Robust and intelligent control strategies for a 3-DOF robotic arm: a comparative study

Abstract

Abstract This paper presents a comparative study of PID, Fuzzy Logic Control (FLC), and Sliding Mode Control (SMC) strategies for trajectory tracking of a nonlinear 3-DOF robotic manipulator. A complete dynamic model is derived using the Euler–Lagrange formulation, incorporating inertia coupling, Coriolis and centrifugal effects, gravitational forces, and joint friction. The developed model is validated against published results, demonstrating close agreement in amplitude and phase characteristics under sinusoidal joint trajectories. The three controllers are implemented under identical conditions and evaluated in both joint space and Cartesian space using step inputs, infinity trajectories, and circular paths. Performance is quantitatively assessed using RMSE, ITAE, and IAE metrics. The results indicate that SMC achieves the highest tracking accuracy, reducing average RMSE by approximately 80% compared to PID, while FLC achieves nearly 50% improvement. In Cartesian tracking, SMC maintains peak position errors below 0.005 m, significantly outperforming PID, which exhibits deviations up to 0.11 m under dynamic motion. Statistical analysis further confirms improved robustness under the considered simulation scenarios and the consistency of SMC across all joints. The findings demonstrate that robust nonlinear control significantly enhances convergence speed, tracking precision, and disturbance rejection capability in planar 3-DOF manipulators. The validated modeling framework and systematic benchmarking provide practical guidance for selecting appropriate control strategies in industrial robotic applications.