Comparison of PID controller tuning methods: analytical/classical techniques versus optimization algorithms

Level control is one of the most used processes in industries. However, it can present nonlinearities, which can make difficult its project. The PID controller is still a commonly used topology due to the non-necessity to know the full system dynamics, only the modelling that well describes the system behavior. The objective of this work is to identify, control and audit a level tank system from a SMAR^® didactic plant. Firstly, system identification techniques (Smith, Bröida, Vitečková and Artificial Neural Network) were used to perform the controllers tuning later, approaching it to a First Order plus Dead Time transfer function (FODT). To tuning PI/PID controllers, optimization methods were used, such as Bat Algorithm, Bacterial Foraging Optimization, Genetic Algorithm, Bee Swarm, Bat Algorithm, Ant Colony Optimization, and Shuffled Frog-Leaping. Beyond optimization methods, analytical/classical PI/PID controllers tuning techniques (Cohen-Coon, Hallman, Internal Model Control (IMC), Chien-Hrones-Reswick (CHR), and Integral of Absolute Error (ITAE)) were also introduced to do this parameterization. In order to compare the simulated and experimental results, non-intrusive performance indexes based on integral errors (IAE, ISE, ITAE and ITSE) were introduced to evaluate and choose the best performance. The results were interesting, showing that the classical identification technique Bröida had the best response. For PID controllers tuning, optimization algorithms overcame analytical/classical tuning techniques performance, proving a better execution, performed by Shuffled Frog-Leaping technique.