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Journal of Fuzzy Systems and ControlJournal of Fuzzy Systems and Control

Robotic manipulators require control systems that are both responsive and precise in order to ensure accurate tracking and stability in dynamic environments. Conventional fuzzy logic controllers that are based on proportional integral derivative (PID) methods frequently encounter difficulties in achieving fast response, minimal steady-state error, and low overshoot. This study presents a comparative evaluation of a PID-driven data-based fuzzy logic controller and a particle swarm optimization (PSO) tuned fuzzy logic controller for a three-axis robotic manipulator implemented in Simulink. Both controllers used Gaussian membership functions within a inference structure. The PSO algorithm was employed to optimize fuzzy input-scaling gains using a composite performance index that incorporated absolute error, control effort, overshoot penalty, and steady-state error. The simulation results indicate that the PSO-tuned controller consistently outperformed the benchmark. On the R-axis, it shortened rise and settling times and reduced overshoot, mean absolute error (MAE), and root mean square error (RMSE). On the T-axis, response speed and error values improved, although overshoot increased, indicating a trade-off between speed and stability. On the Z-axis, the PSO controller achieved a substantial decrease in overshoot, lower error metrics, and faster stabilization. Overall, the PSO-based tuning process preserved steady-state stability while improving transient performance on all axes. These findings show that metaheuristic optimization is an effective and practical method for enhancing fuzzy logic controllers in robotic manipulators. This approach has potential applications in precision manufacturing, service automation, and surgical robotics.

The study demonstrates that the PSO-tuned controller consistently outperforms the PID-driven data-based controller in controlling a three-axis robotic manipulator.This improvement is evident in reduced overshoot, faster rise and settling times, and lower error metrics across most axes.While a trade-off between speed and stability was observed on the T-axis, the PSO-based tuning process effectively preserved steady-state stability while enhancing transient performance.These findings highlight the effectiveness of metaheuristic optimization for improving fuzzy logic controllers in robotic applications.

Future research should focus on refining the performance index to better balance responsiveness and stability, particularly addressing the overshoot observed on the T-axis, potentially through multi-objective optimization strategies. Furthermore, validating the simulation results with real-world hardware implementation using the Seiko D-Tran RT3200 manipulator is crucial to assess the robustness and generalizability of the PSO-tuned controller under realistic conditions, including actuator nonlinearities and external disturbances. Finally, exploring adaptive real-time tuning strategies, coupled with the development of methods for handling manipulators with increased degrees of freedom, will broaden the applicability of this approach and pave the way for more sophisticated and adaptable robotic control systems. These advancements will contribute to the development of more precise, reliable, and efficient robotic systems for a wide range of applications, including manufacturing, healthcare, and service industries.

  1. Constant Force PID Control for Robotic Manipulator Based on Fuzzy Neural Network Algorithm - Dachang... doi.org/10.1155/2020/3491845Constant Force PID Control for Robotic Manipulator Based on Fuzzy Neural Network Algorithm Dachang doi 10 1155 2020 3491845
  2. Handling Four DOF Robot to Move Objects Based on Color and Weight using Fuzzy Logic Control | Journal... doi.org/10.18196/jrc.v4i6.20087Handling Four DOF Robot to Move Objects Based on Color and Weight using Fuzzy Logic Control Journal doi 10 18196 jrc v4i6 20087
  3. Sagging Cable Analysis and Evaluation of 4-degree-of- freedom Cable Robot Using Adaptive Neural Fuzzy... doi.org/10.18178/ijmerr.11.2.73-78Sagging Cable Analysis and Evaluation of 4 degree of freedom Cable Robot Using Adaptive Neural Fuzzy doi 10 18178 ijmerr 11 2 73 78
  4. Tuning of Digital PID Controllers Using Particle Swarm Optimization Algorithm for a CAN-Based DC Motor... doi.org/10.1109/TIE.2019.2934030Tuning of Digital PID Controllers Using Particle Swarm Optimization Algorithm for a CAN Based DC Motor doi 10 1109 TIE 2019 2934030
  5. Enhancement of Underwater Video through Adaptive Fuzzy Weight Evaluation | Journal of Robotics and Control... doi.org/10.18196/jrc.v5i2.20496Enhancement of Underwater Video through Adaptive Fuzzy Weight Evaluation Journal of Robotics and Control doi 10 18196 jrc v5i2 20496
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