Paper Info
Title
Automated generation of feedforward control using feedback linearization of local model networks.
Abstract
An effective but yet simple approach is introduced to automatically attain a dynamic feedforward control law for non-linear dynamic systems represented by discrete-time local model networks (LMN). In this context, feedback linearization is applied to the generic model structure of LMN and the resulting input transformation is used as model inverse. This general and automated approach for model inversion is applicable even when the overall model complexity may be high. Thus, by representing a non-linear dynamic system by an LMN and applying the proposed feedforward control law generation, a dynamic feedforward control for such a non-linear system can be found automatically with the knowledge of measured input-output data only. However, when feedback linearization is considered, the stability of the internal dynamics plays a key role. This paper analyses the occurring internal dynamics for LMN, which directly result from the chosen model structure in identification, and discusses the effects on the transformed system. Finally, the effectiveness of the proposed data-driven feedforward control is demonstrated by a simulation example as well as by an actual application to the pre-distortion of a microelectromechanical systems (MEMS) loudspeaker with electrostatic actuation.
Year
DOI
Venue
2016
10.1016/j.engappai.2016.01.039
Eng. Appl. of AI
Keywords
Field
DocType
Feedforward control,Feedback linearization,Local model networks,System inversion,Internal dynamics
Inverse,Mathematical optimization,Model inversion,Computer science,Control theory,Simulation,Feedback linearization,System inversion,Loudspeaker,Dynamical system,Feed forward,Model complexity
Journal
Volume
Issue
ISSN
50
C
0952-1976
Citations 
PageRank 
References 
0
0.34
19
Authors
4
Name
Order
Citations
PageRank
Nikolaus Euler-Rolle100.68
Igor Skrjanc235452.47
Christoph Hametner3729.35
Stefan Jakubek412619.65