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Quantizer design for interconnected feedback control systems

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控制理论与应用（英文版） 2010年第1期8卷 93-98页

作者： guisheng ZHAI Ning CHEN weihua gui Department of Mechanical Engineering Osaka Prefecture University Sakai Osaka 599-8531 Japan School of Information Science & Engineering Central South University Changsha Hunan 410083 China

In this paper, we consider the design of interconnected H-infinity feedback control systems with quantized signals. We assume that a decentralized static output feedback has been designed for an interconnected continuous-time LTI system so that the closed-loop system is stable and a desired H-infinity disturbance attenuation level is achieved, and that the subsystems＇ measurement outputs are quantized before they are passed to the local controller. We propose a local-output-dependent strategy for updating the quantizers＇ parameters, so that the overall closed-loop system is asymptotically stable and achieves the same H-infinity disturbance attenuation level. Both the pre-designed controllers and the quantizers＇ parameters are constructed in a decentralized manner, depending on local information.

关键词： Interconnected continuous-time LTI system Decentralized H-infinity control Quantizer Quantization Matrix inequality Static output feedback

Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 252-254页

作者： Chenliang Liu Yalin Wang Chunhua Yang weihua gui the School of Automation Central South UniversityChangsha 410083China IEEE

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.

关键词： processes modal adjust

Time -Sharing Power Supply Optimal Decision forElectrolytic Deposition Process of Zinc Based onSimulated Annealing Algorithm

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Journal of Systems Engineering and Electronics 2000年第4期11卷 43-50页

作者： Huang Taisong gui weihua Wang Yalin Yang Chunhua College of Information Engineering Central South University of TechnologyChangsha 410083 P. R. China

According to time-sharing valuation principle (TSVP) of power supply, the relationships of current density and current efficiency at different acidities are obtained based on the processed data of electrolytic deposition process of zinc (EDPZ) with the least square method (LSM). Thus an optimal model of time-sharing power supply system for EDPZ is established, which has been optimized by use of an improved efficient simulated annealing algorithm (SAA). Practical results show that industrial and mining enterprises can obtain enormous economic benefits every year.

关键词： Algorithms Current density Electrodeposition Least squares approximations Mathematical models Optimization Simulated annealing

A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis cell condition

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Frontiers of Chemical Science and Engineering 2017年第3期11卷 414-428页

作者： Weichao Yue Xiaofang Chen weihua gui Yongfang Xie Hongliang Zhang School of Information Science and Engineering Central South University Changsha 410083 China School of Metallurgy and Environment Central South University Changsha 410083 China

Root cause analysis （RCA） of abnormal aluminum electrolysis cell condition has long been a challenging industrial issue due to its inherent complexity in analyzing based on multi-source knowledge. In addition, accurate RCA of abnormal aluminum electrolysis cell condition is the precondition of improving current efficiency. RCA of abnormal condition is a complex work of multi-source knowledge fusion, which is difficult to ensure the RCA accuracy of abnormal cell condition because of dwindling and frequent flow of experienced technicians. In view of this, a method based on Fuzzy- Bayesian network to construct multi-source knowledge solidification reasoning model is proposed. The method can effectively fuse and solidify the knowledge, which is used to analyze the cause of abnormal condition by technicians providing a clear and intuitive framework to this complex task, and also achieve the result of root cause automatically. The proposed method was verified under 20 sets of abnormal cell conditions, and implements root cause analysis by finding the abnormal state of root node, which has a maximum posterior probability by Bayesian diagnosis reasoning. The accuracy of the test results is up to 95%, which shows that the knowledge reasoning feasibility for RCA of aluminum electrolysis cell.

关键词： abnormal aluminum electrolysis cell condi- tion Fuzzy-Bayesian network multi-source knowledge solidification and reasoning root cause analysis

DESIGN OF AN EXPERT CONTROL SYSTEM FOR LEACHING PROCESS

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中国有色金属学会会刊：英文版 1999年第1期9卷 139-146页

作者： Wu, Min Peng, Zhihong gui, weihua Nakano, Michio Jinhua, She Central South Univ of Technology Changsha China

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ＩＮＴＲＯＤＵＣＴＩＯＮＴｈｅｍａｉｎｐｒｏｃｅｓｅｓｉｎｚｉｎｃｈｙｄｒｏｍｅｔａｌｕｒｇｙａｒｅｌｅａｃｈｉｎｇ，ｐｕｒｉｆｉｃａｔｉｏｎａｎｄｅｌｅｃｔｒｏｌｙｓｉｓ［１，２］．Ｔｈｅｌｅａｃｈｉｎｇｉｎｖｏｌｖｅｓｄｉｓｏｌｖｉｎｇｚｉｎｃ?..

关键词： leaching process expert control system mathematical model rule model

Modeling, Optimization, and Control of Solution Purification Process in Zinc Hydrometallurgy

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IEEE/CAA Journal of Automatica Sinica 2018年第2期5卷 564-576页

作者： Bei Sun Chunhua Yang Hongqiu Zhu Yonggang Li weihua gui School of Information Science and Engineering Central South Univer- sity Chartgsha 410083 China IEEE

The solution purification process is an essential step in zinc hydrometallurgy. The performance of solution purification directly affects the normal functioning and economical benefits of zinc hydrometallurgy. This paper summarizes the authors' recent work on the modeling, optimization, and control of solution purification process. The online measurable property of the oxidation reduction potential(ORP) and the multiple reactors, multiple running statuses characteristic of the solution purification process are extensively utilized in this research. The absence of reliable online equipment for detecting the impurity ion concentration is circumvented by introducing the oxidationreduction potential into the kinetic model. A steady-state multiple reactors gradient optimization, unsteady-state operationalpattern adjustment strategy, and a process evaluation strategy based on the oxidation-reduction potential are proposed. The effectiveness of the proposed research is demonstrated by its industrial experiment.

关键词： Index Terms-Nonferrous metallurgy oxidation reduction po-tential (ORP) process control solution purification zinc hy-drometallurgy.

Minimal observability of Boolean networks

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Science China(Information Sciences) 2022年第5期65卷 157-168页

作者： Yang LIU Jie ZHONG Daniel W.C.HO weihua gui College of Mathematics and Computer Science Zhejiang Normal University Department of Mathematics City University of Hong Kong School of Information Science and Engineering Central South University

In this study, the minimum observability of Boolean networks(BNs) is investigated by using the semi-tensor product(STP) of matrices. First, a new system based on the considered BN is obtained to analyze states pair dynamic trajectories, from which a necessary and sufficient condition for the observability of BNs is determined. Second, adding a new observer improves the observability without affecting the observable states. Thus, an algorithm is presented to design an observer for an unobservable BN. In addition, a necessary condition is obtained to determine the minimum number of nodes required to be directly measurable. Further,an algorithm to address the minimal observability is presented. Finally, examples are provided to demonstrate the effectiveness of the obtained results.

关键词： Boolean networks minimal observability semi-tensor product

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Asynchronous Learning-Based Output Feedback Sliding Mode Control for Semi-Markov Jump Systems: A Descriptor Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1358-1369页

作者： Zheng Wu Yiyun Zhao Fanbiao Li Tao Yang Yang Shi weihua gui School of Automation Central South UniversityChangsha 410083China Power Electronics Machines and Control GroupThe University of NottinghamNottingham NG72RDUK Department of Mechanical Engineering University of VictoriaVictoria BC V8W 3P6Canada IEEE

This paper presents an asynchronous output-feed-back control strategy of semi-Markovian systems via sliding mode-based learning *** with most literature results that require exact prior knowledge of system state and mode information,an asynchronous output-feedback sliding sur-face is adopted in the case of incompletely available state and non-synchronization *** holonomic dynamics of the sliding mode are characterized by a descriptor system in which the switching surface is regarded as the fast subsystem and the system dynamics are viewed as the slow *** upon the co-occurrence of two subsystems,the sufficient stochastic admissibility criterion of the holonomic dynamics is derived by utilizing the characteristics of cumulative distribution ***,a recursive learning controller is formulated to guarantee the reachability of the sliding manifold and realize the chattering reduction of the asynchronous switching and sliding ***,the proposed theoretical method is substantia-ted through two numerical simulations with the practical contin-uous stirred tank reactor and F-404 aircraft engine model,respectively.

关键词： Asynchronous switching learning-based control output feedback semi-Markovian jump systems sliding mode con-trol(SMC).

A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring

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Engineering 2021年第9期7卷 1262-1273页

作者： Chunhua Yang Huiping Liang Keke Huang Yonggang Li weihua gui School of Automation Central South UniversityChangsha 410083China

Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles ***,most data-driven process-monitoring methods assume that historical training data and online testing data follow the same *** fact,due to the harsh environment of industrial systems,the collected data from real industrial processes are always affected by many factors,such as the changeable operating environment,variation in the raw materials,and production *** factors often cause the distributions of online monitoring data and historical training data to differ,which induces a model mismatch in the process-monitoring ***,it is difficult to achieve accurate process monitoring when a model learned from training data is applied to actual online *** order to resolve the problem of the distribution divergence between historical training data and online testing data that is induced by changeable operation environments,a robust transfer dictionary learning(RTDL)algorithm is proposed in this paper for industrial process *** RTDL is a synergy of representative learning and domain adaptive transfer *** proposed method regards historical training data and online testing data as the source domain and the target domain,respectively,in the transfer learning *** mean discrepancy regularization and linear discriminant analysis-like regularization are then incorporated into the dictionary learning framework,which can reduce the distribution divergence between the source domain and target *** this way,a robust dictionary can be learned even if the characteristics of the source domain and target domain are evidently different under the interference of a realistic and changeable operation *** a dictionary can effectively improve the performance of process monitoring and mode ***

关键词： Process monitoring Multimode process Dictionary learning Transfer learning