PID and intelligent controllers for optimal timing performances of industrial actuators

PID controllers are widely used in process industries due to its simplicity and robustness. The main problem sometime is tuning the PID parameters in order to improve the settling time, the rise time and the overshoot. In literature, there are procedures to obtain the PID settings which gives the better performance and robustness. Some experiments on this research line show that the controller gain is only a function of the overshoot observed in the setpoint experiment. The challenge is to improve the timing parameters to achieve optimal control performances. Remarkable findings are obtained through the use of Artificial Intelligence techniques as Fuzzy Logic, Genetic Algorithms and Neural Network. The first theory is good for decisional problems, the second one can be used in search algorithms and the Neural Networks have the capability to learn from data. The combination of these approaches can give good results in terms of settling time, rise time and overshoot. In this paper, we propose the design of suitable controllers which target is the improvement of timing performance of industrial actuators. The designed controllers are PID controller, genetic-fuzzy controller and neuro-fuzzy controller. The results show that the PID controller has good overshoot values and shows optimal robustness. The genetic-fuzzy controller gives a good value of settling time and a very good overshoot value. The neural-fuzzy controller gives the best timing parameters improving the control performances of the others two approaches.