Parameter Solution of Fractional Order PID Controller for Home Ventilator Based on Genetic-Ant Colony Algorithm

Considering the practical issues of home ventilators and the advantages of fractional order calculus, this paper implements the fractional order proportional-integral-differential (FOPID) controller to the ventilator pressure system. Given that existing FOPID controller parameter optimization algorithms are complex and lack real-world validation, a genetic-ant colony optimization algorithm is proposed. The paper commences with fractional order calculus derivation and the principles of traditional optimization algorithms. Subsequently, this paper enhances the evolution, crossover, and mutation aspects of the genetic algorithm through theoretical analysis, while incorporating the concept of pheromones to augment the efficacy of the optimization algorithm. A new multi-objective function is proposed, accompanied by the transfer function derivation and calculation for the ventilator pressure system. Simulation experiments compare the results of traditional optimization algorithms and the Genetic-Ant Colony Algorithm (G-ACA) for various controlled objects and objective functions. Finally, the solved FOPID controllers are applied to the actual circuit of the ventilator and compared with the conventional proportional-integral-derivative controllers. The results show that the FOPID controllers optimized by the G-ACA surpass the traditional ones in simulation and practice, validating the proposed objective function.