CONTROL ROTARY INVERTED PENDULUM MODEL BALANCING USING AN IMPROVED FUZZY CONTROLLER BASED ON A QUANTUM SWARM OPTIMIZATION ALGORITHM

Abstract

This paper presents an optimization method for fuzzy logic controllers (FLC) utilizing the quantum-behaved particle swarm optimization (QPSO) algorithm. The proposed controller is implemented for balance control of a rotary inverted pendulum, where the parameters of the triangular membership function are fine-tuned to achieve optimal error and transient response time for the system’s state variables. By integrating quantum mechanics principles with the particle swarm optimization (PSO) framework, QPSO demonstrates robust capabilities in identifying global and local optima for complex nonlinear and non-differentiable problems. To evaluate the algorithm's optimization performance, simulations were conducted using Matlab software, and the algorithm was implemented on an experimental model. Traditional PSO was also included for comparison. Simulation and experimental results show that QPSO achieves faster convergence and superior search outcomes under identical conditions (both with and without noise), along with improved quality indices compared to PSO.