AI Summary & Key Takeaways
AI InsightResults indicate that the PID tuning process significantly improves the manipulator's dynamic characteristics, leading to enhanced stability, reduced overshoot, and reliable reference tracking.
📌 3 Key Takeaways
Abstract
This study investigates the position and motor control of a 3-degree-of-freedom (3-DoF) RRR robotic manipulator. The research focuses on developing and comparing two control strategies: Proportional-Integral-Derivative (PID) control and Sliding Mode Control (SMC). Kinematic modeling of the manipulator is performed using Denavit-Hartenberg parameters, while dynamic modeling is achieved through the Lagrangian ormulation. The effectiveness of both control methods is evaluated through simulations conducted in MATLAB/Simulink, with a focus on assessing their stability and error performance. Results indicate that the PID tuning process significantly improves the manipulator's dynamic characteristics, leading to enhanced stability, reduced overshoot, and reliable reference tracking. Furthermore, the implementation of SMC demonstrates considerable efficacy in managing system uncertainties and attenuating disturbances, ensuring stable and precise control of the robotic system even under challenging conditions. This research validates the robustness and precision offered by these control methodologies for robotic manipulator applications.
Özet
This study investigates the position and motor control of a 3-degree-of-freedom (3-DoF) RRR robotic manipulator. The research focuses on developing and comparing two control strategies: Proportional-Integral-Derivative (PID) control and Sliding Mode Control (SMC). Kinematic modeling of the manipulator is performed using Denavit-Hartenberg parameters, while dynamic modeling is achieved through the Lagrangian ormulation. The effectiveness of both control methods is evaluated through simulations conducted in MATLAB/Simulink, with a focus on assessing their stability and error performance. Results indicate that the PID tuning process significantly improves the manipulator's dynamic characteristics, leading to enhanced stability, reduced overshoot, and reliable reference tracking. Furthermore, the implementation of SMC demonstrates considerable efficacy in managing system uncertainties and attenuating disturbances, ensuring stable and precise control of the robotic system even under challenging conditions. This research validates the robustness and precision offered by these control methodologies for robotic manipulator applications.