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第35届中国控制与决策会议

作者： Tianshu Xu Zhiru Cao Bei Chen Yugang Niu The Key Laboratory of Smart Manufacturing in Energy Chemical Process (East China University of Science and Technology) Ministry of Education The school of Mechatronic Engineering and Automation Shanghai University Shanghai University of Engineering Science

This work discusses the problem of sliding mode sampled control for Markovian jump systems(MJSs),in which the system state is sampled via two randomly switching *** means of the input-delay approach,the controlled MJSs is modeled as a continuous time-delay system.A mode-dependent sliding mode sampled controller is designed to achieve the mean-square exponential ultimate boundedness and the reachability of the specified sliding ***,the corresponding sufficient conditions are derived and an optimization solving algorithm is ***,simulation results via a boost converter circuit are provided to illustrate the designed control scheme.

关键词： Markovian jump systems sampled control input-delay method the mean-square exponential ultimate boundedness

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Adaptive Dynamic Programming and Decentralized Optimal Output Regulation of Two-Time-Scale Interconnected Systems

Adaptive Dynamic Programming and Decentralized Optimal Outpu...

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International Conference on Control, automation and Information Sciences (ICCAIS)

作者： Jianguo Zhao Chunyu Yang Linna Zhou Weinan Gao School of Information and Control Engineering China University of Mining and Technology Xuzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

In this paper, we address the decentralized optimal output regulation problem for two-time-scale (TTS) interconnected systems with unknown slow dynamics through adaptive dynamic programming (ADP). Firstly, singular perturbation theory is leveraged to decompose the TTS interconnected system into a slow subsystem and several interconnected fast time-scale subsystems. Secondly, we develop a decentralized optimal control scheme for the fast time-scale subsystems with interconnections. Also, we devise an off-policy ADP algorithm to learn the optimal control strategy for the slow time-scale subsystem with unknown dynamics. Moreover, we combine the slow and fast control strategies to design the composite decentralized optimal output regulator. Both optimality and stability of the closed-loop system are rigorously analyzed. Finally, simulation results are provided to validate the effectiveness of the proposed methodology.

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Multimodal Decoupling of Stationarity and Nonstationarity with Deep Cascaded Architecture for Varying-Condition process Monitoring

Multimodal Decoupling of Stationarity and Nonstationarity wi...

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第34届中国过程控制会议

作者： Pengyu Song Chunhui Zhao Jinliang Ding Furong Gao College of Control Science and Engineering Zhejiang University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Department of Chemical and Biological Engineering Hong Kong University of Science and Technology

Nonstationarity,i.e.,the time-varying property of the data distribution,is a typical process characteristic caused by operating condition switching,which can conceal stationarity describing time-invariant process mechanisms and thus bring challenges for explicit process *** achieve full decoupling of stationarity and nonstationarity,this study reveals the following facts:1) the coupling between stationarity and nonstationarity is not uniform but can present time-varying modes,defined as multimodal coupling here;2) diverse coupling modes possess intrinsic association since they essentially stem from definite nonstationary driving forces,namely changeable operating ***,this study proposes a multimodal decoupling representation method for learning explicit representations of stationarity and ***,to capture the basic coupling mode caused by nonstationary driving forces,an information refinement module(IRM) is designed in which a shortcut connection is incorporated into the auto-encoding *** the deep cascading of multiple IRMs,multimodal coupling modes can be fully ***,a compatibility learning algorithm is developed,which can balance the stationarity and reconstruction requirements to form a stationary feature learning module(SFLM).With compatibility learning,IRMs can be induced to capture and filter out nonstationarity and output stationary refined information for *** this way,multimodal decoupling of stationarity and nonstationarity can be fully *** effectiveness of the proposed method is illustrated through a numerical example and an actual industrial process.

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Repetitive T-S Fuzzy Model-Based Iterative Learning Control Law Design 63

Repetitive T-S Fuzzy Model-Based Iterative Learning Control ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Maniarski, Robert Paszke, Wojciech Tao, Hongfeng Rogers, Eric Institute of Automation Electronic and Electrical Engineering University of Zielona Góra ul. Szafrana 2 Zielona Góra65-246 Poland Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214000 China University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

ISBN: (纸本)9798350316339

This paper develops an iterative learning control law for a class of nonlinear systems. The approach used to represent the nonlinear system dynamics is a Takagi-Sugeno fuzzy repetitive process that considers the two directions of information propagation. Then, the control action investigated is a state feedback control law combined with a PD-type feed-forward learning control law. Consequently, linear matrix inequality techniques can be used for control design. Furthermore, this approach allows the design of control action to satisfy the requirements on both the error convergence and the transient dynamics. Finally, an example demonstrates the properties of the new design. © 2024 IEEE.

关键词： State feedback

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Inspection trolley based on the distance detection obstacle

Inspection trolley based on the distance detection obstacle

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Chinese Control and Decision Conference, CCDC

作者： Guicheng Wang Ao Sheng Xiaobin Li Min Zhang Changliang Guan Fengfeng Yin School of Electrical and Electronics Engineering Shanghai Institute of Technology Shanghai China Library Shanghai Institute of Technology Shanghai China Zijin Intelligent Control (Xiamen) Technology Co. Ltd. Xiamen China State Key Laboratory of Process Automation in Mining & Metallurgy and Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

In Industry 4.0 background, unmanned full-process automated production has gradually become a hot topic, and the realization of intelligent and information factory requires the joint cooperation of multiple industries, in which the inspection device that replaces traditional manual work is an inescapable topic. At present, domestic and foreign research on intelligent inspection trolley is focused on obstacle avoidance function based on various sensors. In this context, it is introduced an algorithm based on monocular camera as a sensor, using Python language to access OpenCV library, and utilizing Gaussian filter function and Canny operator-based image contour extraction to complete the distance measurement. The algorithm can achieve the distance measurement and obstacle avoidance function at a lower cost. The test results show that the system can automatically identify the distance of obstacles in the inspection process, and according to the visual information captured by the camera in real time, it can independently generate steering action and collision reminder, and can complete the preliminary requirement of intelligent inspection.

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Adaptive Optimal Control for Discrete-Time Linear Systems via Hybrid Iteration

Adaptive Optimal Control for Discrete-Time Linear Systems vi...

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Data Driven Control and Learning Systems (DDCLS)

作者： Omar Qasem Weinan Gao Hector Gutierrez College of Engineering and Science Florida Institute of Technology Melbourne FL USA State Key Laboratory of Integrated Automation for Process Industries Northeastern University Shenyang Liaoning China

In this paper, a novel dynamic programming (DP) and adaptive dynamic programming (ADP) algorithms are proposed, namely hybrid iteration (HI), for discrete-time linear systems. The proposed HI approach fill up the performance gap of two well- known DP algorithms, i.e., policy iteration (PI) and value iteration (VI). In particular, HI drops the need of the prior knowledge of an initial stabilizing control policy required in PI, and at the same time it maintains a fast quadratic convergence rate compared with VI. A data-driven adaptive optimal controller design is also proposed based on the proposed HI algorithm. Simulation results for randomly generated discrete-time linear systems with different system orders demonstrate that the proposed HI approach can significantly save CPU time and reduce the number of learning iterations to converge to the optimal solution comparing with the VI approach. The data-driven HI method is implemented to an application of turbocharged diesel engine with exhaust gas recirculation, and the simulation results illustrate the efficacy of the proposed HI method.

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Towards Active Motion Planning in Interactive Driving Scenarios: A Generic Utility Term of Interaction Activeness

Towards Active Motion Planning in Interactive Driving Scenar...

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IEEE Symposium on Intelligent Vehicle

作者： Xiaocong Zhao Meng Wang Shiyu Fang Jian Sun Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University Shanghai China Chair of Traffic Process Automation Technische Universität Dresden Dresden Germany

Interacting with other vehicles while ensuring safety is a routine task for human drivers, but it can pose a challenge for autonomous vehicles. To address this challenge, we derived a generic utility term of interaction activeness (UTIA) from the driving interaction formulation which considers the rationality of interacting counterparts. Our research shows that incorporating UTIA as a supplementary utility term can improve the active interaction capability of both sampling-based and game-theoretic baseline motion planners without compromising safety. Through simulation experiments, we observed that on average, incorporating the weighted UTIA into the utility function of baseline planners can result in an 8.8% increase in the success rate of exiting a highway within a set distance.

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Addressing Domain Shift via Knowledge Space Sharing for Generalized Zero-Shot Industrial Fault Diagnosis

arXiv

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arXiv 2023年

作者： Zhao, Jiancheng Yue, Jiaqi Feng, Liangjun Zhao, Chunhui Ding, Jinliang College of Control Science and Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

Fault diagnosis is a critical aspect of industrial safety, and supervised industrial fault diagnosis has been extensively researched. However, obtaining fault samples of all categories for model training can be challenging due to cost and safety concerns. As a result, the generalized zero-shot industrial fault diagnosis has gained attention as it aims to diagnose both seen and unseen faults. Nevertheless, the lack of unseen fault data for training poses a challenging domain shift problem (DSP), where unseen faults are often identified as seen faults. In this article, we propose a knowledge space sharing (KSS) model to address the DSP in the generalized zero-shot industrial fault diagnosis task. The KSS model includes a generation mechanism (KSS-G) and a discrimination mechanism (KSS-D). KSS-G generates samples for rare faults by recombining transferable attribute features extracted from seen samples under the guidance of auxiliary knowledge. KSS-D is trained in a supervised way with the help of generated samples, which aims to address the DSP by modeling seen categories in the knowledge space. KSS-D avoids misclassifying rare faults as seen faults and identifies seen fault samples. We conduct generalized zero-shot diagnosis experiments on the benchmark Tennessee-Eastman process, and our results show that our approach outperforms state-of-the-art methods for the generalized zero-shot industrial fault diagnosis problem. Note to Practitioners—This paper is motivated by the difficulty of fault diagnosis in practical industrial scenarios caused by the lack of fault samples for training supervised diagnosis models. The focus of this study is to develop a generalized zero-shot industrial fault diagnosis method, which can diagnose both faults with sufficient training samples and faults without training samples with the help of expert knowledge of these faults. Considering the generalized zero-shot diagnosis models often confuse unseen faults as seen faults, a knowledge spac

关键词： Failure analysis

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Strongly Robust Computational Ghost Imaging Based on Nearest Neighbor Filtering

SSRN

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SSRN 2023年

作者： Wang, Qi Bai, Zongqi Shi, Haoran Mi, Jiashuai Chen, Long Li, Haotian State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China College of Information Science and Engineering Northeastern University Shenyang110819 China

Computational ghost imaging is a new optical imaging technique that obtains a reconstructed image of a target from a set of illumination patterns, but the quality of the reconstructed image is affected by ambient light noise. In this paper, two nearest neighbor filtered illumination patterns generated based on nonlinear noise filtering algorithm are proposed, which can effectively reduce the effect of ambient light in ghost imaging. When additional noise is added, compared with the reconstructed images of random illumination pattern, Hadamard illumination pattern and linear filter illumination pattern, the SNR of the reconstructed images of two different neighbor filtering illumination patterns is increased by 470%, 1863%, 75% and 517%, 2022%, 90%, respectively. In simulated noise environments, neighbor filtering illumination patterns reduce most of the effects of noise and results in high quality reconstructed images for a variety of target objects. In the experiments, these new illumination patterns has a strong robustness, and the reconstructed images obtained are sharper and contain more detailed information than random illumination pattern, Hadamard illumination pattern, and linearly filtered illumination pattern. Nearest neighbor filtering illumination patterns apply the nonlinear filtering method to computational ghost imaging and obtains excellent imaging results, which provides a new denoising idea for computational ghost imaging. © 2023, The Authors. All rights reserved.

关键词： Nonlinear filtering

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A Rolling-Horizon Approach for Mitigation of Exhaust Emissions in Urban Traffic Networks

A Rolling-Horizon Approach for Mitigation of Exhaust Emissio...

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Chinese automation Congress (CAC)

作者： Hualing Liu Zhao Zhou Haili Liang Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai China

With the continuous and rapid increase in the number of motor vehicles in our country, vehicle exhaust emission pollution is one of the important sources of air pollution, so the prevention and control of motor vehicle exhaust pollution is becoming more and more severe. Aiming at the problem of vehicle exhaust emissions in the urban transportation networks, this paper proposes a predictive control method based on the Macroscopic Fundamental Diagram, which can not only reduce vehicle exhaust emissions, but also alleviate the congestion of the transportation network. According to the historical traffic data of the road network, we obtain the macroscopic fundamental diagram of the sub-road networks through the polynomial fitting method, and then construct the traffic dynamics model between multi-sub road networks on this basis. Then, a mixed pollutant emission model is constructed by using the relationship between the pollutant emission rate and the state of the vehicles in the road network. With the objective function of minimizing pollutant emissions, we design a model-based road network optimization control method, and simulate three different traffic scenarios. The results show that the proposed predictive control method can effectively alleviate the congestion of the road network and reduce the pollutant emissions of vehicles.

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