Slip-Clutch Torque Estimation via Real-Time Adaptive Lookup Table

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Wenpeng Wei ◽  
Hussein Dourra ◽  
Guoming Zhu
Keyword(s):  
Friction Coefficient ◽  
Real Time ◽  
Mean Squared Error ◽  
Surface Friction ◽  
Lookup Table ◽  
Recursive Least Squares ◽  
Time Varying ◽  
Squared Error ◽  
Output Torque ◽  
Clutch Torque

Abstract Transfer case clutch is crucial in determining traction torque distribution between front and rear tires for four-wheel-drive (4WD) vehicles. Estimating time-varying clutch surface friction coefficient is critical for traction torque control since it is proportional to the clutch output torque. As a result, this paper proposes a real-time adaptive lookup table strategy to provide the time-varying clutch surface friction coefficient. Specifically, the clutch-parameter-dependent (such as clutch output torque and clutch touchpoint distance) friction coefficient is first estimated with available low-cost vehicle sensors (such as wheel speed and vehicle acceleration); and then a clutch-parameter-independent approach is developed for clutch friction coefficient through a one-dimensional lookup table. The table nodes are adaptively updated based on a fast recursive least-squares (RLS) algorithm. Furthermore, the effectiveness of adaptive lookup table is demonstrated by comparing the estimated clutch torque from adaptive lookup table with that estimated from vehicle dynamics, which achieves 14.8 Nm absolute mean squared error (AMSE) and 2.66% relative mean squared error (RMSE).

scholarly journals APPLICATION OF AN ADAPTIVE STEP-SIZE ALGORITHM IN MODELS OF HYPERINFLATION

2012 ◽  
Vol 16 (S3) ◽  
pp. 355-375 ◽  
Author(s):  
Olena Kostyshyna
Keyword(s):  
New York ◽  
Mean Squared Error ◽  
American Economic Review ◽  
Sufficient Information ◽  
Recursive Algorithms ◽  
Time Varying ◽  
Step Size ◽  
Adaptive Step Size ◽  
Squared Error ◽  
Adaptive Step

An adaptive step-size algorithm [Kushner and Yin,Stochastic Approximation and Recursive Algorithms and Applications, 2nd ed., New York: Springer-Verlag (2003)] is used to model time-varying learning, and its performance is illustrated in the environment of Marcet and Nicolini [American Economic Review93 (2003), 1476–1498]. The resulting model gives qualitatively similar results to those of Marcet and Nicolini, and performs quantitatively somewhat better, based on the criterion of mean squared error. The model generates increasing gain during hyperinflations and decreasing gain after hyperinflations end, which matches findings in the data. An agent using this model behaves cautiously when faced with sudden changes in policy, and is able to recognize a regime change after acquiring sufficient information.

Artificial Neural Networks Estimation for Thicknesses of Multilayer Nano-Scale Films

2019 ◽  
Vol 962 ◽  
pp. 41-48
Author(s):  
Tzong Daw Wu ◽  
Jiun Shen Chen ◽  
Ching Pei Tseng ◽  
Cheng Chang Hsieh
Keyword(s):  
Thin Films ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Film Growth ◽  
Mean Squared Error ◽  
Squared Error ◽  
Thin Film Optics ◽  
The Mean ◽  
Artificial Neural

This study presents a real-time method for determining the thickness of each layer in multilayer thin films. Artificial neural networks (ANNs) were introduced to estimate thicknesses from a transmittance spectrum. After training via theoretical spectra which were generated by thin-film optics and modified by noise, ANNs were applied to estimate the thicknesses of four-layer nanoscale films which were TiO2, Ag, Ti, and TiO2 thin films assembled sequentially on polyethylene terephthalate (PET) substrates. The results reveal that the mean squared error of the estimation is 2.6 nm2, and is accurate enough to monitor film growth in real time.

scholarly journals Study on bifurcation characteristics of multi-clearance bending torsional coupling gear transmission based on EHL

2020 ◽  
Vol 12 (6) ◽  
pp. 168781402093750
Author(s):  
Hao Dong ◽  
Jianwen Zhang ◽  
Libang Wang
Keyword(s):  
Friction Coefficient ◽  
Periodic Motion ◽  
Chaotic Motion ◽  
Hydrodynamic Lubrication ◽  
Surface Friction ◽  
Tooth Surface ◽  
Time Varying ◽  
Mesh Stiffness ◽  
Vibration Model ◽  
Non Linear

In order to study the influence of tooth surface friction on the non-linear bifurcation characteristics of multi-clearance gear drive system, a 6 degree-of-freedom bending torsional coupled vibration model was established. The time-varying mesh stiffness, backlash, support clearance and damping were considered comprehensively in this non-linear vibration model. Loaded tooth contact analysis was used to calculate the time-varying mesh stiffness. Based on the elasto-hydrodynamic lubrication, the time-varying friction coefficient was calculated. Runge–Kutta numerical method was used to solve the dimensionless dynamic differential equation. Using phase diagram, Poincaré diagram, time history diagram, and spectrum diagram, the influence of tooth surface friction on bifurcation characteristics was studied. The results show that the system undergoes a change from 1-periodic motion, multi-periodic motion, to chaotic motion through bifurcation and catastrophe when the speed changes independently. When the friction coefficient of tooth surface changes from 0, 0.05 to 0.09, the chaotic motion of the system is suppressed. Similarly, with the increase in tooth friction, the chaotic motion characteristics are suppressed. Tooth surface friction is the main factor affecting chaotic motion. With the increase in friction coefficient of tooth surface, the chaos characteristic does not change obviously and the vibration amplitude decreases slightly.

scholarly journals Implementation of a Demand-Side Management Solution for South Korea’s Demand Response Program

2020 ◽  
Vol 10 (5) ◽  
pp. 1751 ◽  
Author(s):  
Wonsuk Ko ◽  
Hamsakutty Vettikalladi ◽  
Seung-Ho Song ◽  
Hyeong-Jin Choi
Keyword(s):  
South Korea ◽  
Real Time ◽  
Demand Response ◽  
Mean Squared Error ◽  
Demand Side Management ◽  
Demand Side ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Demand Response Program ◽  
Relative Root

In this paper, we show the development of a demand-side management solution (DSMS) for demand response (DR) aggregator and actual demand response operation cases in South Korea. To show an experience, Korea’s demand response market outline, functions of DSMS, real contracted capacity, and payment between consumer and load aggregator and DR operation cases are revealed. The DSMS computes the customer baseline load (CBL), relative root mean squared error (RRMSE), and payments of the customers in real time. The case of 10 MW contracted customers shows 108.03% delivery rate and a benefit of 854,900,394 KRW for two years. The results illustrate that an integrated demand-side management solution contributes by participating in a DR market and gives a benefit and satisfaction to the consumer.

Kalman-filter-based sensor fusion applied to road-objects detection and tracking for autonomous vehicles

2020 ◽  
pp. 095965182097552
Author(s):  
Wael Farag
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Mean Squared Error ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Root Mean Squared Error ◽  
Time Performance ◽  
Detection And Tracking ◽  
Squared Error

In this article, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. This method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the Unscented Kalman Filter fusion is compared to that of the Extended Kalman Filter fusion showing its superiority. The Unscented Kalman Filter has outperformed the Extended Kalman Filter on all test cases and all the state variable levels (−24% average Root Mean Squared Error). The employed fusion technique shows how outstanding is the improvement in tracking performance compared to the use of a single device (−29% Root Mean Squared Error with lidar and −38% Root Mean Squared Error with radar).

Inverse control of single-input/single-output nonlinear time-varying systems with noise disturbances by multi-dimensional Taylor network

2020 ◽  
Vol 42 (13) ◽  
pp. 2450-2464
Author(s):  
Hong-Sen Yan ◽  
Chao Zhang
Keyword(s):  
Real Time ◽  
Recursive Least Squares ◽  
Time Varying ◽  
Single Input Single Output ◽  
Inverse Control ◽  
The Real ◽  
Single Output ◽  
Time Varying Systems ◽  
Single Input ◽  
Noise Disturbances

In this paper, an inverse control scheme based on the novel dynamic network (multi-dimensional Taylor network (MTN)) is proposed for the real-time tracking control of nonlinear time-varying systems with noise disturbances. Utilized in this scheme are the three MTNs: the adaptive model identifier for system modeling, the adaptive inverse controller for inverse modeling, and the adaptive nonlinear filter for eliminating the noise disturbances, whose weights are modified by the variable forgetting factor recursive least squares (VFF-RLS), back propagation through model (BPTM), normalized least mean square (NLMS) algorithms, respectively. To avoid “compromise”, this scheme is designed into a structure wherein controlling the object dynamic response and eliminating the noise disturbances are implemented in two relatively independent processes. Furthermore, the weight-elimination algorithm is introduced for choice of effective regression items to avoid the dimension explosion, thus overcoming the shortcoming that the number of middle nodes needs to be determined before using the traditional neural network. After a certain number of training, the more streamlined MTNs are observed to contribute to satisfying the real-time requirements of software implementation and engineering application. To ensure that MTN inverse control is strict in theory, the general conditions for the existence of single-input/single-output (SISO) nonlinear inverse systems are identified. Simulation of the MTN inverse control is conducted to confirm the effectiveness of the proposed method.

scholarly journals A Pipeline for Adaptive Filtering and Transformation of Noisy Left-Arm ECG to Its Surrogate Chest Signal

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 866
Author(s):  
Farzad Mohaddes ◽  
Rafael da Silva ◽  
Fatma Akbulut ◽  
Yilu Zhou ◽  
Akhilesh Tanneeru ◽  
...  
Keyword(s):  
Adaptive Filtering ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Recursive Least Squares ◽  
Step Size ◽  
Ecg Signals ◽  
Squared Error ◽  
One Step ◽  
Power Line Interference ◽  
Electrocardiogram Ecg

The performance of a low-power single-lead armband in generating electrocardiogram (ECG) signals from the chest and left arm was validated against a BIOPAC MP160 benchtop system in real-time. The filtering performance of three adaptive filtering algorithms, namely least mean squares (LMS), recursive least squares (RLS), and extended kernel RLS (EKRLS) in removing white (W), power line interference (PLI), electrode movement (EM), muscle artifact (MA), and baseline wandering (BLW) noises from the chest and left-arm ECG was evaluated with respect to the mean squared error (MSE). Filter parameters of the used algorithms were adjusted to ensure optimal filtering performance. LMS was found to be the most effective adaptive filtering algorithm in removing all noises with minimum MSE. However, for removing PLI with a maximal signal-to-noise ratio (SNR), RLS showed lower MSE values than LMS when the step size was set to 1 × 10−5. We proposed a transformation framework to convert the denoised left-arm and chest ECG signals to their low-MSE and high-SNR surrogate chest signals. With wide applications in wearable technologies, the proposed pipeline was found to be capable of establishing a baseline for comparing left-arm signals with original chest signals, getting one step closer to making use of the left-arm ECG in clinical cardiac evaluations.

scholarly journals Validasi Data Curah Hujan Satelit TRMM (Tropical Rainfall Measuring Mission) dengan Data Pos Penakar Hujan di DAS Grindulu, Kabupaten Pacitan, Jawa Timur

2021 ◽  
Vol 1 (2) ◽  
pp. 772-785
Author(s):  
Dieta Putri Jarwanti ◽  
◽  
Ery Suhartanto ◽  
Jadfan Sidqi Fidari ◽  
◽  
...  
Keyword(s):  
Real Time ◽  
Mean Squared Error ◽  
Tropical Rainfall Measuring Mission ◽  
Tropical Rainfall ◽  
Root Mean Squared Error ◽  
Squared Error

Pos penakar hujan di Indonesia lokasinya masih kurang tersebar merata, padahal data hujan yang dihasilkan sangat penting. Maka diperlukan analisis validasi dengan data satelit TRMM karena dapat mencakup wilayah luas, tersedia secara near real-time dan aksesnya yang cepat. Penelitian ini bertujuan untuk memvalidasi data satelit dengan data observasi di DAS Grindulu yang datanya dianggap lengkap dan dapat diandalkan. Nantinya digunakan untuk mengantisipasi data curah hujan observasi yang mungkin error atau tidak tersedia. Metode validasi yang digunakan berupa Root Mean Squared Error (RMSE), Uji Kesalahan Relatif (KR), Nash Sutcliffe Efficiency (NSE) serta Koefisien Korelasi (R). Penelitian ini menggunakan dua tahap perhitungan, yaitu analisis validasi data tidak terkoreksi dan data terkoreksi, dimana data terkoreksi dilakukan kalibrasi data terlebih dahulu, hasil dari validasi data TRMM terkoreksi terbaik terdapat pada periode bulanan dengan rentang kalibrasi 9 tahun dan validasi 1 tahun dengan hasil NSE = 0,929; R = 0,969; RMSE = 46,48; KR = 8,9%. Hasil tersebut menunjukkan bahwa data TRMM terkoreksi menghasilkan nilai yang lebih baik dibandingan data TRMM tidak terkoreksi karena memiliki nilai NSE dan R yang mendekati satu dan nilai RMSE dan Kesalahan Relatifnya rendah. Secara kesluruhan, dapat disimpulkan bahwa data TRMM dapat digunakan sebagai data alternatif hidrologi di DAS Grindulu.

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