Modeling of PID controlled 3DOF robotic manipulator using Lyapunov function for enhancing trajectory tracking and...

Area of Science:

• Robotics
• Control Systems Engineering
• Computational Intelligence

Background:

• Robotic manipulators require precise control for industrial applications.
• Optimizing Proportional-Integral-Derivative (PID) controllers is crucial for enhanced performance.
• Metaheuristic algorithms offer advanced solutions for complex optimization problems.

Purpose of the Study:

• To optimize a Proportional-Integral-Derivative (PID) controller for a 3-DOF rigid-link robotic manipulator (RLM).
• To enhance tracking performance and robustness against uncertainties using the Golden Jackal Optimization (GJO) algorithm.
• To validate the GJO algorithm's efficacy against other state-of-the-art metaheuristic techniques.

Main Methods:

• Simulation of a 3-DOF RLM using Simscape and Lagrange methods.
• Optimization of a PID controller using the novel Golden Jackal Optimization (GJO) algorithm.
• Utilizing a Lyapunov stability function as the objective function for controller tuning.

Main Results:

• The GJO algorithm demonstrated superior performance in minimizing the objective function compared to PSO, ABC, JSO, WOA, AOA, and SCA.
• The optimized PID controller achieved enhanced tracking performance and robustness.
• The system showed significant robustness against disturbances, noise, and payload variations in Pick and Place (PNP) tasks.

Conclusions:

• The Golden Jackal Optimization (GJO) algorithm is an effective method for tuning PID controllers in robotic systems.
• The optimized controller significantly improves the performance and robustness of robotic manipulators for industrial tasks.
• This approach provides a strong foundation for advanced robotic control strategies.