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Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive control

IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 723-733页

作者： Xiongbo Wan Chaoling Zhang Fan Wei Chuan-Ke Zhang Min Wu IEEE the School of Automation China University of Geosciencesthe Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systemsand the Engineering Research Center of Intelligent Technology for Geo-ExplorationMinistry of EducationWuhan 430074China

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic *** addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI *** the designed controller,the involved fuzzy system is ensured to be asymptotically *** examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

关键词： Dynamic event-triggered mechanism(DETM) hybrid dynamic variables model predictive control(MPC) robust positive invariant(RPI)set T-S fuzzy systems

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Reinforcement learning-based unknown reference tracking control of HMASs with nonidentical communication delays

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Science China(Information Sciences) 2023年第7期66卷 46-57页

作者： Yong XU Zheng-Guang WU Wei-Wei CHE Deyuan MENG School of Automation Beijing Institute of Technology Institute of Cyber-Systems and Control Zhejiang University College of Mathematics and Computer Science Zhejiang Normal University Department of Automation Qingdao University School of Automation Science and Electrical Engineering Beihang University(BUAA)

This paper focuses on the optimal output synchronization control problem of heterogeneous multiagent systems(HMASs) subject to nonidentical communication delays by a reinforcement learning *** with existing studies assuming that the precise model of the leader is globally or distributively accessible to all or some of the followers, the leader's precise dynamical model is entirely inaccessible to all the followers in this paper. A data-based learning algorithm is first proposed to reconstruct the leader's unknown system matrix online. A distributed predictor subject to communication delays is further devised to estimate the leader's state, where interaction delays are allowed to be nonidentical. Then, a learning-based local controller, together with a discounted performance function, is projected to reach the optimal output synchronization. Bellman equations and game algebraic Riccati equations are constructed to learn the optimal solution by developing a model-based reinforcement learning(RL) algorithm online without solving regulator equations, which is followed by a model-free off-policy RL algorithm to relax the requirement of all agents' dynamics faced by the model-based RL algorithm. The optimal tracking control of HMASs subject to unknown leader dynamics and communication delays is shown to be solvable under the proposed RL algorithms. Finally, the effectiveness of theoretical analysis is verified by numerical simulations.

关键词： heterogeneous multiagent systems HMAS reinforcement learning RL optimal output synchronization communication delays

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Constrained Moving Path Following control for UAV With Robust control Barrier Function

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1557-1570页

作者： Zewei Zheng Jiazhe Li Zhiyuan Guan Zongyu Zuo Seventh Research Division School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100191 Beijing Institute of Control Engineering Beijing 100094China

This paper studies the moving path following(MPF)problem for fixed-wing unmanned aerial vehicle(UAV)under output constraints and wind *** vehicle is required to converge to a reference path moving with respect to the inertial frame,while the path following error is not expected to violate the predefined *** from existing moving path following guidance laws,the proposed method removes complex geometric transformation by formulating the moving path following problem into a second-order time-varying control problem.A nominal moving path following guidance law is designed with disturbances and their derivatives estimated by high-order disturbance *** guarantee that the path following error will not exceed the prescribed bounds,a robust control barrier function is developed and incorporated into controller design with quadratic program based *** proposed method does not require the initial position of the UAV to be within predefined *** the safety margin concept makes error-constraint be respected even if in a noisy *** proposed guidance law is validated through numerical simulations of shipboard landing and hardware-in-theloop(HIL)experiments.

关键词： Moving path following(MPF) robust control barrier function safety margin shipboard landing unmanned aerial vehicle(UAV)

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An enhanced stochastic error modeling using multi-Gauss–Markov processes for GNSS/INS integration system

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Journal of engineering and Applied Science 2024年第1期71卷 186页

作者： Wu, Youlong Chen, Shuai School of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing China School of Automation Nanjing University of Science and Technology Nanjing China

Angular random walk (ARW), rate random walk (RRW), and bias instability (BI) are the main noise types in inertial measurement units (IMUs) and thus determine the navigation performance of IMUs. BI is the flicker noise, which determines the noise level of an inertial sensor. The traditional error modeling approach involves modeling the ARW and BI processes as RRW or Gauss–Markov (GM) processes, and this approach is applied as a suboptimal filter in the global navigation satellite system (GNSS)/inertial navigation system (INS) extended Kalman filter (EKF). In this paper, the random error identification processes for white noise and colored noise for inertial sensors are separated using the Allan variance and power spectral density methods and the equivalence of the stochastic process differential equations of bias instability and a combination of multiple first-order GM processes are derived. A colored noise compensation method is proposed based on the enhanced EKF model. Experimental results demonstrate that, compared to traditional error models, our proposed model reduces positional drift error in dynamic testing from 195 to 49 m, enhancing positional accuracy by 40.2%. These findings confirm the potential and superiority of our method in complex navigation environments. © The Author(s) 2024.

关键词： Markov processes

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State and parameter identification of linearized water wave equation via adjoint method

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Science China(Information Sciences) 2024年第10期67卷 283-297页

作者： Yang YU Cheng-Zhong XU Hai-Long PEI Jinpeng YU School of Automation Qingdao University LAGEP Batiment CPE University Claude Bernard-Lyon 1 School of Automation Science and Engineering Key Laboratory of Autonomous Systems and Networked ControlMinistry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle SystemSouth China University of Technology

In this paper, we focus on the state and parameter identification problem of a hydrodynamical system. This system is modeled as a linearized water wave equation(LWWE), a hyperbolic state-space model coupled with a Laplace equation. We assume that the wave elevation at two distinct points is the only measurement of water waves. We show that the state and water depth can be reconstructed from this point measurement records. The identification problem is recast as an optimization problem over an infinite-dimensional space. We propose the adjoint method-based identification algorithm to generate an estimated state and water depth. We then performed a numerical simulation to show the effectiveness of our designed algorithm by comparing it with existing studies.

关键词： linearized water wave equation (LWWE) point observation state estimation parameter identification adjoint method numerical simulation

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A Bio-inspired Mutual-hook Strategy for the Soft Finger to Improve Load-bearing Capacity and Grasping Stability

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Journal of Bionic engineering 2024年第3期21卷 1290-1304页

作者： Jie Huang Lingjie Gai Xiaofeng Zong Yunquan Li School of Automation China University of GeosciencesWuhan430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074China The Shien-Ming Wu School of Intelligent Engineering South China University of TechnologyGuangzhou510641China

Soft grippers have great potential applications in daily life,since they can compliantly grasp soft and delicate ***,the highly elastic fingers of most soft grippers are prone to separate from each other while grasping objects due to their low stiffness,thus reducing the grasping stability and load-bearing *** tackle this problem,inspired from the venus flytrap plant,this work proposes a mutual-hook mechanism to restrain the separation and improve the grasping performance of soft *** novel soft gripper design consists of three modules,a soft finger-cot,two Soft Hook Actuators(SHAs)and two sliding ***,the soft finger-cot covers on the soft finger,increasing the contact area with the target object,two SHAs are fixed to the left and right sides of the finger-cot,and the sliding mechanisms are designed to make SHAs stretch *** demonstrate that the proposed design can restrain the separation of soft fingers substantially,and the soft fingers with the finger-cots can grasp objects three times heavier than the soft fingers without the proposed *** proposed design can provide invaluable insights for soft fingers to restrain the separation while grasping,thus improving the grasping stability and the load-bearing capacity.

关键词： Soft robotics Soft bionic finger-cot Soft hook actuator Grasping stability Load-bearing capacity

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Rolling Bearing Fault Diagnosis Based on MTF Encoding and CBAM-LCNN Mechanism

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Computers, Materials & Continua 2025年第3期82卷 4863-4880页

作者： Wei Liu Sen Liu Yinchao He Jiaojiao Wang Yu Gu School of Mechanical and Electrical Engineering Zhoukou Normal UniversityZhoukou466001China School of Information and Control Engineering Jilin Institute of Chemical TechnologyJilin132022China School of Automation Guangdong University of Petrochemical TechnologyMaoming525000China

To address the issues of slow diagnostic speed,low accuracy,and poor generalization performance in traditional rolling bearing fault diagnosis methods,we propose a rolling bearing fault diagnosis method based on Markov Transition Field(MTF)image encoding combined with a lightweight convolutional neural network that integrates a Convolutional Block Attention Module(CBAM-LCNN).Specifically,we first use the Markov Transition Field to convert the original one-dimensional vibration signals of rolling bearings into two-dimensional ***,we construct a lightweight convolutional neural network incorporating the convolutional attention module(CBAM-LCNN).Finally,the two-dimensional images obtained from MTF mapping are fed into the CBAM-LCNN network for image feature extraction and fault *** validate the effectiveness of the proposed method on the bearing fault datasets from Guangdong University of Petrochemical Technology’s multi-stage centrifugal fan and Case Western Reserve *** results show that,compared to other advanced baseline methods,the proposed rolling bearing fault diagnosis method offers faster diagnostic speed and higher diagnostic *** addition,we conducted experiments on the Xi’an Jiaotong University rolling bearing dataset,achieving excellent results in bearing fault *** results validate the strong generalization performance of the proposed *** method presented in this paper not only effectively diagnoses faults in rolling bearings but also serves as a reference for fault diagnosis in other equipment.

关键词： Rolling bearing fault diagnosis markov transition field lightweight convolutional neural network convolutional block attention module

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Fixed-time stabilization of discontinuous spatiotemporal neural networks with time-varying coefficients via aperiodically switching control

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Science China(Information Sciences) 2023年第5期66卷 182-195页

作者： Xiaofang HU Leimin WANG Chuan-Ke ZHANG Xiongbo WAN Yong HE School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

This paper focuses on the challenge of fixed-time control for spatiotemporal neural networks(SNNs) with discontinuous activations and time-varying coefficients. A novel fixed-time convergence lemma is proposed, which facilitates the handling of time-varying coefficients of SNNs and relaxes the restriction on the non-positive definiteness of the derivative of the Lyapunov function. Besides, a more flexible and economical aperiodically switching control technique is presented to stabilize SNNs within a fixed time,efectively reducing the amount of information transmission and control costs. Under the newly established fixed-time convergence lemma and aperiodically switching controller, many more general algebraic conditions are deduced to ensure the fixed-time stabilization of SNNs. Numerical examples are provided to manifest the validity of the results.

关键词： spatiotemporal neural networks discontinuous activations time-varying coefficients fixed-time stabilization aperiodically switching control

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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of Systems Science & Complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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Research on Fault Diagnosis Algorithm of Air Compressor Based on Low-Rank Matrix Recovery

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2024年第12期51卷 2010-2016页

作者： Yu, Tianming Long, Cheng Feng, Guoliang School of Automation Engineering Northeast Electric Power University Jilin Jilin 132012 China School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Jilin 100190 China

Air compressors are crucial in industrial production, but issues such as equipment aging and improper operation can lead to faults, severely impacting efficiency and safety. Traditional MFCC spectrogram-based diagnostic methods are challenged by background noise interference, making accurate fault feature extraction difficult. To address this, this study proposes an air compressor fault identification algorithm based on low-rank matrix recovery. By applying low-rank background modeling to the MFCC spectrogram to eliminate background noise, the algorithm effectively extracts primary features. The MobileNetV2 model is then used to extract features from the MFCC foreground images, followed by dimensionality reduction using Principal Component Analysis (PCA). The reduced-dimensional fault features are analyzed using k-means clustering. Experimental results show that for Data 2, the VGG19 model achieved an accuracy of 95.90%, an F1-score of 0.91, and a recall of 0.93, with a processing time of 1916 seconds. In comparison, the ResNet50 model attained an accuracy of 94.88%, an F1-score of 0.91, and a recall of 0.93, with a processing time of 826 seconds. These results demonstrate that the foreground features obtained through low-rank background modeling exhibit superior performance in fault identification and clustering, enhancing the accuracy and stability of fault diagnosis. Thus, the proposed algorithm shows promising potential for diagnosing and repairing air compressor faults. © (2024), (International Association of Engineers). All rights reserved.

关键词： K-means clustering

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