title: Exponential epsilon-regulation for multi-input nonlinear systems using neural networks.
abstract: This paper considers the problem of robust exponential epsilon-regulation for a class of multi-input nonlinear systems with uncertainties. The uncertainties appear not only in the feedback channel but also in the control channel. Under some mild assumptions, an adaptive neural network control scheme is developed such that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded and, under the control scheme with initial data starting in some compact set, the states of the closed-loop system is guaranteed to exponentially converge to an arbitrarily specified epsilon-neighborhood about the origin. The important contributions of the present work are that a new exponential uniformly ultimately bounded performance is proposed and that the design parameters and initial condition set can be determined easily. The development generalizes and improves earlier results for the single-input case.
authors.forename: Shaosheng
authors.initials: S
authors.lastname: Zhou
authors.forename: James
authors.initials: J
authors.lastname: Lam
authors.forename: Gang
authors.initials: G
authors.lastname: Feng
authors.forename: Daniel W C
authors.initials: DW
authors.lastname: Ho
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted