TY - JOUR
T1 - Stable adaptive compensation with fuzzy CMAC for an overhead crane
AU - Yu, Wen
AU - Moreno-Armendariz, Marco A.
AU - Rodriguez, Floriberto Ortiz
N1 - Funding Information:
M.A. Moreno-Armendariz thanks the support of the Mexican Government (CONACYT, SNI, SIP-IPN, COFAA-IPN, and PIFI-IPN).
PY - 2011/11/1
Y1 - 2011/11/1
N2 - In order to control mechanical systems, this paper proposes a novel fast control strategy. The controller includes a normal proportional and derivative (PD) regulator and a fuzzy cerebellar model articulation controller (CMAC). For an overhead crane, this control can realize both position tracking and anti-swing. Using a Lyapunov method and an input-to-state stability technique, the PD control with CMAC compensation is proven to be robustly stable with bounded uncertainties. Real-time experiments are presented comparing this new stable control strategy with regular crane controllers.
AB - In order to control mechanical systems, this paper proposes a novel fast control strategy. The controller includes a normal proportional and derivative (PD) regulator and a fuzzy cerebellar model articulation controller (CMAC). For an overhead crane, this control can realize both position tracking and anti-swing. Using a Lyapunov method and an input-to-state stability technique, the PD control with CMAC compensation is proven to be robustly stable with bounded uncertainties. Real-time experiments are presented comparing this new stable control strategy with regular crane controllers.
KW - Cerebellar model articulation controller
KW - Neural compensation
KW - Overhead crane
KW - Stability
UR - http://www.scopus.com/inward/record.url?scp=80051552714&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2009.06.032
DO - 10.1016/j.ins.2009.06.032
M3 - Artículo
SN - 0020-0255
VL - 181
SP - 4895
EP - 4907
JO - Information Sciences
JF - Information Sciences
IS - 21
ER -