Development of a Hybrid Optimization Strategy Based on a Bacterial Foraging Algorithm (BFA) and a Particle Swarm Algorithm (PSO) to Tune the Pid Controller of

The current systems to be controlled are becoming more and more complex due to their nonlinearity, their variation in time and the uncertainties they present both in the inputs and in their structure. Due to these reasons, plants can hardly be optimized using classical control techniques. In this work a hybrid optimization of the PID controller constants by means of the bio-inspired algorithms BFA and PSO has been developed for the Ball and Plate (B&P) system model 33-240 Feedback. For the modeling of the system, the Euler- Lagrange equations and the Routh-Hurwitz stability criteria were used; while for the optimization of the PID controller constants, the classical Ziegler Nichols control technique was used, which allowed determining the restrictions of the controller parameters. The optimization was first performed by simulation in MATLAB and then implemented in the real plant by means of the system's own interface in LabVIEW software. The results of the hybrid BFA - PSO algorithm were compared with the results of the BFA and PSO algorithms to determine the actual performance of each algorithm.