Article
Automation & Control Systems
Na Wang, Xiaoping Liu, Cungen Liu, Huanqing Wang, Yucheng Zhou
Summary: In this paper, the problem of adaptive stability control for uncertain high-order fully actuated (HOFA) nonlinear systems with disturbances is solved. The method of almost disturbance decoupling (ADD) is used to cope with unknown disturbances, while the tuning function is applied to design an adaptive update law. A novel adaptive controller is designed based on the full-actuation feature of HOFA systems and Lyapunov stability theory. The effectiveness of the control strategy is validated through a numerical simulation example and spacecraft attitude control.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2023)
Article
Mathematics, Interdisciplinary Applications
Wang Na, Liu Xiaoping, Liu Cungen, Wang Huanqing, Zhou Yucheng
Summary: The article focuses on the almost disturbance decoupling problem for high-order fully actuated nonlinear systems. A control strategy utilizing state feedback control and virtual control laws is designed based on the full-actuation feature and Lyapunov stability theory. The unknown disturbances are handled using the almost disturbance decoupling method. The effectiveness of the control strategy is verified through stability analysis and simulation.
JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
(2022)
Article
Automation & Control Systems
Yiliang Li, Jun-e Feng, Min Meng, Jiandong Zhu
Summary: In this article, the finite-time disturbance decoupling problem of Boolean networks (BNs) and Boolean control networks (BCNs) is studied. A novel necessary and sufficient condition is provided to determine whether a given BN is finite-time disturbance decoupled. An algorithm is devised to find the shortest time that guarantees that a given BN is finite-time disturbance decoupled. In addition, a necessary and sufficient condition for solving the disturbance decoupling problem of BCNs is proposed, and all possible time optimal state feedback controllers are designed.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Xiaoping Liu, Zhengqi Wang, Wilson Wang
Summary: In this paper, a gain-tuning method is developed for almost disturbance decoupling problems of nonlinear systems with zero dynamics. The method linearizes the system, forms a linear matrix inequality, and obtains linear and nonlinear state-feedback controllers to solve the problem efficiently.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Engineering, Mechanical
Zhaopei Gong, Liang Ding, Shaozhen Li, Honghao Yue, Haibo Gao, Zongquan Deng
Summary: This article introduces a maglev vibration isolation platform (MVIP) aimed at attenuating vibration in payload-agnostic tasks under dynamic environments. By proposing a new control strategy, the article addresses coupling issues in payload-agnostic vibration control and offers unique and effective solutions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Automation & Control Systems
Giuseppe Conte, Anna Maria Perdon, Naohisa Otsuka, Elena Zattoni
Summary: This study investigates how to make the output of a linear system with polytopic uncertainties and discontinuities in the state evolution totally insensitive to an unknown disturbance input by state feedback. Suitable geometric notions are introduced to provide a structural, constructive solvability condition, and further solvability conditions are provided by requiring that the time instants at which discontinuities in the state evolution occur are sufficiently far from each other to achieve global robust asymptotic stability of the compensated dynamics.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Thermodynamics
Wen-Qi Wang, Ming-Jia Li, Jia-Qi Guo, Wen-Quan Tao
Summary: In a solar power tower plant, a feedforward-feedback control strategy based on an artificial neural network is proposed to cope with the fluctuation of solar energy. The strategy effectively reduces the fluctuation of the receiver's outlet temperature, confining the temperature deviation within acceptable limits. The results provide an alternative efficient strategy for maintaining a steady outlet temperature under the fluctuation of solar resources.
APPLIED THERMAL ENGINEERING
(2023)
Article
Automation & Control Systems
Yingxin Shou, Bin Xu, Huayan Pu, Jun Luo, Zhongke Shi
Summary: This article proposes a composite learning control approach with a heterogeneous estimator to address the multiple uncertainties in strict-feedback nonlinear systems. By using recorded data-based neural learning and disturbance observer, the approach learns the uncertainties including nonlinear dynamics, unknown control gain function, and time-varying disturbance. The lumped prediction error is constructed and incorporated into the update law through neural approximation and disturbance observation. The proposed approach ensures input limitation by representing the control input's asymmetric saturation nonlinearity with a smooth form model and utilizes a projection algorithm to avoid singularity problem. Rigorous stability analysis of the closed-loop system is conducted, guaranteeing the boundedness of the system tracking error. Tests on a third-order nonlinear system and an autonomous underwater vehicle (AUV) demonstrate that the proposed approach improves system tracking accuracy with expected learning performance.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Pui-Wai Ma, T. -H. Hubert Chan
Summary: We propose a new type of feedforward neural network that is equivariant with respect to the unitary group U(n). The network can handle input and output vectors in Cn with arbitrary dimension n, without the need for convolution layers. Our implementation avoids errors caused by truncated higher order terms in Fourier-like transformations, and each layer can be efficiently implemented using simple calculations. As a proof of concept, we provide empirical results on predicting atomic motion dynamics, demonstrating the practicality of our approach.
Article
Computer Science, Information Systems
Adriano Mourthe, Carlos E. Mello
Summary: Collaborative Filtering has been extensively studied, and neural-based methods have achieved great success in personalized recommendation. However, the complexity of these methods increases the computational cost, and reducing the sparsity and dimensionality of input features is crucial for improving accuracy.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Yulong Sun, Cungen Liu, Guangyue Du, Huanqing Wang, Zhikang Tian
Summary: In this article, an almost disturbance decoupling temperature and humidity tracking controller for air-handling units (AHUs) is constructed based on dynamic nonlinear models using the backstepping method. The controller effectively utilizes known information in the model, attenuates disturbances, and avoids computational explosion caused by fuzzy rules. Experimental results demonstrate the effectiveness and superiority of the proposed controller.
INTERNATIONAL JOURNAL OF CONTROL
(2023)
Article
Telecommunications
Ming Gao, Tanming Liao, Yubin Lu
Summary: This paper introduces a CSI feedback system framework CF-FCFNN based on deep learning, which can recover the original CSI more accurately from compressed CSI, solving the feedback overhead and challenges brought by massive MIMO in 5G.
CHINA COMMUNICATIONS
(2021)
Article
Engineering, Mechanical
Shuli Wei, Jian Wang, Jinping Ou
Summary: The study focuses on enhancing the accuracy of MR damper modeling using a neural network approach, proposing the use of Hilbert transformation to construct instantaneous variables and an indirect modeling method. Comparative analysis shows significant improvements in predicting damper force nonlinearity.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Nuclear Science & Technology
Shifa Wu, Areai Nuerlan, Jiashuang Wan, Pengfei Wang, G. H. Su
Summary: In multi-modular nuclear power plants, the coupling effect between load following reactors and fixed power reactors can affect the operation of the latter during load following or power compensation process. This research proposes a setpoint feedforward-feedback control method to decouple the reactor power output and improve the operation performance of the plant.
PROGRESS IN NUCLEAR ENERGY
(2022)
Article
Automation & Control Systems
Jian Li, Wenqing Xu, Zhaojing Wu
Summary: This paper addresses the fault-tolerant tracking control problem for a class of uncertain robotic systems with time-varying output constraints. The system considers both actuator fault and disturbances, and the dynamic matrices may not be parameterized by unknown parameters or have known nominal parts. Moreover, the reference trajectories and output constraint functions may not be twice continuously differentiable without any time derivatives of them being available for feedback. In this paper, a powerful adaptive control methodology is proposed by incorporating adaptive dynamic compensation technique into the backstepping framework based on Barrier Lyapunov functions. An adaptive state feedback controller is designed with smart choices of adaptive law and virtual controls, which guarantees boundedness of all the states of the closed-loop system and practical tracking of the reference trajectory without violating the output constraints.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Chia-Wei Lin, Tzuu-Hseng S. Li, Chung-Cheng Chen
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2018)
Article
Mathematics, Applied
Chia-Wei Lin, Tzuu-Hseng S. Li, Chung-Cheng Chen
ABSTRACT AND APPLIED ANALYSIS
(2013)
Article
Engineering, Multidisciplinary
Ting-Li Chien, Chung-Cheng Chen, Ming-Chun Tsai, Ying-Chen Chen
APPLIED MATHEMATICAL MODELLING
(2010)
Article
Mathematics, Interdisciplinary Applications
Chung-Cheng Chen, Ting-Li Chien, Ying-Chen Chen, Wen-Jiun Lin, Shu-Hao Yang
CHAOS SOLITONS & FRACTALS
(2009)
Article
Computer Science, Artificial Intelligence
Chiou-Jye Huang, Jung-Shan Lin, Chung-Cheng Chen
EXPERT SYSTEMS WITH APPLICATIONS
(2010)
Article
Automation & Control Systems
Ting-Li Chien, Chung-Cheng Chen, Chiou-Jye Huang
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2010)
Article
Engineering, Mechanical
T. H. S. Li, C. J. Huang, C. C. Chen
INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY
(2010)
Article
Automation & Control Systems
Tzuu-Hseng S. Li, Chiou-Jye Huang, Chung-Cheng Chen
Article
Engineering, Multidisciplinary
Yi-Hsiang Tseng, Chung-Cheng Chen, Chung-Huo Lin, Yuh-Shyan Hwang
MATHEMATICAL PROBLEMS IN ENGINEERING
(2013)
Article
Automation & Control Systems
T-L Chien, C-C Chen, M-C Tsai, Y-C Chen
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING
(2009)
Article
Engineering, Multidisciplinary
Chung-Cheng Chen, Jian Ke, Yen-Ting Chen
MATHEMATICAL PROBLEMS IN ENGINEERING
(2019)
Article
Engineering, Electrical & Electronic
Ke Jian, Chung-Cheng Chen, Yen-Ting Chen, Gui-hong Lin
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
(2020)
Article
Engineering, Electrical & Electronic
Chung-Cheng Chen, Yen-Ting Chen
Summary: This paper introduces a new method for addressing linear nonhomogeneous time-invariant differential equations, which is more efficient than traditional methods and can directly solve the problem. The significant applications of this method are demonstrated through its application to electrical circuit problems, based on recent research in Chen's electrical unifying approach.
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Ting-Li Chien, Chia-Wei Lin, Chiou-Jye Huang, Chung-Cheng Chen
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL
(2012)
Article
Automation & Control Systems
T. L. Chien, C. C. Chen, Y. C. Chen, S. L. Wu
CONTROL AND CYBERNETICS
(2010)