Robust $\mathcal {H}_\infty$ Filtering for Vehicle Sideslip Angle With Quantization and Data Dropouts

Chang, Xiao‐Heng; Yi, Lin

doi:10.1109/tvt.2020.3008222

Cited by 86 publications

(41 citation statements)

References 31 publications

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“…Huihui Pan et al studied the adaptive tracking control method for nonlinear system with uncertain parameter, and designed a disturbance observer based on terminal sliding mode control to deal with external disturbance and actuator saturation [148]. Reference [149] considered the influence of sensor failure, signal quantization and signal packet loss of the vehicle lateral system based on the communication network, a nonlinear system model with uncertainty is modeled using the T-S fuzzy method and the vehicle sideslip angel observer is designed. The above literature studied the disturbance observation methods based on T-S fuzzy modeling method, and proposed a robust control strategy based on observation information.…”

Section: E T-s Fuzzy Medel Based Observationmentioning

confidence: 99%

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao

Tian

Wang³

et al. 2020

IEEE Access

123

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Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This paper introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.

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Section: E T-s Fuzzy Medel Based Observationmentioning

confidence: 99%

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao

Tian

Wang³

et al. 2020

IEEE Access

123

View full text Add to dashboard Cite

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“…In the driving process, a vehicle will always obey Newton's second law, i.e., force equals mass times acceleration (F = M × A) [33,34]. Based on this, the vehicle dynamics balance equation can be derived as…”

Section: F Modeling Vehicle Dynamicsmentioning

confidence: 99%

Nonlinear Model Predictive Control for Heavy-Duty Hybrid Electric Vehicles Using Random Power Prediction Method

Chen¹,

Liao²,

Ma³

2020

IEEE Access

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“…In [28], a two-stage quantization approach was derived to reduce the number of bits required to represent floating-point parameters. Dynamic quantization has been recently explored by Chang and Liu [29] and Xiong et al [30] for vehicle systems, which suggests a growing trend in the use of an adaptive approach for signal quantization. In [29], a dynamic quantizer was developed to adjust the quantization level and parameter used to reduce the steady state limit cycle in in-vehicle networked systems as static quantizers suffer from sensor failure and dropouts.…”

Section: Introductionmentioning

confidence: 99%

Towards More Efficient DNN-Based Speech Enhancement Using Quantized Correlation Mask

2021

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Many studies on deep learning-based speech enhancement (SE) utilizing the computational auditory scene analysis method typically employs the ideal binary mask or the ideal ratio mask to reconstruct the enhanced speech signal. However, many SE applications in real scenarios demand a desirable balance between denoising capability and computational cost. In this study, first, an improvement over the ideal ratio mask to attain more superior SE performance is proposed through introducing an efficient adaptive correlation-based factor for adjusting the ratio mask. The proposed method exploits the correlation coefficients among the noisy speech, noise and clean speech to effectively re-distribute the power ratio of the speech and noise during the ratio mask construction phase. Second, to make the supervised SE system more computationally-efficient, quantization techniques are considered to reduce the number of bits needed to represent floating numbers, leading to a more compact SE model. The proposed quantized correlation mask is utilized in conjunction with a 4-layer deep neural network (DNN-QCM) comprising dropout regulation, pre-training and noise-aware training to derive a robust and high-order mapping in enhancement, and to improve generalization capability in unseen conditions. Results show that the quantized correlation mask outperforms the conventional ratio mask representation and the other SE algorithms used for comparison. When compared to a DNN with ideal ratio mask as its learning targets, the DNN-QCM provided an improvement of approximately 6.5% in the short-time objective intelligibility score and 11.0% in the perceptual evaluation of speech quality score. The introduction of the quantization method can reduce the neural network weights to a 5-bit representation from a 32-bit, while effectively suppressing stationary and non-stationary noise. Timing analyses also show that with the techniques incorporated in the proposed DNN-QCM system to increase its compactness, the training and inference time can be reduced by 15.7% and 10.5%, respectively. INDEX TERMSCorrelation coefficients, deep neural network, dynamic noise-aware training, quantization, speech enhancement, training targets.

show abstract

Robust $\mathcal {H}_\infty$ Filtering for Vehicle Sideslip Angle With Quantization and Data Dropouts

Cited by 86 publications

References 31 publications

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Nonlinear Model Predictive Control for Heavy-Duty Hybrid Electric Vehicles Using Random Power Prediction Method

Towards More Efficient DNN-Based Speech Enhancement Using Quantized Correlation Mask

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