Shunyi Zhao

Prof. Dr. Shunyi Zhao

Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi 214122, China
avatar
Co-authors
avatar
Following
avatar
Followers

Research Focus & Metrics

Explore full metrics on Scilit

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
7 May 2018 in IEEE Transactions on Signal Processing
Verified
Journal Article
Closed access
YSYuriy S. Shmaliy
Yuriy S. Shmaliy
FLFrederic Lehmann
Frederic Lehmann
SZShunyi Zhao
Shunyi Zhao
Choon Ki AhnChoon Ki Ahn
Choon Ki Ahn
This paper provides a comparative analysis for robustness of the Kalman filter (KF), H ∞ filter derived using the game theory, and unbiased finite impulse response (UFIR) filter, which ignores the noise statistics and initial values. A comparison is provided for Gaussian models by studying the effects of errors and disturbing factors on the bias correction gain. It is shown that the rule of thumb of optimal filtering in terms of accuracy, UFIR <; H ∞ = KF, typically does not hold in the realworld implying errors in the noise statistics, mismodeling, temporary uncertainties, and difficulties in filter tuning to optimal mode. Under such conditions, the filters are related to each other as KF ≤ H ∞ <; UFIR. A justification of this statement is provided analytically and confirmed by simulations and experimentally based on two-state polynomial and harmonic models.
14 July 2022 in IEEE Transactions on Cybernetics
Verified
Journal Article
Closed access
TZTianyu Zhang
Tianyu Zhang
SZShunyi Zhao
Shunyi Zhao
Xiaoli LuanXiaoli Luan
Xiaoli Luan
FLFei Liu
Fei Liu
This article proposes a robust Bayesian inference approach for linear state-space models with nonstationary and heavy-tailed noise for robust state estimation. The predicted distribution is modeled as the hierarchical Student- $t$ distribution, while the likelihood function is modified to the Student- $t$ mixture distribution. By learning the corresponding parameters online, informative components of the Student- $t$ mixture distribution are adapted to approximate the statistics of potential uncertainties. Then, the obstacle caused by the coupling of the updated parameters is eliminated by the variational Bayesian (VB) technique and fixed-point iterations. Discussions are provided to show the reasons for the achieved advantages analytically. Using the Newtonian tracking example and a three degree-of-freedom (DOF) hover system, we show that the proposed inference approach exhibits better performance compared with the existing method in the presence of modeling uncertainties and measurement outliers.
7 March 2022 in IEEE Transactions on Industrial Electronics
Verified
Journal Article
Closed access
SZShunyi Zhao
Shunyi Zhao
KLKe Li
Ke Li
Choon Ki AhnChoon Ki Ahn
Choon Ki Ahn
BHBiao Huang
Biao Huang
FLFei Liu
Fei Liu
Sensors provide insights into the industrial processes, while misleading sensor outputs may result in inappropriate decisions or even catastrophic accidents. In this article, the Bayesian estimation algorithms are developed to estimate unforeseen signals in sensor outputs without tuning. The optimal Bayesian estimation method is first derived by incorporating a Gaussian distribution specifying potential unmodeled dynamics into the measurement equation. Since its performance depends on tuning parameters, an iterative Bayesian estimation algorithm is developed using the variational inference technique. Specifically, an inverse Wishart distribution is introduced to describe the predicted covariance of abnormal signals. We then estimate it together with the other independent Gaussian distributions to conditionally approximate the joint posterior distribution, by which the effects of tuning parameters can be replaced adaptively. Testing the proposed algorithms through a simulated electromechanical brake model and a real experimental system shows that the proposed algorithm can satisfactorily estimate additive sensor faults online and services as a sensor monitor that simultaneously provides the locations and magnitudes of faulty signals without tuning.
11 July 2018 in IEEE Transactions on Automatic Control
Verified
Journal Article
Closed access
YMYanjun Ma
Yanjun Ma
SZShunyi Zhao
Shunyi Zhao
BHBiao Huang
Biao Huang
In this paper, we propose a new approach to state estimation of multiple state-space models. Unlike the traditional methods (including the interacting multiple-model algorithm) that approximate a Gaussian mixture distribution with a single Gaussian distribution, the proposed method approximates the joint probability density functions of the state and model identity through Bayesian inference. It is shown that the proposed method reduces the approximation error considerably, and improves estimation accuracy without increasing computational cost. Analysis of its specific features as well as a potential extension is also presented. Numerical examples with a practically oriented simulation are employed to illustrate the effectiveness of the proposed method.
11 May 2020 in IEEE Transactions on Control Systems Technology
Verified
Journal Article
Closed access
SZShunyi Zhao
Shunyi Zhao
YSYuriy S. Shmaliy
Yuriy S. Shmaliy
Choon Ki AhnChoon Ki Ahn
Choon Ki Ahn
FLFei Liu
Fei Liu
An unbiased finite impulse response (UFIR) filtering algorithm is designed in the discrete-time state-space for industrial processes with unknown measurement data covariance. By assuming an inverse-Wishart distribution, the data noise covariance is recursively estimated using the variational Bayesian (VB) approach. The optimal averaging horizon length Nopt is estimated in real time by incorporating the estimated data noise covariance into the full-horizon UFIR filter and specifying Nopt at a point, where the estimation error covariance reaches a minimum. The proposed VB-UFIR algorithm is applied to a quadrupled water tank system and moving target tracking. It is demonstrated that the VB-UFIR filter self-estimates Nopt more accurately than known solutions. Furthermore, the VB-UFIR filter is not prone to divergence and produces more stable and more reliable estimates than the VB-Kalman filter.
24 June 2019 in IEEE Transactions on Industrial Informatics
Verified
Journal Article
Closed access
SZShunyi Zhao
Shunyi Zhao
YSYuriy S. Shmaliy
Yuriy S. Shmaliy
Choon Ki AhnChoon Ki Ahn
Choon Ki Ahn
LLLijia Luo
Lijia Luo
In this paper, an iterative finite impulse response (FIR) filter is proposed for discrete time-varying state-space models, with the purpose of a new initialization strategy for the iterative FIR structure as well as consideration of possible unexpected state dynamics in a finite horizon. A compensation variable that satisfies the Gaussian property is introduced into the state equation, and its probability density function (pdf) is estimated analytically together with the pdf of state variable using the variational Bayesian inference technique. Different from the existing methods, the proposed filter exploits the FIR structure from the perspective of pdf propagation, which provides a new efficient way to use the iterative FIR filtering structure without any particular initialization scheme. Moreover, the effects of uncertainties (caused by initialization and/or possible un-modeled state dynamics) on the filtering output are loosened adaptively. Two examples of applications demonstrate that the proposed algorithm can not only provide optimal estimates when the model used perfectly matches the measurements, but can also exhibit better robustness than the Kalman filter, optimal FIR filter, maximum likelihood FIR filter, and some commonly used robust and/or adaptive Kalman filters when the underlying process suffers from unpredicted uncertainties.