Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A fuzzy controller with evolving structure.
AU - Angelov, Plamen
N1 - The final, definitive version of this article has been published in the Journal, Information Sciences 161 (1-2), 2004, © ELSEVIER.
PY - 2004/4/5
Y1 - 2004/4/5
N2 - An approach to on-line design of fuzzy controllers of Takagi-Sugeno type with gradually evolving structure is treated in the paper. Fuzzy rules, representing the structure of the controller are generated based on data collected during the process of control using newly introduced technique for on-line identification of Takagi-Sugeno systems. The output of the plant under control (including its dynamic) and the respective control signal has been memorised and stored in on-line mode. These data has been used to train in a non-iterative, recursive way the fuzzy controller. The indirect adaptive control approach has been used in combination with the novel on-line identification technique. This approach exploits the quasi-linear nature of Takagi-Sugeno models and builds-up the control rule-base structure and adapts it in on-line mode with recursive, non-iterative learning. The method is illustrated with an example from air-conditioning systems, though it has wider potential applications.
AB - An approach to on-line design of fuzzy controllers of Takagi-Sugeno type with gradually evolving structure is treated in the paper. Fuzzy rules, representing the structure of the controller are generated based on data collected during the process of control using newly introduced technique for on-line identification of Takagi-Sugeno systems. The output of the plant under control (including its dynamic) and the respective control signal has been memorised and stored in on-line mode. These data has been used to train in a non-iterative, recursive way the fuzzy controller. The indirect adaptive control approach has been used in combination with the novel on-line identification technique. This approach exploits the quasi-linear nature of Takagi-Sugeno models and builds-up the control rule-base structure and adapts it in on-line mode with recursive, non-iterative learning. The method is illustrated with an example from air-conditioning systems, though it has wider potential applications.
KW - evolving fuzzy rule-based systems
KW - indirect adaptive control
KW - on-line clustering
KW - on-line identification
KW - recursive least squares estimation
KW - DCS-publications-id
KW - art-586
KW - DCS-publications-credits
KW - dsp-fa
KW - DCS-publications-personnel-id
KW - 82
U2 - 10.1016/j.ins.2003.03.006
DO - 10.1016/j.ins.2003.03.006
M3 - Journal article
VL - 161
SP - 21
EP - 35
JO - Information Sciences
JF - Information Sciences
SN - 0020-0255
IS - 1-2
ER -