TY - JOUR
T1 - A DSC method for strict-feedback nonlinear systems with possibly unbounded control gain functions
AU - Lv, Maolong
AU - Wang, Ying
AU - Baldi, Simone
AU - Liu, Zongcheng
AU - Wang, Zutong
N1 - Accepted Author Manuscript
PY - 2018
Y1 - 2018
N2 - In dynamic surface control (DSC) methods, the control gain functions of systems are always assumed to be bounded, which is a restrictive assumption. This work proposes a novel DSC approach for an extended class of strict-feedback nonlinear systems whose control gain functions are continuous and possibly unbounded. Appropriate compact sets are constructed in such a way that the trajectories of the closed-loop system do not leave these sets, therefore, in these sets, maximums and minimums values of the continuous control gain functions are well defined even if the control gain functions are possibly unbounded. By using Lyapunov theory and invariant set theory, semi-globally uniformly ultimately boundedness is analytically proved: all the signals of closed-loop system will always stay in these compact sets, while the tracking error is shown to converge to a residual set that can be made as small as desired by adjusting design parameters appropriately. Finally, the effectiveness of the designed method is demonstrated via two examples.
AB - In dynamic surface control (DSC) methods, the control gain functions of systems are always assumed to be bounded, which is a restrictive assumption. This work proposes a novel DSC approach for an extended class of strict-feedback nonlinear systems whose control gain functions are continuous and possibly unbounded. Appropriate compact sets are constructed in such a way that the trajectories of the closed-loop system do not leave these sets, therefore, in these sets, maximums and minimums values of the continuous control gain functions are well defined even if the control gain functions are possibly unbounded. By using Lyapunov theory and invariant set theory, semi-globally uniformly ultimately boundedness is analytically proved: all the signals of closed-loop system will always stay in these compact sets, while the tracking error is shown to converge to a residual set that can be made as small as desired by adjusting design parameters appropriately. Finally, the effectiveness of the designed method is demonstrated via two examples.
KW - Adaptive neural control
KW - Dynamic surface control
KW - Invariant set theory
KW - Robust control
UR - http://resolver.tudelft.nl/uuid:0795e7df-2fdc-4015-a34a-aee394e86cc0
UR - http://www.scopus.com/inward/record.url?scp=85032930850&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2017.09.082
DO - 10.1016/j.neucom.2017.09.082
M3 - Article
AN - SCOPUS:85032930850
SN - 0925-2312
VL - 275
SP - 1383
EP - 1392
JO - Neurocomputing
JF - Neurocomputing
ER -