基于扰动观测器的机器人自适应神经网络跟踪控制研究
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摘要
为解决机器人动力学模型未知问题并提升系统鲁棒性,本文基于扰动观测器,考虑动力学模型未知的情况,设计了一种自适应神经网络(Neural network, NN)跟踪控制器.首先分析了机器人运动学和动力学模型,针对模型已知的情况,提出了刚体机械臂通用模型跟踪控制策略;在考虑动力学模型未知的情况下,利用径向基函数(Radial basis function, RBF)神经网络设计基于全状态反馈的自适应神经网络跟踪控制器,并通过设计扰动观测器补偿系统中的未知扰动.利用李雅普诺夫理论证明所提出的控制策略可以使闭环系统误差信号半全局一致有界(Semi-globally uniformly bounded, SGUB),并通过选择合适的增益参数可以将跟踪误差收敛到零域.仿真结果证明所提出算法的有效性并且所提出的控制器在Baxter机器人平台上得到了实验验证.
For solving uncertainties of robotic dynamics and improving system robustness, an adaptive neural network(NN) tracking control is proposed considering uncertainties of robotic dynamics. Firstly, the kinematic model and dynamic model of robots are addressed. When the dynamics of the robots are known, a model-based tracking control strategy is proposed. Then, considering that the robotic dynamics are unknown, an adaptive radial basis function(RBF) neural network tracking control is proposed based on full state feedback to solve uncertainties. Disturbance observer is designed to counteract to unknown disturbance. By utilizing the Lyapunov direct method and the back-stepping method, all error signals are shown to be semi-globally uniformly bounded(SGUB). By choosing proper parameters, the tracking error can converge to a small neighborhood of zero. Simulation results and experiment results on Baxter robot are carried out to show the effectiveness of proposed method.
For solving uncertainties of robotic dynamics and improving system robustness, an adaptive neural network(NN) tracking control is proposed considering uncertainties of robotic dynamics. Firstly, the kinematic model and dynamic model of robots are addressed. When the dynamics of the robots are known, a model-based tracking control strategy is proposed. Then, considering that the robotic dynamics are unknown, an adaptive radial basis function(RBF) neural network tracking control is proposed based on full state feedback to solve uncertainties. Disturbance observer is designed to counteract to unknown disturbance. By utilizing the Lyapunov direct method and the back-stepping method, all error signals are shown to be semi-globally uniformly bounded(SGUB). By choosing proper parameters, the tracking error can converge to a small neighborhood of zero. Simulation results and experiment results on Baxter robot are carried out to show the effectiveness of proposed method.
引文
1 He W, Li Z J, Chen C L P. A survey of human-centered intelligent robots:issues and challenges. IEEE/CAA Journal of Automatica Sinica, 2017, 4(4):602-609
2 Li Y N, Ge S S. Human-robot collaboration based on motion intention estimation. IEEE/ASME Transactions on Mechatronics, 2014, 19(3):1007-1014
3 Li H Y, Chen Z R, Wu L G, Lam H-K. Event-triggered control for nonlinear systems under unreliable communication links. IEEE Transactions on Fuzzy Systems, 2017, 25(4):813-824
4 Hou Zeng-Guang, Zhao Xin-Gang, Cheng Long, Wang QiNing, Wang Wei-Qun. Recent advances in rehabilitation robots and intelligent assistance systems. Acta Automatica Sinica, 2016, 42(12):1765-1779(侯增广,赵新刚,程龙,王启宁,王卫群.康复机器人与智能辅助系统的研究进展.自动化学报, 2016, 42(12):1765-1779)
5 He Wei, Ding Shi-Qiang, Sun Chang-Yin. Research progress on modeling and control of flapping-wing air vehicles. Acta Automatica Sinica, 2017, 43(5):685-696(贺威,丁施强,孙长银.扑翼飞行器的建模与控制研究进展.自动化学报, 2017, 43(5):685-696)
6 Wei Qing-Lai, Zhang Hua-Guang, Liu De-Rong, Zhao Yan.An optimal control scheme for a class of discrete-time nonlinear systems with time delays using adaptive dynamic programming. Acta Automatica Sinica, 2010, 36(1):121-129(魏庆来,张化光,刘德荣,赵琰.基于自适应动态规划的一类带有时滞的离散时间非线性系统的最优控制策略.自动化学报, 2010,36(1):121-129)
7 Xu B, Shi Z K, Yang C G, Sun F C. Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form. IEEE Transactions on Cybernetics, 2014, 44(12):2626-2634
8 Tie Lin, Cai Kai-Yuan, Lin-Yan. A survey on the controllability of bilinear systems. Acta Automatica Sinica, 2011,37(9):1040-1049(铁林,蔡开元,林岩.双线性系统可控性综述.自动化学报, 2011,37 (9):1040-1049)
9 Shen Fei, Cao Zhi-Qiang, Xu De, Zhou Chao. A dynamic model of robotic dolphin based on Kane method and its speed optimization method. Acta Automatica Sinica, 2012,38(8):1247-1256(沈飞,曹志强,徐德,周超.基于Kane方法的机器海豚动力学建模及速度优化方法.自动化学报, 2012, 38(8):1247-1256)
10 Modares H, Ranatunga I, Lewis F L, Popa D O. Optimized assistive human-robot interaction using reinforcement learning. IEEE Transactions on Cybernetics, 2016, 46(3):655-667
11 Sun Fu-Chun, Sun Zeng-Qi, Zhang Bo. Observer-based adaptive control for robot trajectory tracking using neural networks. Acta Automatica Sinica, 1999, 25(3):295-302(孙富春,孙增圻,张钹.基于观测器的机械手神经网络自适应控制.自动化学报, 1999, 25(3):295-302)
12 Li Y N, Ge S S. Impedance learning for robots interacting with unknown environments. IEEE Transactions on Control Systems Technology, 2014, 22(4):1422-1432
13 He W,Dong Y T. Adaptive fuzzy neural network control for a constrained robot using impedance learning. IEEE Transactions on Neural Networks and Learning Systems, 2017,29(4):1174-1186
14 De Santis A, Siciliano B, De Luca A, Bicchi A. An atlas of physical human-robot interaction. Mechanism and Machine Theory, 2008, 43(3):253-270
15 Liu Y J, Tong S C, Li D J, Gao Y. Fuzzy adaptive control with state observer for a class of nonlinear discrete-time systems with input constraint. IEEE Transactions on Fuzzy Systems, 2016, 24(5):1147-1158
16 Wang F Y, Zhang J, Wei Q L, Zheng X H, Li L. PDP:parallel dynamic programming. IEEE/CAA Journal of Automatica Sinica, 2017, 4(1):1-5
17 Tong S C, Wang T, Li Y M, Chen B. A combined backstepping and stochastic small-gain approach to robust adaptive fuzzy output feedback control. IEEE Transactions on Fuzzy Systems, 2013, 21(2):314-327
18 Liu Y J, Tong S C. Adaptive NN tracking control of uncertain nonlinear discrete-time systems with nonaffine deadzone input. IEEE Transactions on Cybernetics, 2015, 45(3):497-505
19 Chen C L P, Wen G X, Liu Y J, Wang F Y. Adaptive consensus control for a class of nonlinear multiagent time-delay systems using neural networks. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(6):1217-1226
20 Han Hong-Gui, Qiao Jun-Fei, Bo Ying-Chun. On structure design for RBF neural network based on information strength. Acta Automatica Sinica, 2012, 38(7):1083-1090(韩红桂,乔俊飞,薄迎春.基于信息强度的RBF神经网络结构设计研究.自动化学报, 2012, 38(7):1083-1090)
21 Ding S B, Wang Z S, Zhang H G. Event-triggered stabilization of neural networks with time-varying switching gains and input saturation. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(10):5045-5056
22 Narendra K S, Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1990, 1(1):4-27
23 Yang C G, Jiang Y M, Li Z J, He W, Su C Y. Neural control of bimanual robots with guaranteed global stability and motion precision. IEEE Transactions on Industrial Informatics,2017, 13(3):1162-1171
24 He W, Dong Y T, Sun C Y. Adaptive neural impedance control of a robotic manipulator with input saturation. IEEE Transactions on Systems, Man, and Cybernetics:Systems,2016, 46(3):334-344
25 Li Z J, Ge S S, Ming A G. Adaptive robust motion/force control of holonomic-constrained nonholonomic mobile manipulators. IEEE Transactions on Systems, Man and Cybernetics, Part B(Cybernetics), 2007, 37(3):607-616
26 Li Z J, Su C Y, Wang L Y, Chen Z T, Chai T Y. Nonlinear disturbance observer-based control design for a robotic exoskeleton incorporating fuzzy approximation. IEEE Transactions on Industrial Electronics, 2015, 62(9):5763-5775
27 Chen M. Robust tracking control for self-balancing mobile robots using disturbance observer. IEEE/CAA Journal of Automatica Sinica, 2017, 4(3):458-465
28 Fu Z J, Xie W F, Rakheja S, Na J. Observer-based adaptive optimal control for unknown singularly perturbed nonlinear systems with input constraints. IEEE/CAA Journal of Automatica Sinica, 2017, 4(1):48-57
29 Li H Y, Shi P, Yao D Y, Wu L G. Observer-based adaptive sliding mode control for nonlinear markovian jump systems,Automatica, 2016, 64:133-142
30 Xu B. Disturbance observer-based dynamic surface control of transport aircraft with continuous heavy cargo airdrop.IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2017, 47(1):161-170
31 Chen W H, Ballance D J, Gawthrop P J, O Reilly J. A nonlinear disturbance observer for robotic manipulators.IEEE Transactions on Industrial Electronics, 2000, 47(4):932-938
32 Yang J, Li S H, Yu X H. Sliding-mode control for systems with mismatched uncertainties via a disturbance observer.IEEE Transactions on Industrial Electronics, 2013, 60(1):160-169
33 Chen M, Ge S S. Adaptive neural output feedback control of uncertain nonlinear systems with unknown hysteresis using disturbance observer. IEEE Transactions on Industrial Electronics, 2015, 62(12):7706-7716
34 Li Y N, Yang C G, Ge S S, Lee T H. Adaptive output feedback NN control of a class of discrete-time MIMO nonlinear systems with unknown control directions. IEEE Transactions on Systems, Man, and Cybernetics, Part B(Cybernetics), 2011, 41(2):507-517
35 Ge S S, Wang C. Adaptive neural control of uncertain MIMO nonlinear systems. IEEE Transactions on Neural Networks, 2004, 15(3):674-692
36 Ju Z F, Yang C G, Ma H B. Kinematics modeling and experimental verification of baxter robot. In:Proceedings of the33 rd Chinese Control Conference(CCC). Nanjing, China:IEEE, 2014. 8518-8523
37 Yang C G, Ganesh G, Haddadin S, Parusel S, Albu-Schaeffer A, Burdet E. Human-like adaptation of force and impedance in stable and unstable interactions. IEEE Transactions on Robotics, 2011, 27(5):918-930
38 Yang C G, Wang X Y, Cheng L, Ma H B. Neural-learningbased telerobot control with guaranteed performance. IEEE Transactions on Cybernetics, 2017, 47(10):3148-3159
2 Li Y N, Ge S S. Human-robot collaboration based on motion intention estimation. IEEE/ASME Transactions on Mechatronics, 2014, 19(3):1007-1014
3 Li H Y, Chen Z R, Wu L G, Lam H-K. Event-triggered control for nonlinear systems under unreliable communication links. IEEE Transactions on Fuzzy Systems, 2017, 25(4):813-824
4 Hou Zeng-Guang, Zhao Xin-Gang, Cheng Long, Wang QiNing, Wang Wei-Qun. Recent advances in rehabilitation robots and intelligent assistance systems. Acta Automatica Sinica, 2016, 42(12):1765-1779(侯增广,赵新刚,程龙,王启宁,王卫群.康复机器人与智能辅助系统的研究进展.自动化学报, 2016, 42(12):1765-1779)
5 He Wei, Ding Shi-Qiang, Sun Chang-Yin. Research progress on modeling and control of flapping-wing air vehicles. Acta Automatica Sinica, 2017, 43(5):685-696(贺威,丁施强,孙长银.扑翼飞行器的建模与控制研究进展.自动化学报, 2017, 43(5):685-696)
6 Wei Qing-Lai, Zhang Hua-Guang, Liu De-Rong, Zhao Yan.An optimal control scheme for a class of discrete-time nonlinear systems with time delays using adaptive dynamic programming. Acta Automatica Sinica, 2010, 36(1):121-129(魏庆来,张化光,刘德荣,赵琰.基于自适应动态规划的一类带有时滞的离散时间非线性系统的最优控制策略.自动化学报, 2010,36(1):121-129)
7 Xu B, Shi Z K, Yang C G, Sun F C. Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form. IEEE Transactions on Cybernetics, 2014, 44(12):2626-2634
8 Tie Lin, Cai Kai-Yuan, Lin-Yan. A survey on the controllability of bilinear systems. Acta Automatica Sinica, 2011,37(9):1040-1049(铁林,蔡开元,林岩.双线性系统可控性综述.自动化学报, 2011,37 (9):1040-1049)
9 Shen Fei, Cao Zhi-Qiang, Xu De, Zhou Chao. A dynamic model of robotic dolphin based on Kane method and its speed optimization method. Acta Automatica Sinica, 2012,38(8):1247-1256(沈飞,曹志强,徐德,周超.基于Kane方法的机器海豚动力学建模及速度优化方法.自动化学报, 2012, 38(8):1247-1256)
10 Modares H, Ranatunga I, Lewis F L, Popa D O. Optimized assistive human-robot interaction using reinforcement learning. IEEE Transactions on Cybernetics, 2016, 46(3):655-667
11 Sun Fu-Chun, Sun Zeng-Qi, Zhang Bo. Observer-based adaptive control for robot trajectory tracking using neural networks. Acta Automatica Sinica, 1999, 25(3):295-302(孙富春,孙增圻,张钹.基于观测器的机械手神经网络自适应控制.自动化学报, 1999, 25(3):295-302)
12 Li Y N, Ge S S. Impedance learning for robots interacting with unknown environments. IEEE Transactions on Control Systems Technology, 2014, 22(4):1422-1432
13 He W,Dong Y T. Adaptive fuzzy neural network control for a constrained robot using impedance learning. IEEE Transactions on Neural Networks and Learning Systems, 2017,29(4):1174-1186
14 De Santis A, Siciliano B, De Luca A, Bicchi A. An atlas of physical human-robot interaction. Mechanism and Machine Theory, 2008, 43(3):253-270
15 Liu Y J, Tong S C, Li D J, Gao Y. Fuzzy adaptive control with state observer for a class of nonlinear discrete-time systems with input constraint. IEEE Transactions on Fuzzy Systems, 2016, 24(5):1147-1158
16 Wang F Y, Zhang J, Wei Q L, Zheng X H, Li L. PDP:parallel dynamic programming. IEEE/CAA Journal of Automatica Sinica, 2017, 4(1):1-5
17 Tong S C, Wang T, Li Y M, Chen B. A combined backstepping and stochastic small-gain approach to robust adaptive fuzzy output feedback control. IEEE Transactions on Fuzzy Systems, 2013, 21(2):314-327
18 Liu Y J, Tong S C. Adaptive NN tracking control of uncertain nonlinear discrete-time systems with nonaffine deadzone input. IEEE Transactions on Cybernetics, 2015, 45(3):497-505
19 Chen C L P, Wen G X, Liu Y J, Wang F Y. Adaptive consensus control for a class of nonlinear multiagent time-delay systems using neural networks. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(6):1217-1226
20 Han Hong-Gui, Qiao Jun-Fei, Bo Ying-Chun. On structure design for RBF neural network based on information strength. Acta Automatica Sinica, 2012, 38(7):1083-1090(韩红桂,乔俊飞,薄迎春.基于信息强度的RBF神经网络结构设计研究.自动化学报, 2012, 38(7):1083-1090)
21 Ding S B, Wang Z S, Zhang H G. Event-triggered stabilization of neural networks with time-varying switching gains and input saturation. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(10):5045-5056
22 Narendra K S, Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1990, 1(1):4-27
23 Yang C G, Jiang Y M, Li Z J, He W, Su C Y. Neural control of bimanual robots with guaranteed global stability and motion precision. IEEE Transactions on Industrial Informatics,2017, 13(3):1162-1171
24 He W, Dong Y T, Sun C Y. Adaptive neural impedance control of a robotic manipulator with input saturation. IEEE Transactions on Systems, Man, and Cybernetics:Systems,2016, 46(3):334-344
25 Li Z J, Ge S S, Ming A G. Adaptive robust motion/force control of holonomic-constrained nonholonomic mobile manipulators. IEEE Transactions on Systems, Man and Cybernetics, Part B(Cybernetics), 2007, 37(3):607-616
26 Li Z J, Su C Y, Wang L Y, Chen Z T, Chai T Y. Nonlinear disturbance observer-based control design for a robotic exoskeleton incorporating fuzzy approximation. IEEE Transactions on Industrial Electronics, 2015, 62(9):5763-5775
27 Chen M. Robust tracking control for self-balancing mobile robots using disturbance observer. IEEE/CAA Journal of Automatica Sinica, 2017, 4(3):458-465
28 Fu Z J, Xie W F, Rakheja S, Na J. Observer-based adaptive optimal control for unknown singularly perturbed nonlinear systems with input constraints. IEEE/CAA Journal of Automatica Sinica, 2017, 4(1):48-57
29 Li H Y, Shi P, Yao D Y, Wu L G. Observer-based adaptive sliding mode control for nonlinear markovian jump systems,Automatica, 2016, 64:133-142
30 Xu B. Disturbance observer-based dynamic surface control of transport aircraft with continuous heavy cargo airdrop.IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2017, 47(1):161-170
31 Chen W H, Ballance D J, Gawthrop P J, O Reilly J. A nonlinear disturbance observer for robotic manipulators.IEEE Transactions on Industrial Electronics, 2000, 47(4):932-938
32 Yang J, Li S H, Yu X H. Sliding-mode control for systems with mismatched uncertainties via a disturbance observer.IEEE Transactions on Industrial Electronics, 2013, 60(1):160-169
33 Chen M, Ge S S. Adaptive neural output feedback control of uncertain nonlinear systems with unknown hysteresis using disturbance observer. IEEE Transactions on Industrial Electronics, 2015, 62(12):7706-7716
34 Li Y N, Yang C G, Ge S S, Lee T H. Adaptive output feedback NN control of a class of discrete-time MIMO nonlinear systems with unknown control directions. IEEE Transactions on Systems, Man, and Cybernetics, Part B(Cybernetics), 2011, 41(2):507-517
35 Ge S S, Wang C. Adaptive neural control of uncertain MIMO nonlinear systems. IEEE Transactions on Neural Networks, 2004, 15(3):674-692
36 Ju Z F, Yang C G, Ma H B. Kinematics modeling and experimental verification of baxter robot. In:Proceedings of the33 rd Chinese Control Conference(CCC). Nanjing, China:IEEE, 2014. 8518-8523
37 Yang C G, Ganesh G, Haddadin S, Parusel S, Albu-Schaeffer A, Burdet E. Human-like adaptation of force and impedance in stable and unstable interactions. IEEE Transactions on Robotics, 2011, 27(5):918-930
38 Yang C G, Wang X Y, Cheng L, Ma H B. Neural-learningbased telerobot control with guaranteed performance. IEEE Transactions on Cybernetics, 2017, 47(10):3148-3159