Evaluation of Optimization Algorithms for Use in a Mobile App Aimed at Learning Autotuning of PID Controllers for Engineering Students

Abstract

Most research in the field of optimization algorithms related to automatic control, both in Hessian and quadratic methods, does not pay enough attention to computational efficiency in data processing, especially in mobile applications on smartphones where their resources are limited. This study seeks to identify the most efficient algorithm in terms of resource consumption for autotuning of PID (proportional, integrator, derivative) controllers in a mobile app aimed at learning engineering students. The BFGS (Broyden-Fletcher-Goldfard-Shanno), simulated annealing, genetic, conjugate gradient, and PSO (particle swarm optimization) algorithms were evaluated. The descriptive scope research and quasi-experimental design showed that the BFGS algorithm is highly efficient and suitable for autotuning of PID controllers in mobile applications, demonstrating consistency in the use of resources with different numbers of testing samples. This study validates a useful optimization method to develop a mobile app that simulates the tuning of PID controllers, being useful for learning topics about automatic control of industrial processes, which allows closing gaps in accessibility and educational availability for engineering students. Future studies should focus on developing mobile applications that use smartphone sensors to generate signals in real time and integrate them with augmented reality environments to build immersive and interactive autotuning systems.