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第三十四届中国控制会议

作者： ZHANG Yunong LIU Ming JIN Long ZHANG Yinyan TAN Hongzhou School of Information Science and Technology Sun Yat-sen University(SYSU) SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Autonomous Systems and Networked Control Ministry of Education

ISBN: (纸本)9789881563897

Recently, the synchronization of two chaotic systems is attracting more and more attentions because of its potential applications in communication security, aerospace industry and many other fields. In this paper, by combining Zhang dynamics(ZD) and gradient dynamics(GD), a novel effective controller-design method termed Zhang-gradient(ZG) method is firstly developed for the chaos synchronization problem with multiple inputs(i.e., three or two inputs). Via this ZG method, the traditional three-input chaos synchronization problem can be successfully solved with desirable convergence rate and satisfactory accuracy. In addition, by taking advantage of the coupling property of the chaotic system, a creative extension of the ZG method is investigated to solve the thorny two-input chaos synchronization problem. Simulation verifications further demonstrate that the controllers based on the ZG method can not only achieve satisfactory synchronization accuracy and exponential convergence rate on the three-input chaos synchronization problem but also successfully solve the chaos synchronization problem with only two inputs.

关键词： Chaos Synchronization,L ü Chaotic System,Chen Chaotic System,ZG(Zhang-Gradient) Method

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Artificial Intelligence-based Fault Detection and Diagnosis: Towards Application in a Chemical Process

Artificial Intelligence-based Fault Detection and Diagnosis:...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Liu, Yiqi Li, Zhi Chen, Hongtian The School of Automation Science & Engineering Key Laboratory of Autonomous Systems and Networked Control Ministry of Education South China University of Technology Guangzhou510640 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350337754

As the chemical processes become more and more complicated, it is crucial but also difficult to detect and diagnose faults in the system than ever. With the development of machine learning algorithms and cloud computing, to monitor chemical processes completely with the help of big data become true. However, most machine learning methods suffer from unexplainable problem. By employing Explainable Artificial Intelligence(XAI) techniques, this paper applies two kinds of unsupervised machine learning methods, One-Class Support Vector Machine and Depth-based Isolation Forest Feature Importance, to detect and diagnosis faults in a chemical process. By combining two different fault explanation methodologies, accuracy and reliability of the fault diagnosis results can be achieved by the validation in a simulated continuous stirred tank reactor. © 2023 IEEE.

关键词： Support vector machines

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Consensus Performance of First-Order Agents

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IEEE TRANSACTIONS ON AUTOMATIC control 2024年第8期69卷 5446-5453页

作者： Ding, Yanling Peng, Hui Qi, Tian Chen, Jie City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Guangdong Univ Technol Sch Automat Guangdong Prov Key Lab Intelligent Decis & Cooper Guangzhou 510006 Peoples R China South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510640 Peoples R China

This article concerns consensus problems of linear time-invariant systems with stochastic noises and deterministic disturbances. We consider first-order dynamic agents interconnected by a directed graph network subject to interagent time delay, and we seek to determine the error performance achievable by the consensus feedback protocol, whereas the performance quantifies the disruption of consensus by noises and disturbances. The H-2 and H-infinity norms of the multiagent system transfer function matrices are employed as measures of the consensus error. For the H-2 consensus performance, we obtain an analytical expression of the consensus error, while for the and H-infinity consensus performance, we show that it can be determined by solving a sequence of independent unimodal quasi-convex problems. The results help demonstrate how, in the presence of stochastic noises and deterministic disturbances, the agent's unstable dynamics may interact over a delayed network to limit the consensus performance attainable.

关键词： Consensus delay networks multiagent systems (MASs) robustness analysis Consensus delay networks H-2 and H-infinity multiagent systems (MASs) robustness analysis

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Multi-Object Selective Assembly Based on Hybrid Particle Swarm Algorithm

Multi-Object Selective Assembly Based on Hybrid Particle Swa...

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第37届中国控制会议

作者： Liu Weidong Liu Haiming Guo Jinhui Key laboratory of autonomous systems and networked control Ministry of education South China university of technology

Considering the batch assembly with multiple function requirements（FR）, the multi-object selective assembly（SA） technology based on the hybrid particle swarm algorithm is introduced in this paper. Firstly, the SA model is discussed, and a multi-object optimization problem is illustrated based on the assembly chains;additionally, the hybrid particle swarm algorithm is used to solve the multi-object selective assembly problem. The matching matrix of batch parts is designed as the particle. The local non-inferior particles and the global non-inferior particles of each iteration population are calculated. The particle population is update by crossover and mutation operations. After all the iterations, the final global non-inferior particles are selected as the matching strategy of batch parts. Based on the optimized matching strategy, the assembly success rate is calculated, and each assembly accuracy and its precision is guaranteed. The process of multi-object SA based on hybrid particle swarm algorithm is introduced, and an example of batch cabin sections are illustrated to obtain the optimized matching strategy of batch parts. Contrasted to the sequential strategy and the random strategy, it can find that the multi-object SA method based on hybrid particle swarm algorithm has advantage in guaranteeing the assembly success rate, and comprehensively decreasing each assembly accuracy.

关键词： multi-object selective assembly hybrid particle swarm algorithm assembly chain non-inferior particles matching strategy of batch parts

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Working Properties of Compliant Actuators Based on Magnetorheological Elastomer 6

Working Properties of Compliant Actuators Based on Magnetorh...

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6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Li, Chongbin Feng, Ying Yoong, Hou-Pin Liang, Mingwei Chen, Jiapeng South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China Univ Malaysia Sabah Fac Engn Kota Kinabalu 88400 Sabah Malaysia

ISBN: (纸本)9780738133645

Novel designs and control methods have been addressed in areas from sensing technology to robotics with the development of smart materials and compliant actuating components, which are showing some good actuating performances compared with the traditional actuating components utilizing air valves, hydraulic pumps and electro-motors. In this paper, the working properties of a class of compliant actuators utilizing liquid alloy conductors, and magnetorheological elastomers (MREs) are discussed. The magnetorheological (MR) effect in the complaint MRE actuators are analyzed so the working properties between input (voltage)and the output (displacement) can be modeled analytically.

关键词： Coils Actuators Force Muscles Robot sensing systems Valves Sensors

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Process Monitoring of Operational Cost for Wastewater Treatment Processes Using Variants of ARMA Models Based Soft-sensors 11

Process Monitoring of Operational Cost for Wastewater Treatm...

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11th IEEE Data Driven control and Learning systems Conference, DDCLS 2022

作者： Lu, Yu Liu, Yiqi Li, Dong Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Technical University of Denmark Department of Chemical and Biochemical Engineering Lyngby Denmark

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Process monitoring of operation cost index (OCI) is of great importance for wastewater treatment plants (WWTPs), which is not only able to support financial budget, but also to optimize local operation. This paper proposed four variants of auto-regressive and moving average (ARMA), based on recursive least squares algorithm (RLS), ARMA based on recursive extended least squares algorithm (RELS), nonlinear auto-regressive neural network (NARNN), and nonlinear auto-regressive neural network with external input (NARXNN) respectively, to predict the operating cost in WWTPs. The proposed methods were validated in the simulation platform, Benchmark Simulation Model No.2-P (BSM2-P). On account of the strong nonlinearity of the wastewater treatment process, the nonlinear model, like NARXNN, achieved better performance in terms of mean square error (MSE) and correlation coefficient (R). © 2022 IEEE.

关键词： Wastewater treatment

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Euler-Discretized GZ-Type Complex Neuronet Computing Real-Time Varying Complex Matrix Inverse 36

Euler-Discretized GZ-Type Complex Neuronet Computing Real-Ti...

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第36届中国控制会议

作者： Yunong Zhang Guofu Wu Binbin Qiu Wan Li Penghao He School of Information Science and Technology Sun Yat-sen University(SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Reseach Institute Key Laboratory of Machine Intelligence and Advanced Computing(Sun Yat-sen University) Ministry of Education

ISBN: (纸本)9781538629185

In this paper,a GZ-type complex neuronet（generated by combining gradient dynamic system and Zhang dynamic system） is illustrated and discretized into an Euler-discretized（i.e.,discretized by Euler forward rule） GZ-type complex neuronet for computing real-time varying complex matrix *** results not only show the efficacy of the proposed Eulerdiscretized GZ-type neuronet but also verify that the error pattern is square.

关键词： GZ-Type Neuronet Complex Matrix Inverse Euler Forward Rule Computer Simulation Error Pattern

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Identification of water depth and velocity potential for water waves

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systems & control LETTERS 2019年 125卷 29-36页

作者： Yu, Yang Pei, Hai-Long Xu, Cheng-Zhong South China Univ Technol Minist Educ Key Lab Autonomous Syst & Networked Control Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Guangzhou 510640 Guangdong Peoples R China Univ Claude Bernard Lyon 1 LAGEP Batiment CPE43 Blvd 11 Novembre 1918 F-69622 Villeurbanne France

The purpose of this paper is to investigate the identification of the water depth and the water velocity potential in a coastal region by using the linearized water wave equation (LWWE). Existence and uniqueness of the solutions to the partial differential equation LWWE are shown by using the semigroup theory. Moreover the analytical solution is found by the separation of variables method. We assume that the surface wave elevation is measurable. We like to recover the water depth and the water velocity potential from the measurement. This identification problem is shown to be well-posed by proving the parameters' identifiability by the surface elevation. Based on the classical gradient descent method we elaborate an identification algorithm to recover simultaneously both the water depth and the velocity potential. Numerical simulations are carried out to illustrate effectiveness of the algorithm. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Water wave equation Infinite dimensional system Water depth Velocity potential Parametric identification Gradient descent method

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Signum-function array activated ZNN with easier circuit implementation and finite-time convergence for linear systems solving

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INFORMATION PROCESSING LETTERS 2017年第Aug.期124卷 30-34页

作者： Zhang, Yunong Ding, Yaqiong Qiu, Binbin Zhang, Yinyan Li, Xiaodong Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China SYSU CMU Shunde Int Joint Res Inst Foshan 528300 Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China

A new type of Zhang neural network (ZNN), which is activated by the signum-function array, is proposed for linear systems solving. Such a signum-function array activated ZNN is developed on the basis of a vector-valued error function instead of a scalar-valued norm based energy function. Besides, a theorem is provided to illustrate the excellent finite time convergence property of the new-type ZNN. In addition, the corresponding circuit schematic of the signum-function array activated ZNN is given. For better illustration, a representative simulative example is presented and the corresponding simulation result is shown to substantiate the efficacy of the proposed new-type ZNN for linear systems solving. Besides, the comparative simulation result further shows the desired finite-time convergent performance. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Algorithms Real-time systems Zhang neural network (ZNN) Signum-function array Linear systems solving Finite-time convergence

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An Incremental Learning Framework to Enhance Teaching by Demonstration Based on Multimodal Sensor Fusion

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FRONTIERS IN NEUROROBOTICS 2020年 14卷 55页

作者： Li, Jie Zhong, Junpei Yang, Jingfeng Yang, Chenguang South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou Peoples R China Nottingham Trent Univ Sch Sci & Technol Nottingham England Guangzhou Chinese Acad Sci Shenyang Inst Automat Guangzhou Peoples R China Univ West England Bristol Robot Lab Bristol Avon England

Though a robot can reproduce the demonstration trajectory from a human demonstrator by teleoperation, there is a certain error between the reproduced trajectory and the desired trajectory. To minimize this error, we propose a multimodal incremental learning framework based on a teleoperation strategy that can enable the robot to reproduce the demonstration task accurately. The multimodal demonstration data are collected from two different kinds of sensors in the demonstration phase. Then, the Kalman filter (KF) and dynamic time warping (DTW) algorithms are used to preprocessing the data for the multiple sensor signals. The KF algorithm is mainly used to fuse sensor data of different modalities, and the DTW algorithm is used to align the data in the same timeline. The preprocessed demonstration data are further trained and learned by the incremental learning network and sent to a Baxter robot for reproducing the task demonstrated by the human. Comparative experiments have been performed to verify the effectiveness of the proposed framework.

关键词： incremental learning network teaching by demonstration teleoperation data fusion robot learning

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