Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee...

Area of Science:

• Robotics and Control Systems
• Optimization Algorithms
• Artificial Intelligence

Background:

• Proportional-Integral-Derivative (PID) controllers are widely used in robotic systems.
• Optimizing PID controller gains is crucial for system performance.
• Existing optimization methods and objective functions have limitations.

Purpose of the Study:

• To introduce and compare two optimization techniques: basic Artificial Bee Colony (ABC) and multi-elite guided ABC (MGABC).
• To determine optimal Proportional-Integral-Derivative (PID) controller gains for a 3 degrees of freedom (DOF) rigid link manipulator (RLM).
• To evaluate a novel Lyapunov-based objective function (LBF) against traditional error-based functions.

Main Methods:

• Implemented basic Artificial Bee Colony (ABC) and multi-elite guided Artificial Bee Colony (MGABC) algorithms.
• Utilized a novel Lyapunov-based objective function (LBF) for optimization.
• Evaluated controller performance in trajectory tracking for a 3 DOF RLM system.
• Compared LBF against IAE, ISE, ITAE, MAE, and MRSE objective functions.

Main Results:

• MGABC algorithm demonstrated superior convergence and avoidance of local optima compared to basic ABC.
• The Lyapunov-based objective function (LBF) significantly improved controller performance in trajectory tracking.
• The optimized system exhibited robustness to disturbances, payload uncertainty, and joint flexibility without vibrations.

Conclusions:

• The MGABC algorithm is an effective optimization technique for PID controllers.
• The Lyapunov-based objective function (LBF) offers significant advantages over traditional error-based functions.
• The proposed methods provide a robust and adaptable approach for PID controller optimization in robotic applications.