dc.contributor.author | Das, S | |
dc.contributor.author | Pan, I | |
dc.contributor.author | Das, S | |
dc.contributor.author | Gupta, A | |
dc.date.accessioned | 2018-01-18T14:13:29Z | |
dc.date.issued | 2011-10-27 | |
dc.description.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) PI(λ)D(μ) controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H(2)-norm-based reduced parameter modeling 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 PI(λ)D(μ) 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. | en_GB |
dc.description.sponsorship | This work has been supported by the Department of Science and Technology (DST), Government of India, under the PURSE programme. | en_GB |
dc.identifier.citation | Vol. 51 (2), pp. 237 - 261 | en_GB |
dc.identifier.doi | 10.1016/j.isatra.2011.10.004 | |
dc.identifier.uri | http://hdl.handle.net/10871/31067 | |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier for ISA | en_GB |
dc.relation.url | https://www.ncbi.nlm.nih.gov/pubmed/22036301 | en_GB |
dc.rights | Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved. | en_GB |
dc.subject | Algorithms | en_GB |
dc.subject | Computer Simulation | en_GB |
dc.subject | Engineering | en_GB |
dc.subject | Equipment Design | en_GB |
dc.subject | Genetics | en_GB |
dc.subject | Industry | en_GB |
dc.subject | Models, Statistical | en_GB |
dc.title | Improved model reduction and tuning of fractional-order PI(λ)D(μ) controllers for analytical rule extraction with genetic programming | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2018-01-18T14:13:29Z | |
exeter.place-of-publication | United States | en_GB |
dc.description | This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record. | en_GB |
dc.identifier.journal | ISA Transactions | en_GB |