Comparing Fuzzy logic with classical controller designs
IEEE Transactions on Systems, Man and Cybernetics
PID type fuzzy controller and parameters adaptive method
Fuzzy Sets and Systems
A PID type fuzzy controller with self-tuning scaling factors
Fuzzy Sets and Systems
Auto-tuning of fuzzy logic controllers for self-regulating processes
Fuzzy Sets and Systems
Tuning and analysis of a fuzzy PI controller based on gain and phase margins
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Brief Parallel structure and tuning of a fuzzy PID controller
Automatica (Journal of IFAC)
Fuzzy Immune PID Temperature Control of HVAC Systems
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Fuzzy adaptive control for the actuators position control and modeling of an expert system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An error-based on-line rule weight adjustment method for fuzzy PID controllers
Expert Systems with Applications: An International Journal
Self-adaptive interval type-2 neural fuzzy network control for PMLSM drives
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.08 |
The modelling, numerical simulation and intelligent control of an expert HVAC (heating, ventilating and air-conditioning) system having two different zones with variable flow-rate were performed by considering the ambient temperature in this study. The sub-models of the system were obtained by deriving heat transfer equations of heat loss of two zones by conduction and convection, cooling unit and fan. All models of the variable flow-rate HVAC system were generated by using MATLAB/SIMULINK, and proportional-integral-derivative (PID) parameters were obtained by using Fuzzy sets. For comfortable of people the temperatures of the two different zones were decreased to 5^oC from the ambient temperature. The successful results were obtained by applying self-tuning proportional-integral-derivative (PID)-type fuzzy adaptive controller if comparing with the fuzzy PD-type and the classical PID controller. The obtained results were presented in a graphical form.