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The Research on Uncertain Robot with Several Kinds of Intelligent Control Strategies

Author: TangDeZhi
Tutor: WangHongRui；SongWeiGong
School: Yanshan University
Course: Control Theory and Control Engineering
Keywords: Uncertain Robot Fuzzy Control Sliding oversight items Fuzzy Neural Network Sliding mode observer Backstepping method
CLC: TP242
Type: Master's thesis
Year: 2003
Downloads: 224
Quote: 1
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Abstract


Robot control problems both in theoretical circles or the engineering sector over the years has been much attention. When the robot system model is accurately known , the feedback linearization technique can be a good solution to the control problem the realistic robot during operation of the various parameters of the dynamic model , however , may change , but also by environmental disturbances and load changes , and many other uncertainties , it is necessary to improve the existing control methods . In this thesis, a complete dynamic model of the robot system , uncertain robot system as the research object , on the basis of the existing literature , focus on a variety of robust control strategy based on intelligent algorithm . In this thesis, Chapter 1 describes the overview of the development of the robot and robot control theory overview ; Chapter 2 gives the mathematical knowledge required for controller design and robot dynamic model ; Chapter 3, a fuzzy adaptive control with sliding mode supervision the combination of control methods , including fuzzy control its simple set of rules and membership function effectively compensate for the uncertainty of the system , and then use a low- chattering sliding mode supervision for elimination approximation error of fuzzy control system ; Chapter 4 presents the robot the backstepping method based on the uncertainty fuzzy neural network control , fuzzy neural network learning system ideal feedback linearization control law , and a robust term compensation fuzzy neural network learning error , the entire controller the design process is based on backstepping design method , to ensure global stability of the system ; Chapter 5 for industrial robots only joint position measurements , the speed of RBF neural network - based sliding mode observer to observe the joint signal , the sliding mode method into the design of the observer improve its anti-jamming capability , RBF neural network compensates for the uncertainties of the robot system , to avoid the calculation of the regression matrix and a priori knowledge of the inertia matrix and take full account of the mutual influence of the controller and Observer , to ensure the the reconstructed speed signal instead of the actual speed signal used in the feedback loop of the control strategy , the simulation results prove the validity of the proposed method .

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