چکیده (انگلیسی):

Proportional Integral Derivative (PID) controllers are widely used in industrial processes for their simplicity
and robustness. The main application problems are the tuning of PID parameters to obtain good settling
time, rise time and overshoot. 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 Networks. The combination of these theories can give good
results in terms of settling time, rise time and overshoot. In this study, suitable controllers able of improving
timing performance of second order plants are proposed. 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. Further improvements are achieved
designing a real-time optimization algorithm which works on a genetic-neuro-fuzzy controller.