Optimized Robot Control: A Comparative Study of PID, Fuzzy Logic, and Hybrid FLC-PID Techniques

Mechanical Engineering and Machine Science


Аuthors

Alwardat M. Y.1*, Alwan H. M.2**

1. Peter the Great St. Petersburg Polytechnic University, 29, Polytechnicheskaya str., St. Petersburg, 195251, Russia
2. University of Technology, Baghdad, Iraq

*e-mail: moh.alwardat@yahoo.com
**e-mail: hassana@mail.ru

Abstract

This study addresses the critical challenge of singularity avoidance in robotic manipulators with six degrees of freedom by proposing an advanced intelligent control framework that integrates trajectory optimization with robust control strategies. The paper explores the adverse effects of singular configurations—conditions under which the manipulator loses degrees of freedom and control precision—and presents a comprehensive analysis of methods to mitigate these issues. The research primarily compares three control techniques: classical Proportional-Integral-Derivative (PID) control, Fuzzy Logic Control (FLC), and a novel hybrid FLC-PID approach, each evaluated through detailed simulation studies.
The article begins by reviewing the fundamental concepts of robotic kinematics, emphasizing the importance of both forward and inverse kinematics in determining the position and orientation of the manipulator’s end-effector. It further elaborates on the derivation and role of the Jacobian matrix, which is crucial for relating joint velocities to end-effector motions and serves as the basis for singularity analysis. When the Jacobian becomes singular (its determinant approaches zero or its rank decreases), the manipulator’s ability to execute precise movements is compromised, potentially leading to instability and increased tracking errors.
To overcome these challenges, the study investigates various control strategies. The conventional PID controller is noted for its simplicity and effectiveness in managing system dynamics; however, its performance degrades near singular configurations due to the inability to adapt to rapid system nonlinearities. FLC, on the other hand, introduces a degree of adaptability by employing a set of linguistic rules that handle uncertainties and non-linearities in real time, thereby improving the smoothness of the trajectory tracking. The proposed hybrid FLC-PID controller merges the stability of PID control with the adaptive capabilities of fuzzy logic, dynamically adjusting its parameters based on the proximity to singular regions.
Simulation results demonstrate that the hybrid FLC-PID controller significantly outperforms the standalone methods. Quantitative analysis reveals that the hybrid approach reduces average tracking errors by approximately 60% compared to the PID controller, enhances overall system stability by 80%, and improves energy efficiency by 20% under conditions prone to singularity. These improvements are supported by comparative graphs, detailed performance tables, and extensive modeling that illustrate the robustness and precision of the proposed system.
In conclusion, the article establishes that the hybrid FLC-PID strategy not only mitigates the detrimental effects of singularities but also optimizes trajectory tracking, rendering it highly suitable for practical applications in dynamic and complex robotic environments. The findings suggest that integrating intelligent control methods with traditional approaches can substantially elevate the performance and reliability of robotic manipulators in industrial, medical, and autonomous systems.

Keywords:

Manipulator, Singularity Avoidance, Trajectory optimization, Intelligent control, PID control, Fuzzy logic control, Hybrid FLC-PID

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