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A Study on the Control and Realization for Bicycle Robot Nonlinear System

Author: YuXiuLi
Tutor: WeiShiMin
School: Beijing University of Posts and Telecommunications
Course: Mechanical and Electronic Engineering
Keywords: Cascade Sliding Mode Control Stable sliding mode control Hierarchical Sliding Mode Control ARX ??model ANFIS system identification
CLC: TP242
Type: PhD thesis
Year: 2010
Downloads: 157
Quote: 1
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Bicycle Robot Robot academia in recent years proposed a new intelligent transport (or traffic) tool concept, because the robot itself is a bicycle driven with lateral instability due to non-complete system, its two vertical layout, and no sliding contact with the ground, while bicycle dynamics model with symmetry characteristics, its dynamics is more complex, since the bicycle robot stability control very difficult. Domestic Dynamic Modeling for bicycles, control algorithms and system identification of (relatively) small, and bicycle robot dynamics modeling and control and other issues has long been a hot research field of robotics. In this paper, bicycle robot dynamic modeling, control and system identification problem as the research object, using the Lagrange method to establish a way of turning the bicycle handlebar adjustment robot dynamics model, and the model built by sliding control, while using the collected body and handlebar inclination angle data, linear ARX model-based system identification and ANFIS Adaptive Fuzzy Neural Network Simulation of nonlinear system identification. Bicycle physical prototype robot designed and completed the bicycle robot control experiments. The text of the main contents and results are as follows: 1, using Lagrangian methods rely on regulation to establish a bicycle handlebar ensure a balanced robot SISO nonlinear dynamic model. Approximate linearization method using the nonlinear model was linearized designed based on LQR linear system controller, the bicycle robot in a small range of changes can be effectively controlled. 2 analyzes the characteristics of SISO nonlinear dynamic model and proposed based on the cascade sliding, sliding and stable hierarchical sliding mode controller for bicycle robot model control method, which can more effectively achieve a stable bike robot control. 3, based on a linear ARX model and system identification of nonlinear system identification ANFIS model works, the use of motion capture vehicle stability and steering angle of the vehicle body inclination data, bicycle robot system identification. Based on TS Model ANFIS network can be a good approximation of the actual bicycle robot nonlinear system, and identification of high precision, better linear ARX model identification method. Obtained through the identification between the input and output of the fuzzy inference conclusions for the future for the effective control of bicycle robot system provides a certain reference value. 4, ARM9-based embedded systems, microcontroller, high-precision sensors and other hardware platforms MTi bicycle robot designed experimental prototype, completed the data acquisition system to single-input single-output bicycle robot nonlinear system as an example, for the corresponding control Algorithm experiments. This study from the non-linear, due to the angle of the bicycle drive provides a new robot control ideas, the use of system identification methods bicycle robot model to model, enrich our laboratory research on bicycle robot for further research laid the foundation.

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