A New Real-Time Pinch Detection Algorithm Based on Model Reference Kalman Prediction and SRMS for Electric Adjustable Desk
This paper presents a new algorithm based on model reference Kalman torque prediction algorithm combined with the sliding root mean square (SRMS). It is necessary to improve the accuracy and reliability of the pinch detection for avoiding collision with the height adjustable desk and accidents on us...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-08-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/17/4699 |
| _version_ | 1797556772483891200 |
|---|---|
| author | Minming Gu Yajie Wei Haipeng Pan Yujia Ying |
| author_facet | Minming Gu Yajie Wei Haipeng Pan Yujia Ying |
| author_sort | Minming Gu |
| collection | DOAJ |
| description | This paper presents a new algorithm based on model reference Kalman torque prediction algorithm combined with the sliding root mean square (SRMS). It is necessary to improve the accuracy and reliability of the pinch detection for avoiding collision with the height adjustable desk and accidents on users. Motors need to regulate their position and speed during the operation using different voltage by PWM (Pulse Width Modulation) to meet the requirement of position synchronization. It causes much noise and coupling information in the current sampling signal. Firstly, to analyze the working principle of an electric height adjustable desk control system, a system model is established with consideration of the DC (Direct Current) motor characteristics and the coupling of the system. Secondly, to precisely identify the load situation, a new model reference Kalman perdition method is proposed. The load torque signal is selected as a pinch state variable of the filter by comparing the current signal. Thirdly, to meet the need of the different loads of the electric table, the sliding root means square (SRMS) of the torque is proposed to be the criterion for threshold detection. Finally, to verify the effectiveness of the algorithm, the experiments are carried out in the actual system. Experimental results show that the algorithm proposed in this paper can detect the pinched state accurately under different load conditions. |
| first_indexed | 2024-03-10T17:07:42Z |
| format | Article |
| id | doaj.art-3aac8bca104346a194235f684da2900c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T17:07:42Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3aac8bca104346a194235f684da2900c2023-11-20T10:45:27ZengMDPI AGSensors1424-82202020-08-012017469910.3390/s20174699A New Real-Time Pinch Detection Algorithm Based on Model Reference Kalman Prediction and SRMS for Electric Adjustable DeskMinming Gu0Yajie Wei1Haipeng Pan2Yujia Ying3Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaFaculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaFaculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaNanhu College, Jiaxing University, Jiaxing 314001, ChinaThis paper presents a new algorithm based on model reference Kalman torque prediction algorithm combined with the sliding root mean square (SRMS). It is necessary to improve the accuracy and reliability of the pinch detection for avoiding collision with the height adjustable desk and accidents on users. Motors need to regulate their position and speed during the operation using different voltage by PWM (Pulse Width Modulation) to meet the requirement of position synchronization. It causes much noise and coupling information in the current sampling signal. Firstly, to analyze the working principle of an electric height adjustable desk control system, a system model is established with consideration of the DC (Direct Current) motor characteristics and the coupling of the system. Secondly, to precisely identify the load situation, a new model reference Kalman perdition method is proposed. The load torque signal is selected as a pinch state variable of the filter by comparing the current signal. Thirdly, to meet the need of the different loads of the electric table, the sliding root means square (SRMS) of the torque is proposed to be the criterion for threshold detection. Finally, to verify the effectiveness of the algorithm, the experiments are carried out in the actual system. Experimental results show that the algorithm proposed in this paper can detect the pinched state accurately under different load conditions.https://www.mdpi.com/1424-8220/20/17/4699anti-pinch detectionmodel reference adaptive kalman predictionsliding root mean square (SRMS)height adjustable desk |
| spellingShingle | Minming Gu Yajie Wei Haipeng Pan Yujia Ying A New Real-Time Pinch Detection Algorithm Based on Model Reference Kalman Prediction and SRMS for Electric Adjustable Desk Sensors anti-pinch detection model reference adaptive kalman prediction sliding root mean square (SRMS) height adjustable desk |
| title | A New Real-Time Pinch Detection Algorithm Based on Model Reference Kalman Prediction and SRMS for Electric Adjustable Desk |
| title_full | A New Real-Time Pinch Detection Algorithm Based on Model Reference Kalman Prediction and SRMS for Electric Adjustable Desk |
| title_fullStr | A New Real-Time Pinch Detection Algorithm Based on Model Reference Kalman Prediction and SRMS for Electric Adjustable Desk |
| title_full_unstemmed | A New Real-Time Pinch Detection Algorithm Based on Model Reference Kalman Prediction and SRMS for Electric Adjustable Desk |
| title_short | A New Real-Time Pinch Detection Algorithm Based on Model Reference Kalman Prediction and SRMS for Electric Adjustable Desk |
| title_sort | new real time pinch detection algorithm based on model reference kalman prediction and srms for electric adjustable desk |
| topic | anti-pinch detection model reference adaptive kalman prediction sliding root mean square (SRMS) height adjustable desk |
| url | https://www.mdpi.com/1424-8220/20/17/4699 |
| work_keys_str_mv | AT minminggu anewrealtimepinchdetectionalgorithmbasedonmodelreferencekalmanpredictionandsrmsforelectricadjustabledesk AT yajiewei anewrealtimepinchdetectionalgorithmbasedonmodelreferencekalmanpredictionandsrmsforelectricadjustabledesk AT haipengpan anewrealtimepinchdetectionalgorithmbasedonmodelreferencekalmanpredictionandsrmsforelectricadjustabledesk AT yujiaying anewrealtimepinchdetectionalgorithmbasedonmodelreferencekalmanpredictionandsrmsforelectricadjustabledesk AT minminggu newrealtimepinchdetectionalgorithmbasedonmodelreferencekalmanpredictionandsrmsforelectricadjustabledesk AT yajiewei newrealtimepinchdetectionalgorithmbasedonmodelreferencekalmanpredictionandsrmsforelectricadjustabledesk AT haipengpan newrealtimepinchdetectionalgorithmbasedonmodelreferencekalmanpredictionandsrmsforelectricadjustabledesk AT yujiaying newrealtimepinchdetectionalgorithmbasedonmodelreferencekalmanpredictionandsrmsforelectricadjustabledesk |