Adaptive neural control for a class of nonlinear systems with uncertain hysteresis inputs and time-varying state delays.
In this paper, adaptive variable structure neural control is investigated for a class of nonlinear systems under the effects of time-varying state delays and uncertain hysteresis inputs. The unknown time-varying delay uncertainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design, and the effect of the uncertain hysteresis with the Prandtl-Ishlinskii (PI) model representation is also mitigated using the proposed control. By utilizing the integral-type Lyapunov function, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded (SGUUB). Extensive simulation results demonstrate the effectiveness of the proposed approach.
Ren
Beibei
B
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore. renbeibei@nus.edu.sg
Ge
Shuzhi Sam
SS
Lee
Tong Heng
TH
Su
Chun-Yi
CY
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012371:Robotics / Q000379:methods; D012984:Software / Q000639:trends; D013997:Time Factors