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Movement Control of Amphibious Robot Using IMC Based PID Controller with Fuzzy Logic Optimization


Affiliations
1 Department of Instrumentation Engineering, Ramrao Adik Institute of Technology, India
     

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For the Amphibious bot movement control, this study provides an Internal Model Control (IMC) tuned PID controller using fuzzy logic optimization method. IMC approach is used to tune the PID controller parameter as per the robot movements for robust control. Fuzzy logic is used to optimize the input error for controlling output. The proposed control approach, which is based on an accurate robot model, resulted, design of the stable and robust control system. The proposed method produces better outcomes than the present methods. It is measured against performance indices. Comparative results show a significant improvement in the system’s overshoot, rising time, and settling time. It is also performing better and better robustness against disturbances.

Keywords

Movement Control, Ambiguous Robot, PID Controller, IMC, Fuzzy Logic, Time Domain Analysis
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  • F. Gao and T. Liu, “Enhanced IMC Design of Load Disturbance Rejection for Integrating and Unstable Processes with Slow Dynamics”, ISA Transactions, Vol. 50, No. 2, pp. 239-248, 2011.
  • R. Singh M. Kumar and D. Prasad, “Performance Enhancement of IMC-PID Controller Design for Stable and Unstable Second-Order Time Delay Processes”, Journal of Central South University, Vol. 43, pp. 1-14, 2020.
  • Shivam Jain and Hote Yogesh Vijay, “PID Controller Design for Load Frequency Control: Past, Present and Future Challenges”, IFAC-Papers, Vol. 23, No. 1, pp. 604-608, 2018.
  • Shahab Abdulla, Peng Wen and Wei Xiang. “The Design and Investigation of Model based Internal Model Control for the Regulation of Hypnosis”, Proceedings of IEEE International Conference on Nano/Molecular Medicine and Engineering, pp. 192-197, 2010.
  • Vipul Shah and Himanshukumar Patel, “Passive Fault Tolerant Control System using Feed-Forward Neural Network for Two-Tank Interacting Conical Level Control System against Partial Actuator Failures and Disturbances”, IFAC-Papers, Vol. 22, No. 2, pp. 78-89, 2019.
  • U.M. Nath and and R.K. Mudi Chanchal Day, “Designing of Fuzzy Rule-Based Switching Mechanism for IMC Controller for Temperature Controlling Process”, Procedia Computer Science, Vol. 167, pp. 1363-1369, 2020.
  • Alshaboti and M.U. Baroudi, “Multi-Robot Task Allocation System: Fuzzy Auction-Based and Adaptive Multi-Threshold Approaches”, SN Computer Science, Vol. 2, No. 2, pp. 1-9, 2021.
  • X. Lin S. Guo, J. Du and C. Yue, “Adaptive Fuzzy Sliding Mode Control for Spherical under Water Robots”, Proceedings of IEEE International Conference on Mechatronics and Automation, pp. 1-14, 2012.
  • Chunfeng Yue, Shuxiang Guo and Maoxun Li, “Ansysuent-Based Modeling and Hydro Dynamic Analysis for a Spherical Underwater Robot”, Proceedings of IEEE International Conference on Mechatronic sand Automation, pp. 1577-1581, 2013.
  • L. Huang, “Direction and Control Adjustment for Smooth Motion of Wheeled Mobile Robots”, International Journal of Intelligent Systems Technologies and Applications, Vol. 11, No. 3, pp. 1-15, 2010.
  • Vinod Kumar Panchal Monica Sood, “Meta-Heuristic Techniques for Path Planning: Recent Trends and Advancements”, International Journal of Intelligent Systems Technologies and Applications, Vol. 23, No. 2, pp. 1-15, 2020.
  • B. Behkam and M. Sitti, “Design Methodology for Biomimetic Repulsion of Miniature Swimming Robots”, Journal of Dynamic Systems Measurement and Control, Vol. 3, No. 1, pp. 1-6, 2006.
  • Shuxiang Guo, Shilian Mao, Liwei Shi and Maoxun Li, “Design and Kinematic Analysis of an Amphibious Spherical Robot”, Proceedings of IEEE International Conference on Mechatronics and Automation, pp. 2214-2219,2012.
  • R. Ranganayakulu, A. Seshagiri Rao and G. Uday Bhaskar Babu, “An Improved Fractional Filter IMC/PID Controller Design for Enhanced Performance of Integrating Plus Time
  • Delay Processes”, Indian Chemical Engineer, Vol. 62, No. 2, pp. 184-201, 2020.
  • P. Giannone, S. Graziani, C. Bonomo, L. Fortuna and S. Strazzeri, “A Nonlinear Model for Ionic Polymer Metal Composites as Actuators”, Journal of Smart Material and Structures, Vol. 21, pp. 1-14, 2007.
  • L. Ye and J. Bing, “Analytical Optimization of IMC-PID Design based on Performance/Robustness Trade O Tuning Strategy for the Modified Smith Structure”, Journal of Low Frequency Noise, Vibration and Active Control, Vol. 39, No. 1, pp. 158-173, 2020.
  • G. S. Phadke R.R. Bambulkar and S. Salunkhe, “Movement Control of Robot using Fuzzy PID Algorithm”, Proceedings of IEEE International Conference on Mechatronics and Automation, pp. 1-8, 2016.
  • B. Verma and P.K. Padhy, “Indirect IMC-PID Controller Design”, IET Control Theory and Applications, Vol. 13, No. 2, pp. 297-305, 2019.

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  • Movement Control of Amphibious Robot Using IMC Based PID Controller with Fuzzy Logic Optimization

Abstract Views: 88  |  PDF Views: 2

Authors

Gargi Phadke
Department of Instrumentation Engineering, Ramrao Adik Institute of Technology, India
Shamal Salunkhe
Department of Instrumentation Engineering, Ramrao Adik Institute of Technology, India
Supriya Bhuran
Department of Instrumentation Engineering, Ramrao Adik Institute of Technology, India

Abstract


For the Amphibious bot movement control, this study provides an Internal Model Control (IMC) tuned PID controller using fuzzy logic optimization method. IMC approach is used to tune the PID controller parameter as per the robot movements for robust control. Fuzzy logic is used to optimize the input error for controlling output. The proposed control approach, which is based on an accurate robot model, resulted, design of the stable and robust control system. The proposed method produces better outcomes than the present methods. It is measured against performance indices. Comparative results show a significant improvement in the system’s overshoot, rising time, and settling time. It is also performing better and better robustness against disturbances.

Keywords


Movement Control, Ambiguous Robot, PID Controller, IMC, Fuzzy Logic, Time Domain Analysis

References