Article
Automation & Control Systems
Mingjie Li, Ping Zhou, Liangyong Wang, Ye Yuan
Summary: This article proposes a new method for controlling pulp quality using a data-driven multiobjective predictive optimal control to simultaneously control fiber length distribution and Canadian standard freeness. The method involves developing a stochastic distribution model, designing a predictive controller, and analyzing system stability to achieve effective results. Industrial experiments demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Automation & Control Systems
Jinglin Zhou, Hong Yue
Summary: The study proposes a solution to the soft-bound interval control problem by transforming it into an output probability density function tracking control problem to control the output variable within a bounded region. Additionally, fault tolerant control methods are investigated for systems with faults, and an integrated design for fault estimation and FTC is developed based on a double proportional integral structure. Extensive simulation studies are conducted to examine the effectiveness and key design factors of the proposed approach.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Engineering, Marine
Chunlong Huang, Wenwen Zhou, Qian Cheng, Ran Cao
Summary: This paper investigates the amplitude distribution characteristics of water entry sound to develop a detection method based on probability distribution function change. The results show that water entry sound exhibits significant non-Gaussian characteristics, and the stable distribution model is more suitable than the normal distribution model for water entry noise. The characteristic exponent a plays a crucial role in describing the probability distribution function of water entry sound.
Article
Biotechnology & Applied Microbiology
Matteo Cornacchia, Gabriele Moser, Ezio Saturno, Andrea Trucco, Paola Costamagna
Summary: This article introduces the development of innovative membrane filtration plants for municipal wastewaters and emphasizes the importance of reliable filtration models in the designing phase. The experimental particle size distribution (PSD) of two wastewater samples is reported using the laser diffraction technique. A comparative analysis is conducted to fit the experimental PSDs using various probability density functions (PDFs), and the three-parameter lognormal and Burr PDFs are found to provide satisfactory fitting.
ENVIRONMENTAL TECHNOLOGY & INNOVATION
(2022)
Article
Automation & Control Systems
Dan Bao, Xiaoling Liang, Shuzhi Sam Ge, Baolin Hou
Summary: This article proposes an adaptive neural trajectory tracking control scheme for n-DOF robotic manipulators subjected to parameter variations, unknown functions, and time-varying external disturbances. The computed torque control (CTC) method is used to reduce the system's nonlinearity. Radial basis function neural networks (RBFNNs) are constructed to approximate the uncertainties due to parameter variations and unknown functions. The effectiveness of the proposed method is validated through simulations on a seven-degrees of freedom robotic manipulator.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Geological
Xiaowei Wang
Summary: This study utilized field case history data from different regions to develop empirical probability distribution models for soil-layer thicknesses of liquefiable ground, and found that the lognormal distribution is appropriate. Means and standard deviations of the established models for regions such as China, Japan, New Zealand, and the US have been tabulated for ease of implementation.
JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING
(2021)
Article
Automation & Control Systems
Bo Tian, Yan Wang, Lei Guo
Summary: A novel control algorithm is proposed for a class of nonlinear stochastic systems, which can handle multiple disturbances including dynamic disturbances and non-Gaussian noise. An observer is designed to estimate the external disturbances and incorporate the disturbance compensation into the feedback control strategy, optimizing the performance index and ensuring closed-loop stability.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Yiming Fei, Dongyu Li, Yanan Li, Jiangang Li
Summary: This paper proposes an adaptive phase compensator to improve the performance of the deterministic learning-based adaptive feedforward control system. By applying the adaptive phase compensator to the hidden layer of the neural network, the nonlinear approximation capability of the network is effectively improved, leading to better learning and control performance. Simulation studies confirm the effectiveness of the proposed phase compensation method.
Article
Computer Science, Artificial Intelligence
Qingyu Shi, Xia Huang, Bo Meng, Zhen Wang
Summary: This paper proposes a neural network-based iterative learning control algorithm for tracking nonlinear SISO discrete-time systems with unknown models. The algorithm uses a generalized regression neural network as an estimator and a radial basis function neural network as a controller to solve the key parameters and control input of the system, respectively. The proposed algorithm simplifies the solving processes and drives the tracking error of the system to approach zero, as demonstrated by numerical examples and a path tracking experiment of an unmanned vehicle.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Chemistry, Analytical
Guangwei Zhang, Ping Li, Guolin Li, Ruili Jia
Summary: With the continuous advancement of electronic technology, terahertz technology has been gradually applied in radar systems. This paper studies the statistical characteristics of amplitude distribution of terahertz radar clutter, uses selected axial integral bispectrum feature for radar glancing angle recognition with a recognition rate of 91%, and verifies the wide applicability of the selected feature.
Article
Automation & Control Systems
Lingzhi Wang, Guo Xie, Fucai Qian, Jun Liu, Kun Zhang
Summary: This study presents a novel shape control approach for the probability density function (PDF) of nonlinear stochastic systems. The approach provides a formula for the PDF shape controller and utilizes the exact analytical solution of the Fokker-Planck-Kolmogorov (FPK) equation. Comparative simulation experiments demonstrate the effectiveness and feasibility of the proposed approach, showing clear advantages in PDF shape control performance compared to other methods.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Mechanics
Wei Li, Yu Guan, Dongmei Huang, Natasa Trisovic
Summary: This paper proposes a method using Gaussian Radial Basis Functions Neural Network (RBFNN) to solve FPK and BK equations and obtain the transient probability density function and reliability function of a generalized Van der Pol system under FOPID controller. The numerical results demonstrate the efficiency and accuracy of Gaussian RBFNN in solving FPK and BK equations. The order of fractional integration and derivative are critical parameters in controlling the system response, and the changes in fractional order parameters can indeed enhance the system's response to a certain extent and lead to bifurcation.
INTERNATIONAL JOURNAL OF NON-LINEAR MECHANICS
(2023)
Article
Automation & Control Systems
Hoai Vu Anh Truong, Manh Hung Nguyen, Duc Thien Tran, Kyoung Kwan Ahn
Summary: This paper presents an adaptive backstepping-based model-free control method for enhancing system tracking performance of general high-order nonlinear systems subject to disturbances and unstructured uncertainties. The proposed methodology combines backstepping control with radial basis function neural network-based time-delayed estimation to overcome the obstacle of unknown system dynamics. Command filtering is also used to address the complexity explosion in the design of backstepping control. New control laws are established to reduce the effects of approximation errors. The stability of the closed-loop system is guaranteed through the Lyapunov theorem, and the superiority of the proposed methodology is confirmed through comparative simulation with other model-free approaches.
Article
Chemistry, Analytical
Dongxi Zheng, Wonsuk Jung, Sunghoon Kim
Summary: Radial basis function neural networks are widely used, and the number and centers of basis functions play a crucial role in the network's accuracy and speed. This study introduces a modified nearest neighbor-based clustering algorithm for training these networks, which is computationally efficient, adaptive, and does not require parameter tuning. Simulation results show that this approach improves clustering and optimization of abnormal samples, leading to higher accuracy in curve fitting compared to conventional methods. Additionally, the effectiveness of this approach is demonstrated in path tracking control for a magnetic microrobot, resulting in significant improvements in test and training accuracy.
Article
Automation & Control Systems
Pascual Noradino Montes Dorantes, Gerardo Maximiliano Mendez, Marco Aurelio Jimenez Gomez, Adriana Mexicano Santoyo
Summary: This paper presents a methodology that utilizes central composite design and radial basis function neural networks to evaluate quality features in industrial image processing. Experimental results show higher accuracy and lower error rates compared to traditional approaches.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Automation & Control Systems
Ping Zhou, Ruiyao Zhang, Jin Xie, Jiping Liu, Hong Wang, Tianyou Chai
Summary: This article introduces a novel integrated PCA-ICA method for monitoring and diagnosing abnormal furnace conditions in BF ironmaking by comprehensively considering and combining the characteristics of PCA and ICA. The proposed method shows good results in both monitoring and diagnosing the abnormal furnace conditions.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Engineering, Civil
Hong Wang, Meixin Zhu, Wanshi Hong, Chieh Wang, Gang Tao, Yinhai Wang
Summary: This study proposes an adaptive multi-input and multi-output traffic signal control method that can improve network-wide traffic operations and is more computationally feasible than existing centralized signal control methods. The method minimizes traffic delay and incremental changes in control input by considering intersection interactions and using an adaptive linear-quadratic regulator (LQR).
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Wanshi Hong, Gang Tao, Hong Wang, Chieh Wang
Summary: This article proposes a new traffic signal control algorithm that uses a recurrent neural network to approximate the dynamics of unknown traffic systems. An online-learning scheme is used to accurately identify the traffic system model and design optimal signal-timing controllers. Simulation studies show that this method can reduce vehicle travel delays and improve traffic system robustness compared to widely used actuated traffic signal control schemes.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Energy & Fuels
Jie Bao, Hong Yue
Summary: The development of LIDAR technology has expanded control options for wind turbines. This paper presents the development of a predictive controller based on LIDAR measurements and compares it with other control schemes. Simulation studies show that the developed LIDAR-based predictive control improves turbine performance and satisfies control constraints.
Article
Green & Sustainable Science & Technology
Peter Taylor, Hong Yue, David Campos-Gaona, Olimpo Anaya-Lara, Chunjiang Jia
Summary: As the number of turbines in offshore wind farms increases, the complexity of cable routing optimization problems also increases. This study proposes a novel optimization algorithm based on the ant-colony heuristic, which improves computational performance by introducing decomposition techniques informed by the problem formulation and achieves near-optimal solutions.
IET RENEWABLE POWER GENERATION
(2023)
Article
Energy & Fuels
Feng Du, Hong Yue, Jiangfeng Zhang
Summary: User awareness and behaviour have a strong impact on energy savings, especially in large-scale mass rollout programmes for new energy products. This study investigates the influence of advertisement control on residential energy savings in large population networks. A mathematical model is used to predict the expected energy savings and adoption rate of energy efficient products based on advertisement. The optimisation results show significant potential benefits of using advertisement as a means to promote energy efficient products through social networks.
Article
Energy & Fuels
Oliver Tulloch, Hong Yue, Abbas Mehrad Kazemi Amiri, Roderick Read
Summary: This work introduces a unique rotary kite turbine with tensile rotary power transmission (TRPT). The power extraction, transmission, and ground station are modeled in a modular framework. Three models of the TRPT system are proposed to assess torque loss and improve design. The developed models are validated and optimized based on experimental data.
Article
Automation & Control Systems
Yunfeng Kang, Lina Yao, Jinglin Zhou, Hong Wang
Summary: In this paper, a fault isolation, diagnosis and fault tolerant control algorithm is proposed for nonlinear multiple multiplicative faults stochastic distribution control systems employing Takagi-Sugeno fuzzy system. A fault detection algorithm is introduced to discover the fault occurrence time and a fault isolation observer is built to produce the residual. The validity of the designed algorithm is demonstrated through a simulation example.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Engineering, Marine
Marvin Wright, Qing Xiao, Saishuai Dai, Mark Post, Hong Yue, Bodhi Sarkar
Summary: This paper describes the design and construction of a magnetically coupled modular bio-inspired underwater robot called the Modular Magnetic Bio-Inspired Underwater Vehicle (MMBAUV). It uses a traveling wave to mimic efficient Body Caudal Fin (BCF) swimming and manoeuvring, while its modularity allows for flexible system setup and cost reduction. The novel feature of this design is the use of a permanent synchronous magnetic coupling between neighbouring modules with a rotational degree of freedom (DoF). Lab testing results demonstrate the design's functionality, thrust generation, and manoeuvrability.
Review
Physics, Multidisciplinary
Ling-Feng Shi, Adnan Zahid, Aifeng Ren, Muhammad Zulfiqar Ali, Hong Yue, Muhammad Ali Imran, Yifan Shi, Qammer H. Abbasi
Summary: This article presents a comprehensive review of THz technologies for investigating the intrinsic characteristics of different materials, emphasizing the significance of THz wave guides, sources, detectors, and components. It also highlights the future trends and challenges in THz technology. The paper concludes that THz technology has great potential and offers new opportunities for characterizing composite materials.
Article
Computer Science, Artificial Intelligence
Zhun Yin, Tong Liu, Chieh Wang, Hong Wang, Zhong-Ping Jiang
Summary: This article introduces a deep learning-based control algorithm, DL velocity-based model predictive control (VMPC), to reduce traffic congestion with slowly time-varying traffic signal controls. The algorithm involves system identification using DL and traffic signal control using VMPC. A modeling error entropy loss is used as the training criteria, inspired by the theory of stochastic distribution control (SDC) proposed by the fourth author. Simulation results demonstrate that the algorithm effectively reduces traffic congestion with slowly varying traffic signal input. An ablation study shows that the algorithm outperforms other model-based controllers in terms of prediction error, signal varying speed, and control effectiveness.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yunfeng Kang, Lina Yao, Hong Wang
Summary: A fault isolation, estimation, and fault-tolerant control scheme for nonlinear time-varying delay stochastic distribution control systems is proposed in this article. The Takagi-Sugeno fuzzy model is used to represent the nonlinear dynamics of the system, and corresponding methods for fault detection, isolation, and estimation are designed. The system is decomposed and an augmented state vector is utilized to simplify fault handling. An adaptive observer is also designed to obtain fault information, and the observer gain matrix is calculated using linear matrix inequality. Experimental results show that the proposed scheme can effectively achieve fault-tolerant control.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Qichun Zhang, Hong Wang
Summary: This article introduces a novel data-based approach to address the non-Gaussian stochastic distribution control problem, presenting a new probability density function transformation and two optimization algorithms.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Ping Zhou, Shuai Zhang, Liang Wen, Jun Fu, Tianyou Chai, Hong Wang
Summary: This article introduces a novel Kalman filter-based robust model-free adaptive predictive control (MFAPC) method for controlling molten iron quality in the blast furnace (BF) ironmaking process. The method extends the existing single-variable MFAPC method to multivariable systems using a compact-form dynamic linearization approach. A robust MFAPC is proposed to address data loss and measurement noise issues in quality detection, combining dynamic linearization with a Pseudo-Jacobian matrix for predicting missing data and using a Kalman filter to filter measurement noise.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Wanshi Hong, Indrasis Chakraborty, Hong Wang, Gang Tao
Summary: This paper proposes a co-optimization scheme to optimize fuel efficiency for HEVs, using future speed prediction for parameter tuning and considering optimization constraints for the emission system. This novel co-optimization algorithm achieved an average further 9.22% fuel savings for the Toyota Prius Hybrid Simulink model.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2021)
