基于确定学习的机器人任务空间自适应神经网络控制

吴玉香; 王聪

doi:10.3724/SP.J.1004.2013.00806

基于确定学习的机器人任务空间自适应神经网络控制

doi: 10.3724/SP.J.1004.2013.00806

吴玉香^,,
王聪

1.
华南理工大学自动化科学与工程学院广州 510640

基金项目:

国家自然科学基金(60934001, 61225014, 61075082)资助

详细信息

通讯作者:
吴玉香

计量
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- 被引次数: 0
出版历程
- 收稿日期: 2011-12-16
- 修回日期: 2012-10-25
- 刊出日期: 2013-06-20

Deterministic Learning Based Adaptive Network Control of Robot in Task Space

WU Yu-Xiang^,,
WANG Cong

1.
College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640

Funds:

Supported by National Natural Science Foundation of China(60934001, 61225014, 61075082)

摘要

摘要: 针对产生回归轨迹的连续非线性动态系统, 确定学习可实现未知闭环系统动态的局部准确逼近. 基于确定学习理论, 本文使用径向基函数(Radial basis function, RBF)神经网络为机器人任务空间跟踪控制设计了一种新的自适应神经网络控制算法, 不仅实现了闭环系统所有信号的最终一致有界, 而且在稳定的控制过程中, 沿着回归跟踪轨迹实现了部分神经网络权值收敛到最优值以及未知闭环系统动态的局部准确逼近. 学过的知识以时不变且空间分布的方式表达、以常值神经网络权值的方式存储, 可以用来改进系统的控制性能, 也可以应用到后续相同或相似的控制任务中, 节约时间和能量. 最后, 用仿真说明了所设计控制算法的正确性和有效性.
- 确定学习 /
- 径向基函数神经网络 /
- 自适应神经网络控制 /
- 机器人
Abstract: Deterministic learning can achieve locally-accurate approximation of the unknown closed-loop system dynamics while attempting to control a class of nonlinear systems producing recurrent trajectories. Based on deterministic learning, an adaptive neural control algorithm is proposed for unknown robots in task space using radial basis function (RBF) networks. The designed adaptive neural controller can not only guarantee all signals in the closed-loop system uniformly ultimately bounded, but also achieve convergence of partial network weights to their optimal values. It can also learn the unknown closed-loop system dynamics in a stable control process along recurrent tracking orbits. The learned knowledge stored as constant network weights can be reused in a same or similar control task to improve the control performance and to save time and energy. Simulation results demonstrate the effectiveness of the proposed approach.
- Deterministic learning /
- Radial basis function (RBF) networks /
- adaptive neural network control /
- robot

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参考文献(29)

[1]	Lewis F L, Liu K, Yesildirek A. Neural net robot controller with guaranteed tracking performance. IEEE Transactions on Neural Networks, 1995, 6(3): 703-715
[2]	Sun F C, Sun Z Q, Woo P Y. Neural network-based adaptive controller design of robotic manipulators with an observer. IEEE Transactions on Neural Networks, 2001, 12(1): 54-67
[3]	Peng Ji-Gen, Ni Yuan-Hua, Qiao Hong. Neural network control of flexible-joint robot manipulators. Acta Automatica Sinica, 2007, 33(2): 175-180 (彭济根, 倪元华, 乔红. 柔性关节机操手的神经网络控制. 自动化学报, 2007, 33(2): 175-180)
[4]	Yu Jian-Cheng, Li Qiang, Zhang Ai-Qun, Wang Xiao-Hui. Neural network adaptive control for underwater vehicles. Control Theory & Applications, 2008, 25(1): 9-13 (俞建成, 李强, 张艾群, 王晓辉. 水下机器人的神经网络自适应控制. 控制理论与应用, 2008, 25(1): 9-13)
[5]	Ge S S, Hang C C, Woon L C. Adaptive neural network control of robot manipulators in task space. IEEE Transactions on Industrial Electronics, 1997, 44(6): 746-752
[6]	Cheng L, Hou Z G, Tan M. Adaptive neural network tracking control of manipulators using quaternion feedback. In: Proceedings of the 2008 IEEE International Conference on Robotics and Automation. Pasadena, CA: IEEE, 2008. 3371-3376
[7]	Liu Ying-Zhuo, Wang Yue-Chao, Xi Ning. Application of Lyapunov-like methodology for a humanoid robot tracking in task space. Control Theory & Applications, 2004, 21(3): 351-356(刘英卓, 王越超, 席宁. 类 Lyapunov理论在类人形机器人任务空间内跟踪的应用. 控制理论与应用, 2004, 21(3): 351-356)
[8]	Feng Bao-Min, Ma Guang-Cheng, Wen Qi-Yong, Wang Chang-Hong. Design of robust intelligent controller for space robot in task space. Journal of Astronautics, 2007, 28(4): 914-919 (丰保民, 马广程, 温奇咏, 王常虹. 任务空间内空间机器人鲁棒智能控制器设计. 宇航学报, 2007, 28(4): 914-919)
[9]	Wang C, Hill D J. Learning from neural control. IEEE Transactions on Neural Networks, 2006, 17(1): 130-146
[10]	Wang C, Hill D J. Deterministic Learning Theory for Identification, Recognition and Control. Boca Raton: CRC Press, 2009
[11]	Liu T F, Wang C, Hill D J. Learning from neural control of nonlinear systems in normal form. Systems & Control Letters, 2009, 58(9): 633-638
[12]	Wang C, Wang M, Liu T F, Hill D J. Learning from ISS-modular adaptive NN control of nonlinear strict-feedback systems. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(10): 1539-1550
[13]	Wang C, Chen T R. Rapid detection of small oscillation faults via deterministic learning. IEEE Transactions on Neural Networks, 2011, 22(8): 1284-1296
[14]	Yuan C Z, Wang C. Persistency of excitation and performance of deterministic learning. Systems & Control Letters, 2011, 60(12): 952-959
[15]	Yuan C Z, Wang C. Performance of deterministic learning in noisy environments. Neurocomputing, 2012, 78(1): 72-82
[16]	Wang Cong, Chen Tian-Rui, Liu Teng-Fei. Deterministic learning and data-based modeling and control. Acta Automatica Sinica, 2009, 35(6): 693-706 (王聪, 陈填锐, 刘腾飞. 确定学习与基于数据的建模及控制. 自动化学报, 2009, 35(6): 693-706)
[17]	Shilnikov L P. Methods of Qualitative Theory in Nonlinear Dynamics, Part II. Singapore: World Scientific, 2001
[18]	Wu Y X, He Q Z, Wang C. Adaptive neural learning control of rigid-link electrically-driven robot manipulators. In: Proceedings of the 30th Chinese Control Conference. Yantai, China: IEEE, 2011. 6616-6622
[19]	Zeng W, Wang C. Human gait recognition via deterministic learning. Neural Networks, 2012, 35: 92-102
[20]	Wang C, Gu W J, Xue Z G. Deterministic learning from robust adaptive NN control of robot manipulators. In: Proceedings of the 8th World Congress on Intelligent Control and Automation. Ji'nan, China: IEEE, 2010. 526-531
[21]	Park J, Sandberg I W. Universal approximation using radial-basis-function networks. Neural Computation, 1991, 3(2): 246-257
[22]	Gorinevsky D. On the persistency of excitation in radial basis function network identification of nonlinear systems. IEEE Transactions on Neural Networks, 1995, 6(5): 1237-1244
[23]	Kurdila A J, Narcowich F J, Ward J D. Persistency of excitation in identification using radial basis function approximants. SIAM Journal on Control and Optimization, 1995, 33(2): 625-641
[24]	Farrell J A. Stability and approximator convergence in nonparametric nonlinear adaptive control. IEEE Transactions on Neural Networks, 1998, 9(5): 1008-1020
[25]	Loría A, Panteley E. Uniform exponential stability of linear time-varying systems: revisited. Systems & Control Letters, 2002, 47(1): 13-24
[26]	Lewis F L, Abdallah C T, Dawson D M. Control of Robot Manipulators. New York: Macmillan, 1993
[27]	Slotine J J, Li W P. Applied Nonlinear Control. Englewood Cliffs: Prentice-Hall, 1991
[28]	Khalil H K. Nonlinear Systems (Third edition). Upper Saddle River: Prentice-Hall, 2002
[29]	Huang S J, Lee J S. A stable self-organizing fuzzy controller for robotic motion control. IEEE Transactions on Industrial Electronics, 2000, 47(2): 421-428