-This study presents a well-developed optimization methodology based on the dynamic inertia weight Artificial Bee Colony algorithm (ABC) to design an optimal PID controller for a robotic arm manipulator. The dynamical analysis of robotic arm manipulators investigates a coupling relation between the joint torques applied by the actuators and the position and acceleration of the robot arm. An optimal PID control law is obtained from the proposed (ABC) algorithm and applied to the robotic system. The designed controller optimizes the trajectory of the robot's end effector for a time-variant input and makes the robot robust in the presence of external disturbance.
This paper introduces a novel algorithm called variable population size artificial bee colony (VPS-ABC) optimization algorithm. VPS-ABC is proposed to overcome the impact of the effect of initial population and improve the convergence rate of classical ABC. The main idea is based on reducing the number of food sources gradually and moving the bees towards the global best food source in each re-initialization process. Moreover, an analysis for convergence of the ABC algorithm is proofed in details. The convergence analysis is based on the relation between ABC variants and the general solution of the food source regeneration equation. To show the fitness of the proposed algorithm, a comparison is made between VPS-ABC versus classical ABC, PSO, and GA algorithms in tuning the proportional-integral-derivative (PID) controllers. Simulation results show that VPS-ABC algorithm is highly competitive, often outperforming PSO and GA algorithms.
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