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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 黃漢邦 | zh_TW |
| dc.contributor.advisor | Han-Pang Huang | en |
| dc.contributor.author | 羅漢為 | zh_TW |
| dc.contributor.author | Carlos Javier Espinola Rotela | en |
| dc.date.accessioned | 2025-08-21T16:41:22Z | - |
| dc.date.available | 2025-08-22 | - |
| dc.date.copyright | 2025-08-21 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-05 | - |
| dc.identifier.citation | [1] Leonid Reznik. Fuzzy Controllers. Butterworth-Heinemann, Oxford, UK, 1997.
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Denavit and R. S. Hartenberg. A kinematic notation for lower-pair mechanisms based on matrices. Journal of Applied Mechanics, 22(2):215–221, June 1955. [24] Yuanqing Li, Yueming Hu, Zhiming Song, and Chuanjun Hou. Inverse kinematics and coordination of a hybrid redundant manipulator based on virtual decomposition method. Mathematical Problems in Engineering, 2018:1–13, 2018. [25] Yi-Hsiu Lee. Safe cooperative control and dynamics path planning of dual manipulators. Master’s thesis, Department of Mechanical Engineering, College of Engineering, National Taiwan University, Taipei, Taiwan, January 2021. [26] Jun Nakanishi, Rick Cory, Michael Mistry, Jan Peters, and Stefan Schaal. Operational space control: A theoretical and empirical comparison. The International Journal of Robotics Research, 27(6):737–757, June 2008. [27] Tomomichi Sugihara. Solvability-unconcerned inverse kinematics by levenberg-marquardt method. Journal of the Robotics Society of Japan, 29(3):269–277, 2011. [28] Wisam Subhi Al Dayyeni and Hussam Saleh Mahmood. Pid control perspective: Techniques and uses. Edison Journal for Electrical and Electronics Engineering, 3(1):1–8, 2025. [29] Karl J. Åström and Tore Hägglund. PID Controllers: Theory, Design, and Tuning. Instrument Society of America, Research Triangle Park, NC, 1995. [30] Zeynep Yilmazer Hitit, Ismet Kocer, Gokce Kus, Nermin Zeynep Arslan, Elif Pinar Dal, and Habipcan Koz. Optimal pid control with anti-windup in neutralization process. International Advanced Researches and Engineering Journal, 7(3):138–145, December 2023. [31] MathWorks. Reinforcement learning toolbox, 2023. Release R2023b. [32] Ebrahim H. Mamdani. Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the Institution of Electrical Engineers, 121(12):1585–1588, 1974. [33] Stephen Brawner. Solidworks to URDF exporter, 2024. [34] MathWorks. Robotics system toolbox: Trajectory generation. MATLAB Documentation, 2024. Accessed: 2025-04-10. [35] Di Zhao, Zhenyu Ding, Wenjie Li, Sen Zhao, and Yuhong Du. Cascaded fuzzy reward mechanisms in deep reinforcement learning for comprehensive path planning in textile robotic systems. MDPI Journal of Robotics, 15(4):233–249, 2023. [36] Ken Shoemake. Animating rotation with quaternion curves. ACM SIGGRAPH Computer Graphics, 19(3):245–254, July 1985. [37] Luigi Biagiotti and Claudio Melchiorri. Trajectory Planning for Automatic Machines and Robots. Springer, Berlin, Germany, 2008. [38] MathWorks. Robotics system toolbox: Coordinate transformations. MATLAB Documentation, 2024. Accessed: 2025-04-10. [39] MathWorks. Robotics System Toolbox UserCMD#39;s Guide, 2024. Accessed: 2025-06-07. [40] Charles W. Wampler. Manipulator inverse kinematics solutions based on vector formulations and damped least-squares methods. IEEE Transactions on Systems, Man, and Cybernetics, 16(1):93–101, 1986. [41] Stefano Chiaverini, Bruno Siciliano, and Olav Egeland. Review of the damped least-squares inverse kinematics with experiments on an industrial robot manipulator. IEEE Transactions on Control Systems Technology, 2(2):123–134, 1994. [42] Kevin M. Lynch and Frank C. Park. Modern Robotics: Mechanics, Planning, and Control. Cambridge University Press, Cambridge, UK, 2017. [43] Fuyw. Furl: Visual-language models as fuzzy rewards. arXiv preprint, arXiv:2406.00645, 2023. [44] Jinxin Zhan, Xiaodong Yue, Zihao Li, and Kai Han. Accelerating deep reinforcement learning with fuzzy logic rules. In Lecture Notes in Artificial Intelligence, volume 14687, pages 455–467. Springer, 2023. [45] T. Nguyen. Fuzzy logic guided reward function variation: An oracle for testing reinforcement learning programs. arXiv preprint, arXiv:2406.19812, 2024. [46] Gaël Guennebaud, Benoît Jacob, et al. Eigen library. http://eigen.tuxfamily.org/, 2024. Accessed: 2025-03-20. [47] Yean-Yia Lin. Vibration suppression of a six-axis manipulator using input shaping approach. Master’s thesis, National Taiwan University, Taipei, Taiwan, 7 2018. In Chinese: 輸入塑形應用於六軸機械手臂之抑振控制. [48] Chen-Yu Tzou. Safely cooperative planning and control of multi-robot manipulators. Master’s thesis, National Taiwan University, Taipei, Taiwan, 7 2017. In Chinese: 多機械手臂之安全協同規劃與控制. [49] Chien-Yao Tseng. Arm-slam: an application combining robot arm and slam. Master’s thesis, National Taiwan University, Taipei, Taiwan, 7 2024. In Chinese: Arm-SLAM: 機器手臂與 SLAM 結合之應用. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99177 | - |
| dc.description.abstract | 改善機器人操控器的效能與運動精確性，一直是控制系統領域中的重要研究主題之一。本文提出了一種應用於六自由度機器人操控器的自適應控制架構設計，目的是提升其在變動環境中的追蹤能力與反應效率。此控制系統整合了強化學習與模糊邏輯技術，以實現即時調整比例-微分（PD）控制器的控制參數。所設計的模糊獎勵機制可用於即時評估整體控制表現，考慮因素包括偏差大小、其變化速率以及施加的控制力。該模糊評估系統結合了 TD3（Twin Delayed Deep Deterministic Policy Gradient)強化學習演算法，這是一種基於actor-critic架構的先進策略學習方法。透過與外部環境的互動，系統能逐步學習出最適的控制策略，並根據模糊獎勵提供的回饋來調整 PD 控制器的增益設定。在 Simulink 環境中結合 MATLAB 工具箱所進行的模擬實驗，驗證了該控制器的有效性。透過對六自由度機器手臂的實驗，展示了其在實際應用中的控制表現。這些模擬比較了結合模糊邏輯與強化學習的自適應控制策略與傳統固定增益PID控制器之間的性能差異。結果顯示，該創新方法在追蹤精度方面有顯著提升，且更能適應系統動態的變化。此一控制框架在機器人手臂及其他複雜機電系統的自主控制領域中，具備潛在的應用價值與貢獻。 | zh_TW |
| dc.description.abstract | Improving the performance and accuracy of robotic manipulators has been a very important research areas in control systems engineering. This article presents the development of an adaptive control framework for a six-DOF robotic manipulator, aiming to improve its motion accuracy and responsiveness in a dynamic environment. The proposed controller focuses on the online tuning of a proportional derivative controller parameters combining reinforcement learning with fuzzy logic. The fuzzy reward system is designed to evaluate system performance in real time, considering error, error rate, and applied control force. Fuzzy rewards are combined with a reinforcement learning method, the Twin Delayed Deep Deterministic Policy Gradient algorithm operates using an actor-critic methodology. TD3 lets the system achieve a better control policy which engages with the environment and adjusts the PD controller’s gains based on feedback from fuzzy rewards. The simulations conducted in Simulink with the use of Matlab toolboxes confirmed the efficacy of the controller. The experiments done with the six-DOF robot arm showed its performance in real-life applications. These simulations assessed the performance of an adaptive control strategy that utilizes fuzzy logic and reinforcement learning against a conventional fixed-gain PID controller. The findings indicated that the novel approach notably enhanced tracking precision and adaptability to changes in system dynamics. This control framework could potentially contribute in the area of autonomous control systems for robotic arms and other intricate mechatronic systems. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-21T16:41:22Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-21T16:41:22Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Contents
摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . . . 3 Chapter 2 Background and Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1 Proportional-Derivative (PD) Controllers . . . . . . . . . . . . . . 8 2.1.1 Principles of PD Control . . . . . . . . . . . . . . . . . . 8 2.1.2 Stability and Performance Analysis . . . . . . . . . . . . 10 2.1.3 Advantages and Limitations . . . . . . . . . . . . . . . . 11 2.2 Introduction to Fuzzy Logic Controllers . . . . . . . . . . . . . . . 12 2.2.1 Mathematical Foundation of Fuzzy Sets . . . . . . . . . . 12 2.2.2 Fuzzy Set Operations . . . . . . . . . . . . . . . . . . . . 13 2.2.3 Membership Functions . . . . . . . . . . . . . . . . . . . 14 2.2.4 Fuzzy Logic Controller Architecture . . . . . . . . . . . . 16 2.2.5 Fuzzy Inference Systems: Mamdani vs Sugeno . . . . . . 18 2.2.6 Types of Fuzzy Inference Systems . . . . . . . . . . . . . 19 2.2.7 Comparative Analysis of Fuzzy Inference Systems . . . . 21 2.2.8 Application Considerations in Robotic Control . . . . . . 22 2.3 Reinforcement Learning for Control Systems . . . . . . . . . . . . 23 2.3.1 Overview of Reinforcement Learning (RL) Techniques . . 23 2.3.2 Agent-Environment Interaction . . . . . . . . . . . . . . 24 2.3.3 Mathematical Framework . . . . . . . . . . . . . . . . . 25 2.3.4 Value Functions and Bellman Equations . . . . . . . . . . 27 2.3.5 Temporal Difference Learning . . . . . . . . . . . . . . . 29 2.3.6 Policy Gradient Methods . . . . . . . . . . . . . . . . . . 30 2.3.7 Deep Deterministic Policy Gradient (DDPG) . . . . . . . 30 2.3.8 Twin Delayed Deep Deterministic Policy Gradient Enhancements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.3.9 Comparison of DDPG and TD3 . . . . . . . . . . . . . . 35 2.3.10 Practical Applications . . . . . . . . . . . . . . . . . . . 35 2.3.11 Other RL Algorithms . . . . . . . . . . . . . . . . . . . . 36 Chapter 3 Kinematics and Dynamics of the Robot Arm . . . . . . . . . . . . . . . . . . . . . 37 3.1 Kinematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.1.1 Forward Kinematics . . . . . . . . . . . . . . . . . . . . 40 3.1.2 Jacobian Matrix . . . . . . . . . . . . . . . . . . . . . . . 40 3.1.3 Inverse Kinematics . . . . . . . . . . . . . . . . . . . . . 42 3.1.4 Singular and Joint Limit Avoidance . . . . . . . . . . . . 44 3.2 Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2.1 Computed Torque Control with PID . . . . . . . . . . . . 45 3.2.2 Task-Space PID Control . . . . . . . . . . . . . . . . . . 46 3.3 Challenges and Control Objectives . . . . . . . . . . . . . . . . . 48 3.3.1 Non-linearities, Joint Coupling, and External Disturbances 48 Chapter 4 PD Controller Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.1 Mathematical Foundation of PD Control . . . . . . . . . . . . . . 49 4.1.1 PD Control Law . . . . . . . . . . . . . . . . . . . . . . 49 4.1.2 Integration with Computed Torque Control . . . . . . . . 50 4.1.3 Closed-Loop Analysis . . . . . . . . . . . . . . . . . . . 50 4.2 Limitations and Motivations for Adaptive Control . . . . . . . . . 51 4.2.1 Fundamental Limitations of Fixed-Gain PD Control . . . 51 4.2.2 Performance Trade-offs . . . . . . . . . . . . . . . . . . 53 Chapter 5 TD3-Based PD Controller with Fuzzy Reward System . . . . . . . . . . . . 54 5.1 Agent, State, and Action Space Definition . . . . . . . . . . . . . 54 5.1.1 Multi-Agent Architecture . . . . . . . . . . . . . . . . . . 54 5.1.2 TD3 Algorithm for PD Gain Tuning . . . . . . . . . . . . 56 5.1.2.1 TD3 Algorithm Implementation . . . . . . . . . . . . . 67 5.1.3 State Space Definition . . . . . . . . . . . . . . . . . . . 69 5.1.4 Action Space Definition . . . . . . . . . . . . . . . . . . 70 5.2 Environment Modeling . . . . . . . . . . . . . . . . . . . . . . . 71 5.2.1 Simulation Environment Setup . . . . . . . . . . . . . . . 71 5.2.2 Episode Structure . . . . . . . . . . . . . . . . . . . . . . 71 5.3 Fuzzy Reward System Design . . . . . . . . . . . . . . . . . . . . 72 5.3.1 Fuzzy Control Surface Visualization . . . . . . . . . . . . 73 5.3.2 Fuzzy Rules for Reward Calculation . . . . . . . . . . . . 77 5.3.3 Defuzzification . . . . . . . . . . . . . . . . . . . . . . . 79 5.3.4 Complete Reward Function . . . . . . . . . . . . . . . . 79 5.4 TD3 Algorithm Implementation . . . . . . . . . . . . . . . . . . . 81 5.4.1 Network Architecture . . . . . . . . . . . . . . . . . . . . 81 5.4.2 Training Configuration . . . . . . . . . . . . . . . . . . . 82 5.4.3 Hardware and Software Setup . . . . . . . . . . . . . . . 83 5.4.4 Training Process . . . . . . . . . . . . . . . . . . . . . . 83 5.5 Implementation in MATLAB/Simulink . . . . . . . . . . . . . . . 85 5.5.1 Simulink Model Architecture . . . . . . . . . . . . . . . 85 5.5.2 Training vs Application Phase Equations . . . . . . . . . 86 5.5.3 Real-Time Implementation Considerations . . . . . . . . 87 Chapter 6 Simulation Results and Performance Analysis . . . . . . . . . . . . . . . . . . . . 89 6.1 Simulation Model and Setup . . . . . . . . . . . . . . . . . . . . . 89 6.2 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . 91 6.2.1 Trajectory Generation and Planning . . . . . . . . . . . . 92 6.2.2 Robot Dynamics and Control Implementation . . . . . . . 94 6.3 Performance Analysis and Results . . . . . . . . . . . . . . . . . . 96 6.4 Visualization and Graphical Analysis . . . . . . . . . . . . . . . . 99 6.5 Performance Summary and Comparative Analysis . . . . . . . . . 100 6.6 Robot Visualization and Trajectory Analysis . . . . . . . . . . . . 104 6.7 Computational Efficiency . . . . . . . . . . . . . . . . . . . . . . 106 6.7.1 Profiling Setup and Methodology . . . . . . . . . . . . . 106 6.8 Discussion and Analysis Summary . . . . . . . . . . . . . . . . . 108 Chapter 7 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 7.1 Software Platform . . . . . . . . . . . . . . . . . . . . . . . . . . 111 7.1.1 Eigen Library . . . . . . . . . . . . . . . . . . . . . . . . 111 7.1.2 Open Source Graphics Library . . . . . . . . . . . . . . . 111 7.2 Hardware Platform . . . . . . . . . . . . . . . . . . . . . . . . . . 113 7.2.1 Kinematic Configuration . . . . . . . . . . . . . . . . . . 113 7.2.2 Dynamic Properties . . . . . . . . . . . . . . . . . . . . . 114 7.2.3 Motor Specifications . . . . . . . . . . . . . . . . . . . . 114 7.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . 115 7.3.1 Situation 1 . . . . . . . . . . . . . . . . . . . . . . . . . 115 7.3.2 Situation 2 . . . . . . . . . . . . . . . . . . . . . . . . . 115 7.3.3 Situation 3 . . . . . . . . . . . . . . . . . . . . . . . . . 117 7.4 RMSE Analysis Results . . . . . . . . . . . . . . . . . . . . . . . 119 7.4.1 Square Drawing Experimental Results . . . . . . . . . . . 122 7.4.2 Star Drawing Experimental Results . . . . . . . . . . . . 123 7.4.3 Line Drawing Experimental Results . . . . . . . . . . . . 124 7.4.4 Experimental Performance Analysis Summary . . . . . . 124 Chapter 8 Conclusion and Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 8.1 Summary of Contributions . . . . . . . . . . . . . . . . . . . . . . 126 8.2 Limitations and Challenges . . . . . . . . . . . . . . . . . . . . . 127 8.3 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 | - |
| dc.language.iso | en | - |
| dc.subject | 模糊邏輯控制 | zh_TW |
| dc.subject | 動態環境 | zh_TW |
| dc.subject | 控制參數最佳化 | zh_TW |
| dc.subject | 比例微分控制 | zh_TW |
| dc.subject | 自適應控制 | zh_TW |
| dc.subject | 機器人操作臂 | zh_TW |
| dc.subject | 位置與力的追蹤 | zh_TW |
| dc.subject | TD3 演算 法 | zh_TW |
| dc.subject | 自調式控制器 | zh_TW |
| dc.subject | 強化學習 | zh_TW |
| dc.subject | 即時控制 | zh_TW |
| dc.subject | Robotic Manipulator | en |
| dc.subject | TD3 Algorithm | en |
| dc.subject | Self-Tuning Controller | en |
| dc.subject | Control Parameter Optimization | en |
| dc.subject | Dynamic Environment | en |
| dc.subject | Real-time Control | en |
| dc.subject | Reinforcement Learning | en |
| dc.subject | Proportional-Derivative Control | en |
| dc.subject | Adaptive Control | en |
| dc.subject | Position and Force Tracking | en |
| dc.subject | Fuzzy Logic Control | en |
| dc.title | 基於 TD3 演算法與模糊獎勵系統的 PD 控制器參數最 佳化之設計與應用 | zh_TW |
| dc.title | Design and Application of PD Controller Parameter Optimization using TD3 Algorithm with Fuzzy Reward System | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林峻永;劉孟昆;李廣齊 | zh_TW |
| dc.contributor.oralexamcommittee | Chun-Yeon Lin;Meng-Kun Liu;Kuang-Chyi Lee | en |
| dc.subject.keyword | 機器人操作臂,模糊邏輯控制,強化學習,自調式控制器,TD3 演算 法,位置與力的追蹤,自適應控制,比例微分控制,即時控制,動態環境,控制參數最佳化, | zh_TW |
| dc.subject.keyword | Robotic Manipulator,Fuzzy Logic Control,Reinforcement Learning,Self-Tuning Controller,TD3 Algorithm,Position and Force Tracking,Adaptive Control,Proportional-Derivative Control,Real-time Control,Dynamic Environment,Control Parameter Optimization, | en |
| dc.relation.page | 134 | - |
| dc.identifier.doi | 10.6342/NTU202503257 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-08-07 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 機械工程學系 | - |
| dc.date.embargo-lift | N/A | - |
| Appears in Collections: | 機械工程學系 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-113-2.pdf Restricted Access | 19.42 MB | Adobe PDF |
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