Inertial Parameter Estimation of an Excavator with Adaptive Updating Rule Using Performance Analysis of Kalman Filter

Oh, Kwangseok; Seo, Ja Ho

doi:10.1007/s12555-017-0087-1

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…Kalman filter is a recursive tracking estimator. The approach estimates the states of a process by using an updated regularized linear inversion scheme [ 38 , 52 , 66 , 67 ]. Therefore, the performance of our proposed method was compared with that of a Kalman filter.…”

Section: Resultsmentioning

confidence: 99%

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Adaptive filtering of physiological noises in fNIRS data

2018

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The study presents a recursive least-squares estimation method with an exponential forgetting factor for noise removal in functional near-infrared spectroscopy data and extraction of hemodynamic responses (HRs) from the measured data. The HR is modeled as a linear regression form in which the expected HR, the first and second derivatives of the expected HR, a short-separation measurement data, three physiological noises, and the baseline drift are included as components in the regression vector. The proposed method is applied to left-motor-cortex experiments on the right thumb and little finger movements in five healthy male participants. The algorithm is evaluated with respect to its performance improvement in terms of contrast-to-noise ratio in comparison with Kalman filter, low-pass filtering, and independent component method. The experimental results show that the proposed model achieves reductions of 77% and 99% in terms of the number of channels exhibiting higher contrast-to-noise ratios in oxy-hemoglobin and deoxy-hemoglobin, respectively. The approach is robust in obtaining consistent HR data. The proposed method is applied for both offline and online noise removal.

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Section: Resultsmentioning

confidence: 99%

“…Additionally, β ( t ) is estimated by the RLSE approach. According to previous works in the control field, the RLSE algorithm gives a good performance in parameter estimation [ 49 – 52 ] and could be utilized in real-time applications [ 53 – 56 ]. Therefore, this algorithm is chosen to estimate unknown parameter vector β ( t ).…”

Section: Methodsmentioning

confidence: 99%

Adaptive filtering of physiological noises in fNIRS data

2018

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“…The outlet pressure and flow rate of the hydraulic pumps can be used to calculate the power demand of the hydraulic system P p , as follows ( [44,45]):…”

Section: Excavator Hydraulic Modelmentioning

confidence: 99%

Energy Management Strategy of a PEM Fuel Cell Excavator with a Supercapacitor/Battery Hybrid Power Source

et al. 2019

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Construction machines are heavy-duty equipment and a major contributor to the environmental pollution. By using only electric motors instead of an internal combustion engine, the problems of low engine efficiency and air pollution can be solved. This paper proposed a novel energy management strategy for a PEM fuel cell excavator with a supercapacitor/battery hybrid power source. The fuel cell is the main power supply for most of the excavator workload while the battery/supercapacitor is the energy storage device, which supplies additional required power and recovers energy. The whole system model was built in a co-simulation environment, which is a combination of MATLAB/Simulink and AMESim software, where the fuel cell, battery, supercapacitor model, and the energy management algorithm were developed in a Simulink environment while the excavator model was designed in an AMESim environment. In this work, the energy management strategy was designed to concurrently account for power supply performance from the hybrid power sources as well as from fuel cells, and battery lifespan. The control design was proposed to distribute the power demand optimally from the excavator to the hybrid power sources in different working conditions. The simulation results were presented to demonstrate the good performance of the system. The effectiveness of the proposed energy management strategy was validated. Compared with the conventional strategies where the task requirements cannot be achieved or system stability cannot be accomplished, the proposed algorithms perfectly satisfied the working conditions.

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“…The direct calculation method and proportional integral regulator method (Liang et al, 2019) were proposed to identify the moment of inertia. The recursive least square (RLS) method (Oh and Seo, 2018) was used for inertia identification, the updating rule of forgetting factor is improved based on online analysis of Kalman filter performance. Li and Gu (2012) proposed an inertia identification method based on disturbance observer for a speed regulation system, then a fuzzy adaptive law was developed based on the identification results.…”

Section: Introductionmentioning

confidence: 99%

Adaptive control and disturbance compensation for gear transmission servo systems with large-range inertia variation

Wang

et al. 2021

Transactions of the Institute of Measurement and Control

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In gear transmission servo systems, backlash effect and inertia variation often generate low precision, oscillations or even affect the stability. Focusing on this issue, an adaptive terminal integral sliding mode control (ATISMC) strategy and a discrete linear extended state observer (DLESO) are proposed in this article. First, different from using traditional dynamic model, a characteristic model is established with online parameter identification to describe inertia variation. Then, a DLESO is proposed to observe and compensate the modeling error caused by parameter identification and backlash effect. An ATISMC is newly designed based on the characteristic model to suppress the uncertainties and stabilize the whole closed-loop system. Both the uniform ultimate boundedness of observation error and the practical finite-time stability of the system is proved. Finally, simulation and experimental results demonstrate that the proposed strategy can adapt to large-range inertia variation and suppress the backlash effect.

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Inertial Parameter Estimation of an Excavator with Adaptive Updating Rule Using Performance Analysis of Kalman Filter

Cited by 13 publications

References 14 publications

Adaptive filtering of physiological noises in fNIRS data

Adaptive filtering of physiological noises in fNIRS data

Energy Management Strategy of a PEM Fuel Cell Excavator with a Supercapacitor/Battery Hybrid Power Source

Adaptive control and disturbance compensation for gear transmission servo systems with large-range inertia variation

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