title: Global exponential stability of recurrent neural networks for synthesizing linear feedback control systems via pole assignment.
abstract: Global exponential stability is the most desirable stability property of recurrent neural networks. The paper presents new results for recurrent neural networks applied to online computation of feedback gains of linear time-invariant multivariable systems via pole assignment. The theoretical analysis focuses on the global exponential stability, convergence rates, and selection of design parameters. The theoretical results are further substantiated by simulation results conducted for synthesizing linear feedback control systems with different specifications and design requirements.
authors.affiliation: Dept. of Autom. and Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin.
authors.forename: Yunong
authors.initials: Y
authors.lastname: Zhang
authors.forename: Jun
authors.initials: J
authors.lastname: Wang