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Self-organizing approximation-based control for higher order systems
- Source :
- IEEE transactions on neural networks. 18(4)
- Publication Year :
- 2007
-
Abstract
- Adaptive approximation-based control typically uses approximators with a predefined set of basis functions. Recently, spatially dependent methods have defined self-organizing approximators where new locally supported basis elements were incorporated when existing basis elements were insufficiently excited. In this paper, performance-dependent self-organizing approximators will be defined. The designer specifies a positive tracking error criteria. The self-organizing approximation-based controller then monitors the tracking performance and adds basis elements only as needed to achieve the tracking specification. The method of this paper is applicable to general nth-order input-state feedback linearizable systems. This paper includes a complete stability analysis and a detailed simulation example.
- Subjects :
- Adaptive control
Basis (linear algebra)
Computer Networks and Communications
Basis function
General Medicine
Nonlinear control
Models, Theoretical
Computer Science Applications
Decision Support Techniques
Feedback
Tracking error
Function approximation
Nonlinear Dynamics
Artificial Intelligence
Control theory
Control system
Computer Simulation
Neural Networks, Computer
Software
Algorithms
Mathematics
Subjects
Details
- ISSN :
- 10459227
- Volume :
- 18
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on neural networks
- Accession number :
- edsair.doi.dedup.....0e18b444b4515cf4953774d8b1c4295e