研究生: |
吳坤瑞 Wu, Kun-Jui |
---|---|
論文名稱: |
以PSO優化PID控制器參數設計應用於機械手臂之模糊模型 The PSO Optimized PID Controller Parameter Design Applied to Fuzzy Model of Robotic Arm |
指導教授: |
陳美勇
Chen, Mei-Yung |
口試委員: |
陳美勇
Chen, Mei-Yung 王俊勝 Wang, Jiun-Shen 張文哲 Chang, Wen-Jer 練光祐 Lian, Kuang-Yow |
口試日期: | 2024/07/31 |
學位類別: |
碩士 Master |
系所名稱: |
機電工程學系 Department of Mechatronic Engineering |
論文出版年: | 2024 |
畢業學年度: | 112 |
語文別: | 中文 |
論文頁數: | 65 |
中文關鍵詞: | Denavit-Hartenberg (D-H) 約定 、粒子群最佳化演算法 、機械手臂 |
英文關鍵詞: | Denavit-Hartenberg (D-H) convention, particle swarm optimization (PSO), Robot Manipulator |
研究方法: | 實驗設計法 |
DOI URL: | http://doi.org/10.6345/NTNU202401767 |
論文種類: | 學術論文 |
相關次數: | 點閱:59 下載:0 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
本文使用 Denavit-Hartenberg (D-H) 約定推導六自由度機械手臂的運動模型,包括運動學與動力學。為了克服運動模型的高度非線性問題,我們透過了 T-S 模糊系統來建立非線性系統之線性化模型。並利用此線性化模型,我們可以通過平行分佈式 PID 控制器來控制機械手臂。
根據連續軌跡的要求、各手臂的長度以及關節旋轉的角度限制,運動形式的設計需要擬合機器人手臂的運動模型。機械手臂系統中的 PID 控制器其參數係通過粒子群最佳化演算法(PSO)求得的。根據系統轉移函數,優化後的控制器參數可以在機械手臂運轉時抵抗系統的不確定性,使機械臂在運作時有能更高效且更平穩。
利用 Matlab 中的 Simulink 對系統進行模擬,分析範圍包括定點跟踪和軌跡跟踪。與傳統的 PID 控制器相比,結果顯示所提出的控制器參數具有更小的穩定性誤差且有較佳的調和性與較小的振動,並依此參數操作機械手臂觀察手臂之運動情形,其結果為手臂運作很均勻無抖動現象。
This paper uses the Denavit-Hartenberg (D-H) convention to derive the motion model, including the kinematics and dynamics, of the 6-DOF robotic arm. In order to overcome the highly nonlinear issue of the motion model, we linearize the nonlinear system by T-S fuzzy modeling.
Based on the linearized model, we can control the robotic arm through a parallel distributed PID controller. According to the requirements of the continuous trajectory, the length limits of each arm, and the angle limits of the joint rotation, the design of motion form needs to fit the motion model of the robot arm.
The parameters of the PID controller are found by the particle swarm optimization (PSO). According to the system transfer function, the controller with the optimized parameters can resist the uncertainty of the system, and make the robot arm move more efficiency and smoothly.
The system’s simulated in Matlab is used to simulate the system, and the analysis scope includes fixed-point tracking and trajectory tracking. Compared with the traditional PID controller, the results show that the proposed controller parameters have smaller stability errors, better harmonic and smaller vibration and operate the robotic arm according to these parameters to observe the movement of the arm,The result is that the arm operates evenly without shaking.
Hamza Khan. Saad Jamshed Abbasi, and Karam Dad Kallu, “Robust Control Design of 6-DOF Robot for Nuclear Power Plant Dismantling,” 2019 International Conference on Robotics and Automation in Industry (ICRAI) Year: Publication Year: 2019,Page(s):1 - 7.
Alam, M.S. and Tokhi, M.O.,“Hybrid Fuzzy logic control with genetic optimization for a single link flexible manipulator”, Journal of Engineering Applications of Artificial intelligence 2,pp. 858- 873,2008.
Federico Marinia, Beata Walczakb, “Particle swarm optimization (PSO). A tutorial”, Chemometrics and Intelligent Laboratory Systems 149 (2015) 153–165 Volume 149, Part B, 15 December 2015, Pages 153-165
Y. Guangyou, "A Modified Particle Swarm Optimizer Algorithm," 2007 8th International Conference on Electronic Measurement and Instruments, Xi'an, China, 2007, pp. 2-675-2-679, doi: 10.1109/ICEMI.2007.4350772.
O.Djaneye-Boundjou, X. Xu and R.Ordóñez, "Automated particle swarm optimization based PID tuning for control of robotic arm," 2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS), Dayton, OH, USA, 2016, pp. 164-169, doi: 10.1109/NAECON.2016.7856792.
Kennedy and R. Eberhart, "Particle swarm optimization,"Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia, 1995, pp. 1942-1
John J. Craig, “Introduction to Robotics: Mechanics and Control,” London ,Pearson Education International, 2005.
Ya Lei Sun and Meng Joo Er, "Hybrid fuzzy control of robotics systems," in IEEE Transactions on Fuzzy Systems, vol. 12, no. 6, pp. 755-765, Dec. 2004, doi:10.1109/TFUZZ.2004.836097948 vol.4, doi: 10.1109/ICNN.1995.488968.
Claudio Urrea , John Kern and Johanna Alvarado,” Design and Evaluation of a New Fuzzy Control Algorithm Applied to a Manipulator Robot”, Journals Applied Sciences Volume 10 Issue 21DOI:10.3390/app10217482
Nowaková, J.; Pokorný, M.; Pieš, M. Conventional controller design based on Takagi–Sugeno fuzzy models. J. Appl. Log. 2015, 13, 148–155. DOI:10.1016/j.jal.2014.11.008.
K. Tanaka, T. Hori and H. O. Wang, "A fuzzy Lyapunov approach to fuzzy control system design," Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), Arlington, VA, USA, 2001, pp. 4790-4795 vol.6, doi: 10.1109/ACC.2001.945740.
Nasr M. Ghaleb and Ayman A. Aly,” Modeling and Control of 2-DOF Robot Arm”, International Journal of Emerging Engineering Research and Technology Volume 6, Issue 11, 2018, PP 24-31 ISSN 2349-4395 (Print) & ISSN 2349-4409.
Chung-Shi Tseng, Bor-Sen Chen and Huey-Jian Uang, "Fuzzy tracking control design for nonlinear dynamic systems via T-S fuzzy model," in IEEE Transactions on Fuzzy Systems, vol. 9, no. 3, pp. 381-392, June 2001, doi: 10.1109/91.928735.
L. -W. Huang, C. -Y. Cheng and G. -R. Yu, "SOS-based design of fuzzy tracking controller for a two-link robot arm," Proceedings 2011 International Conference on System Science and Engineering, Macau, China, 2011, pp. 437-442, doi: 10.1109/ICSSE.2011.5961943.
.M. I. El-Hawwary, A. L. Elshafei, H. M. Emara and H. A. A. Fattah, "Adaptive Fuzzy Control of the Inverted Pendulum Problem," in IEEE Transactions on Control Systems Technology, vol. 14, no. 6, pp. 1135-1144, Nov. 2006, doi: 10.1109/TCST.2006.880217
L. -W. Huang, C. -Y. Cheng and G. -R. Yu, "SOS-based design of fuzzy tracking controller for a two-link robot arm," Proceedings 2011 International Conference on System Science and Engineering, Macau, China, 2011, pp. 437-442, doi: 10.1109/ICSSE.2011.5961943.