In this paper, a particle swarm optimization called multi-objective particle swarm optimization (MOPSO) with an accelerated update methodology is employed to tune Proportional Integral Derivative (PID) controller for a light control system. Nowadays, the demand of energy has exponentially increased and therefore is necessary to use the electric energy efficiently. Here, we tackle the use of light in offices where it is preferable to provide uniform illumination over the entire workplace by combining both natural and artificial lighting. The focus of this research is to regulate the light amount in a room at a constant level, irrespective of the disturbances from outside such as weather conditions. Thus, a control system for closed-loop regulation of the light amount in building rooms is designed. The main benefits would be a higher level of comfort and a continuous saving of energy. The obtained results verify that the MOPSO is able to perform appropriately in complex systems such as light control environment.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    PID based particle swarm optimization in offices light control


    Beteiligte:

    Erscheinungsdatum :

    2018-01-01


    Anmerkungen:

    IFAC PAPERSONLINE ; ISSN: 2405-8963



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Particle Swarm Optimization

    Gerhard Venter / Jaroslaw Sobieszczanski-Sobieski | AIAA | 2003


    Particle Swarm Optimization

    Venter, G. / Sobieszczanski-Sobieski, J. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2002


    Particle Swarm Optimization

    Venter, Gerhard / Sobieszczanski-Sobieski, Jaroslaw | AIAA | 2002


    Trajectory tracking control based on improved particle swarm optimization

    Wang, Yuxiao / Chao, Tao / Wang, Songyan et al. | IEEE | 2016


    Dedicated Bus Arterial Coordination Control Based on Particle Swarm Optimization

    Zhu, Guiqing / Li, Ruiqi / Sha, Na et al. | IEEE | 2023