Optimization of PID Parameters Based on Ant Colony Optimization Algorithm for Ball and Beam System

الملخص

This paper investigates the control of the inherently unstable ball and beam system, a canonical benchmark problem in control engineering known for its nonlinear dynamics and challenging control requirements. The study's primary objective is to develop and rigorously compare different control strategies for achieving precise ball positioning on the beam.  The research begins with the derivation of both linear and nonlinear mathematical models of the ball and beam system. These models incorporate the intricate dynamics of the DC servomotor, responsible for tilting the beam, and the coupled mechanical dynamics governing the ball's movement along the beam's surface.  The core of the research focuses on evaluating the performance of a Proportional-Integral-Derivative (PID) controller, a widely used control strategy, tuned using three distinct methods: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and a traditional trial-and-error approach.  Extensive simulations conducted within the MATLAB/Simulink environment allow for a detailed comparison of these tuning methods, using key performance indicators such as settling time, rise time, overshoot, and steady-state error. The findings contribute significantly to the broader understanding of optimal control strategies for unstable nonlinear systems and offer valuable insights into the relative strengths and weaknesses of different optimization algorithms for efficient PID controller parameter tuning.  The results provide a practical guide for selecting appropriate tuning methods based on specific performance requirements and computational constraints.