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Recent Developments in Event-Triggered control of Nonlinear Systems:An Overview

无人系统（英文） 2023年第1期11卷 27-56页

作者： Pengpeng Zhang Tengfei Liu Jie Chen Zhong-Ping Jiang Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

Event-triggered control is a most popular paradigm for transferring feedback information in an economical"as needed"*** study of event-triggered control can be traced back to the *** significant advances on the topic of control over networks and the topic of nonlinear control systems over the last two decades,event-triggered control has quickly emerged as a major theoretical subject in control *** of event-triggered control are wide-spread ranging from embedded control systems and industrial control processes to unmanned systems and cyber-physical transportation *** this paper,we first review developments in the synthesis of event-triggered sampling *** event triggering mechanisms,such as static event trigger,dynamic event trigger,time-regularized event trigger,and event trigger with positive threshold offsets,are systematically ***,we study how to design a stabilizing controller that is robust with respect to the sampling ***,we review some recent results in the directions of self-triggered control,event-triggered tracking control and cooperative control,and event-triggered control of stochastic systems and partial differential equation *** applications of event-triggered control are also discussed.

关键词： Event-triggered control event-triggered sampling mechanism event-triggered controller

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Iterative Learning Hybrid Robust Predictive control for Multi-Phase Batch processes with Asynchronous Switching via a Lyapunov-Razumikhin Approach

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 345-358页

作者： Li, Hui Wang, Shiqi Shi, Huiyuan Li, Ping Su, Chengli School of Electronics and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Information and Control Engineering Liaoning Petrochemical University Fushun113001 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Information Engineering Liaodong University Dandong118001 China

For a class of the industrial cyber-physical system, i.e., multi-phase batch processes with small time-delays and asynchronous switching, this study develops a two-dimensional iterative learning hybrid robust predictive control method. Compared with the previous Lyapunov-Krasovsky function-based method, stability conditions given by the Lyapunov-Razumikhin function-based method is less conservative intensive in dealing with small time-delays. Not only that, compared to the traditional iterative learning method, the rolling-optimized gains of the control law make it possible for the designed controller to have fewer learning cycles, and the control performance is even better. Meanwhile, the minimum and the maximum dwelling times are obtained in conjunction with the exponential stability analysis of the system. Since the obtained dwelling times, an advanced switching signal is provided to avoid asynchronous switching situations. The simulation results for the injection molding process are finally utilized to illustrate the effectiveness and feasibility of the developed approach. © 2023 The Authors.

关键词： Time delay

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Novel Cooperative Particle Swarm Optimization with Interactive Learning Strategy 1

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Qiliang Liu, Yiheng Xu, Hao Zhang, Pinggai Wu, Qinlin Wei, Lisheng Anhui Key Laboratory of Electric Drive and Control School of Electrical Engineering Anhui Polytechnic University Anhui Wuhu241000 China Industrial Process Control Optimization and Automation Engineering Research Center School of Electronic Engineering Chaohu University Anhui Chaohu238024 China School of Computer Science and Artificial Intelligence Chaohu University Anhui Chaohu238024 China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

Particle Swarm Optimization (PSO) is an optimization method grounded in swarm intelligence, designed to mimic the social behaviors of bird flocks during hunting. By sharing information about their positions, the entire swarm can explore and search diverse areas within a given space. This method features rapid convergence, minimal parameters, and straightforward implementation, making it a potent and efficient metaheuristic technique. Nevertheless, conventional PSO algorithms, despite their simplicity and efficiency, often become trapped in local optima when tackling complex multimodal problems, which limits their global search capability. To mitigate these limitations, a new Cooperative Particle Swarm Optimization algorithm with Interactive Learning (CPSOIL) is introduced. This approach incorporates an interactive learning strategy, allowing particles to learn from their neighbors by perceiving the surrounding particles during updates. Moreover, a mechanism has been added to modify the particle velocity update when the algorithm encounters local optima, thereby preserving population diversity, enhancing the likelihood of escaping local optima, and improving algorithm accuracy. Comparisons with other leading algorithms on the CEC2005 benchmark functions indicate that, in most cases, the enhanced algorithm outperforms its counterparts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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A Norm Optimal Iterative Learning control Approach with Efficient Computation 13

A Norm Optimal Iterative Learning Control Approach with Effi...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Gao, Luyuan Zhuang, Zhihe Tao, Hongfeng Chen, Yiyang Paszke, Wojciech Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214122 China School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Institute of Automation University of Zielona Gora Zielona Gora Poland

ISBN: (纸本)9798350361674

Iterative learning control (ILC) is excellently applied to output tracking for systems that perform repetitive tasks. Classical norm optimal ILC (NOILC) approach yields optimal control inputs by minimizing a quadratic cost function. However, this approach requires the introduction of supervectors, i.e., lifting technique, for system description, which raises a high computational load problem. The contribution of this paper is to propose a non-lifted NOILC (N-NOILC) approach by redesigning a cost function that does not involve the lifting technique, achieving the goal of efficient computation. Therefore, the proposed approach is particularly suitable for large tracking tasks with long trial duration and high sampling frequency. Moreover, considering the model uncertainty in practice, robust convergence is analyzed. Numerical simulations are shown to demonstrate the properties and advantages of the proposed approach. © 2024 IEEE.

关键词： Adversarial machine learning

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Identification of Abnormal Operating Conditions in Fused Magnesium Furnaces Using a Deep Multiview TSK Fuzzy System

Identification of Abnormal Operating Conditions in Fused Mag...

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2024 China automation Congress, CAC 2024

作者： Xi, Chao Wu, Gaochang Fan, Zizhu Xu, Feng Wang, Hui Tang, Xiaokun School of Electrical and Automation Engineering East China Jiaotong University Nanchang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China College of Computer Science and Technology Shanghai University of Electric Power Shanghai China Key Laboratory of Advanced Control and Optimization of Jiangxi Province East China Jiaotong University Nanchang China

ISBN: (纸本)9798350368604

The smelting process of the fused magnesium furnace(FMF) is prone to semi-molten abnormal conditions, which may lead to low product quality and production efficiency, and is also prone to safety accidents. At present, the operating conditions of FMF mainly rely on manual observation of the furnace shell features for diagnosis, and it is hard to timely and effectively identify abnormalities in the smelting process. Given the difficulty in identifying abnormal operating conditions of FMF, this paper proposes a deep multiview fuzzy classification method(DMFC) that can automatically identify abnormal operating conditions. First, based on the three-phase current data, CNN and Transformer are used to extract features as multiple views. Secondly, the Takagi-Sugeno-Kang(TSK) fuzzy model is used as a single-view model, and the evidence theory is used for dynamic fusion of multiple views. This method can effectively fuse multiple deep features, and has strong scalability and a certain degree of interpretability. Through verification of the real historical data of multiple FMFs, our model has relatively high accuracy, false detection rate(FDR), and missed detection rate(MDR). © 2024 IEEE.

关键词： Smelting

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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Robust PD-type iterative learning control design for uncertain batch processes subject to nonrepetitive disturbances 41

Robust PD-type iterative learning control design for uncerta...

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第41届中国控制会议

作者： Robert Maniarski Wojciech Paszke Shoulin Hao Hongfeng Tao Institute of Automation Electronic and Electrical EngineeringUniversity of Zielona Góra Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper develops PD-type iterative learning control schemes for a class of uncertain batch processes subject to nonrepetitive *** means of two-dimensional/repetitive setting,the sufficient conditions for batch-to-batch error convergence and H disturbance attenuation are formulated and ***,the procedure for computing the desired control law matrices is formulated in terms of solvability of linear matrix *** proposed control law is able to fulfil the imposed design specifications,i.e.,they are suitable for the batch processes with time-varying uncertainties as well as non-repetitive *** illustrative example is used to validate the proposed control scheme and demonstrates a possible applicability of the developed results.

关键词： iterative learning control repetitive processes batch processes robust control nonrepetitive disturbances

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Trend-Aware Patch Feature Fusion Transformer for Fault Diagnosis of Vibration Signals

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IEEE Sensors Journal 2025年

作者： Zhang, Ruicong Chu, Fei Qiu, Yazhi He, Dakuo Wang, Fuli China University of Mining and Technology School of Information and Control Engineering Xuzhou221000 China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing100160 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang110000 China

The Vision Transformer (ViT) has demonstrated reliable performance in industrial equipment fault diagnosis by capturing global features and long-range dependencies in vibration signals. Nonetheless, when processing vibration signals, ViT primarily focuses on the correlations between patches, while neglecting the trend variation information hidden between signal units. This limitation hinders the model’s ability to extract latent fault information. To address this issue, we propose a trend-aware patch feature fusion Transformer (TAPformer) for fault diagnosis of vibration signals. Specifically, we design both patch-level and signal-level trend-aware self-attention blocks and adopt a weighted approach to integrate coarse-grained patch-level trend features with fine-grained signal-level trend features at the output of each self-attention head. This approach enables the model to capture fault information hidden within trend variations, effectively enhancing its fault detection capabilities. TAPformer establishes optimal weighted fusion hyperparameters through ablation experiments and is tested on four public datasets. Compared to eight advanced models, TAPformer achieves the highest accuracy across all four datasets, demonstrating the effectiveness and generalization capability of the proposed method. © 2001-2012 IEEE.

关键词： Fault detection

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Breaking Limitation of Convergence in Continuous Optimization: Predefined-Time Gradient Flows 63

Breaking Limitation of Convergence in Continuous Optimizatio...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Zhang, Renyongkang Guo, Ge Gao, Zhenyu Yu, Miao Zhang, Lu Wang, Zhengsong Han, Meng Yin, Yanyan Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China Curtin University School of Electrical Engineering Computing and Mathematical Sciences PerthWA6102 Australia

ISBN: (纸本)9798350316339

This paper investigates the continuous-time unconstrained optimization via dynamical gradient flow systems. For strongly convex objective functions, a gradient flow with predefined time convergence is proposed, which is suitable for relaxing the objective function to only satisfy the PolyakLojasiewicz (P-L) condition. A predefined-time convergent Newton's flow is designed for the minimization of strictly convex functions. The proposed result is extended for strongly convex objective functions with time-varying parameters. In our schemes, the upper bound of convergence time can be used as a prior choice without involving system initialization and complex parameter calculations. The convergence of algorithms is rigorously proved by using the Lyapunov method. Numerical simulation illustrates the effectiveness of the proposed methods. © 2024 IEEE.

关键词： Lyapunov methods

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Neural learning control for discrete-time nonlinear systems in pure-feedback form

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Science China(Information Sciences) 2022年第2期65卷 124-138页

作者： Min WANG Haotian SHI Cong WANG Jun FU School of Automation Science and Engineering South China University of Technology School of Control Science and Engineering Shandong University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

This study focuses on state-feedback and output-feedback neural learning control problems for discrete-time nonlinear systems in the pure-feedback form. First, an extended result for the exponential stability for a class of discrete-time linear time-varying(LTV) systems with n-step delays is proposed to verify the exponential convergence of estimated weights. Subsequently, both state-feedback and outputfeedback adaptive neural network(NN) controllers are constructed by combining the classical n-step and n-step input-output predictors. After ensuring convergence of the system output to a recurrent reference signal, the radial basis function subvector of NN is verified to satisfy the persistent exciting condition using the system state equation and the implicit function theorem. By combining the extended stability corollary of an LTV system, the estimated weights are verified to exponentially converge to their optimal values. By constructing "learning rules" and using a "mod" function, the estimated weights with a convergent sequence are synthetically represented and stored as the experience knowledge, which is reused to construct neural learning controllers. The proposed neural learning controllers not only accomplish similar control tasks but also reduce the burden of online computation compared with the conventional adaptive NN ***, simulation results are presented to demonstrate the effectiveness of the presented schemes.

关键词： discrete-time nonlinear systems pure-feedback systems learning control neural networks persistent excitation

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