Evolutionary-Based BEL Controller Applied to a Magneto-Rheological Structural System

• Published in: Actuators Vol. 7; no. 2; p. 29
• Main Authors: Braz César, Manuel, Paulo Coelho, João, Gonçalves, José
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-06-2018
• Subjects: Actuation; Algorithms; Architectural engineering; Artificial intelligence; Automation; Brain; brain emotional learning controller; Brain research; Buildings; Computer simulation; Construction; Control systems design; Controllers; Design optimization; Earthquakes; Electric currents; Evolutionary algorithms; Fluids; International conferences; Laboratories; Magnetic fields; Order parameters; Particle swarm optimization; Rheological properties; Rheology; Seismic engineering; Seismic response; Stiffness; structural control; Vibration; France; Portugal
• ISSN: 2076-0825; 2076-0825
• DOI: 10.3390/act7020029

This work addresses the problem of finding the best controller parameters in order to improve the response of a single degree-of-freedom structural system under earthquake excitation. The control paradigm considered is based on brain emotional learning (BEL) and the actuation over the building dynamics is carried out by changing the stiffness of a magneto-rheological damper. A typical BEL-based controller requires the definition of several parameters which can prove difficult and non-intuitive to obtain. For this reason, an evolutionary-based search technique has been added to the current problem framework in order to automate the controller design. In particular, the particle swarm optimization method is chosen as the evolutionary based technique to be integrated within the current control paradigm. The obtained results suggest that, indeed, it is possible to parametrize a BEL controller using an evolutionary-based algorithm. Moreover, a simulation shows that the obtained results can outperform the ones obtained by manual tuning each controller parameter individually.