Article
Automation & Control Systems
Iman Askari, Mulugeta A. Haile, Xuemin Tu, Huazhen Fang
Summary: The implicit particle filter aims to alleviate particle degeneracy by identifying particles in high-probability regions of the target distribution. In this study, we explore the connection between the particle update step in the implicit particle filter and the Kalman filter, and propose a novel realization of the implicit particle filter based on a bank of nonlinear Kalman filters. This realization offers improved computational tractability and efficiency.
Article
Engineering, Chemical
Mahshad Valipour, Luis A. Ricardez-Sandoval
Summary: This work proposes a constrained abridged Gaussian sum extended Kalman filter that utilizes Gaussian mixture models to enhance estimation in constrained nonlinear applications. Computational experiments show that the proposed scheme is computationally efficient and suitable for applications with active constraints on states, non-Gaussian process uncertainties, and measurement noises.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2021)
Article
Economics
Kuo-Hsuan Chin
Summary: This study applies a Bayesian approach to estimate a small-scale New Keynesian Dynamic Stochastic General Equilibrium (DSGE) model and compares the likelihood function and forecasting performance of linear and nonlinear models. The results indicate that it is unclear whether the nonlinearized DSGE model has a better fit than the linearized one during the transition periods. In terms of forecasting performance, neither the linearized nor nonlinearized DSGE models outperform VAR models in predicting GDP growth rate, inflation, and interest rates.
JOURNAL OF FORECASTING
(2022)
Article
Automation & Control Systems
Xin Chen, Shunyi Zhao, Fei Liu
Summary: This paper discusses the robust identification of linear systems using the recursive expectation-maximization algorithm, formulating a recursive Q-function based on maximum likelihood principle and accommodating outliers with Student's t-distribution. The parameter vector, noise variance, and degree of freedom are recursively estimated, and the effectiveness of the proposed algorithm is demonstrated through a numerical example and simulation of a CSTR system.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Automation & Control Systems
Xin Liu, Sicheng Lou, Wei Dai
Summary: This note presents a robust identification strategy for the nonlinear state-space model (NSSM) based on meaningful conclusions from a previous paper. The strategy utilizes the heavy-tailed Student's t-distribution to model system noises and solves the parameter estimation problem using the expectation maximization (EM) algorithm with t-distribution decomposition and particle smoother. The mathematical decomposition of the t-distribution brings two major advantages: efficient calculation of the desired Q-function and a clearer explanation of the robustness of the identification strategy.
Article
Engineering, Mechanical
Jing Chen, Yawen Mao, Manfeng Hu, Liuxiao Guo, Quanmin Zhu
Summary: This study proposes a decomposition optimization-based expectation maximization algorithm for switching models. The identities of each sub-model are estimated in the expectation step, while the parameters are updated using the decomposition optimization method in the maximization step. Compared with the traditional expectation maximization algorithm and the gradient descent expectation maximization algorithm, the decomposition optimization-based expectation maximization algorithm avoids the matrix inversion and eigenvalue calculation; thus, it can be extended to complex nonlinear models and large-scale models. Convergence analysis and simulation examples are given to show the effectiveness of the proposed algorithm.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Mechanical
Jacek Michalski, Piotr Kozierski, Wojciech Giernacki, Joanna Zietkiewicz, Marek Retinger
Summary: This paper introduces a new particle filter algorithm (MultiPDF) for state estimation of nonlinear systems, which divides the main particle filter into smaller sub-filters with a smaller number of particles for each one. Experimental results show that parallelization accelerates computation and improves estimation quality.
NONLINEAR DYNAMICS
(2021)
Article
Engineering, Chemical
Ruijing Han, Yousef Salehi, Biao Huang, Vinay Prasad
Summary: The approach presented integrates slow measurements with variable time delays and fast measurements to improve parameter estimation accuracy. By utilizing a particle filter-based approach within the expectation maximization algorithm framework, the method demonstrates effectiveness and applicability in various scenarios such as simulation examples, hybrid tank experiments, and industrial case studies.
Article
Engineering, Electrical & Electronic
Xiaoxuan Wang, Yingmin Yi, Li Wu, Chun-Yi Su, Yankai Li, Bojun Liu
Summary: This paper introduces a cost reference particle filter with multi-probability distribution (CRPF_MPD) to address the lack of particle diversity problem in particle filtering. By utilizing multi-probability distribution, information interaction and particle selection, the method achieves improved state estimation accuracy and robustness for dynamic systems with unknown noise statistics.
DIGITAL SIGNAL PROCESSING
(2023)
Article
Chemistry, Analytical
Ojonugwa Adukwu, Darci Odloak, Amir Muhammed Saad, Fuad Kassab Junior
Summary: The focus of this work is to extend nonlinear state estimation methods to gas-lifted systems. The study compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) in estimating the nonlinear states. It was found that UKF provided slightly better estimates than EKF, while PF performed the worst. The gas-lifted system exhibited casing heading instability, and the results showed that either EKF or UKF could be used for nonlinear state estimation, with UKF being preferred if computational cost is not considered.
Article
Statistics & Probability
Jean-Francois Begin, Mathieu Boudreault
Summary: The study introduces a deterministic nonlinear filtering algorithm based on a high-dimensional version of Kitagawa's method to evaluate likelihood functions for models involving stochastic volatility and jumps. Numerical results demonstrate the algorithm's precision and efficiency compared to particle filtering, showing smooth functions over parameter space. The algorithm is applied to estimate maximum likelihood parameters for models with stochastic volatility, jumps, and jump arrival intensity using S&P 500 data, revealing significant increases in jump arrival intensity during the Great Recession affecting volatility and returns clustering.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2021)
Article
Multidisciplinary Sciences
Slawomir Koziel, Anna Pietrenko-Dabrowska
Summary: This study investigates the benefits of incorporating variable-resolution electromagnetic simulation models into nature-inspired algorithms for optimization of antenna structures. The results show that appropriate resolution adjustment profiles can achieve significant computational savings without noticeable degradation of the search process reliability.
SCIENTIFIC REPORTS
(2023)
Article
Environmental Sciences
Abhinanda Roy, K. S. Kasiviswanathan, Sandhya Patidar, Adebayo J. J. Adeloye, Bankaru-Swamy Soundharajan, Chandra Shekhar P. Ojha
Summary: This paper presents a novel modeling framework that integrates the Bayesian-based Particle Filter technique and Random Forest algorithm with the conceptual hydrological model (HBV) to minimize the prediction uncertainty in streamflow simulation. The framework was analyzed on Nepal and India's Sunkoshi and Beas River basins, and consistently improved the model performance while minimizing uncertainty.
WATER RESOURCES RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Lin Zhou, Yayong Tang
Summary: The APX-EM algorithm is a hybrid accelerator proposed to speed up the convergence of EM algorithm, based on PNCG and PX-EM algorithm, achieving faster convergence rate by sacrificing some simplicity. The convergence of APX-EM algorithm is discussed, including a global convergence result under suitable conditions.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2021)
Article
Multidisciplinary Sciences
Mohammad Reza Sharifi, Saeid Akbarifard, Kourosh Qaderi, Mohamad Reza Madadi
Summary: The study employed five robust evolutionary algorithms for optimal operation of a multi-reservoir system and found that the MSA algorithm performed the best in terms of objective function value, CPU run-time, and convergence rate. This suggests that the application of robust EAs, particularly the MSA algorithm, is recommended to improve the operation policies of multi-reservoir systems.
SCIENTIFIC REPORTS
(2021)
Article
Computer Science, Artificial Intelligence
Yu Hui, Ronghu Chi, Biao Huang, Zhongsheng Hou
Summary: This study addresses the challenges of heterogeneous dynamics, strongly non-linear and non-affine structures, and cooperation-antagonism networks in multi-agent systems output consensus. It introduces a heterogeneous linear data model and an adaptive learning consensus protocol to improve system performance effectively.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Shunyi Zhao, Ke Li, Choon Ki Ahn, Biao Huang, Fei Liu
Summary: This article develops Bayesian estimation algorithms for estimating unforeseen signals in sensor outputs without tuning. A novel iterative algorithm using inverse Wishart distribution and variational inference technique is proposed to adaptively replace the effects of tuning parameters.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Wanke Yu, Chunhui Zhao, Biao Huang, Min Wu
Summary: In this study, a robust dissimilarity distribution analytics (RDDA) method is proposed for incipient industrial fault detection. The probabilistic model of the RDDA method is formulated with Laplace distribution, which provides better robustness compared to Gaussian distribution based models. By using variational inference, maximum likelihood estimations of latent variables and model parameters can be derived. A monitoring strategy is established based on static and dynamic statistics, utilizing dissimilarity between distributions of datasets. The proposed RDDA method is more suitable for practical industrial applications due to its consideration of missing data problems. Experimental results demonstrate the method's ability to accurately detect incipient faults using historical data with missing values.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Ronghu Chi, Huaying Li, Na Lin, Biao Huang
Summary: In this work, a data-driven indirect iterative learning control (DD-iILC) is proposed for a repetitive nonlinear system using a proportional-integral-derivative (PID) feedback control in the inner loop. A linear parametric iterative tuning algorithm for the set-point is developed from an ideal nonlinear learning function using an iterative dynamic linearization (IDL) technique. An adaptive iterative updating strategy of the parameter in the linear parametric set-point iterative tuning law is presented by optimizing an objective function for the controlled system. The convergence is proven using contraction mapping and mathematical induction, and the theoretical results are verified through simulations.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Junyao Xie, Oguzhan Dogru, Biao Huang, Chris Godwaldt, Brett Willms
Summary: Data-driven soft sensors have been widely used in the process industry for quality variable estimation. However, building reliable soft sensors for complex industrial processes under limited data conditions is challenging. To address this issue, we propose a reinforcement learning framework that leverages samples from source domains to solve the cross-domain soft sensor problem. The proposed framework incorporates a method for sample selection and soft sensor design, taking into account correlation and estimation error metrics.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Automation & Control Systems
Ronghu Chi, Huaying Li, Dong Shen, Zhongsheng Hou, Biao Huang
Summary: In this article, the authors propose an indirect adaptive iterative learning control scheme to enhance the performance of P-type controllers by learning from set points. Adaptive mechanism is included to regulate the learning gain using real-time measurements. The proposed methods are used for both linear and nonlinear systems, with theoretical analysis and simulation studies provided to validate the results.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Engineering, Chemical
Seshu K. Damarla, Xi Sun, Fangwei Xu, Ashish Shah, Biao Huang
Summary: Control valve, affected by stiction, causes oscillations in closed-loop signals, leading to reduced product quality, plant throughput, and increased environmental impact. Therefore, it is crucial to detect and quantify stiction in control valves. In this study, four noninvasive and practical methods are developed using statistical tests such as F-test, t-test, modified Hotelling T2-test, and reverse arrangement test. These methods are applied to benchmark control loops from various industries and compared with existing methods. The results show that the proposed methods perform equally well or better than existing methods, with the t-test-based method and the modified Hotelling T2-test-based method being particularly effective. The proposed methods not only detect stiction but also quantify its severity, providing timely notifications to operators and assisting maintenance engineers in scheduling plant shutdowns. These methods are applicable to all control loops except for level loops.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Automation & Control Systems
Huaying Li, Ronghu Chi, Zhongsheng Hou, Biao Huang
Summary: This article proposes a higher order indirect adaptive iterative learning control scheme for nonlinear nonaffine systems, which improves the control performance by using a P-type controller and iterative learning to update set points, and introduces an iterative dynamic linearization method to transform into a linear parametric learning controller.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Energy & Fuels
Stefan Jespersen, Zhenyu Yang, Dennis Severin Hansen, Mahsa Kashani, Biao Huang
Summary: To reduce the environmental impact of offshore oil and gas, stricter regulations on hydrocarbon discharge are being implemented. One approach to reducing oil discharge is by improving control systems through the introduction of new oil-in-water sensing technologies and advanced control methods. However, obtaining valid control-oriented models for de-oiling hydrocyclones has proven to be challenging, as existing models are often based on droplet trajectory analysis and do not account for the dynamics or require the measurement of droplet size distribution.
Article
Automation & Control Systems
Hongtian Chen, Biao Huang
Summary: This study develops three novel data-driven approaches for the development of fault-tolerant soft sensors in automation systems. The approaches, namely MSaS, SSaS, and IMSaS, aim to address the issue of unpredictable faults and their impact on soft sensor performance. MSaS constructs an optimal estimator of faults, SSaS removes influences from unknown sensor faults using a constructed subspace, and IMSaS is an improved version of MSaS that eliminates the effects of past prediction errors. These fault-tolerant soft sensors rely solely on system measurements and are evaluated through performance analysis and case studies.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Automation & Control Systems
Vamsi Krishna Puli, Biao Huang
Summary: Extraction of underlying patterns from measured variables is important for data-driven control applications. The proposed model can separate oscillating patterns and nonstationary variations. The methodology is applied to solve a fouling monitoring problem for an industrial oil production process.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Automation & Control Systems
Yousef Salehi, Kaiyu Zhou, Biao Huang, Xuehua Zhang
Summary: The study proposes a computer vision model to estimate flotation froth concentration, addressing practical challenges such as contaminated images, camera noise, and outliers. By restoring contaminated images, extracting froth image features, and building a regression model, the algorithm can accurately estimate froth concentration. This algorithm provides a time-saving alternative to laboratory analysis, making it essential for advanced process control applications.
JOURNAL OF PROCESS CONTROL
(2023)
Article
Automation & Control Systems
Qingyang Dai, Chunhui Zhao, Biao Huang
Summary: Due to frequent changes in operating conditions, industrial processes often exhibit time-varying behaviors, resulting in shifting data distributions. Conventional adaptive methods struggle to distinguish normal shifts from real faults when the distribution shifts widely. This study proposes an incremental variational Bayesian Gaussian mixture model (IncVBGMM) for adaptive monitoring to accommodate the changing data distribution caused by different degrees of time-varying behaviors. The proposed method effectively differentiates various types of faults from normal shifts and adapts to the time-varying dynamics.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Na Lin, Huaying Li, Ronghu Chi, Zhongsheng Hou, Biao Huang
Summary: In this work, a data-driven virtual reference setting learning (DDVRSL) method is proposed to enhance the PD feedback controller of the repetitive nonlinear system. The method utilizes an ideal nonlinear learning law and an iterative adaptation law to estimate parameters and improve robustness against uncertainties. The proposed method does not require exact mechanistic model knowledge and its convergence is proven through mathematical analysis and simulations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Ronghu Chi, Xiaolin Guo, Na Lin, Biao Huang
Summary: This article addresses the challenges of data-driven control design in the presence of strong uncertainties, hard nonlinearities, and model dependency. It proposes a dynamic linearization (DL) method and an extended state observer (ESO) to handle an unknown nonlinear nonaffine system. The article presents a modified linear data model (mLDM) that accurately captures the input-output dynamics, including both linear parameter increments and unmodeled uncertainties and disturbances. The theoretical results are mathematically proven and validated through simulations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
