全自主足球机器人变负载情况下双闭环调速系统建模
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摘要
全自主足球机器人是当前人工智能和机器人领域的研究热点之一。全自主机器人足球比赛的特点是每个机器人完全自治,即每个机器人必须自带各种传感器、控制器、驱动器、电源等设备[1]。它集高新技术、娱乐和比赛为一体,是人工智能、机器人学、计算机视觉等领域,新理论、新方法的良好实验平台。
在全自主足球机器人系统中,底层驱动控制系统的好坏直接影响到机器人的运动性能和比赛结果,因此建立底层驱动系统模型是十分必要的。目前绝大部分控制系统的设计是在离线的情况下进行的,因此建立与实际系统比较贴近的模型,代替实际被控对象进行控制器设计,是控制系统设计首先需要解决的关键问题之一。
运用特征分析和“类等效”的建模方法,从被控对象的主要特征量出发,建立结构合理,参数精确的模型,这种方法极大的减小了仿真模型和实际系统的差异,大大缩短仿真到实时控制之间的进程。在全自主足球机器人比赛过程中,由于底层驱动系统的外部负载经常会发生变化,为使离线设计的控制器能够更好的贴近真实系统,需要建立变负载下底层驱动系统模型。
基于以上背景,本课题从全自主足球机器人的实际应用中出发,引出双闭环调速系统作为研究对象。双闭环调速系统是构成直流电机驱动系统的典型方案,往往作为执行机构的重要组成部分,建立变负载下双闭环调速系统的模型具有广泛的实际意义。
本文在运用特征分析和“类等效”的建模方法,建立的恒定负载模型基础上,深入分析该模型在变负载情况下其模型参数变化情况,通过电机系统驱动电流和电机转动状态建立负载与模型参数之间的函数关系,利用改进的遗传算法和曲线拟合的工程方法对模型参数进行辨识,从而得到变负载情况下直流电机双闭环调速系统模型。
最后,我们在General Bar全自动足球机器人上,建立以MAXON电机、双闭环驱动器为主要硬件的实验平台。对General Bar全自主足球机器人的变负载下双闭环调速系统进行建模。通过实际系统实验证实了模型的有效性,可以很好的代替实际系统。
Soccer Robot is one of hot issue in technology research nowadays. It contains the high technology and the entertainment. Also, it is a right experiment platform for the new theories and methods in the area of artificial intelligence, robotic and the computer vision.
Autonomous Soccer Robot is one of the hot fields in technology research nowadays. The feature of Autonomous Robot Soccer competition is autonomous completely, that is, each robot must take varieties of sensors, controllers, drives, power supplies and other equipments with itself. It contains the high technology and the entertainment. Also, it is a right experiment platform for the new theories and methods in the area of artificial intelligence, robotic and the computer vision.
Whether the bottom drive control is good or not has a great influence on the performance of the motion & the results of the competition. Thus the bottom drive control modeling is very essential. Nowadays most of the control system is designed off-line. Therefore making the model close to the actual system, instead of the actual controlled object, is one of the most important problems of the control system design.
The characteristic analysis and the modeling method of the "Quasi-Equivalent", utilizing the main characters of the controlled device, can get a reasonable structural and parameters precise model. The method can reduce the difference of the model and the real object; shorten the transition form the simulation to the real-time control. In the competition of the Autonomous Soccer Robot, the external load of the bottom drive system is always changing. In order to make the off-line-design controller closer to the actual system, it needs modeling the bottom drive system with time-variety load.
Based on the above background, double loop DC motor control system (DLM) is posed from the application of the autonomous soccer robot. The DLM is the typical approach to compose the DC motor drive system. It is very important part of the actuators. The modeling of the DLM with time-variety load has very broad meaning.
In this paper, basing the model of the double loop DC motor control system that using the characteristic analysis, the modeling method of the "Quasi-Equivalent" which is fit for Constant load, analyzing the changes of parameters in the model when it’s under changing load conditions. Through the states of drive motor current and motors turning, it builds the functional relations between the model’s parameters, doing the parameter identification with improved genetic algorithm and using the method of curve fitting, finally, we get the model of double loop DC motor speed control system under change load conditions.
Finally, on the General Autonomous Soccer Robot we establish the MAXON motor and dual closed-loop drive as the main hardware platform. We model the dual closed-loop and variable speed control system with the time-variety load on the General Bar Autonomous Soccer Robot. Through the experiments of practical system we confirm the validity of the model, it can be a good substitute for the actual system.
在全自主足球机器人系统中,底层驱动控制系统的好坏直接影响到机器人的运动性能和比赛结果,因此建立底层驱动系统模型是十分必要的。目前绝大部分控制系统的设计是在离线的情况下进行的,因此建立与实际系统比较贴近的模型,代替实际被控对象进行控制器设计,是控制系统设计首先需要解决的关键问题之一。
运用特征分析和“类等效”的建模方法,从被控对象的主要特征量出发,建立结构合理,参数精确的模型,这种方法极大的减小了仿真模型和实际系统的差异,大大缩短仿真到实时控制之间的进程。在全自主足球机器人比赛过程中,由于底层驱动系统的外部负载经常会发生变化,为使离线设计的控制器能够更好的贴近真实系统,需要建立变负载下底层驱动系统模型。
基于以上背景,本课题从全自主足球机器人的实际应用中出发,引出双闭环调速系统作为研究对象。双闭环调速系统是构成直流电机驱动系统的典型方案,往往作为执行机构的重要组成部分,建立变负载下双闭环调速系统的模型具有广泛的实际意义。
本文在运用特征分析和“类等效”的建模方法,建立的恒定负载模型基础上,深入分析该模型在变负载情况下其模型参数变化情况,通过电机系统驱动电流和电机转动状态建立负载与模型参数之间的函数关系,利用改进的遗传算法和曲线拟合的工程方法对模型参数进行辨识,从而得到变负载情况下直流电机双闭环调速系统模型。
最后,我们在General Bar全自动足球机器人上,建立以MAXON电机、双闭环驱动器为主要硬件的实验平台。对General Bar全自主足球机器人的变负载下双闭环调速系统进行建模。通过实际系统实验证实了模型的有效性,可以很好的代替实际系统。
Soccer Robot is one of hot issue in technology research nowadays. It contains the high technology and the entertainment. Also, it is a right experiment platform for the new theories and methods in the area of artificial intelligence, robotic and the computer vision.
Autonomous Soccer Robot is one of the hot fields in technology research nowadays. The feature of Autonomous Robot Soccer competition is autonomous completely, that is, each robot must take varieties of sensors, controllers, drives, power supplies and other equipments with itself. It contains the high technology and the entertainment. Also, it is a right experiment platform for the new theories and methods in the area of artificial intelligence, robotic and the computer vision.
Whether the bottom drive control is good or not has a great influence on the performance of the motion & the results of the competition. Thus the bottom drive control modeling is very essential. Nowadays most of the control system is designed off-line. Therefore making the model close to the actual system, instead of the actual controlled object, is one of the most important problems of the control system design.
The characteristic analysis and the modeling method of the "Quasi-Equivalent", utilizing the main characters of the controlled device, can get a reasonable structural and parameters precise model. The method can reduce the difference of the model and the real object; shorten the transition form the simulation to the real-time control. In the competition of the Autonomous Soccer Robot, the external load of the bottom drive system is always changing. In order to make the off-line-design controller closer to the actual system, it needs modeling the bottom drive system with time-variety load.
Based on the above background, double loop DC motor control system (DLM) is posed from the application of the autonomous soccer robot. The DLM is the typical approach to compose the DC motor drive system. It is very important part of the actuators. The modeling of the DLM with time-variety load has very broad meaning.
In this paper, basing the model of the double loop DC motor control system that using the characteristic analysis, the modeling method of the "Quasi-Equivalent" which is fit for Constant load, analyzing the changes of parameters in the model when it’s under changing load conditions. Through the states of drive motor current and motors turning, it builds the functional relations between the model’s parameters, doing the parameter identification with improved genetic algorithm and using the method of curve fitting, finally, we get the model of double loop DC motor speed control system under change load conditions.
Finally, on the General Autonomous Soccer Robot we establish the MAXON motor and dual closed-loop drive as the main hardware platform. We model the dual closed-loop and variable speed control system with the time-variety load on the General Bar Autonomous Soccer Robot. Through the experiments of practical system we confirm the validity of the model, it can be a good substitute for the actual system.
引文
[1]贾建强,陈卫东,席裕庚.全自主足球机器人系统关键技术综述[J].上海交通大学学报, 2003,(S2).
[2]李小坚. Robocup中型组机器人控制体系结构和驱动系统的设计与实现[D].北方工业大学, 2004.
[3]谢云,杨宜民.全自主机器人足球系统的研究综述[J].机器人, 2004,(05).
[4]侯清涛,厉广伟,李金屏. Robocup中型组机器人足球技术探讨[J].济南大学学报(自然科学版) , 2008,(03).
[5]郭锐,吴敏,彭军,彭姣,曹卫华.一种新的多智能体Q学习算法[J].自动化学报, 2007, 33(4): 367-372.
[6]余存. RoboCup中型组足球机器人视觉系统的研究与设计[D].山东:山东大学, 2007.
[7]张春晖.机器人足球策略系统研究与实现[D].东北大学博士学位论文, 2002, 50-51.
[8] Halld L. Mathematical technique in multi2sensor data fusion [M].London: Artech House, 2000. 15-21.
[9] Wu Weiguo, Chen Huitang, Woo Peng-Yung. Optimal motion planning for a wheeled mobile robot[C]. Proceedings-IEEE International Conference on Robotics and Automation, 1999, 1: 41-46.
[10] Gao J B. Some remark on Kalman filters for the multi-sensor fusion [J]. Information Fusion, 2002,3(3):191– 201.
[11] Megalooikonomou V, Yesha Y. Space efficient quantization for distributed estimation by a multi2sensor fusion system [J].Information Fusion, 2004,5(5):299-308.
[12] D.Floreano, J.Urzelai. Evolutionary robots with on-line self-organization and behavioral fitness [J]. Neural Networks,2000.5,13 (4): 431-443.
[13] Joanne H. Walker, Simon M.Garrett, Myra S. Wilson. The balance between initial training and lifelong adaptation in evolving robot controllers[J]. IEEE Transactions on Systems, Man, and Cybernetics, 2006.4,36 (2):423-432.
[14]谢云.全自主机器人足球比赛系统的通信与多传感器信息融合技术[D].广东:广东工业大学, 2006.6.
[15]吴伟,刘兴刚,王忠实,徐心和.多传感器融合实现机器人精确定位[J].东北大学学报(自然科学版), 2007.2, 28(2): 161-164.
[16]王晓宇,闫继宏.两轮自平衡机器人多传感器数据融合方法研究[J].传感技术学报, 2007.3, 20(3): 668-672.
[17]陈伯时.电力拖动自动控制系统[M].北京:机械工业出版社,1997:48-55.
[18] Wu Weiguo, Chen Huitang, Woo Peng-Yung. Optimal motion planning for a wheeled mobile robot[C]. Proceedings-IEEE International Conference on Robotics and Automation, 1999, 1: 41-46.
[19]曹洋,方帅,徐心和.加速度约束条件下的非完整移动机器人运动控制[J].控制与决策, 2006, 21(2): 93-96.
[20]朱咏杰,付宜利,王树国,常疆.一种改进的移动机器人运动控制算法[J].哈尔滨工业大学学报. 2004, 36(1): 4-6.
[21]黄永志,陈卫东.两轮移动机器人运动控制系统的设计与实现[J].机器人, 2004, 26(1): 40-44.
[22]何艳丽,吴敏,曹卫华.自主机器人直线行进过程的数字PID控制[J].现代电子技术, 2005, (13): 58-60.
[23] L Huang. Speed Control of Differentially Driven Wheeled Mobile Robots-Model-Based adaptive approach [J]. Journal of Robotic Systems, 2005, 22(6): 323-332.
[24] Omar Al-Ayasrahl, T. Alukaidey, R. Salman, G. Pissanidis. Dual feed-back and feed-forward synchronized cross-coupled motion control for two-wheel mobile robot[C]. Proceedings of the 2005 IEEE International Workshop on Robotic Sensors: Robotic and Sensor Environments, ROSE 2005. IEEE. Canada, 2005: 55-60.
[25]李永龙,李祖枢,王牛.直流电机双闭环调速系统(DLM)的建模与辨识[J].控制理论与应用, 2008,25(6).
[26]古建功.遗传算法在仿人智能控制中的应用[D].重庆:重庆大学, 2006.
[27]牛全民,亓迎川.基于状态观测器实现转速及负载转矩估计的直流调速系统[J].电子技术应用, 2000,(05)
[28]李祖枢.仿人智能控制理论与多级摆的摆起控制[M].人工智能:回顾与展望.北京:科学出版社, 2006.
[29]李祖枢,涂亚庆.仿人智能控制[M].北京:国防工业出版社, 2003.
[30]李祖枢.具有可调参数的模型降价新方法[J].自动化学报, 1982,(2):81-92.
[31]李祖枢,周其鉴,任伟.动态系统“类等效”模型测辨及多可调参数的Pade模型降阶[J].自动化学报, 1985,(1): 53-62.
[32]王牛,李祖枢,李永龙,潘娅.带驱动直流电机两轮机器人运动系统仿真[J].系统仿真学报, 2008,(17)
[33]王牛.基于动觉智能图式足球机器人运动控制[D].重庆大学博士学位论文, 2008
[34]古建功,李祖枢,张华.三关节单杠体操机器人的动力学参数辨识[C].中国人工智能学会第11届全国学术年会论文集,北京邮电大学出版社, 2005: 594-599.
[35]郑文.曲线拟合[D],西南大学高校教师硕士学位论文, 2008.
[36]刘和平. TMS320LF240X DSP结构,原理及应用[M].北京:北京航空航天大学出版社, 2002.
[37] Jinn-Tsong Tsai. Tung-Kuan Liu. Jyh-Horng Chou. Hybrid Taguchi-Genetic Algorithm for Global Numerical Optimization [J]. IEEE trans. Evolutionary computation, Aug 2004, 8(4): 365-377.
[38] Y.W. Leung and Y.Wang. An orthogonal genetic algorithm with quantization for global numerical optimization [J]. IEEE Trans. Evol. Comput, Feb. 2001, 5(1), 41-53.
[39]于芳.基于动觉图式的仿人智能控制在移动机器人路径规划中的研究[D].重庆大学, 2007.
[40]李永龙.基于IGA的“类等效”建模在全自主足球机器人双闭环调速系统中的应用研究[D].重庆大学, 2007.
[2]李小坚. Robocup中型组机器人控制体系结构和驱动系统的设计与实现[D].北方工业大学, 2004.
[3]谢云,杨宜民.全自主机器人足球系统的研究综述[J].机器人, 2004,(05).
[4]侯清涛,厉广伟,李金屏. Robocup中型组机器人足球技术探讨[J].济南大学学报(自然科学版) , 2008,(03).
[5]郭锐,吴敏,彭军,彭姣,曹卫华.一种新的多智能体Q学习算法[J].自动化学报, 2007, 33(4): 367-372.
[6]余存. RoboCup中型组足球机器人视觉系统的研究与设计[D].山东:山东大学, 2007.
[7]张春晖.机器人足球策略系统研究与实现[D].东北大学博士学位论文, 2002, 50-51.
[8] Halld L. Mathematical technique in multi2sensor data fusion [M].London: Artech House, 2000. 15-21.
[9] Wu Weiguo, Chen Huitang, Woo Peng-Yung. Optimal motion planning for a wheeled mobile robot[C]. Proceedings-IEEE International Conference on Robotics and Automation, 1999, 1: 41-46.
[10] Gao J B. Some remark on Kalman filters for the multi-sensor fusion [J]. Information Fusion, 2002,3(3):191– 201.
[11] Megalooikonomou V, Yesha Y. Space efficient quantization for distributed estimation by a multi2sensor fusion system [J].Information Fusion, 2004,5(5):299-308.
[12] D.Floreano, J.Urzelai. Evolutionary robots with on-line self-organization and behavioral fitness [J]. Neural Networks,2000.5,13 (4): 431-443.
[13] Joanne H. Walker, Simon M.Garrett, Myra S. Wilson. The balance between initial training and lifelong adaptation in evolving robot controllers[J]. IEEE Transactions on Systems, Man, and Cybernetics, 2006.4,36 (2):423-432.
[14]谢云.全自主机器人足球比赛系统的通信与多传感器信息融合技术[D].广东:广东工业大学, 2006.6.
[15]吴伟,刘兴刚,王忠实,徐心和.多传感器融合实现机器人精确定位[J].东北大学学报(自然科学版), 2007.2, 28(2): 161-164.
[16]王晓宇,闫继宏.两轮自平衡机器人多传感器数据融合方法研究[J].传感技术学报, 2007.3, 20(3): 668-672.
[17]陈伯时.电力拖动自动控制系统[M].北京:机械工业出版社,1997:48-55.
[18] Wu Weiguo, Chen Huitang, Woo Peng-Yung. Optimal motion planning for a wheeled mobile robot[C]. Proceedings-IEEE International Conference on Robotics and Automation, 1999, 1: 41-46.
[19]曹洋,方帅,徐心和.加速度约束条件下的非完整移动机器人运动控制[J].控制与决策, 2006, 21(2): 93-96.
[20]朱咏杰,付宜利,王树国,常疆.一种改进的移动机器人运动控制算法[J].哈尔滨工业大学学报. 2004, 36(1): 4-6.
[21]黄永志,陈卫东.两轮移动机器人运动控制系统的设计与实现[J].机器人, 2004, 26(1): 40-44.
[22]何艳丽,吴敏,曹卫华.自主机器人直线行进过程的数字PID控制[J].现代电子技术, 2005, (13): 58-60.
[23] L Huang. Speed Control of Differentially Driven Wheeled Mobile Robots-Model-Based adaptive approach [J]. Journal of Robotic Systems, 2005, 22(6): 323-332.
[24] Omar Al-Ayasrahl, T. Alukaidey, R. Salman, G. Pissanidis. Dual feed-back and feed-forward synchronized cross-coupled motion control for two-wheel mobile robot[C]. Proceedings of the 2005 IEEE International Workshop on Robotic Sensors: Robotic and Sensor Environments, ROSE 2005. IEEE. Canada, 2005: 55-60.
[25]李永龙,李祖枢,王牛.直流电机双闭环调速系统(DLM)的建模与辨识[J].控制理论与应用, 2008,25(6).
[26]古建功.遗传算法在仿人智能控制中的应用[D].重庆:重庆大学, 2006.
[27]牛全民,亓迎川.基于状态观测器实现转速及负载转矩估计的直流调速系统[J].电子技术应用, 2000,(05)
[28]李祖枢.仿人智能控制理论与多级摆的摆起控制[M].人工智能:回顾与展望.北京:科学出版社, 2006.
[29]李祖枢,涂亚庆.仿人智能控制[M].北京:国防工业出版社, 2003.
[30]李祖枢.具有可调参数的模型降价新方法[J].自动化学报, 1982,(2):81-92.
[31]李祖枢,周其鉴,任伟.动态系统“类等效”模型测辨及多可调参数的Pade模型降阶[J].自动化学报, 1985,(1): 53-62.
[32]王牛,李祖枢,李永龙,潘娅.带驱动直流电机两轮机器人运动系统仿真[J].系统仿真学报, 2008,(17)
[33]王牛.基于动觉智能图式足球机器人运动控制[D].重庆大学博士学位论文, 2008
[34]古建功,李祖枢,张华.三关节单杠体操机器人的动力学参数辨识[C].中国人工智能学会第11届全国学术年会论文集,北京邮电大学出版社, 2005: 594-599.
[35]郑文.曲线拟合[D],西南大学高校教师硕士学位论文, 2008.
[36]刘和平. TMS320LF240X DSP结构,原理及应用[M].北京:北京航空航天大学出版社, 2002.
[37] Jinn-Tsong Tsai. Tung-Kuan Liu. Jyh-Horng Chou. Hybrid Taguchi-Genetic Algorithm for Global Numerical Optimization [J]. IEEE trans. Evolutionary computation, Aug 2004, 8(4): 365-377.
[38] Y.W. Leung and Y.Wang. An orthogonal genetic algorithm with quantization for global numerical optimization [J]. IEEE Trans. Evol. Comput, Feb. 2001, 5(1), 41-53.
[39]于芳.基于动觉图式的仿人智能控制在移动机器人路径规划中的研究[D].重庆大学, 2007.
[40]李永龙.基于IGA的“类等效”建模在全自主足球机器人双闭环调速系统中的应用研究[D].重庆大学, 2007.