Abstract
In this paper, an approach for system identification and automatic control systems
design based on a combination of Particle Swarm Optimization (PSO) together with
Principal Components Analysis (PCA) is proposed. This methodology, referred to
as PSO-PCA optimization approach, incorporates the PCA concept on the PSO algorithm, which can be implemented off-line or even on-line. Different variants of the
PSO-PCA algorithm will be presented and compared with the classical PSO, in order
to evaluate the performance of the underlying algorithms, in simulations and in real
experiments.
design based on a combination of Particle Swarm Optimization (PSO) together with
Principal Components Analysis (PCA) is proposed. This methodology, referred to
as PSO-PCA optimization approach, incorporates the PCA concept on the PSO algorithm, which can be implemented off-line or even on-line. Different variants of the
PSO-PCA algorithm will be presented and compared with the classical PSO, in order
to evaluate the performance of the underlying algorithms, in simulations and in real
experiments.
| Original language | English |
|---|---|
| Title of host publication | Focus on Swarm Intelligence Research and Applications |
| Publisher | Nova Science Publishers, Inc. |
| Pages | 177-192 |
| Number of pages | 16 |
| ISBN (Print) | 978-1-53612-452-1 |
| Publication status | Published - 2017 |
Keywords
- Constrained nonlinear optimization
- Control design
- Particle swarm optimization
- Principal components analysis
- System identification
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Cite this
Palma, L. B., Antunes, R., Brito, V., & Gil, P. J. C. D. S. (2017). PSO-PCA Optimization Approach for Control Design. In Focus on Swarm Intelligence Research and Applications (pp. 177-192). Nova Science Publishers, Inc..