Article
Computer Science, Artificial Intelligence
Zhiwen Chen, Ketian Liang, Steven X. Ding, Chao Yang, Tao Peng, Xiaofeng Yuan
Summary: This article introduces the current research status of canonical correlation analysis (CCA) and deep neural network-aided CCA (DNN-CCA) in multivariate analysis, discusses the characteristics and differences of various DNN models combined with CCA, and provides suggestions for method selection.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Zhiwen Chen, Chang Liu, Steven X. Ding, Tao Peng, Chunhua Yang, Weihua Gui, Yuri A. W. Shardt
Summary: A new method for monitoring and fault detection of multimode processes is proposed in the article, integrating K-means into just-in-time learning to build local models and addressing limitations of traditional canonical correlation analysis methods in handling processes with multiple operating points. Its effectiveness is demonstrated in an industrial benchmark process, showing better fault detection rate compared to conventional methods.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Computer Science, Artificial Intelligence
Xianchao Xiu, Lili Pan, Ying Yang, Wanquan Liu
Summary: This study proposes a new joint sparse constrained CCA model that integrates l(2,0)-norm joint sparse constraints into classical CCA for improved fault detection performance. The proposed approach fully exploits the joint sparse structure to determine the number of extracted variables and utilizes an efficient algorithm for computation. Extensive numerical studies demonstrate the efficiency and speed of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Chemical
Bing Song, Tao Guo, Hongbo Shi, Yang Tao, Shuai Tan
Summary: With the development of sensor technology and industrial processes, process monitoring has become crucial for ensuring product quality and improving economic efficiency. This study proposes a model called Neighborhood Embedding Canonical Correlation Analysis (NECCA) that combines canonical correlation analysis (CCA) with a neighborhood structure feature extraction algorithm. By incorporating neighborhood information into the traditional CCA model, the NECCA model achieves a more comprehensive feature representation. The rationality and effectiveness of the proposed model are demonstrated through a typical test case.
JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS
(2023)
Article
Automation & Control Systems
Hongchao Cheng, Jing Wu, Daoping Huang, Yiqi Liu, Qilin Wang
Summary: A novel method called Rab-CCA is proposed for monitoring wastewater treatment processes, which includes a robust decomposition method and an adaptive statistical control limit to improve the performance of standard process monitoring methods, reducing missed alarms and false alarms simultaneously.
Article
Computer Science, Artificial Intelligence
Lijia Luo, Weida Wang, Shiyi Bao, Xin Peng, Yigong Peng
Summary: The traditional CCA lacks robustness against outliers. This study proposes robust CCA methods that utilize weighted covariance matrices to reduce the negative impact of outliers. The RCCA is further extended to include l1-norm constraints, resulting in robust sparse CCA (RSCCA). The proposed methods are then applied to fault detection and diagnosis in industrial processes. Two case studies demonstrate the high robustness of RCCA and RSCCA against outliers and the reliability of the robust FDD method even with low-quality training data containing outliers.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Engineering, Chemical
Simin Li, Shuang-hua Yang, Yi Cao
Summary: Most industrial systems today are nonlinear and dynamic, and traditional fault detection techniques are limited in simultaneously extracting both nonlinear and dynamic features. This work proposes a novel nonlinear dynamic process monitoring method called canonical variate kernel analysis (CVKA), which combines the CVA method for linear dynamic feature extraction and kernel principal component analysis for nonlinear feature extraction. Experimental results on a TE process case study demonstrate the excellent performance of CVKA compared to other common approaches in dynamic nonlinear process monitoring for TE-like processes.
Article
Automation & Control Systems
Bing Song, Hongbo Shi, Shuai Tan, Yang Tao
Summary: The article introduces a novel data-driven method called multisubspace orthogonal canonical correlation analysis, which can real-time judge whether faults affect product quality. By dividing the process variable space into four subspaces, conducting orthogonal CCA for feature extraction and monitoring statistics, the method is developed and tested successfully.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Automation & Control Systems
Xianchao Xiu, Zhonghua Miao, Ying Yang, Wanquan Liu
Summary: This article proposes an efficient nonlinear process monitoring method by integrating DAENNs, CCA, and SCO. The method is demonstrated on the TE process and the diesel generator process, achieving an increased fault detection rate of 8.00% for the fault IDV(11) compared to classical CCA.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Acoustics
Jun Lin, Huanlin Liu, Shihua Huang, Zhenhua Nie, Hongwei Ma
Summary: This paper presents a novel method for structural damage detection in beam bridges using limited sensors. The method involves cutting out measured acceleration responses with a moving window, extracting principal components through subspace analysis, and obtaining correlation coefficients between the components using canonical correlation analysis. A new damage factor is defined based on the output of the correlation analysis. Numerical and experimental results demonstrate the successful identification of single and multiple damages using a limited number of sensors.
JOURNAL OF SOUND AND VIBRATION
(2022)
Article
Engineering, Chemical
Xuguang Hu, Ping Wu, Haipeng Pan, Yuchen He
Summary: In this paper, a sparse dynamic canonical correlation analysis (SDCCA) method is proposed for fault detection. By introducing sparsity, the interpretability of canonical variates is enhanced. The superiority of the method is demonstrated through a comparative study of the Tennessee Eastman process benchmark.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING
(2023)
Article
Mathematics
Stan Lipovetsky
Summary: Introduces a multivariate technique called Canonical Concordance Correlation Analysis (CCCA) which maximizes the Lin's concordance correlation coefficient between two sets of variables to measure similarity and agreement. It considers not only the maximum correlation but also the closeness of mean values and variances of the aggregates. CCCA is a generalized eigenproblem that provides a different solution from CCA when the means of the aggregates are different. It has properties and applications for solving applied statistical problems that require closeness of mean values and variances, along with maximum canonical correlations between two data sets.
Article
Computer Science, Artificial Intelligence
Fabien Girka, Arnaud Gloaguen, Laurent Le Brusquet, Violetta Zujovic, Arthur Tenenhaus
Summary: This paper presents a new method called Tensor GCCA (TGCCA) for analyzing higher-order tensors. Two algorithms for TGCCA are provided, along with convergence guarantees. The efficiency and usefulness of TGCCA are evaluated on simulated and real data and compared favorably to state-of-the-art approaches.
INFORMATION FUSION
(2024)
Article
Automation & Control Systems
Shaoqi Wang, Chenchen Zhou, Yi Cao, Shuang-Hua Yang
Summary: Combustion state monitoring in industrial MSWI power generation process is challenging due to the time lag between the combustion chamber and steam generation stage, as well as the continuous addition of raw waste. To overcome these difficulties, a MBDCCA method is proposed, which extends CCA and incorporates dynamic enhancement. An online monitoring approach based on MBDCCA algorithm is designed and applied in an industrial MSWI plant to demonstrate its effectiveness.
CONTROL ENGINEERING PRACTICE
(2023)
Article
Engineering, Environmental
Ping Wu, Xujie Zhang, Jiajun He, Siwei Lou, Jinfeng Gao
Summary: The paper presents a novel locality preserving randomized canonical correlation analysis (LPRCCA) method for real-time nonlinear process monitoring which maps original data to a randomized low-dimensional feature space and integrates local geometric structure information to improve data mining performance, reducing computational cost and showing significant advantages over kernel-based methods.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2021)
Article
Automation & Control Systems
Subhashis Nandy
Summary: This research focuses on the design and stability analysis of nonlinear controllers for an electrically driven marine cycloidal propeller, along with estimating various parameters using the Extended Kalman Filter. The controller is defined using an efficient physics-based model and is able to accurately process multiple control signals. The robustness of the controller is assessed using Monte Carlo simulation, and its performance is evaluated through validation investigations.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Lucas C. Borin, Guilherme Hollweg, Caio R. D. Osorio, Fernanda M. Carnielutti, Ricardo C. L. F. Oliveira, Vinicius F. Montagner
Summary: This work presents a new automated test-driven design procedure for robust and optimized current controllers applied to LCL-filtered grid-tied inverters. The design of control gains is guided by high-fidelity simulations and particle swarm optimization algorithm, considering various normal and abnormal operating conditions. The proposed design ensures superior performance compared with other current control designs.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Wei He, Xiang Wang, Mohammad Masoud Namazi, Wangping Zhou, Josep M. Guerrero
Summary: The main objective of this paper is to develop a reduced-order adaptive state observer for a large class of DC-DC converters with constant power load, in order to estimate their unavailable states and unknown parameter and achieve an output feedback control scheme. The observer is designed using a generalized parameter estimation based observer technique and dynamic regressor extension and mixing method. The comparison study shows that the observer has the advantage of verifying the observability of the systems for exponential convergence without any extra excitation condition.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Te Zhang, Bo Zhu, Lei Zhang, Qingrui Zhang, Tianjiang Hu
Summary: This paper introduces a control technique called time-varying uncertainty and disturbance estimator (TV-UDE) which extends the classic UDE approach to handle more complicated issues. By combining TV-UDE with a nominal dynamic output-feedback controller, robust control for uncertain second-order attitude control systems without velocity measurements is achieved. Numerical simulations and physical experiments on a 2-DOF AERO attitude helicopter platform demonstrate the effectiveness of the proposed design in reducing steady-state errors and avoiding issues caused by high-gain estimation.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Kanishke Gamagedara, Taeyoung Lee, Murray Snyder
Summary: This paper presents the developments of flight hardware and software for a multirotor unmanned aerial vehicle capable of autonomously taking off and landing on a moving vessel in ocean environments. The flight hardware consists of a general-purpose computing module connected to a low-cost inertial measurement unit, real-time kinematics GPS, motor speed controller, and a camera through a custom-made printed circuit board. The flight software is developed in C++ with multi-threading to execute control, estimation, and communication tasks simultaneously. The proposed flight system is verified through autonomous flight experiments on a research vessel in Chesapeake Bay, utilizing real-time kinematics GPS for relative positioning and vision-based autonomous flight for shipboard launch and landing.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Yun Zhu, Kangkang Zhang, Yucai Zhu, Pengfei Jiang, Jinming Zhou
Summary: In this study, a three-term Dynamic Matrix Control (DMC) algorithm using quadratic programming is developed and compared with the traditional two-term DMC algorithm. Simulation studies and real-life tests show that the three-term DMC algorithm outperforms the two-term DMC algorithm in control effectiveness.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Jayu Kim, Taehoon Lee, Cheol-Joong Kim, Kyongsu Yi
Summary: This paper presents a data-based model predictive control method for a semi-active suspension system. The method utilizes a continuous damping controller and a stiffness controller to improve ride comfort and reduce vehicle pitch motion. Gaussian process regression is also used to compensate for model parameter uncertainties. The algorithm has been verified through computer simulations and vehicle tests, demonstrating its effectiveness and robustness.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Kunpeng Zhang, Jikang Gao, Zongqi Xu, Hui Yang, Ming Jiang, Rui Liu
Summary: A improved dynamic programming model is proposed in this paper for joint operation optimization of virtual coupling of heavy-haul trains. By simultaneously optimizing the headway and energy savings, as well as performing locomotive engineering advisory analysis, significant improvements in train performance can be achieved.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Demian Garcia-Violini, Yerai Pena-Sanchez, Nicolas Faedo, Fernando Bianchi, John V. Ringwood
Summary: This study presents a model invalidation methodology for wave energy converters (WECs) that can effectively handle dynamic uncertainty and external noise. The results indicate that neglecting dynamic uncertainty can lead to overestimation of performance, highlighting the importance of accurate dynamic description for estimating control performance.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Shengyang Lu, Yue Jiang, Xiaojun Xu, Hanxiang Qian, Weijie Zhang
Summary: This paper proposes an adaptive heading tracking control strategy based on wheelbase changes for unmanned ground vehicles (UGVs) with variable configuration. The strategy adjusts the wheelbase according to different working conditions to optimize driving performance. The impact of changing wheelbase on sideslip angle and heading angle is analyzed, and a robust-active disturbance rejection control method is developed to achieve desired front-wheel steering angle. A torque distribution method based on tire load rate and real-time load is applied to enhance longitudinal stability.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Domenico Dona, Basilio Lenzo, Paolo Boscariol, Giulio Rosati
Summary: This paper proposes a new method for designing minimum energy trajectories for servo-actuated systems and demonstrates its accuracy and effectiveness through numerical comparisons and experimental validation.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Haolin Wang, Luyao Zhang, Yao Mao, Qiliang Bao
Summary: This paper proposes a method of transforming the core element of ADRC, ESO, into a novel fuzzy self-tuning observer structure to improve the stability of LOS in the electro-optical tracking system. It effectively solves the conflict between disturbance rejection ability and noise attenuation ability in traditional ESO.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Alejandro Toro-Ossaba, Juan C. Tejada, Santiago Rua, Juan David Nunez, Alejandro Pena
Summary: This work presents the development of a myoelectric Model Reference Adaptive Controller (MRAC) with an Adaptive Kalman Filter for controlling a cable driven soft elbow exoskeleton. The proposed MRAC controller is effective in both passive and active control modes, showing good adaptability and control capabilities.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Mehrad Jaloli, Marzia Cescon
Summary: This study presents an advanced multi-agent reinforcement learning (RL) strategy for personalized glucose regulation, which is shown to improve glucose regulation and reduce the risk of severe hyperglycemia compared to traditional therapy.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Yingming Tian, Kenan Du, Jianfeng Qu, Li Feng, Yi Chai
Summary: This paper investigates the control strategy for PMSM with position sensor fault in railway. A learning observer-based control strategy is proposed, which achieves high-precision estimation of electromotive force and accelerates speed response.
CONTROL ENGINEERING PRACTICE
(2024)