并联机器人多目标协同智能控制研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
随着机器人技术的迅速发展,并联机器人的优势日益突现,正逐步广泛应用于航空、航天、深海、危险化工、核工业、医疗手术、精密制造业等高精、尖端领域。因此,人们对并联机器人的定位精度、运行平稳性、设备稳定性、容错能力、自适应性、多机器人协调性等工作性能也提出了更高的要求,其控制问题逐步成为机器人领域中最重要的研究方向之一。同时在现代复杂的信息环境下,以往单目标、少目标的传统控制方法已经无法满足并联机器人多性能指标的控制要求,越来越多的机器人控制系统需要智能化程度更高、实用性更强的多目标智能控制算法。本文从并联机器人工程实践中一些有待解决的实际问题出发,借鉴具有多目标协同调节特性的生物网络机制,对并联机器人的一些多目标协同智能控制问题进行研究。主要研究成果归纳如下:
     (1)研究了并联机器人运动支链的自适应控制器设计。首先基于内分泌甲状腺激素调节机制设计了一种多级协同调节自适应控制器,能够实现控制器各参数之间的协同自适应调节及各级控制器之间的协同补偿,从而提高机器人控制器的响应速度、控制精度和稳定性。其次基于内分泌调控网络结构提出了一种新颖的速、位协同智能控制器,进一步改进参数自适应调节方法及速、位协同控制能力。
     (2)研究了并联机器人的位姿、速度、加速度协同智能控制。基于神经内分泌系统的多环反馈机制和多目标协同调节机制,设计了一种多环反馈的多目标协同智能控制系统,并应用于真实的多通道并联机器人设备上。实验结果表明,该系统能够让并联机器人的子通道控制性能和全局控制性能都能得到较大的提高,能够较好地实现位姿、速度、加速度之间的多目标协同智能控制。
     (3)研究了并联机器人的精准协同容错控制。基于人体生理止血机制的多目标协同调节原理,设计了一种应用于冗余并联机器人的精准容错控制系统,确保冗余并联机器人发生局部故障时,能够继续完成精准的容错任务,实现正常和故障工作状态之间的协同。对比实验结果表明,相对于传统的PID控制器,所提出的容错控制系统对子通道控制和并联机器人整体控制均有更高的控制精度和容错能力。
     (4)研究了并联机器人的力、位解耦协同智能控制。基于人体体内双边解耦协同调节机制,提出了一种应用于微创手术主-从机器人系统的力、位解耦协同智能控制系统,解决了达芬奇等微创手术机器人无力反馈信号等问题。实验结果表明:提出的控制系统能够在无力传感器情况下,利用运动信号获得较精准的反馈力;基于生物调节启发的解耦、协同机制具有较好的力、位协同控制性能;让操作者准确获得额外的力反馈信号,能够确保机器人平稳地完成力位协同任务。
     最后,总结了全文的工作内容,指出了目前研究中存在的缺陷与不足,并对今后的研究展望和研究重点进行了讨论。
With the rapid development of robot technology, the advantage of parallel robot becomes more observably. Parallel robots are now widely applied to high precision and cutting-edge fields, such as aeronautics, aerospace, deep sea, hazardous chemicals, nuclear, medical treatment, and precision manufacturing, etc. That people requires higher standards for parallel robots' working performance such as positioning accuracy, running smoothness, equipment stability, fault tolerance, adaptability, and coordination of multi-robot. The control problem gradually becomes one of the most important research directions in the robotic field. Meanwhile, under the modern complex information environment, the single objective and few objective traditional control methods are hard to satisfy parallel robot multi-objective control performance requirements. More and more robot control system needs better intelligent and practical multi-objective control algorithm. To resolve some parallel robot problems in engineering practice, the author does a lot of researches on parallel robot multi-objective cooperative intelligent control according to some biological network regulation mechanisms which owing multi-objective cooperative regulation characteristics. Mainly summarized as follows:
     (1) Study on adaptive controller design of the parallel robot motion branched chain. First, based on the endocrine thyroid hormone regulation mechanism, a multi-level cooperative regulation adaptive controller is proposed. The controller can regulate control parameters and make cooperative compensates automatically to improve response, accuracy and stability of the robot controller. What's more, based on endocrine regulation network structure, a novel position-velocity cooperative intelligent controller is proposed, which improve the parameters regulation method and cooperative ability of the position and velocity.
     (2) Study on position, velocity and acceleration cooperative intelligent control of the parallel robot. Based on multi-loop feedback and multi-objective cooperative regulation mechanism of neuroendocrine system, a multi-loop and multi-objective cooperative intelligent control system is presented and applied to an actual multi-channel parallel robot. The experimental result shows that the proposed system greatly improves the control performance in sub-channels and global robot that can easy to achieve multi-objective cooperative intelligent control of the position, velocity and acceleration.
     (3) Study on precise cooperative fault-tolerant control of the parallel robot. Based on multi-objective cooperative regulation principle in human hemostasis system, a precise fault-tolerant system is proposed for redundant parallel manipulator to continue their fault-tolerant task accurately with the presence of failure. That robot can achieve cooperative control between the normal state and the fault state. The comparative experiment result shows that compared with traditional PID controller, the proposed fault-tolerant system has better control precision and fault-tolerant capability both in robot sub-channel and global.
     (4) Study on force-position decoupling and cooperation intelligent control of the parallel robot. Based on bilateral decoupling and cooperation bio-regulation mechanism in human body, a force and position decoupling and cooperation intelligent control system is proposed for minimally invasive surgical master-slave robot system. The new system solves the lacking force feedback problem in minimally invasive surgical robots such as da Vinci system. The experimental results indicate the proposed control system can acquire precise force feedback by motion signal without force sensor; Based on bio-regulation inspired decoupling and cooperation mechanism has a good force-position cooperative control performance; Operator gets accurate force feedback to make sure robot performing task steady.
     At last, a summary of the thesis is made, and the deficiency in the project and the further development are narrated respectively.
引文
[1]蔡自兴.机器人学(第二版)[M].北京:清华大学出版社,2009.
    [2]丛爽,尚伟伟.并联机器人:建模控制优化与应用[M].北京:电子工业出版社,2010.
    [3]Stewart D. A platform with six degrees freedom [C]. Proceedings of the Institution of Mechanical Engineering, London,1965,180 (1):371-386.
    [4]高峰,杨加伦,葛巧德.并联机器人型综合的GF集理论[M].北京:科学出版社,2011.
    [5]郝矿荣,丁永生.机器人几何代数模型与控制[M].北京:科学出版社,2011.
    [6]丁学恭.机器人控制研究[M].浙江:浙江大学出版社,2006.
    [7]陆震.冗余自由度机器人原理及应用[M].北京:电子工业出版社,2007.
    [8]Zhang D, Gao Z. Optimal kinematic calibration of parallel manipulators with pseudo error theory and cooperative coevolutionary network [J]. IEEE Transactions on Industrial Electronics,2012,59 (8):3221-3231.
    [9]Zhang D, Lei J. Kinematic analysis of a novel 3-DOF actuation redundant parallel manipulator using artificial intelligence approach [J]. Robotics and Computer-Integrated Manufacturing,2011,27 (1):157-163.
    [10]Yabuta T, Tsujimura T. On the characteristics of the robot manipulator controller using neural network [C]. International Workshop on Industrial Applications of Machine Intelligence and Vision (MIV-89), Tokyo, Japan,1989:76-81.
    [11]Hassan M, Notash L. Design modification of parallel manipulators for optimum fault tolerance to joint jam [J]. Mechanism and Machine Theory,2005,40 (5): 559-577.
    [12]Khoukhi A. Data-driven multi-stage motion planning of parallel kinematic machines [J]. IEEE Transactions on Control Systems Technology,2010,18 (6): 1381-1389.
    [13]Bethea B T, Okamura A M, Kitagawa M, et al. Application of haptic feedback to robotic surgery [J]. Journal of Laparoendoscopic and Advanced surgical techniques Part:A,2004,14 (3):191-195.
    [14]Okamura A M. Methods for haptic feedback in teleoperated robot-assisted surgery [J]. Industrial Robots,2004,31 (6):499-508.
    [15]Tavakoli M, Patel R V, Moallem M. Haptic interaction in robot-assisted endoscopic surgery:a sensorized end-effector [J]. The International Journal of Medical Robotics and Computer Assisted Surgery,2005,1 (2):53-63.
    [16]Kitagawa M, Dokko D, Okamura A M, et al. Effect of sensory substitution on suture-manipulation forces for robotic surgical systems [J]. The journal of Thoracic and Cardiovascular Surgery,2005,129 (1):151-158.
    [17]Hagen M E, Meehan J J, Inan I, et al. Visual clues act as a substitute for haptic feedback in robotic surgery [J]. Surgical Endoscopy,2007,22 (6):1505-1508.
    [18]Reily C E, Akinbiyi T, Burschka D, et al. Effects of visual force feedback on robot-assisted surgical task performance [J]. The Journal of Thoracic and Cardiovascular Surgery,2008,135 (1):196-202.
    [19]Van der Meijden O A, Schijven M P. The value of haptic feedback in conventional and robot-assisted minimal invasive surgery and virtual reality training:a current review [J]. Surgical Endoscopy,2009,23 (6):1180-1190.
    [20]Cheng C-H, Shu S-L. Application of GA-based neural network for attitude control of a satellite [J]. Aerospace Science and Technology,2010,14 (4): 241-249.
    [21]Rong H-J, Sundararajan N, Saratchandran P, et al. Adaptive Fuzzy fault-tolerant controller for aircraft autolanding under failures [J]. IEEE Transactions on Aerospace and Electronic Systems,2007,43 (4):1586-1603.
    [22]Kimura S, Takahashi M, Okuyama T, et al. A fault-tolerant control algorithm having a decentralized autonomous architecture for space hyper-redundant manipulators [J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans,1998,28 (4):521-527.
    [23]Yuan J Y, Liu C A, Li G X. Dynamics coordinated control method of multi-arm free-flying space robot with external force acting [C]. The Sixth World Congress on Intelligent Control and Automation, Dalian, China,2006,2: 8933-8937.
    [24]Notash L, Huang L. On the design of fault tolerant parallel manipulators [J]. Mechanism and Machine Theory,2003,38 (1):85-101.
    [25]Zhao X H, S. X. Peng. A successive approximation algorithm for the direct position analysis of parallel manipulators [J]. Mechanism and Machine Theory, 2000,35(8):1095-1101.
    [26]Geng Z, Haynes L. Neural network solution for the forward kinematics problemof a Stewart platform [C]. Proceeding of the 1991 IEEE International Conference on Robotics and Automation, Sacramento, California, USA, 1991,3:2650-2655.
    [27]Parikh P J, Lam S S Y. A Hybrid Strategy to Solve the Forward Kinematics Problem in Parallel Manipulators [J]. IEEE Transactions on Robotics,2005,21 (1):18-25.
    [28]Zhang P-Y, Lu T-S, Li-BoSong. RBF networks-based inverse kinematics of 6R manipulator [J]. The International Journal of Advanced Manufacturing Technology,2005,26 (1-2):144-147.
    [29]Yee C S, Lim K-b. Forward kinematics solution of Stewart platform using neural networks [J]. Neurocomputing,2000,16:333-349.
    [30]Boudreau R, N.Turkkan. Solving the forward kinematics of parallel manipulators with a genetic algorithm [J]. Journal of Robotic Systems,2006,13 (2):111-125.
    [31]宋伟刚,任静,朱冠亚.2DOF空间3-RPS并联机器人位置运动学混合算法[J].东北大学学报(自然科学版),2008,29(12):1762-1765.
    [32]Jamwal P K, Xie S, Aw K C. Kinematic design optimization of a parallel ankle rehabilitation robot using modified genetic algorithm [J]. Robotics and Autonomous Systems,2009,57 (10):1018-1027.
    [33]Parikh P J, Lam S S. Solving the forward kinematics problem in parallel manipulators using an iterative artificial neural network strategy [J]. The International Journal of Advanced Manufacturing Technology,2009,40 (5-6): 595-606.
    [34]Wang K. Application of genetic algorithms to robot kinematics calibration [J]. International Journal of Systems Science,2009,40 (2):147-153.
    [35]Sangveraphunsiri V, Chooprasird K. Dynamics and control of a 5-DOF manipulator based on an H-4 parallel mechanism [J]. The International Journal of Advanced Manufacturing Technology,2011,52 (1-4):343-364.
    [36]Shang W, Cong S, Kong F. Identification of dynamic and friction parameters of a parallel manipulator with actuation redundancy [J]. Mechatronics,2010,20 (2): 192-200.
    [37]Yeh Y C, Li T H S, Chen C Y. Adaptive Fuzzy Sliding-Mode Control of Dynamic Model Based Car-Like Mobile Robot [J]. International Journal of Fuzzy Systems,2009,11 (4):272-286.
    [38]Peng C C, Chen C L. Dynamic controller design for a class of nonlinear uncertain systems subjected to time-varying disturbance [J]. Nonlinear Dynamics,2009,57 (3):411-423.
    [39]Rodrigue-Vazque K, Fonseca K M, Fleming P J. Identifying the Structure of Nonlinear Dynamic systems Using Multi-objective Genetie Programming [J]. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans,2004,34 (4):531-545.
    [40]Zeinali M, Notash L. Adaptive sliding mode control with uncertainty estimator for robot manipulators [J]. Mechanism and Machine Theory,2010,45 (1): 80-90.
    [41]Hassan M, Notash L. Optimizing fault tolerance to joint jam in the design of parallel robot manipulators [J]. Mechanism and Machine Theory,2007,42 (10): 1401-1417.
    [42]Jamisola R S, Maciejewski A A, Roberts R G. A path planning strategy for kinematically redundant manipulators anticipating joint failures in the presence of obstacles [C]. IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, Nevada, October,2003:142-148.
    [43]Maciejewski A A. Fault tolerant properties of kinematically redundant manipulators [C]. IEEE International Conference on Robotics and Automation, West Lafayette, USA,1990:638-642.
    [44]Gwinnett J E. Amusement devices [P]. US Patent,1931, No.1789680.
    [45]张跃敏,谢刚.并联机器人机构研究概述[J].机械工程师,2008,(7):8-9.
    [46]李艳,王勇,陈正洪,et al.并联机器人智能控制研究现状[J].机床与液压,2008,36(12):180-182.
    [47]Li Y, Wang Y, Chen Z H. Research on Trajectory Tracking of a Parallel Robot Based on Neural Network PID Control [C]. IEEE International Conference on Automation and Logistics, Qingdao, China,2008:504-508.
    [48]Cakara T, Kokerb R, Demira H I. Parallel robot scheduling to minimize mean tardiness with precedence constraints using a genetic algorithm [J]. Advances in Engineering Software,2008,39 (1):47-54.
    [49]Bezinea H, Derbelb N, Alimi A M. Fuzzy control of robot manipulators::some issues on design and rule base size reduction [J]. Engineering Applications of Artificial Intelligence,2002,15 (5):401-416.
    [50]Tin Y H, Zhu J Y, Wei Z X. Intelligent Strategy of Force & Position Parallel Control for a Robot [J]. CIRP Annals-Manufacturing Technology,1997,46 (1): 279-282.
    [51]Ahn K K, Nguyen H T C. Intelligent switching control of a pneumatic muscle robot arm using learning vector quantization neural network [J]. Mechatronics, 2007,17 (4-5):255-262.
    [52]Mitra S, Hayashi Y. Bioinformatics with soft computing [J]. IEEE Transactions on System, Man and Cybernetics, Part C:Applications and Reviews,2006,36 (5):616-635.
    [53]丁永生.计算智能:理论、技术与应用[M].北京:科学出版社,2004.
    [54]丁永生.自然计算与网络智能[M].北京:科学出版社,2008.
    [55]丁永生.基于生物网络的智能控制与优化[M].北京:科学出版社,2010.
    [56]丁永生.计算智能的新框架:生物网络结构[J].智能系统学报,2007,2(2):26-30.
    [57]张妮,徐文尚,王文文.人工智能技术发展及应用研究综述[J].煤矿机械,2009,30(2):4-7.
    [58]丁菊玲,勒中坚,李钟华,et al.人工内分泌系统研究综述[J].科技广场,2009,3:26-28.
    [59]申晓宁,郭毓,陈庆伟,et al.基于多目标协同进化算法的多机器人路径规划[J].南京航空航天大学学报,2008,40(2):245-249.
    [60]王跃宣,刘连臣,牟盛静.处理带约束的多目标进化算法[J].清华大学学报,2005,45(1):103-106.
    [61]李艳君,吴铁军.一种混合动力学系统多目标优化控制问题的求解方法[J].自动化学报,2002,28(4):606-609.
    [62]姜山,程君实,陈佳品,et a1.基于多目标遗传算法的仿人机器人中枢神经运动控制器的设计[J].机器人,2001,23(1):59-67.
    [63]Ding Y, Lu X, Hao K, et al. Target coverage optimisation of wireless sensor networks using a multi-objective immune co-evolutionary algorithm [J]. International Journal of Systems Science,2011,42 (9):1531-1541.
    [64]Valdes J J, Barton A J. Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining:Application to geophysical prospecting [J]. Neural Networks,2007,20 (4):498-508.
    [65]Sari A T, S. M, alovi, et al. Fuzzy multi-objective algorithm for multiple solution of distribution systems voltage control [J]. International Journal of Electrical Power & Energy Systems,2003,25 (2):145-153.
    [66]Blumel A L, Hughes E J, White B A. Multi-objective Evolutionary Design of Fuzzy Auto pilot Controller [J]. Lecture Notes in Computer Science,2001,1993: 668-680.
    [67]刘宝.基于生物网络的智能控制系统及其应用[D].上海:东华大学,2006.
    [68]Liang X, Ding Y-S, Ren L-H, et al. A bio-inspired multilayered intelligent cooperative controller for stretching process of fiber production [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C:Applications and Reviews,2012,42 (3):367-377.
    [69]Jo S. Adaptive biomimetic control of robot arm motions [J]. Neurocomputing, 2008,71 (16-18):3625-3630.
    [70]Tyurin K V, Khanin M A. Hemostasis as an optimal system [J]. Mathematical Biosciences,2006,204 (2):167-184.
    [71]Viollet S, Franceschini N. A high speed gaze control system based on the Vestibulo-Ocular Reflex [J]. Robotics and Autonomous Systems,2005,50 (4): 147-161.
    [72]黄昔光,何广平,谭晓兰,et al.并联机器人机构研究现状分析[J].北方工业大学学报,2009,21(3):25-31.
    [73]吴博,吴盛林,赵克定.并联机器人控制策略的现状和发展趋势[J].机床与液压,2005,(10):5-8.
    [74]王义萍,陈庆伟,胡维礼.机器人行为选择机制综述[J].机器人,2009,31(5):472-480.
    [75]方勇纯.机器人视觉伺服研究综述[J].智能系统学报,2008,3(2):109-114.
    [76]Zhu M C, Li Y C. Decentralized adaptive fuzzy sliding, mode control for reconfigurable modular manipulators [J]. International Journal of Robust and Nonlinear Control,2009,20 (4):472-488.
    [77]孙坚.基于生物智能的六自由度并联机构运动控制及其仿真研究[D].上海:东华大学,2009.
    [78]Abellan-Nebot J V, Subiron F R. A review of machining monitoring systems based on artificial intelligence process models [J]. The International Journal of Advanced Manufacturing Technology,2010,47 (1-4):237-257.
    [79]Larsson S, Kjellander J A P. Motion control and data capturing for laser scanning with an industrial robot [J]. Robotics and Autonomous Systems,2006, 54 (6):453-460.
    [80]Ye J. Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot [J]. Neurocomputing,2008,71 (7-9):1561-1565.
    [81]张锐,吴成东.机器人智能控制研究进展[J].沈阳建筑工程学院学报(自然科学版),2003,19(1):61-64.
    [82]Shang W, Cong S, Zhang Y. Nonlinear friction compensation of a 2-DOF planar parallel manipulator [J]. Mechatronics,2008,18 (7):340-346.
    [83]Shang W, Cong S. Nonlinear computed torque control for a high-speed planar parallel manipulator [J]. Mechatronics,2009,19 (6):987-992.
    [84]丛爽,王杨,尚伟伟.自适应控制策略在并联机构上的应用[J].制造业自动化,2007,29(7):45-49.
    [85]焦晓红,耿秋实,方一鸣,et al.并联机器人的鲁棒自适应控制[J].机器人技术与应用,2002,(4):22-24.
    [86]焦晓红,李运峰,方一鸣,et al.一种机器人鲁棒自适应控制法[J].机器人技术与应用,2002,(3):40-43.
    [87]WU J, XU G, YIN Z. Robust adaptive control for a nonholonomic mobile robot with unknown parameters [J]. Journal of Control Theory and Applications,2009, 7 (2):212-218.
    [88]Wang Z, Zeng H, Ho D W C, et al. Multiobjective control of a four-link flexible manipulator:a robust H∞ approach [J]. IEEE Transactions on Control Systems Technology,2002,10 (6):866-875.
    [89]Zuo Y, Wang Y, Liu X, et al. Neural network robust H∞ tracking control strategy for robot manipulators [J]. Applied Mathematical Modelling,2010,34: 1823-1838.
    [90]Canudas W C, Fixot N. Robot control via robust estimated state feedback [J]. IEEE Transactions on Automatic Control,1991,36 (12):1497-1501.
    [91]Zhao D, Li S, Gao F. Finite time position synchronised control for parallel manipulators using fast terminal sliding mode [J]. International Journal of Systems Science,2009,40 (8):829-843.
    [92]Hung N, Im J S, Jeong S K, et al. Design of a Sliding Mode Controller for an Automatic Guided Vehicle and Its Implementation [J]. International Journal of Control Automation and Systems,2009,8 (1):81-90.
    [93]Boubakir A, Boudjema F, Labiod S. A Neuro-fuzzy-sliding Mode Controller Using Nonlinear Sliding Surface Applied to the Coupled Tanks System [J]. International Journal of Automation and Computing,2009,6(1):72-80.
    [94]Jacob, Cheung W F, Hung. Y S. Robust learning control of a high precision parallel manipulator [J]. Automatica,2009,19 (1):42-55.
    [95]Jolly K G, Kumar R S, Vijayakumar R. An artificial neural network based dynamic controller for a robot in a multi-agent system [J]. Neurocomputing, 2009,73 (1-3):283-294.
    [96]Shahdi A, Sirouspour S. Adaptive/Robust Control for Time-Delay Teleoperation [J]. IEEE Transactions on Robotics,2009,25 (1):196-205.
    [97]Zhang Y, Chen Z, Yang P. Multivariable nonlinear proportional-integral-derivative decoupling control based on recurrent neural networks [J]. Chinese journal of chemical engineering,2004,12 (5):677-681.
    [98]Ding Y, Liu B. An intelligent bi-cooperative decoupling control approach based on modulation mechanism of internal environment in body [J]. IEEE Transactions on Control Systems Technology,2011,19 (3):692-698.
    [99]Ding Y S, Liu B, Ren L H. Intelligent decoupling control system inspired from modulation of the growth hormone in neuroendocrine system [J]. Dynamics of Continuous, Discrete and Impulsive Systems, Series B:Applications & Algorithms,2007,14 (5):679-693.
    [100]何景峰,谢文建,韩俊伟.六自由度并联机器人输出解耦控制[J].哈尔滨工业大学学报,2006,38(3):395-398.
    [101]Xie D, Qu D, Xu F, et al. Tracking Controller Design of SCARA Robot Based on Decoupling Method [J]. Journal of System Simulation,2006,18 (S2): 931-935.
    [102]刘宝,张中炜,丁永生.基于生长激素双向调节原理的解耦控制[J].东南大学学报(自然科学版),2006,36(S1):5-8.
    [103]刘宝,王君红,丁永生.一种基于生理调节机制的智能协同解耦控制器[J].信息与控制,2009,38(5):539-545.
    [104]Carvajal J, Chen G, Ogmen H. Fuzzy PID controller:Design, performance evaluation, and stability analysis [J]. Information Sciences,2000,123 (3-4): 249-270.
    [105]黄友锐,曲立国.PID控制器参数整定与实现[M].北京:科学出版社,2010.
    [106]Cheng S L, C. Hwang. Designing PID controllers with a minimum IAE criterion by a differential evolution algorithm [J]. Chemical Engineering Communications,1998,170 (1):83-115.
    [107]Yu L L, Cai Z X, Jiang Z Y, et al. An advanced fuzzy immune PID-type tracking controller of a nonholonomic mobile robot [C].2007 IEEE International Conference on Automation and Logistics, Jinan, China,2007:66-71.
    [108]Nahapetian N, Motlagh M R J, Analoui M. PID gain tuning using genetic algorithms and Fuzzy logic for robot manipulator control [C]. International Conference on Advanced Computer Control, Singapore,2009:346-350.
    [109]Sun Z, Xing R, Zhao C, et al. Fuzzy auto-tuning PID control of multiple joint robot driven by ultrasonic motors [J]. Ultrasonics,2007,46 (4):303-312.
    [110]李艳,王勇,陈正洪.平面二自由度冗余驱动并联机器人控制实验研究[J].武汉理工大学学报(交通科学与工程),2009,33(4):623-626.
    [111]郭崇滨,郝矿荣,丁永生.基于神经内分泌的并联机器人智能控制系统[J].机电工程,2010,29(7):1-4,8.
    [112]Guo C, Hao K, Ding Y, et al. A position-velocity cooperative intelligent controller based on the biological neuroendocrine system [J]. Lecture Notes in Computer Science,2011,6677 (3):112-121.
    [113]K. M R, R. P N. A robust self-tuning scheme for PI and PD type fuzzy controllers [J]. IEEE Transactions on Fuzzy System,1996, (1):23-35.
    [114]Zhang Y, Cong S, Shang W, et al. Modeling, identification and control of a redundant planar 2-DOF parallel manipulator [J]. International Journal of Control, Automation, and Systems,2007,5 (5):559-569.
    [115]M.Mahfouf, Linkens D A, M.F.Abbod. Multi-objective genetic optimisation of GPC and SOFLC tuning parameters using a Fuzzy-based ranking method [J]. IEEE Control Theory and Applications,2000,147 (3):344-354.
    [116]Wai R J, Huang Y C, Yang Z W, et al. Adaptive fuzzy-neural-network velocity sensorless control for robot manipulator position tracking [J]. IET Control Theory & Applications,2010,4 (6):1079-1093.
    [117]Jeong S-K, You S-S. Precise position synchronous control of multi-axis servo system [J]. Mechatronics,2008,18:129-140.
    [118]Rossi C, Savino S. Robot trajectory planning by assigning positions and tangential velocities [J]. Robotics and Computer-Integrated Manufacturing, 2013,29(1):139-156.
    [119]Pathak K, Franch J, Agrawal S K. Velocity and position control of a wheeled inverted pendulum by partial feedback linearization [J]. IEEE Transactions on Robotics,2005,21 (3):505-513.
    [120]Tsai M-S, Nien H-W, Yau H-T. Development of integrated acceleration/deceleration look-ahead interpolation technique for multi-blocks NURBS curves [J]. The International Journal of Advanced Manufacturing Technology,2011,56 (5-8):601-618.
    [121]Farkhatdinov I, Ryu J-H. Hybrid Position-Position and Position-Speed Command Strategy for the Bilateral Teleoperation of a Mobile Robot [C]. International Conference on Control, Automation and Systems, COEX, Seoul, Korea, Oct.17-20,,2007:2442-2447.
    [122]Moreno-Valenzuela J. Velocity field control of robot manipulators by using only position measurements [J]. Journal of the Franklin Institute,2007,344 (8): 1021-1038.
    [123]Fakhry H H, Wilson W J. A modified resolved acceleration controller for position-based visual servoing [J]. Mathematical and Computer Modelling, 1996,24(5-6):1-9.
    [124]Staicu S. Inverse dynamics of the 3-PRR planar parallel robot [J]. Robotics and Autonomous Systems,2009,57 (5):556-563.
    [125]Noshadi A, Mailah M, Zolfagharian A. Intelligent active force control of a 3-RRR parallel manipulator incorporating fuzzy resolved acceleration control [J]. Applied Mathematical Modelling,2012,36 (6):2370-2383.
    [126]Guo C, Hao K, Ding Y. Neuroendocrine based Cooperative Intelligent Control System for Multi-objective Integrated Control of a Parallel Manipulator [J]. Mathematical Problems in Engineering,2012, no.467402:1-18.
    [127]Chuang H-Y, Chien K-H. A real-time NURBS motion interpolator for position control of a slide equilateral triangle parallel manipulator [J]. The International Journal of Advanced Manufacturing Technology,2007,34 (7-8):724-735.
    [128]Luo L, ShigangWang, Mo J, et al. On the modeling and composite control of flexible parallel mechanism [J]. The International Journal of Advanced Manufacturing Technology,2006,29 (7-8):786-793.
    [129]Notash L. Joint sensor fault detection for fault tolerant parallel manipulators [J]. Journal of Robotic Systems,2000,17 (3):149-157.
    [130]Yu D L, Chang T K, Yu D W. Adaptive neural model-based fault tolerant control for multi-variable processes [J]. Engineering Applications of Artificial Intelligence,2005,18:393-411.
    [131]Roberts R G, Yu H G, Maciejewski A A. Fundamental limitations on designing optimally fault-tolerant redundant manipulators [J]. IEEE Transactions on Robotics,2008,24 (5):1224-1237.
    [132]Visinsky M L, Cavallaro J R, Walker I D. A dynamic fault tolerance framework for remote robots [J]. IEEE Transactions on Robotics and Automation,1995,11 (4):477-490.
    [133]Zhao J, Zhang K, Yao X. Study on fault tolerant workspace and fault tolerant planning algorithm based on optimal initial position for two spatial coordinating manipulators [J]. Mechanism and Machine Theory,2006,41 (5):584-595.
    [134]Roberts R G, Maciejewski A A. A local measure of fault tolerance for kinematically redundant manipulators [J]. IEEE Transactions on Robotics and Automation,1996,12 (4):543-552.
    [135]Groom K N, Maciejewski A A, Balakrishnan V. Real-time failuretolerant control of kinematically redundant manipulators [J]. IEEE Transactions on Robotics and Automation,1999,15 (6):1109-1116.
    [136]Notash L. A methodology for actuator failure recovery in parallel manipulators [J]. Mechanism and Machine Theory,2011,46 (4):454-465.
    [137]Tinos R, Terra M H, Bergerman M. A fault tolerance framework for cooperative robotic manipulators [J]. Control Engineering Practice,2007,15 (5):615-525.
    [138]Yi Y, Mclnroy J E, Jafari F. Generating classes of locally orthogonal Gough-Stewart platforms [J]. IEEE Transactions on Robotics,2005,21 (5): 812-820.
    [139]Ting Y, Tosunoglu S, Freeman R. Torque redistribution and time regulation methods for actuator saturation avoidance of fault-tolerant parallel robots [J]. Journal of Robotic Systems,1995,12 (12):807-820.
    [140]English J D, Maciejewski A A. Fault tolerance for kinematically redundant manipulators:Anticipating free-swinging joint failures [J]. IEEE Transactions on Robotics and Automation,1998,14 (4):566-575.
    [141]Lewis C L, Maciejewski A A. Fault tolerant operation of kinematically redundant manipulators for locked joint failures [J]. IEEE Transactions on Robotics and Automation,1997,13 (4):622-629.
    [142]Yi Y, McInroy J E, Chen Y. Fault tolerance of parallel manipulators using task space and kinematic redundancy [J]. IEEE Transactions on Robotics,2006,22 (5):1017-1021.
    [143]Ukidve C S, McInroy J E, Jafari F. Orthogonal Gough-Stewart platforms with optimal fault tolerant manipulability [C]. IEEE International Conference on Robotics and Automation, Orlando, FL, USA, May 15-19,2006: 3801-3806.
    [144]Zhang K, Jiang B, Staroswiecki M. Dynamic output feedback fault tolerant controller design for Takagi-Sugeno fuzzy systems with actuator faults [J]. IEEE Transactions on Fuzzy systems,2010,18 (1):194-201.
    [145]Lin C-M, Chen C-H. Robust fault-tolerant control for a biped robot using a recurrent cerebellar model articulation controller [J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics,2007,37 (1):110-123.
    [146]Song Q, Yin L. Robust adaptive fault accommodation for a robot system using a radial basis function neural network [J]. International Journal of Systems Science,2001,32 (2):195-204.
    [147]Goertz R C. Fundamentals of general-purpose remote manipulators [J]. Journal of Nucleonics,1952,10 (11):36-42.
    [148]Maples J, Becker J. Experiments in force control of robotic manipulators [C]. IEEE International Conference on Robotics and Automation,1986,3: 695-702.
    [149]Mason M T. Compliance and force control for computer controlled manipulators [J]. IEEE Transactions on Systems, Man and Cybernetics,1981,11 (6): 418-432.
    [150]Raibert M H, Craig J J. Hybrid position/force control of manipulators [J]. Journal of Dynamic Systems, Measurement, and Control,1981,103 (2): 126-134.
    [151]Patel R V. A Robust Position and Force Control Strategy for 7-DOF Redundant Manipulators [J]. IEEE/ASME Transactions on Mechatronics,2009,14 (5): 575-589.
    [152]孔民秀,游玮,杜志江,et al.基于速度闭环的自适应力位控制算法[J].哈尔滨工业大学学报,2010,42(3):409-413.
    [153]李二超,李战明,李炜.面向位控机器人的视觉/力觉混合控制[J].机床与液压,2010,38(11):10-11.
    [154]Burn k, Short M, Bicker R. Adaptive and nonlinear fuzzy force control techniques applied to robots operating in uncertain environments [J]. Journal of Robotic Systems,2003,20 (7):391-400.
    [155]Kumar N, Panwar V, Sukavanam N, et al. Neural network based hybrid force/position control for robot manipulators [J]. International Journal of Precision Engineering and Manufacturing,2011,12 (3):419-426.
    [156]殷跃红,朱剑英.智能机器力觉及力控制研究综述[J].航空学报,1999,20(1):1-7.
    [157]Ganesh G, Albu-Schaffer A, Haruno M, et al. Biomimetic motor behavior for simultaneous adaptation of force, impedance and trajectory in interaction tasks [C].2010 IEEE International Conference on Robotics and Automation, London, UK,3-7 May,2010:2705-2711.
    [158]Huang J, Yamada D, Nakamura Y, et al. Cooperative impedance control of a finger-arm robot by regulating finger's manipulability [J]. Journal:of System Design and Dynamics,2009,3 (5):756-767.
    [159]陈铁华.六自由度力反馈双向伺服控制策略研究[D].长春:吉林大学,2010.
    [160]A. L D. Stability and transparency in bilateral teleoperation [J]. IEEE Transactions on Robotics and Automation,1993,9 (5):624-637.
    [161]Sirouspour S. Modeling and control of cooperative teleoperation systems [J]. IEEE Transactions on Robotics,2005,21 (6):1220-1225.
    [162]Nuno E, Ortega R, Barabanov N, et al. A globally stable PD controller for bilateral teleoperators [J]. IEEE Transactions on Robotics,2008,24 (3): 753-758.
    [163]Nuno E, Basanez L, Ortega R. Passivity-based control for bilateral Teleoperation:A tutorial [J]. Automatica,2011,47 (3):485-495.
    [164]Hashtrudi-Zaad K, Salcudean S E. Transparency in Time-Delayed Systems and the Effect of Local Force Feedback for Transparent Teleoperation [J]. IEEE Transactions on Robotics and Automation,2002,18 (1):108-114.
    [165]Hashtrudi-Zaad K, Salcudean S E. Analysis of control architectures for teleoperation systems with impedance/admittance master and slave manipulators [J]. The International Journal of Robotics Research,2001,20 (6):419-445.
    [166]Salcudean S E, Zhu M, Zhu W-H, et al. Transparent Bilateral Teleoperation under Position and Rate Control [J]. The International Journal of Robotics Research,2000,19(12):1185-1202.
    [167]Chopra N, Spong M W, Ortega R, et al. On tracking performance in bilateral teleoperation [J]. IEEE Transactions on Robotics,2006,22 (4):861-866.
    [168]Kitagawa M, Okamura A M, Bethea B T, et al. Analysis of Suture Manipulation Forces for Teleoperation with Force Feedback [J]. Lecture Notes in Computer Science,2002,2488:155-162.
    [169]Farkhatdinov I, Ryu J-H, Poduraev J. A user study of command strategies for mobile robot teleoperation [J]. Intelligent Service Robotics,2009,2 (2):95-104.
    [170]Neal M, Timmis J. Timidity:A useful emotional mechanism for robot control [J]. Informatica (Slovenia),2003,27 (2):197-204.
    [171]周军,丁希仑.基于遗传算法的双臂机器人模糊力/位混合控制[J].机器人,2008,30(4):318-325.
    [172]朱大年.生理学[M].北京:人民卫生出版社,2008.
    [173]Liu B, Ren L H, Ding Y S. A novel intelligent controller based on modulation of neuroendocrine system [J]. Lecture Notes in Computer Science,2005,3498 (3): 119-124.
    [174]Berrichi A, Amodeo L, Yalaoui F, et al. Bi-objective optimization algorithms for joint production and maintenance scheduling:application to the parallel machine problem [J]. Journal of Intelligent Manufacturing,2009,20 (4): 389-400.
    [175]Zeng Y, Huai W. Application of artificial neural network to predict the friction factor of open channel flow [J]. Communications in Nonlinear Science and Numerical Simulation,2009,14 (5):2373-2378.
    [176]Lampinen J, Vehtari A. Bayesian approach for neural networks—review and case studies [J]. Neural Networks,2001,14 (3):257-274.
    [177]Akyol D E, Bayhan G M. A review on evolution of production scheduling with neural networks [J]. Computers & Industrial Engineering,2007,53 (1):95-122.
    [178]Yildirim S, Eskia I. Noise analysis of robot manipulator using neural networks [J]. Robotics and Computer-Integrated Manufacturing,2010,26 (4):282-290.
    [179]Yildirim S. Design of a proposed neural network control system for trajectory controlling of walking robots [J]. Simulation Modelling Practice and Theory, 2008,16 (3):368-378.
    [180]Rakkiyappan R, Balasubramaniam P. On exponential stability results for fuzzy impulsive neural networks [J]. Fuzzy Sets and Systems,2010,161 (13): 1823-1835.
    [181]Jafarian M, Ranjbar A M. Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine [J]. Renewable Energy,2010,35 (9):2008-2014.
    [182]Er M J, Tan T P, Loh S Y. Control of a mobile robot using generalized dynamic fuzzy neural networks [J]. Microprocessors and Microsystems,2004,28 (9): 491-498.
    [183]Santa K, Fatikow S, Felso G. Control of microassembly-robots by using fuzzy-logic and neural networks [J]. Computers in Industry,1999,39 (3): 219-227.
    [184]石辛民,郝整清.模糊控制及其MATLAB仿真[M].北京:清华大学出版社北京交通大学出版社,2008.
    [185]曹双贵,田锦明.基于BP神经网络的Fuzzy-PID'恒温控制器[J].机电工程,2009,26(12):82-84.
    [186]王伦文,张铃.构造型神经网络综述[J].模式识别与人工智能,2008,21(1):49-55.
    [187]玄光男,程润伟.遗传算法与工程优化[M].北京:清华大学出版社,2003.
    [188]雷英杰,张善文.MATLAB遗传算法工具箱及应用[M].西安:西安电子科技大学出版社,2005.
    [189]Renner G, Ekart A. Genetic algorithms in computer aided design [J]. Computer-Aided Design,2003,35 (8):709-726.
    [190]Laabidi K, Bouani F. Genetic algorithms for multi-objective predictive control [C]. First International Symposium on Control, Communications and Signal Processing, Hammamet, Tunisia,2004:149-152.
    [191]N.Keeratlvuttiunirongm, N.Chaiyaratana, V.Varavithya. Multiobjective co-operative co-evolutionary genetic algorithm [J]. Lecture Notes in Computer Science,2002, (2439):288-297.
    [192]Nantawatana W, Nachol C. Closed-loop time-optimal path planning using a multi-objective diversity control oriented genetic algorithm [C].2002 IEEE International Conference on Systems,Man and Cybernetics, Tunisia,2002, 6:346-352.
    [193]Martinez R, Castillo O, Aguilarbr L T. Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms [J]. Information Sciences,2009,179 (13):2158-2174.
    [194]Vo T Q, Kima H S, Lee B R. Propulsive Velocity Optimization of 3-Joint Fish Robot Using Genetic-Hill Climbing Algorithm [J]. Journal of Bionic Engineering,2009,6 (4):415-429.
    [195]吴勇,郝矿荣,丁永生,et al.基于遗传算法的机器人动态视觉检测系统[J].微型电脑应用,2009,25(9):17-20.
    [196]谢克明,郭红波,谢刚,et al.人工免疫算法及其应用[J].计算机工程与应用,2005,20:77-80.
    [197]何珍梅,徐雪松.人工免疫系统研究综述[J].华东交通大学学报,2007,24(4):79-83.
    [198]Dan G, Lall S B. Neuroendocrine Modulation of Immune System [J]. Indian Journal of Pharmacology,1998,30:129-140.
    [199]Musilek P, Lau A, Reformat M, et al. Immune programming [J]. Information Sciences,2006,176:972-1002.
    [200]Harta E, Timmisb J. Application areas of AIS:The past, the present and the future [J]. Applied Soft Computing Journal 2008,8 (1):191-201.
    [201]Xie F, Hou Y, Xu Z, et al. Fuzzy-immune control strategy of a hydro-viscous soft start device of a belt conveyor [J]. Mining Science and Technology (China), 2009,19 (4):544-548.
    [202]Chen P-C. Using immune network in nonlinear system identification for a 3D parallel robot [J]. Information Technology Journal,2009,8 (6):895-902.
    [203]Razali S, Meng Q, Yang S-H. Multi-robot cooperation using immune network with memory [C].2009 IEEE International Conference on Control and Automation, Christchurch, New Zealand,2009:145-150
    [204]Ding Y, Wang Z, Ye H. Optimal control of a fractional-order HIV-immune system with memory [J]. IEEE Transactions on Control Systems Technology, 2012,20 (3):763-769.
    [205]Ding Y, Ren L. Fuzzy Self-Tuning Immune Feedback Controller for Tissue Hyperthermia [C]. The Ninth IEEE International Conference on Fuzzy Systems, San Antonio, USA,2000,1:534-538.
    [206]Isbiguro A, Kuboshiki S, Ichikawa S. Gait control of Hexapod walking robots using mutual-coupled immune networks [J]. Advanced Robotics,2006,10 (2): 179-195.
    [207]Timmis J, Neal M. A resource limited artificial immune system for data analysis [J]. Knowledge-Based Systems 2001,14 (3-4):121-130.
    [208]Stear E B. Application of control theory to endocrine regulation and control [J]. Annals of Biomedical Engineering,1975,3 (4):439-455.
    [209]谢启文.现代神经内分泌学[M].上海:上海医科大学出版社,1999.
    [210]Keenan D M, Licinio J, Veldhuis J D. A feedback-controlled ensemble model of the stress-responsive hypothalamo-pituitary-adrenal axis [J]. Proceedings of the National Academy of Sciences,2001,98 (7):4028-4033.
    [211]Farhy L S. Modeling of Oscillations of Endocrine Networks with Feedback [J]. Methods in Enzymology,2004,384:54-81.
    [212]黄国锐,徐敏,张荣,et al.基于内分泌调节机制的机器人行为规划算法及其应用研究[J].小型微型计算机系统,2004,2(25):262-265.
    [213]王继鹏.内分泌调节机制的机器人足球比赛策略开发[J].中国水运(理论版),2006,5(4):135-136.
    [214]Vargas P, Moioli R, Castro L N d, et al. Artificial homeostatic system:A novel approach [J]. Lecture Notes in Computer Science,2005,3630:754-764.
    [215]Cordova F M, Canete L R. The challenge of designing nervous and endocrine systems in robots [J]. International Journal of Computers, Communications & Control,2006,1 (2):33-40.
    [216]Tang D, Gu W, Wang L, et al. A neuroendocrine-inspired approach for adaptive manufacturing system control [J]. International Journal of Production Research, 2011,49(5):1255-1268.
    [217]Farhy L S, Straume M, Johnson M L, et al. A construct of interactive control of the GH axis in the male [J]. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology,2001,281 (1):R38-R51.
    [218]郭崇滨,郝矿荣,丁永生.微创手术机器人的力/位解耦协同智能控制[J].机器人,2013,35(1):73-80.
    [219]Mukherjee A, Zhang J. A reliable multi-objective control strategy for batch processes based on bootstrap aggregated neural network models [J]. Journal of Process Control,2007,18 (7-8):720-734.
    [220]陈瑞祥,任立红,丁永生.一种6自由度冗余驱动并联机器人的神经内分泌智能控制[J].制造业自动化,2008,30(5):46-50.
    [221]黄桓,丁永生,郝矿荣,et a1.一种并联机器人的神经内分泌智能控制器[J].机械设计与研究,2008,24(6):35-38.
    [222]刘宝,丁永生,王君红.一种基于内分泌超短反馈机制的智能控制器[J].计算机仿真,2008,25(1):188-191.
    [223]黄桓.六自由度并联机器人内分泌控制策略的研究[D].上海:东华大学,2009.
    [224]Parsa S S, Daniali H M, Ghaderi R. Optimization of parallel manipulator trajectory for obstacle and singularity avoidances based on neural network [J]. The International Journal of Advanced Manufacturing Technology,2010,51 (5-8):811-816.
    [225]Liu B, Ding Y, Wang J. Intelligent Network Control System Inspired from Neuroendocrine-Immune System [C]. Sixth International Conference on Fuzzy Systems and Knowledge Discovery, Tianjin, China,14-16 Aug, 2009,7:136-140.
    [226]Savino W, Dardenne M. Neuroendocrine control of thymus physiology [J]. Endocrine Reviews,2000,21 (4):412-443.
    [227]Cortes C E, Saezb D, Milla F, et al. Hybrid predictive control for real-time optimization of public transport systems'operations based on evolutionary multi-objective optimization [J]. Transportation Research Part C:Emerging Technologies,2010,18 (5):757-769.
    [228]Prummel M F, Brokken L J S, Wiersinga W M. Ultra short-loop feedback control of thyrotropin secretion [J]. Thyroid,2004,14 (10):825-829.
    [229]Dong H, Wang Z, Lam J, et al. Fuzzy-model-based robust fault detection with stochastic mixed time delays and successive packet dropouts [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,2012,42 (2):365-376.
    [230]Precupa R-E, Hellendoornb H. A survey on industrial applications of fuzzy control [J]. Computers in Industry,2011,62 (3):213-226.
    [231]Takosoglu J E, Dindorf R F, Laski P A. Rapid prototyping of fuzzy controller pneumatic servo-system [J]. The International Journal of Advanced Manufacturing Technology,2009,40 (3-4):349-361.
    [232]Dhillon B S, Fashandi A R M. Safety and reliability assessment techniques in robotics [J]. Robotica,1997,15 (6):701-708.
    [233]Ukidve C S, McInroy J E, Jafari F. Using redundancy to optimize manipulability of Stewart platforms [J]. IEEE/ASME Transactions on Mechatronics,2008,13 (4):475-479.
    [234]Korayem M H, Nikoobin A. Maximum payload path planning for redundant manipulator using indirect solution of optimal control problem [J]. The International Journal of Advanced Manufacturing Technology,2009,44 (7-8): 725-736.
    [235]Mclnroy J E, O'Brien J F, Neat G W. Precise, fault-tolerant pointing using a Stewart platform [J]. IEEE/ASME Transactions on Mechatronics,1999,4 (1): 91-95.
    [236]Zhou N, Hao K, Guo C, et al. Visual servo control system of 2-DOF parallel robot [J]. Advances in Intelligent and Soft Computing,2012,114:425-433.
    [237]Anderson R J, Spong M W. Bilateral control of teleoperators with time delay [J]. IEEE Transactions on Automatic Control,1989,34 (5):494-501.
    [238]Ackermann U. Regulation of arterial blood pressure [J]. Surgery,2004,22 (5): 120a-120f.
    [239]Campbell I. Body temperature and its regulation [J]. Anaesthesia Intensive Care Medicine,2008,9 (6):259-263.