Paper Info
Title
Modeling and adaptive control of nonlinear dynamical systems using radial basis function network.
Abstract
In this paper, the use of radial basis function network (RBFN) for simultaneous online identification and indirect adaptive control of nonlinear dynamical systems is demonstrated. The motivation of using RBFN comes from the simplicity of its structure and simpler mathematical formulation, which gives it an advantage over multi-layer feed-forward neural network (MLFFNN). Since most processes are nonlinear, the use of conventional proportional-integral-derivative controller is not useful. Most of the time plantu0027s dynamics information is not available. This creates another limitation on the use of conventional control techniques, which works only if plantu0027s dynamics information is available. The proposed controller is tested for parameter variations and disturbance effects. Simulation results showed that RBFN is able to capture the unknown dynamics as well as simultaneously able to adaptively control the plant. It is also found to compensate the effects of parameter variations and disturbances. The comparative analysis is also done with MLFFNN in each simulation example, and it is found that performance of RBFN is better than that of MLFFNN.
Year
DOI
Venue
2017
10.1007/s00500-016-2447-9
Soft Comput.
Keywords
Field
DocType
Radial basis function network, Nonlinear system identification and control, Gradient descent principle, Multi-layer feed-forward neural network, Robustness
Online identification,Radial basis function network,Control theory,Nonlinear system,Computer science,Control theory,Robustness (computer science),Nonlinear dynamical systems,Artificial intelligence,Adaptive control,Artificial neural network,Machine learning
Journal
Volume
Issue
ISSN
21
15
1433-7479
Citations 
PageRank 
References 
1
0.35
26
Authors
3
Name
Order
Citations
PageRank
Rajnish Kumar122922.32
Smriti Srivastava213719.60
J. R. P. Gupta3516.26