Intelligent Control Methods for Robot Manipulator Trajectory Planning With Singularity Avoidance

The presence of singularities in robotic manipulators poses significant challenges to stability, trajectory tracking, and control precision, often leading to loss of manipulability and operational inefficiency. This paper explores the optimization of trajectories and the implementation of advanced intelligent control methods to avoid singular configurations in manipulators with six degrees of freedom. By employing Proportional-Integral-Derivative PID control, Fuzzy Logic Control FLC, and a hybrid FLC-PID approach, the study investigates their effectiveness in maintaining stability and precision across diverse trajectory patterns, including complex paths prone to singularities. The hybrid FLC-PID controller is designed to dynamically adapt control parameters in real time based on proximity to singularities, enhancing the manipulator's ability to perform tasks with high accuracy and reliability. The modeling and comparative analysis results show that when using FLC-PID, average tracking errors are reduced by 60%, system stability increases by 80%, and energy efficiency improves by 20% in conditions prone to singularity.