Fault-tolerant control of an electrohydraulic brake using virtual pressure sensor

Ho, Lok Man; Satzger, Clemens; Castro, Ricardo de

doi:10.1109/icras.2017.8071920

Cited by 6 publications

(2 citation statements)

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“…Usually, they are used to replace physical sensors [ 25 , 27 , 28 ]. They can also be used as part of fault detection methodologies, where their output is compared to the corresponding sensor [ 24 , 29 , 30 , 31 , 32 ]. The concept of virtual sensors is also present in studies in the context of wearable sensors [ 27 , 33 ] and physiological signals [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Moura

Balbinot

2018

Sensors

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A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior.

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Section: Introductionmentioning

confidence: 99%

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Moura

Balbinot

2018

Sensors

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“…Ding et al [21] employed least mean square method to predict wheel pressure and tire-road friction based on hydraulic response models developed using control parameters in ABS. Ho et al [22] proposed a fault tolerant control method in response to pressure sensor failure of an EHB system. A virtual pressure sensor based on a least squares estimation algorithm using motor position and pressure signals during healthy conditions was developed.…”

Section: Introductionmentioning

confidence: 99%

Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach

et al. 2019

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Driver perception, decision, and control behaviors are easily affected by traffic conditions and driving style, showing the tendency of randomness and personalization. Brake intention and intensity are integrated and control-oriented parameters that are crucial to the development of an intelligent braking system. In this paper, a composite machine learning approach was proposed to predict driver brake intention and intensity with a proper prediction horizon. Various driving data were collected from Controller Area Network (CAN) bus under a real driving condition, which mainly contained urban and rural road types. ReliefF and RReliefF (they don’t have abbreviations) algorithms were employed as feature subset selection methods and applied in a prepossessing step before the training. The rank importance of selected predictors exhibited different trends or even negative trends when predicting brake intention and intensity. A soft clustering algorithm, Fuzzy C-means, was adopted to label the brake intention into categories, namely slight, medium, intensive, and emergency braking. Data sets with misplaced labels were used for training of an ensemble machine learning method, random forest. It was validated that brake intention could be accurately predicted 0.5 s ahead. An open-loop nonlinear autoregressive with external input (NARX) network was capable of learning the long-term dependencies in comparison to the static neural network and was suggested for online recognition and prediction of brake intensity 1 s in advance. As system redundancy and fault tolerance, a close-loop NARX network could be adopted for brake intensity prediction in the case of possible sensor failure and loss of CAN message.

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Series Motor Four Quadrants Drive DC Chopper

Arof¹,

Sazali²,

Diyanah³

et al. 2021

Advanced Structured Materials

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Fault-tolerant control of an electrohydraulic brake using virtual pressure sensor

Cited by 6 publications

References 13 publications

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach

Series Motor Four Quadrants Drive DC Chopper

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