Adaptive neural control for a class of nonlinear systems with uncertain hysteresis inputs and time-varying state delays

Authors:
Beibei Ren;Shuzhi Sam Ge;Tong Heng Lee;Chun-Yi Su
Affiliations:
Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore;Department of Mechanical and Industrial Engineering, Concordia University, Montreal, QC, Canada
Venue:
IEEE Transactions on Neural Networks
Year:
2009

Citing 17
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Abstract

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.