KSOM-WLN vs. PID-CLIK: intelligent and conventional approaches for trajectory tracking control of spatial robotic manipulator

This paper presents a comparative study between intelligent and conventional control strategies for accurate 3D trajectory tracking with joint limit avoidance in spatial robotic manipulators. The proposed intelligent approach integrates a Kohonen Self-Organizing Map (KSOM) neural network with a Weighted Least Norm (WLN) scheme referred to as KSOM-WLN to effectively address redundancy resolution while significantly reducing the computational load typically associated with Jacobian pseudo-inverse calculations. For comparative evaluation, three conventional methods are developed: PID-Controlled Closed-Loop Inverse Kinematics with Task Priority (PID-CLIK-TP), PID-CLIK with Weighted Least Norm (PID-CLIK-WLN) and Adaptive (gain-scheduled) PID-CLIK. A 5-degree-of-freedom spatial robotic manipulator is modeled in MATLAB to assess the control performance across seven 3D trajectory types: straight-line, circular, elliptical, triangular, rectangular, Lissajous, and spring-shaped. The simulation results confirm that the KSOM–WLN method consistently outperforms conventional approaches, achieving lower root mean square error (RMSE) and higher correlation coefficient (CC) values across all trajectory types. The KSOM-WLN method computational efficiency, requiring approximately 0.01 seconds per trajectory point, significantly faster than the 0.34 seconds observed for PID-CLIK methods. Experimental validation confirms that the KSOM-WLN method ensures smooth, efficient, and highly accurate 3D trajectory tracking.