TY - GEN
T1 - An on-line rule weighting mechanism based on normalized acceleration for fuzzy PID controllers
AU - Karasakal, Onur
AU - Guzelkaya, Mujde
AU - Eksin, Ibrahim
AU - Yesil, Engin
PY - 2010
Y1 - 2010
N2 - In this study, an on-line rule weighting method is proposed for fuzzy PID controllers. For this purpose, the relative importance or influence of the fired fuzzy rules is determined for certain regions of the transient phase of the unit step response of the closed loop system. Meta-rules for the adjustment of the rule weights are derived according to the error value and the relative information on the fastness or slowness of the system response to obtain an efficient and appropriate control signal. Then, the rule weight values are adjusted via a fuzzy inference mechanism in an on-line manner. The inputs of the fuzzy mechanism are the "normalized system error" and the "normalized acceleration". The "normalized acceleration" gives the "relative rate" information about the fastness or slowness of the system response. The effectiveness of the proposed self tuning method is demonstrated on two processes by simulations.
AB - In this study, an on-line rule weighting method is proposed for fuzzy PID controllers. For this purpose, the relative importance or influence of the fired fuzzy rules is determined for certain regions of the transient phase of the unit step response of the closed loop system. Meta-rules for the adjustment of the rule weights are derived according to the error value and the relative information on the fastness or slowness of the system response to obtain an efficient and appropriate control signal. Then, the rule weight values are adjusted via a fuzzy inference mechanism in an on-line manner. The inputs of the fuzzy mechanism are the "normalized system error" and the "normalized acceleration". The "normalized acceleration" gives the "relative rate" information about the fastness or slowness of the system response. The effectiveness of the proposed self tuning method is demonstrated on two processes by simulations.
KW - Fuzzy PID controller
KW - Fuzzy rule weighting
KW - Normalized acceleration
KW - Self-tuning
UR - http://www.scopus.com/inward/record.url?scp=84901946364&partnerID=8YFLogxK
U2 - 10.3182/20100826-3-tr-4015.00053
DO - 10.3182/20100826-3-tr-4015.00053
M3 - Conference contribution
AN - SCOPUS:84901946364
SN - 9783902661852
T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)
SP - 282
EP - 287
BT - IFAC International Workshop on the Adaptation and Learning in Control and Signal Processing, ALCOSP 2010 - Proceedings
PB - IFAC Secretariat
T2 - 10th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2010
Y2 - 26 August 2010 through 28 August 2010
ER -