title: Feedback-linearization-based neural adaptive control for unknown nonaffine nonlinear discrete-time systems.
abstract: A new feedback-linearization-based neural network (NN) adaptive control is proposed for unknown nonaffine nonlinear discrete-time systems. An equivalent model in affine-like form is first derived for the original nonaffine discrete-time systems as feedback linearization methods cannot be implemented for such systems. Then, feedback linearization adaptive control is implemented based on the affine-like equivalent model identified with neural networks. Pretraining is not required and the weights of the neural networks used in adaptive control are directly updated online based on the input-output measurement. The dead-zone technique is used to remove the requirement of persistence excitation during the adaptation. With the proposed neural network adaptive control, stability and performance of the closed-loop system are rigorously established. Illustrated examples are provided to validate the theoretical findings.
authors.affiliation: Key Laboratory of Modern Complex Equipment Design and Extreme Manufacturing, Ministry of Education, Changsha 410083, China. hdeng@csu.edu.cn
authors.forename: Hua
authors.initials: H
authors.lastname: Deng
authors.forename: Han-Xiong
authors.initials: HX
authors.lastname: Li
authors.forename: Yi-Hu
authors.initials: YH
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005246:Feedback; D016014:Linear Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted