- Volume 9 Issue 6
DOI QR Code
An Evolutionary Optimized Algorithm Approach to Compensate the Non-linearity in Linear Variable Displacement Transducer Characteristics
- Murugan, S. (Dept. of Electrical and Electronics Engineering, Einstein College of Engineering) ;
- Umayal, S.P. (Dept. of Electrical and Electronics Engineering, ULTRA College of Engineering and Technology for Women)
- Received : 2014.06.16
- Accepted : 2014.08.13
- Published : 2014.11.01
Linearization of transducer characteristic plays a vital role in electronic instrumentation because all transducers have outputs nonlinearly related to the physical variables they sense. If the transducer output is nonlinear, it will produce a whole assortment of problems. Transducers rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. Attempts have been made by many researchers to increase the range of linearity of transducers. This paper presents a method to compensate nonlinearity of Linear Variable Displacement Transducer (LVDT) based on Extreme Learning Machine (ELM) method, Differential Evolution (DE) algorithm and Artificial Neural Network (ANN) trained by Genetic Algorithm (GA). Because of the mechanism structure, LVDT often exhibit inherent nonlinear input-output characteristics. The best approximation capability of optimized ANN technique is beneficial to this. The use of this proposed method is demonstrated through computer simulation with the experimental data of two different LVDTs. The results reveal that the proposed method compensated the presence of nonlinearity in the displacement transducer with very low training time, lowest Mean Square Error (MSE) value and better linearity. This research work involves less computational complexity and it behaves a good performance for nonlinearity compensation for LVDT and has good application prospect.
Linear Variable Displacement Transducer (LVDT);Nonlinearity;Extreme Learning Machine (ELM);Differential Evolution (DE) algorithm;ANN trained by Genetic Algorithm (GA)
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