Improved Model Reduction and Tuning of Fractional Order PI$\lambda$D$\mu$ Controllers for Analytical Rule Extraction with Genetic Programming

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@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

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