Abstract:
This paper addressess, a study on the bio inspired intelligent controller namely Brain Emotional Learning Based Intelligent Controller (BELBIC) and its design for spring ...Show MoreMetadata
Abstract:
This paper addressess, a study on the bio inspired intelligent controller namely Brain Emotional Learning Based Intelligent Controller (BELBIC) and its design for spring mass damper system which has parameter uncertainty. The mass-damper-spring system is a common control experimental device frequently used. This paper gives the possible structured (parametric) uncertainties in the spring-mass-damper system. The model free controller based on the brain-emotional-learning algorithm adapts the computational model of limbic system in the mammalian brain. Here an attempt is made to design the controller for a uncertain system (spring-mass-damper). The performance of the developed controller is compared with that of a PID controller.
Published in: 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies
Date of Conference: 08-10 May 2014
Date Added to IEEE Xplore: 26 January 2015
ISBN Information:
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- IEEE Keywords
- Index Terms
- Control System ,
- Emotional Memory ,
- Uncertain Systems ,
- Intelligent Control ,
- Control For Uncertain Systems ,
- Model System ,
- Parameter Uncertainty ,
- Brain Systems ,
- Bioinspired ,
- Limbic System ,
- Mammalian Brain ,
- Proportional-integral-derivative ,
- Common Devices ,
- Active Control ,
- Thalamus ,
- Cortical Areas ,
- Sensory Input ,
- Orbitofrontal Cortex ,
- Sensory Signals ,
- Plant Yield ,
- Mass Position ,
- Connection Weights ,
- Reward Signal ,
- Permanent Magnet Synchronous Motor ,
- Newton’s Second Law ,
- Learning Rate Parameter ,
- Control Technology
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Control System ,
- Emotional Memory ,
- Uncertain Systems ,
- Intelligent Control ,
- Control For Uncertain Systems ,
- Model System ,
- Parameter Uncertainty ,
- Brain Systems ,
- Bioinspired ,
- Limbic System ,
- Mammalian Brain ,
- Proportional-integral-derivative ,
- Common Devices ,
- Active Control ,
- Thalamus ,
- Cortical Areas ,
- Sensory Input ,
- Orbitofrontal Cortex ,
- Sensory Signals ,
- Plant Yield ,
- Mass Position ,
- Connection Weights ,
- Reward Signal ,
- Permanent Magnet Synchronous Motor ,
- Newton’s Second Law ,
- Learning Rate Parameter ,
- Control Technology
- Author Keywords