Adaptive Single-Neuron Proportional-Integral-Derivative Controller Based on Memristor

Xiao Yan Fang; Li Dan Wang; Shu Kai Duan

doi:10.4028/www.scientific.net/AMM.284-287.2205

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 284-287Adaptive Single-Neuron...

Adaptive Single-Neuron Proportional-Integral-Derivative Controller Based on Memristor

Abstract:

The traditional proportional-integral-derivative (PID) controllers are still the most popular controllers in the majority of manufacturing industries. However, its control parameters are difficult to regulate with the environment changing. The resistance value of the memristor dynamically depends on its charge and flux changing, which can be regarded as a tunable parameter element in the modern control system. In this paper the newest circuit element is applied to replace the synapse weight and realize self-regulation of PID controller parameters with the environment changes in the control network. It is possessed of many characteristics such as simple structure, good robustness, high reliability and adaption to the changing environment. Then the position tracking curves and parameter varying curves are obtained by experimental simulation according to the different learning rules. The experimental simulation results show that the memristive PID control network can realize on-line control and identification with higher control reliability and better adaptability.

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Periodical:

Applied Mechanics and Materials (Volumes 284-287)

Pages:

2205-2209

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.284-287.2205

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Online since:

January 2013

Authors:

Xiao Yan Fang, Li Dan Wang, Shu Kai Duan

Keywords:

Memristor, Neural Network (NN), PID Controller, Single Neuron, Synapse Weight

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References

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