Robust fuzzy logic control of mechanical systems
Fuzzy Sets and Systems - Theme: Fuzzy control
Design of fuzzy PID controllers using modified triangular membership functions
Information Sciences: an International Journal
Fuzzy coordinated PI controller: application to the real-time pressure control process
Advances in Fuzzy Systems - Regular issue
Metal chamber temperature control by using fuzzy PID gain auto-tuning strategy
WSEAS Transactions on Systems and Control
Auto-tuning and fuzzy PID temperature controllers for hollow metal block
CIMMACS'08 Proceedings of the 7th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Genetic fuzzy self-tuning PID controllers for antilock braking systems
Engineering Applications of Artificial Intelligence
Survey paper: A survey on industrial applications of fuzzy control
Computers in Industry
Selftuning nonlinear controller
FS'05 Proceedings of the 6th WSEAS international conference on Fuzzy systems
Fuzzy virtual coupling design for high performance haptic display
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
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A methodology, based on fuzzy logic, for the tuning of proportional-integral-derivative (PID) controllers is presented. A fuzzy inference system is adopted to determine the value of the weight that multiplies the set-point for the proportional action, based on the current output error and its time derivative. In this way, both the overshoot and the rise time in set-point following can be reduced. The values of the proportional gain and the integral and derivative time constant are determined according to the well-known Ziegler-Nichols formula so that a good load disturbance attenuation is also assured. The methodology is shown to be effective for a large range of processes and is valuable for industrial settings since it is intuitive, it requires only a small extra computational effort, and it is robust with regard to parameter variations. The tuning of the parameters of the fuzzy module can be easily done by hand or by means of an autotuning procedure based on genetic algorithms