TY - GEN
T1 - Multivariable adaptive control of bead geometry in GMA welding
AU - Song, Jae Bok
AU - Hardt, David E.
PY - 1991
Y1 - 1991
N2 - This paper considers the problem of controlling bead geometry of a Gas-Metal Arc Weld (GMAW). Specifically, the desire is control bead width and depth in real-time, and travel speed of the torch and wire feedrate are chosen as the inputs. Previous work has shown that process dynamics are highly non-linear, and this is quantified for this problem through a series of experiments and off-line parameter identification experiments. Since the range of parameter variations can be as large as ± 40%, adaptive control is investigated to achieve high performance. A multi-variable deadbeat adaptive control algorithm is investigated and implemented to achieve the desired weld bead geometries. Control weighting factors are introduced to reduce the excessive control effort that is likely to exist in the deadbeat algorithm. Simulation results indicate that the weighted deadbeat adaptive controller works well for the welding process, and that the control weighting factors are essential to practical, non-saturating control performance.
AB - This paper considers the problem of controlling bead geometry of a Gas-Metal Arc Weld (GMAW). Specifically, the desire is control bead width and depth in real-time, and travel speed of the torch and wire feedrate are chosen as the inputs. Previous work has shown that process dynamics are highly non-linear, and this is quantified for this problem through a series of experiments and off-line parameter identification experiments. Since the range of parameter variations can be as large as ± 40%, adaptive control is investigated to achieve high performance. A multi-variable deadbeat adaptive control algorithm is investigated and implemented to achieve the desired weld bead geometries. Control weighting factors are introduced to reduce the excessive control effort that is likely to exist in the deadbeat algorithm. Simulation results indicate that the weighted deadbeat adaptive controller works well for the welding process, and that the control weighting factors are essential to practical, non-saturating control performance.
UR - http://www.scopus.com/inward/record.url?scp=0026390677&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0026390677&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0026390677
SN - 0791808521
T3 - American Society of Mechanical Engineers, Production Engineering Division (Publication) PED
SP - 123
EP - 134
BT - Welding and Joining Processes
A2 - Kannatey-Asibu, Elijah Jr.
A2 - Cho, Hyung Suck
A2 - Fukuda, Shuichi
PB - Publ by ASME
T2 - Winter Annual Meeting of the American Society of Mechanical Engineers
Y2 - 1 December 1991 through 6 December 1991
ER -