[4]张福学.机器人学:智能机器人传感技术.北京:电子工业出版社,1996
[5]杜祥瑛.工业机器人及其应用.北京:机械工业出版社,1986
[6]金广业.工业机器人与控制.沈阳:东北工学院出版社,1991
[7]张碧波.浅谈包装码垛自动生产线的发展、应用现状及发展趋势.中国高新技术企业技术论坛,75-77
[8]余达大.工业机器人应用工程.北京:冶金工业出版社,1999
[9]张效祖.工业机器人的现状与发展趋势.WMEM,2004(5):33-36.
[10]张培艳.工业机器人操作与应用实践教程.上海:上海交通大学出版社,2009
[11]余达太,马香峰.工业机器人应用工程.冶金工业出版社.1999
[12]胡洪国.码垛技术综述.组合机床与自动化加工技术 20006:7为
[13]张有良.码垛机械手的设计及电气控制.包装与食品机械,2007(5):23-26
[14]杨宪惠.现场总线技术及其应用.北京:清华大学出版社,1998.
[15]陈在平,岳有军.工业控制网络与现场总线技术.北京:机械工业出版社,2006.
[16]王慧铎,何衍庆.现场总线控制系统原理及应用.北京:化学工业出版社,2005.
[17]李正军.现场总线与工业以太网及其应用系统设计.北京:人民邮电出版社,2006.
[18]夏德海.现场总线的现状及其发展趋势.电气时代,2006(8):16-19.
[19]石宗英,徐文立,杜继宏,温旭.基于现场总线的仿人型机器人控制系统.计算机工程与应用,2002.2:195-197.
[20]白云飞,曲尔光.现场总线的技术特点和发展趋势.机械管理开发,2007(1):81-82.
[21]董砚,孙鹤旭,刘作军.基于现场总线的工业机器人监控系统研究.微计算机信息,2005,21(83):87-90.
[22]陈志平.面向工业的实时以太网方案——Ethernet Powerlink.自动化博览:69-72.
[23]奚清漪,麦云飞.基于Ethernet Powerlink的伺服通信控制.工业控制计算机,2006,19(7):23-24.
[24]冯海川,张承慧,崔纳新,崔胜国.Powerlink在纸机传动控制系统中的应用.中国造纸,2007,26(1):41-45.
[25]刘星桥,凌俊杰,高健,孙伟,沈跃.基于PROFIBUS现场总线的装箱机械手控制系统.电气传动,2005,35(9):58-61.
[26]钟华,吴镇炜,李斌,卜春光.基于CAN的仿人机器人控制器的设计与实现.仪器仪表学报,2004,25(4):913-915.
[27]邓遵义,宁祎,刘保国.基于CAN总线的分布式机器人控制系统设计.机器人技术,2006,22(62):255-257.
[28]齐蓉,肖维蓉.可编程计算机控制器高级技术.西安:西北工业大学出版社 2002.
[29]李方圆.变频器自动化工程实践.北京:电子工业出版社,2007.4
[30]林春芳.电气控制与PLC原理及应用.上海:上海交通大学出版社,2008.1
[31]柳洪义,罗忠,王菲.现代机械工程自动控制.北京:科学出版社,2008.9
[32]郝丽娜,巩亚东,李虎.机械装备电气控制技术.北京:科学出版社,2006.8
[33]张华龙.图解PLC与电气控制入门.北京:人民邮电出版社,2008.9
[34]师黎,陈铁军,李晓媛,姚丽娜.智能控制理论及应用.北京:清华大学出版社,2009.4
[35]孙增圻.智能控制理沦与技术.北京:清华大学出版社,1997.
[36]喻宗泉,喻晗.神经网络控制.西安:西安电子科技大学出版社,2009.1
[37]王耀南.机器人智能控制工程.北京:科学出版社,2004.6
[38]毛宗源.机器人的智能控制方法.北京:国防工业出版社,2002.
[39]张毅,罗元,郑太雄.移动机器人技术及其应用.北京:电子工业出版社,2007.
[40]陆祥生,杨秀莲.机械手理论及应用.北京:中国铁道出版社,1985.
[41]孙富春,孙增圻,张钹.机械手神经网络稳定自适应控制的理论与方法.北京:高等教育出版社,2005.
[42]何玉彬,李新忠.神经网络控制技术及其应用.北京:科学出版社,2000.
[43]韩力群.人工神经网络理论、设计及应用(第二版).北京:化学工业出版社,2007.
[44]王伟.人工神经网络原理入门与应用.北京:北京航空航天大学出版社,1995.
[45]Young H.Kim,Frank L.Lewis,Darren M.Dawson.Intelligent optimal control of robotic manipulators using neural networks.Automatica,2000(36):1355-1364.
[46]R.S.afaric,K.Jezernik,M.Pec.Neural network control for direct-drive robot mechanisms.Engineering Applications of Arti(?)cial Intelligence,1998(11):735- 745.
[47]Changman Son.Comparison of intelligent control planning algorithms for robot's part micro-assembly task.Engineering Applications of Artificial Intelligence,2006(19):41-52.
[48]Sahin Yildirim.Design of a proposed neural network control system for trajectory controlling of walking robots.Simulation Modelling Practice and Theory,2008(16):368-378.
[49]Pramod Gupta,Naresh K.Sinha.Intelligent control of robotic manipulators:experimental study using neural networks.Mechatronics,2000,(10):289-305.
[50]M.J.Er,Tien Peng Tan,Sin Yee Loh.Control of a mobile robot using generalized dynamic fuzzy neural networks.Microprocessors and Microsystems,2004(28):491-498.
[51]夏鲁刚.机器人智能控制方法研究及控制器设计.机械工程师,2006(12):47-49.
[52]刘成良,张凯,付庄,曹其新,殷跃红.神经网络在机器人运动控制中的应用研究.机械科学与技术,2003,22(2):226-228.
[53]Sahin Yildirim.Adaptive robust neural controller for robots.Robotics and Autonomous Systems,2004(46):175-184.
[54]TU Diep Cong Thanh,Kyoung Kwan Ahn.Nonlinear PID control to improve the control performance of 2 axes pneumatic artificial muscle manipulator using neural network.Mechatronics,2006,(16):577-587.
[55]王洪斌,宋佐时,王洪瑞.基于模糊神经网络的机器人逆运动学问题.自动化与仪器仪表,2002(4):8-12.
[56]X.P.Chengl,R.V.Patel.Neural network based tracking control of a flexible macro-micro manipulator system.Neural Networks,2003(16):271-286.
[57]Jun Ye.Tracking control for nonholonomic mobile robots:Integrating the analog neural network into the backstepping technique.Neurocomputing,2008(71):3373-3378.
[58]Hong Suha,Yae Won Kim.A visual servoing algorithm using fuzzy logics and fuzzy-neural networks.Mechatronics,2000(10):1-18.
[59]姜志兵,赵英凯,李方方.神经网络在机器人逆运动学中的应用.机械与电子,2005(11):43-46.
[60]Manfred L.Husty,Martin Pfurner,Hans-Peter Schroker.A new and efficient algorithm for the inverse kinematics of a general serial 6R manipulator.Mechanism and Machine Theory,2007(42):66-81.
[61]S.N.Huang,K.K.Tan,T.H.Lee.Adaptive neural network algorithm for control design of rigid-link electrically driven robots.Neurocomputing,2008(71):885-894.
[62]姜新农,王文香.基于免疫遗传的BP网络在机械手逆运动学中的应用.机械与电子,2006(1):48-51.
[2]蔡自兴.机器人学.北京:清华大学出版社,2000.9
[3]刘极峰.机器人技术基础.北京:高等教育出版社,2006.
[4]张福学.机器人学:智能机器人传感技术.北京:电子工业出版社,1996
[5]杜祥瑛.工业机器人及其应用.北京:机械工业出版社,1986
[6]金广业.工业机器人与控制.沈阳:东北工学院出版社,1991
[7]张碧波.浅谈包装码垛自动生产线的发展、应用现状及发展趋势.中国高新技术企业技术论坛,75-77
[8]余达大.工业机器人应用工程.北京:冶金工业出版社,1999
[9]张效祖.工业机器人的现状与发展趋势.WMEM,2004(5):33-36.
[10]张培艳.工业机器人操作与应用实践教程.上海:上海交通大学出版社,2009
[11]余达太,马香峰.工业机器人应用工程.冶金工业出版社.1999
[12]胡洪国.码垛技术综述.组合机床与自动化加工技术 20006:7为
[13]张有良.码垛机械手的设计及电气控制.包装与食品机械,2007(5):23-26
[14]杨宪惠.现场总线技术及其应用.北京:清华大学出版社,1998.
[15]陈在平,岳有军.工业控制网络与现场总线技术.北京:机械工业出版社,2006.
[16]王慧铎,何衍庆.现场总线控制系统原理及应用.北京:化学工业出版社,2005.
[17]李正军.现场总线与工业以太网及其应用系统设计.北京:人民邮电出版社,2006.
[18]夏德海.现场总线的现状及其发展趋势.电气时代,2006(8):16-19.
[19]石宗英,徐文立,杜继宏,温旭.基于现场总线的仿人型机器人控制系统.计算机工程与应用,2002.2:195-197.
[20]白云飞,曲尔光.现场总线的技术特点和发展趋势.机械管理开发,2007(1):81-82.
[21]董砚,孙鹤旭,刘作军.基于现场总线的工业机器人监控系统研究.微计算机信息,2005,21(83):87-90.
[22]陈志平.面向工业的实时以太网方案——Ethernet Powerlink.自动化博览:69-72.
[23]奚清漪,麦云飞.基于Ethernet Powerlink的伺服通信控制.工业控制计算机,2006,19(7):23-24.
[24]冯海川,张承慧,崔纳新,崔胜国.Powerlink在纸机传动控制系统中的应用.中国造纸,2007,26(1):41-45.
[25]刘星桥,凌俊杰,高健,孙伟,沈跃.基于PROFIBUS现场总线的装箱机械手控制系统.电气传动,2005,35(9):58-61.
[26]钟华,吴镇炜,李斌,卜春光.基于CAN的仿人机器人控制器的设计与实现.仪器仪表学报,2004,25(4):913-915.
[27]邓遵义,宁祎,刘保国.基于CAN总线的分布式机器人控制系统设计.机器人技术,2006,22(62):255-257.
[28]齐蓉,肖维蓉.可编程计算机控制器高级技术.西安:西北工业大学出版社 2002.
[29]李方圆.变频器自动化工程实践.北京:电子工业出版社,2007.4
[30]林春芳.电气控制与PLC原理及应用.上海:上海交通大学出版社,2008.1
[31]柳洪义,罗忠,王菲.现代机械工程自动控制.北京:科学出版社,2008.9
[32]郝丽娜,巩亚东,李虎.机械装备电气控制技术.北京:科学出版社,2006.8
[33]张华龙.图解PLC与电气控制入门.北京:人民邮电出版社,2008.9
[34]师黎,陈铁军,李晓媛,姚丽娜.智能控制理论及应用.北京:清华大学出版社,2009.4
[35]孙增圻.智能控制理沦与技术.北京:清华大学出版社,1997.
[36]喻宗泉,喻晗.神经网络控制.西安:西安电子科技大学出版社,2009.1
[37]王耀南.机器人智能控制工程.北京:科学出版社,2004.6
[38]毛宗源.机器人的智能控制方法.北京:国防工业出版社,2002.
[39]张毅,罗元,郑太雄.移动机器人技术及其应用.北京:电子工业出版社,2007.
[40]陆祥生,杨秀莲.机械手理论及应用.北京:中国铁道出版社,1985.
[41]孙富春,孙增圻,张钹.机械手神经网络稳定自适应控制的理论与方法.北京:高等教育出版社,2005.
[42]何玉彬,李新忠.神经网络控制技术及其应用.北京:科学出版社,2000.
[43]韩力群.人工神经网络理论、设计及应用(第二版).北京:化学工业出版社,2007.
[44]王伟.人工神经网络原理入门与应用.北京:北京航空航天大学出版社,1995.
[45]Young H.Kim,Frank L.Lewis,Darren M.Dawson.Intelligent optimal control of robotic manipulators using neural networks.Automatica,2000(36):1355-1364.
[46]R.S.afaric,K.Jezernik,M.Pec.Neural network control for direct-drive robot mechanisms.Engineering Applications of Arti(?)cial Intelligence,1998(11):735- 745.
[47]Changman Son.Comparison of intelligent control planning algorithms for robot's part micro-assembly task.Engineering Applications of Artificial Intelligence,2006(19):41-52.
[48]Sahin Yildirim.Design of a proposed neural network control system for trajectory controlling of walking robots.Simulation Modelling Practice and Theory,2008(16):368-378.
[49]Pramod Gupta,Naresh K.Sinha.Intelligent control of robotic manipulators:experimental study using neural networks.Mechatronics,2000,(10):289-305.
[50]M.J.Er,Tien Peng Tan,Sin Yee Loh.Control of a mobile robot using generalized dynamic fuzzy neural networks.Microprocessors and Microsystems,2004(28):491-498.
[51]夏鲁刚.机器人智能控制方法研究及控制器设计.机械工程师,2006(12):47-49.
[52]刘成良,张凯,付庄,曹其新,殷跃红.神经网络在机器人运动控制中的应用研究.机械科学与技术,2003,22(2):226-228.
[53]Sahin Yildirim.Adaptive robust neural controller for robots.Robotics and Autonomous Systems,2004(46):175-184.
[54]TU Diep Cong Thanh,Kyoung Kwan Ahn.Nonlinear PID control to improve the control performance of 2 axes pneumatic artificial muscle manipulator using neural network.Mechatronics,2006,(16):577-587.
[55]王洪斌,宋佐时,王洪瑞.基于模糊神经网络的机器人逆运动学问题.自动化与仪器仪表,2002(4):8-12.
[56]X.P.Chengl,R.V.Patel.Neural network based tracking control of a flexible macro-micro manipulator system.Neural Networks,2003(16):271-286.
[57]Jun Ye.Tracking control for nonholonomic mobile robots:Integrating the analog neural network into the backstepping technique.Neurocomputing,2008(71):3373-3378.
[58]Hong Suha,Yae Won Kim.A visual servoing algorithm using fuzzy logics and fuzzy-neural networks.Mechatronics,2000(10):1-18.
[59]姜志兵,赵英凯,李方方.神经网络在机器人逆运动学中的应用.机械与电子,2005(11):43-46.
[60]Manfred L.Husty,Martin Pfurner,Hans-Peter Schroker.A new and efficient algorithm for the inverse kinematics of a general serial 6R manipulator.Mechanism and Machine Theory,2007(42):66-81.
[61]S.N.Huang,K.K.Tan,T.H.Lee.Adaptive neural network algorithm for control design of rigid-link electrically driven robots.Neurocomputing,2008(71):885-894.
[62]姜新农,王文香.基于免疫遗传的BP网络在机械手逆运动学中的应用.机械与电子,2006(1):48-51.