GAOT-ECM: Extension For Control And Modeling

GAOT-ECM implements genetic algorithms for industrial plant identification and PID controller design

GAOT-ECM (GAOT - Extension For Control And Modeling) is an extension of the Genetic Algorithm Optimization Toolbox (GAOT) by C.R. Houck, J.A. Joines, M.G. Kay, that implements genetic algorithms for industrial plant identification and PID controller design.
Case studies and examples included!
This extension brings forth the practical aspect of using genetic algorithms in aiding plant modeling and PID control design for real world industrial processes. Plants such water tanks, heaters, fans and motors are usually hard to model or tune on-site, especially after prolonged use of the equipment when degradation of performances is inevitable. Therefore, this toolbox extension introduces a series of practical steps that can be taken by control engineers in order to re-design viable PID controllers for their plants.
Inside the GAOT-ECM archive you will find two main folders: (1) Identification (for model identification) and (2) Control (for controller design).
(1) runs a plant model identification genetic algorithm for LTI second order and first order with time delay systems.
(2) runs a PID controller design genetic algorithm under various customizable performance criteria.
The demonstration files offer real world case studies from which the user can learn step-by-step how to apply genetic algorithms to model identification and PID controller design.
GAOT-ECM was implemented and tested under Matlab 7 (R2011b).
Thanks for trying the toolbox!
Acknowledgements: GAOT-ECM Toolbox(GAOT - Extension For Control And Modeling ) was inspired by Genetic Algorithm Optimization Toolbox (GAOT) by C.R. Houck, J.A. Joines, M.G. Kay.

Citing information: 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 (Ed. H.E.Ponce Espinosa), Series Studies in Systems, Decision and Control, vol. 40, pp. 73-119, Springer International Publishing, doi: 10.1007/978-3-319-26230-7_4