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  • 學位論文

形狀記憶合金致動器的建模、控制及其在六足仿生機器人上之應用

Modeling and Control of Shape Memory Alloy Actuator and its Application to a Hexapod Biomimetic Robot

指導教授 : 顏家鈺
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摘要


本論文旨在對形狀記憶合金致動器進行控制,並對一個以形狀記憶合金為致動器的六足仿生機器人進行導航。我們深入研究形狀記憶合金致動器的自我感應特性,此特性源自形狀記憶合金致動器於致動過程中所產生的電阻變化。我們提出了一套方法來量測以脈波寬度調變進行驅動的形狀記憶合金致動器其自感應特性曲線,對此特性曲線進行建模後,不需額外安裝感測器,即可控制形狀記憶合金的長度。此外,針對形狀記憶合金致動器的遲滯現象,我們研發了一個反遲滯補償器以進行補償,並且提出了一個切換控制器架構,此控制器可於前饋反遲滯補償器及自感應回授PID控制器間進行切換。實驗結果證明此切換控制器的性能較PID回授控制器優越。我們製做了一台以形狀記憶合金為致動器的六足仿生機器人SMABOT IV,利用感測器融合技術估測機器人的姿態,所使用的感測器融合技術包含離散時間Kalman濾波器以及離散時間H∞濾波器。離散時間Kalman濾波器針對估測誤差的變異量進行最小化,而離散時間H∞濾波器則是針對最大誤差進行最小化。透過Allan變異量分析對感測器的雜訊進行識別並用以設計離散時間Kalman濾波器,使得離散時間Kalman濾波器擁有較佳的估測性能。將所估測的機器人姿態配合腳步測程法所得之里程加以運算進而估測機器人的位移。

並列摘要


The major aim of the study is to control the SMA actuator, and implement the navigation of a shape memory alloy (SMA)-based hexapod biomimetic robot. We investigate the self-sensing property of the SMA actuator, which is due to the electric resistance variation during its actuating process. A procedure for obtaining the self-sensing characteristic curve of the SMA actuator driving by PWM signal is proposed. By modeling the self-sensing characteristic curve, we can control the displacement of the SMA actuator without using additional sensors. In addition, an inverse hysteresis compensator of the SMA actuator is developed to compensate its hysteresis phenomenon. We proposed a switching controller to control the displacement of the SMA actuator, which consists of a feedforward inverse hysteresis compensator and a PID feedback controller with self-sensing feedback. The experimental results indicate that the switch controller provides better tracking performance than the PID-controller. An SMA-based hexapod biomimetic robot, SMABOT IV, is built, and sensor fusion algorithms are used to estimate its attitude, which include a discrete-time Kalman filter that minimizes the estimation error variance, and a discrete-time H∞ filter that minimizes the worst-case estimation error. After identifying the noise characteristics of the sensors using Allan variance analysis, the Kalman filter provides the better performance. The displacement of the SMABOT IV is then estimated by incorporating the attitude data with the legged odometry.

參考文獻


[14] S.-H. Liu, Y.-T. Chen, and J.-Y. Yen, "Sensor fusion in a Six-legged Bio-mimicking Robot," in Proceedings of the 17th IFAC World Congress (IFAC'08), Seoul, Korea, pp. 15624-15629, Jul. 6-11, 2008.
[104] R. J. Schilling, Fundamentals of robotics, Prentice Hall International, Englewood Cliffs, N.J., 1998.
[79] M. H. Elahinia, T. M. Seigler, D. J. Leo, and M. Ahmadian, "Nonlinear stress-based control of a rotary SMA-actuated manipulator," Journal of Intelligent Material Systems and Structures, vol. 15, no. 6, pp. 495-508, Jun. 2004.
[44] J. E. Clark, J. G. Cham, S. A. Bailey, E. M. Froehlich, P. K. Nahata, R. J. Full, and M. R. Cutkosky, "Biomimetic design and fabrication of a hexapedal running robot," in Proceedings of IEEE International Conference on Robotics and Automation, Seoul, Korea, vol. 4, pp. 3643-3649, May 21-26, 2001.
[3] G. B. Song and N. Ma, "Robust control of a shape memory alloy wire actuated flap," Smart Materials & Structures, vol. 16, no. 6, pp. N51-N57, Dec. 2007.

被引用紀錄


黃澤世(2010)。形狀記憶合金致動器的特性分析與控制應用〔博士論文,國立臺灣大學〕。華藝線上圖書館。https://doi.org/10.6342%2fNTU.2010.03533

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