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System Design and Control of an Electric Seatless Unicycle
|關鍵字:||Seatless Electric Unicyle;Fuzzy Basis Function Networks (FBFN);Interval Type 2 Fuzzy Neural Networks (IT2FNN);State Feedback Control;無座式電動獨輪車;模糊基底類神經網路;區間第二型模糊類神經網路;狀態回授控制||引用:|| Z. Sheng, and , K. Yamafuji, 'Postural stability of a human riding a unicycle and its emulation by a Robot,' IEEE Transactions Robotics and Automation, vol.13 no.5, pp.709-720, Oct.1997  H. B. Brown, Jr, and Y. Xu, 'A single-wheel, gyroscopically stabilized robot,' IEEE Robotics & Automation Magazine, vol.4, no.3, pp.39-44, Sep.1997.  K. Pathak, and S. K. Agrawal, 'An integrated path-planning and control approach for nonholonomic unicycles using switched local potentials,' IEEE Transactions Robotics and Automation, vol.21 no.6, pp.1201–1208, Dec.2005.  S. Cedervall, Hu Xiaoming, 'Nonlinear Observers for Unicycle Robots with Range Sensors,' IEEE Transactions Automatic Control, vol.52, no.7, pp.1325 – 1329, July 2007.  Y. Xu, and K.W. 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Chan, 'Intelligent Backstepping Sliding-Mode Control Using Recurrent Interval Type 2 Fuzzy Neural Networks for a Ball-Riding Robot,' in Proceedings of 2012 International Conference on Fuzzy Theory and Its Applications National Chung Hsing University, Taichung, Taiwan, Nov.16-18, 2012.  C.K. Chan,C. C. Tsai, 'Intelligent backstepping sliding-mode control using recurrent interval type 2 fuzzy neural networks for a ball robot with a four-motor inverse-mouse ball drive,' Proceedings of SICE 2012 Annual conference, Akita, Japan, 20-23 August, 2012.  TMS320F28335 product page, http://www.ti.com/product/tms320f28335/ [Online], 2013. Available:||摘要:||
This thesis presents techniques and methodologies for system design, mathematical modeling, adaptive self-balancing and speed controllers of an electric seatless unicycle. System design, key components and control architecture of the unicycle are described in details. A novel mathematic model of the vehicle is established using Lagrangian mechanics by considering the motor torque as an input. Two types of controllers based on the linearized and nonlinear models of the vehicle are designed to achieve self-balancing and speed-command control, respectively. In the presence of system uncertainties, two types of intelligent adaptive motion controllers using fuzzy-basis function networks (FBFN) and interval type 2 fuzzy neural networks (IT2FNN) are respectively proposed to achieve self-balancing and speed control. Simulations and experimental results are conducted to verify the effectiveness and performance of the proposed control methods.
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