Dynamic modeling and motion control strategy simulation analysis of robots
Dajun Tao
Flexiv Ltd.,Santa Clara
DOI: https://doi.org/10.59429/esta.v12i1.9659
Keywords: Robotic dynamic modeling; Motion control strategies; Trajectory tracking; Model predictive control
Abstract
Objective: This paper investigates the methods for robotic dynamic modeling and evaluates the performance of motion control strategies, focusing on accuracy, stability, and real-time capabilities in complex trajectory tracking. The aim is to provide theoretical support for optimizing motion control strategies. Methods: A six-degree-of-freedom robotic manipulator dynamic model is established based on the Lagrangian method. Three control strategies—PID control, sliding mode control (SMC), and model predictive control (MPC)— are implemented for simulation experiments. Their performance is compared in terms of trajectory error, torque fluctuation, and computational cost. Results: The experiments demonstrate that MPC achieves the highest trajectory accuracy (RMS error: 0.009 m) and the lowest torque fluctuation (0.14 Nm), but has a longer computation time (4.3 ms/step). Sliding mode control exhibits strong robustness with moderate computational efficiency, while PID control provides the best real-time performance but with relatively larger errors (0.018 m). Conclusion: MPC is suitable for high-precision and complex tasks, sliding mode control is appropriate for scenarios requiring strong robustness, and PID control is more suitable for industrial environments with high realtime demands. This study provides a reference for the application of dynamic modeling and control strategies.
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