Tracking control based on recurrent neural networks for nonlinear systems with multiple inputs and unknown deadzone
type: Item
instance of: scholarly article
title: Tracking control based on recurrent neural networks for nonlinear systems with multiple inputs and unknown deadzone
zbMATH Open document ID: 1256.93054
DOI: 10.1155/2012/471281
author: J. Humberto Pérez-Cruz
author: Eduardo Ruiz-Velazquez
author: José De Jesús Rubio
author: G. Solís-Perales
published in: Abstract and Applied Analysis
publication date: 2013-02-04T00:00:00Z
review text: Summary: This paper deals with the problem of trajectory tracking for a broad class of uncertain nonlinear systems with multiple inputs each one subject to an unknown symmetric deadzone. On the basis of a model of the deadzone as a combination of a linear term and a disturbance-like term, a continuous-time recurrent neural network is directly employed in order to identify the uncertain dynamics. By using a Lyapunov analysis, the exponential convergence of the identification error to a bounded zone is demonstrated. Subsequently, by a proper control law, the state of the neural network is compelled to follow a bounded reference trajectory. This control law is designed in such a way that the singularity problem is conveniently avoided and the exponential convergence to a bounded zone of the difference between the state of the neural identifier and the reference trajectory can be proven. Thus, the exponential convergence of the tracking error to a bounded zone and the boundedness of all closed-loop signals can be guaranteed. One of the main advantages of the proposed strategy is that the controller can work satisfactorily without any specific knowledge of an upper bound for the unmodeled dynamics and/or the disturbance term.
Mathematics Subject Classification ID: 93C10
Mathematics Subject Classification ID: 93C15
Mathematics Subject Classification ID: 93C40
zbMATH DE Number: 6134097
Wikidata QID: Q58695265
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/471281
OpenAlex ID: W1999056513
cites work: Q4001920
cites work: Neural networks for modelling and control of dynamic systems. A practitioner's handbook
cites work: Q4653997
cites work: System identification using multilayer differential neural networks: a new result
cites work: Influence of control valve delay and dead zone on the stability of a simple hydraulic positioning system
cites work: Nonlinear mathematical modeling in pneumatic servo position applications
cites work: Adaptive control of plants with unknown dead-zones
cites work: Q4239325
cites work: Q437594
cites work: Q4210718
cites work: Robust adaptive control of a class of nonlinear systems with unknown dead-zone.
cites work: Robust adaptive neurocontrol of SISO nonlinear systems preceded by unknown deadzone
cites work: Deadzone compensation in motion control systems using neural networks
cites work: Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities
description: scientific article; zbMATH DE number 6134097