ECCE: Evolutionary Computing in Control Engineering (2014-present)
Evolutionary computing fits into the intelligent systems core idea of developing (more or less) intelligent computer systems by implementing simple rules for simple entities whose interaction will derive complex behaviors. In this case, complexity is achieved through artificial evolution, with application to search methods for problem solving. The possible solutions to a considered problem are coded into a population, which is then artificially evolved through the core mechanisms of selection, mutation and recombination.
ECCE endeavours to apply evolutionary computing techniques to the development control systems, including system identification and controller design for a wide array of plants and processes.
Project leader Monica Patrascu
Core team Andreea Ion, Ioan Marica, Ionuț Banu, Cristi Stoican, Diana Spînu
This project is being supported through the Complex Systems Laboratory. Find out how to contribute to funding this project further.
The travels of Ph.D. students have been partially funded by the Sectoral Operational Programme Human Resources Development 2007-2013 of the Ministry of European Funds through the Financial Agreement POSDRU/187/1.5/S/155420.
- Patrascu M., Ion A. 2017 – Self-adaptation in Genetic Algorithms for Control Engineering: the Case of Time Delay Systems, 21th International Conference on Control Systems and Computer Science, CSCS21, Bucharest, Romania, 10.1109/CSCS.2017.10
- Patrascu M., Ion A. 2016 – Evolutionary Modeling of Industrial Plants and Design of PID Controllers. Case Studies and Practical Applications, Nature-Inspired Computing for Control Systems, Series Studies in Systems, Decision and Control, vol. 40, pp. 73-119, Springer, dx.doi.org/10.1007/978-3-319-26230-7_4
- Ion A., Patrascu M., Constantinescu V. 2015 – Genetic Decision Mechanism for Reasoning and Behaviour Generation in Adaptive Intelligent Agents, IEEE Evolving and Adaptive Intelligent Systems EAIS 15, Douai, France, pp. 1-8, dx.doi.org/10.1109/EAIS.2015.7368790
- Patrascu M. 2015 – Genetically enhanced modal controller design for seismic vibration in nonlinear multi-damper configuration, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 229, is. 2, pp. 158-168, dx.doi.org/10.1177/0959651814550540
- GAOT-ECM: Extension For Control And Modeling (implements genetic algorithms for industrial plant identification and PID controller design; Patrascu M., Ion A.)
- GAOT-ECM: Seismic Vibration Case Study (Patrascu M., Ion A.)