Created by W.Langdon from gp-bibliography.bib Revision:1.7177

- @Article{Das2012237,
- author = "Saptarshi Das and Indranil Pan and Shantanu Das and Amitava Gupta",
- title = "Improved Model Reduction and Tuning of Fractional Order {PI}{$\lambda$}{D}{$\mu$} Controllers for Analytical Rule Extraction with Genetic Programming",
- journal = "ISA Transactions",
- volume = "51",
- number = "2",
- pages = "237--261",
- year = "2012",
- month = mar,
- ISSN = "0019-0578",
- DOI = "doi:10.1016/j.isatra.2011.10.004",
- URL = "http://www.sciencedirect.com/science/article/pii/S0019057811001194",
- URL = "http://arxiv.org/abs/1202.5683",
- URL = "http://arxiv.org/pdf/1202.5683v1",
- keywords = "genetic algorithms, genetic programming, Automatic rule generation, Fractional-order proportional-integral-derivative (FOPID) controller, PID, Model reduction, Optimal time domain tuning, FOPID tuning rule",
- size = "25 pages",
- abstract = "Genetic algorithm (GA) has been used in this study for a new approach of suboptimal model reduction in the Nyquist plane and optimal time domain tuning of proportional-integral-derivative (PID) and fractional-order (FO) P I lambda D mu controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H2-norm-based reduced parameter modelling technique. With the tuned controller parameters and reduced-order model parameter dataset, optimum tuning rules have been developed with a test-bench of higher-order processes via genetic programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort. From the reported Pareto optimal front of the GP-based optimal rule extraction technique, a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP-based tuning rules has been compared with the original GA-based control performance for the PID and P I lambda D mu controllers, handling four different classes of representative higher-order processes. These rules are very useful for process control engineers, as they inherit the power of the GA-based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three-dimensional plots of the required variation in PID/fractional-order PID (FOPID) controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP-based tuning rules has also been shown with credible simulation examples.",
- oai = "oai:arXiv.org:1202.5683",
- }

Genetic Programming entries for Saptarshi Das Indranil Pan Shantanu Das Amitava Gupta