Accurate and fast estimation for field-dependent nonlinear damping force of meandering valve-based magnetorheological damper using extreme learning machine method
| Autor: | Seung-Bok Choi, Ubaidillah, Saiful Amri Mazlan, Fitrian Imaduddin, Irfan Bahiuddin, Mohd Hatta Mohammed Ariff, Khairunnisa Bte Mohmad |
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| Rok vydání: | 2021 |
| Předmět: |
010302 applied physics
Artificial neural network Computer science Computer Science::Neural and Evolutionary Computation Metals and Alloys 02 engineering and technology 021001 nanoscience & nanotechnology Condensed Matter Physics 01 natural sciences Standard deviation Surfaces Coatings and Films Electronic Optical and Magnetic Materials Damper Nonlinear system Data point Control theory 0103 physical sciences Magnetorheological fluid Magnetorheological damper Electrical and Electronic Engineering 0210 nano-technology Instrumentation Extreme learning machine |
| Zdroj: | Sensors and Actuators A: Physical. 318:112479 |
| ISSN: | 0924-4247 |
| DOI: | 10.1016/j.sna.2020.112479 |
| Popis: | The application of artificial neural network (ANN) models in magnetorheological (MR) damper has gained interest in various studies because of the high accuracy in predicting the damping force, especially for control purposes. However, the existing neural network models have apparent drawbacks such as relatively long training time and the possibility to be trapped in local solutions. Therefore, this paper aims to propose a new method to deal with a highly nonlinear behavior of MR damper using an extreme learning machine (ELM) method. The ELM method is applied to a meandering valve-based MR damper for damping force prediction, which has been recently developed. A simulation scheme is selected with damping force as the output, and current, velocity, and displacement as the inputs. The simulations are then carried out based on fatigue dynamic tests data in various frequencies and currents. The training times for more than nineteen thousand data points using the ELM method with 10, 100, 1000 hidden neuron numbers are less than 1.70 s, which is faster than the conventional ANN. Based on 50 times training processes, the ELM and ANN models have comparable average accuracies with R2 values of more than 0.95. ELM also has shown less value R2 standard deviation showing its advantage to reduce the possibility of being trapped in local solution compared to the conventional ANN. |
| Databáze: | OpenAIRE |
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