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Study on Two New Control Method of Underactuated Robot

Author: LiuLiLing
Tutor: WuZhongQiang
School: Yanshan University
Course: Navigation, Guidance and Control
Keywords: Underactuated robot Finite time control Neural Network Adaptive Control T-S model Non- fragile guaranteed cost control H_ ∞ control
CLC: TP242
Type: Master's thesis
Year: 2010
Downloads: 103
Quote: 0
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Abstract


The underactuated robot name implies , its part of the joint is no driving device , which means that some of the joints are passive , also called freedom . Its main feature is the dimension of the input space is less than the space dimension is constructed , passive joint constraint equations acceleration nonintegrable order nonholonomic constraint characteristics . Compared to the full- drive robots , control more difficult . The advantage of this robot is designed to reduce drive device , reduce the weight of the robot , and reduce the cost of the increased flexibility , more compact structure , reduce energy consumption , these advantages make underactuated robot very research value , become a hotspot in the field of robotics research . The underactuated robot controlled object , the position tracking control studies , mainly to complete the work of the following aspects : link underactuated robot system Lagrangian dynamics model . Underactuated robot system TS fuzzy model , the combination of non - fragile control , guaranteed cost control as well as H_ ∞ control thought , given underactuated robot non- fragile guaranteed cost H_ ∞ controller based on TS fuzzy model . The fuzzy controller can not only ensure the stability of the closed-loop system and certain quadratic performance index upper bound , but also has the meaning of non- fragile features and H_ ∞ interference inhibition . The simulation results show that the effect of the control law , even if the system has a disturbance and controller parameters perturbation , still can effectively achieve the position tracking . A three-link planar underactuated robot system Lagrangian dynamics model . Combined with the limited - time control, neural network control and adaptive control thinking , given the limited time with model uncertainty nonlinear underactuated robot system adaptive neural network controller . The controller uses the RBF network approximation model uncertainty using adaptive methods online to adjust the weights of the neural network , it has a strong robustness . The simulation results indicate that the role of the control law , in a limited time within the system can be effectively achieved position tracking .

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Robotics > Robot
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