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      Fuzzy Logic Self-Tuning PID Control for a Single-Link Flexible Joint Robot Manipulator in the presence of Uncertainty

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      https://www.riss.kr/link?id=A101196342

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      다국어 초록 (Multilingual Abstract)

      Nowadays, flexible joint robots (FJR) manipulators are widely used at industry; however, these robots have several problems. These problems are in the joint and with links. Another problem is their complex dynamics that make control of this robot have...

      Nowadays, flexible joint robots (FJR) manipulators are widely used at industry; however, these robots have several problems. These problems are in the joint and with links. Another problem is their complex dynamics that make control of this robot have some challenges. Non-linearity, interaction between loops and flexibility in the joint cause this problem. The present paper has focused to improve the tracking performance of these robots. Therefore, at the first step, we need to use physical relations of system and determine a model for the FJR. In this paper, the Fuzzy Logic Self- Tuning PID (FLST-PID) controller will be introduced to keep the rotating angle of the link of FJR at desired position. In the classic PID forms, the parameter values of the controller i.e. Kp , Ki , Kd are calculated in many various methods like Ziegler-Nichols and are constant. In FLST-PID, the parameter values computed by intelligent methods like fuzzy logic and they vary during the controlling process.
      For demonstrating the ability of the proposed controller, some classic controller like PID, LQR and State Feedback will be designed for FJR and the response of the system with these controllers will be compared. Moreover, by considering some uncertainty on systems parameters, the comparison will be performed once again. Simulation results confirm the claims and show that the proposed controller has the best response for the system especially in uncertainty conditions.

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      목차 (Table of Contents)

      • Abstract
      • 1. INTRODUCTION
      • 2. System modeling
      • 3. Design State Feedback, LQR and PID for FJR
      • 4. Self-tuning PID controller base on fuzzy logic
      • Abstract
      • 1. INTRODUCTION
      • 2. System modeling
      • 3. Design State Feedback, LQR and PID for FJR
      • 4. Self-tuning PID controller base on fuzzy logic
      • 5. Effect of uncertainty in system response
      • 6. Conclusion
      • 7. References
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