3RPS/UPS并联机器人神经网络观测器反演控制
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摘要
为了增强4D互动立体游戏仿真模拟平台的刚度和运动性能,将带冗余结构的3RPS/UPS并联机器人应用其中。首先对其结构进行介绍及逆运动学分析,然后针对传统PID控制在控制精度方面的不足,提出了一种基于神经网络观测器的反演控制方法。最后利用MATLAB对其进行建模以及系统仿真实验,并与传统PID控制以及一般的RBF神经网络自适应控制进行对比。由仿真结果可以看出,根据RBF神经网络观测器估计系统状态值,并应用反演控制理论设计控制器,能实现很好的状态观测,从而实现无需速度信号的位置跟踪。该方法也能够在一定程度上提高精度,且其整体控制效果优于传统PID控制器,相比于一般的RBF神经网络自适应控制也有了一定的改进。
In order to enhance the stiffness and kinematic performance of 4D interactive stereo game simulation platform,the 3RPS/UPS parallel robot with redundant structure is applied. Firstly, this paper introduces the structure of 3RPS/UPS parallel mechanism and analyzes the inverse kinematics. Then, in view of the shortcomings of traditional PID control in the control precision, this paper proposes a back-stepping control of the state observer based on the RBF neural network.Finally, MATLAB is used to model and simulate the system, and the back-stepping control is compared with the traditional PID control and general neural network adaptive control. The simulation results show that estimating the system state according to the RBF neural network observer and designing the controller with the back-stepping control theory can achieve good state observation as well as the location tracking without velocity signal. This control strategy can improve the accuracy to a certain extent, and its overall control effect is better than the traditional PID controller. Compared with the general RBF neural network adaptive control, the method also has some improvement.
In order to enhance the stiffness and kinematic performance of 4D interactive stereo game simulation platform,the 3RPS/UPS parallel robot with redundant structure is applied. Firstly, this paper introduces the structure of 3RPS/UPS parallel mechanism and analyzes the inverse kinematics. Then, in view of the shortcomings of traditional PID control in the control precision, this paper proposes a back-stepping control of the state observer based on the RBF neural network.Finally, MATLAB is used to model and simulate the system, and the back-stepping control is compared with the traditional PID control and general neural network adaptive control. The simulation results show that estimating the system state according to the RBF neural network observer and designing the controller with the back-stepping control theory can achieve good state observation as well as the location tracking without velocity signal. This control strategy can improve the accuracy to a certain extent, and its overall control effect is better than the traditional PID controller. Compared with the general RBF neural network adaptive control, the method also has some improvement.
引文
[1]赵星宇.三自由度伺服驱动运动模拟平台的研究[D].沈阳:沈阳工业大学,2012.
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[15]刘青,倪骁骅,郭祥东.基于神经网络状态观测器的反演控制在双关节机械手控制中的应用[J].机床与液压,2015(3):24-28.
[16]刘永,张立毅.BP和RBF神经网络的实现及其性能比较[J].电子测量技术,2007(4):77-80.
[17]李晚龙.六自由度Stewart平台分散智能控制研究[D].哈尔滨:哈尔滨工程大学,2010.
[18]Moody J,Darken C J.Fast learning in networks of locallytuned processing units[J].Neural Computation,1989,1(2):281-294.
[2]梁桥康,吴贵元,邹坤霖,等.3RPS/UPS结构并联机器人设计与分析[J].湖南大学学报(自然科学版),2016(10):110-118.
[3]Fradkov A L,Miroshnik I V,Nikiforov V O.Nonlinear and adaptive control of complex systems[M].Berlin:Springer Science&Business Media,2013.
[4]Lightbody G,Irwin G W.Neural networks for nonlinear adaptive control[C]//Proc IFAC Symposium on Algorithms and Architectures for Real-Time Control,2014:1-13.
[5]Bellman R E.Adaptive control processes:a guided tour[M].Princetin:Princeton University Press,2015.
[6]Demuth H B,Beale M H,De Jess O,et al.Neural network design[M].[S.l.]:Martin Hagan,2014.
[7]He W,Ge S S,Li Y,et al.Neural network control of a rehabilitation robot by state and output feedback[J].Journal of Intelligent&Robotic Systems,2015,80(1):15-31.
[8]Liu Y J,Tong S,Chen C L P.Adaptive fuzzy control via observer design for uncertain nonlinear systems with unmodeleddynamics[J].IEEE Transactions on Fuzzy Systems,2013,21(2):275-288.
[9]Tong S.Adaptive fuzzy control for a class of unknown nonlinear dynamical systems[J].Fuzzy Sets and Systems,2015,263:49-70.
[10]Wang T,Zhang Y,Qiu J,et al.Adaptive fuzzy backstepping control for a class of nonlinear systems with sampled and delayed measurements[J].IEEE Transactions on Fuzzy Systems,2015,23(2):302-312.
[11]Hansen L P,Sargent T J.Robust control and model uncertainty[J].The American Economic Review,2001,91(2):60-66.
[12]Gu D W,Petkov P,Konstantinov M M.Robustcontrol design with MATLAB?[M].Berlin:Springer Science&Business Media,2014.
[13]Chen M,Shi P,Lim C C.Robust constrained control for MIMO nonlinear systems based on disturbance observer[J].IEEE Transactions on Automatic Control,2015,60(12):3281-3286.
[14]李光,符浩.无模型机械臂BP神经网络状态观测及反演跟踪控制[J].中国机械工程,2016(7):859-865.
[15]刘青,倪骁骅,郭祥东.基于神经网络状态观测器的反演控制在双关节机械手控制中的应用[J].机床与液压,2015(3):24-28.
[16]刘永,张立毅.BP和RBF神经网络的实现及其性能比较[J].电子测量技术,2007(4):77-80.
[17]李晚龙.六自由度Stewart平台分散智能控制研究[D].哈尔滨:哈尔滨工程大学,2010.
[18]Moody J,Darken C J.Fast learning in networks of locallytuned processing units[J].Neural Computation,1989,1(2):281-294.