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arXiv 2021年

作者： Faulwasser, Timm Lucia, Sergio Darup, Moritz Schulze Mönnigmann, Martin Institute of Energy Systems Energy Efficiency and Energy Economics TU Dortmund University Dortmund Germany Laboratory of Process Automation Systems TU Dortmund University Dortmund Germany Control and Cyberphysical Systems TU Dortmund University Dortmund Germany Automatic Control and Systems Theory Ruhr University Bochum Bochum Germany

Since the earliest conceptualizations by Lee and Markus, and Propoi in the 1960s, Model Predictive control (MPC) has become a major success story of systems and control with respect to industrial impact and with respect to continued and wide-spread research interest. The field has evolved from conceptually simple linear-quadratic (convex) settings in discrete and continuous time to nonlinear and distributed settings including hybrid, stochastic, and infinite-dimensional systems. Put differently, essentially the entire spectrum of dynamic systems can be considered in the MPC framework with respect to both-system theoretic analysis and tailored numerics. Moreover, recent developments in machine learning also leverage MPC concepts and learning-based and data-driven MPC have become highly active research areas. However, this evident and continued success renders it increasingly complex to live up to industrial expectations while enabling graduate students for state-of-the-art research in teaching MPC. Hence, this position paper attempts to trigger a discussion on teaching MPC. To lay the basis for a fruitful debate, we subsequently investigate the prospect of covering MPC in undergraduate courses;we comment on teaching textbooks;and we discuss the increasing complexity of research-oriented graduate teaching of MPC. Copyright © 2021, The Authors. All rights reserved.

关键词： Model predictive control

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A self-triggered control scheme for Markov jump systems under multiple range performance restrictions

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IFAC-PapersOnLine 2020年第2期53卷 2783-2788页

作者： Haiying Wan Hamid Reza Karimi Xiaoli Luan Fei Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi214122 China Department of Mechanical Engineering Politecnico di Milano Milan Italy

This paper proposes a multi-frequency controller design scheme for Markov jump systems (MJSs) based on the self-triggered strategy in a resource-aware way. Firstly, a derandomization technique is introduced to make sure the transition probability information is included in the finite frequency specification analysis. Then, a self-triggered policy is developed to update the control input of the system via the history measurement. Finally, sufficient conditions are deduced that guarantee the multiple range frequency performances and the reduction of computation and communication occupation for the controlled MJSs, simultanously. The cart- spring system is employed to illustrate the effectiveness of the proposed approach.

关键词： Self-triggered control finite frequency performance Markov jump systems derandomization disturbance rejection

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Finite-time Boundedness of T-S Fuzzy Systems Subject to Injection Attacks: A Sliding Mode control Method ⁎

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IFAC-PapersOnLine 2020年第2期53卷 5075-5080页

作者： Zhina Zhang Jun Song Yuanyuan Zou Yugang Niu The Key Laboratory of Advanced Control and Optimization for Chemical Process East China University of Science and Technology Shanghai 200237 China Department of Automation Shanghai Jiao Tong University Shanghai 200240 China

This paper studies the sliding mode control for T-S fuzzy systems in the framework of finite-time boundedness. It is assumed that the control signals are transmitted via vulnerable channels, where injection attacks might happen. A fuzzy sliding mode controller is firstly synthesized to guarantee the finite-time reachability of the prescribed sliding surface and attenuate the effect of the injection attacks. By introducing a partitioning strategy, the finite-time boundedness over both the reaching phase and the sliding motion phase are analyzed. Furthermore, sufficient criteria are derived such that the closed-loop system is finite-time bounded over the whole specified finite-time interval despite of the injection attacks. An optimal algorithm is further provided for searching ideal control gains with fewer energy demands. Finally, a simulation example verifies the proposed sliding mode control approach.

关键词： T-S fuzzy system sliding mode control finite-time boundedness partitioning strategy injection attacks

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Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater’s Condition

arXiv

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arXiv 2023年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with adversarial constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize network regret and cumulative constraint violation. A novel distributed online algorithm is proposed and it achieves an O(T max{c,1−c}) network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the number of rounds and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T 1−c). Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to O(log(T)), and the network cumulative constraint violation bound is reduced to O(√log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is the first to achieve reduced (network) cumulative constraint violation bounds for (distributed) online convex optimization with adversarial constraints under Slater’s condition. Finally, the theoretical results are verified through numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

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Recursive Weighted Null-Space Fitting Method for Identification of Multivariate Systems ⁎

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IFAC-PapersOnLine 2021年第7期54卷 345-350页

作者： Mengyuan Fang Miguel Galrinho Håkan Hjalmarsson Zhejiang Engineering Laboratory of Industrial Internet Communication Technology Zhejiang Sci-Tech University Hangzhou China. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China OHB Sweden AB Kista Sweden Centre for Advanced Bio Production and Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Recursive identification of structured multivariate models is known to be difficult due to the general non-convexity of the likelihood function. In this work, we propose a recursive multivariate weighted null-space fitting method for identification of structured multivariate models. The proposed method first uses recursive least squares to estimate a high order non-parametric model, then a parametric model is obtained through weighted least squares from the non-parametric model. In this way, the method avoids directly optimizing a non-convex likelihood function and has guaranteed global convergency. Moreover, the proposed method is flexible in model structures and has the same finite sample performance as its off-line counterpart. We use simulation examples to illustrate the performance.

关键词： Recursive system identification multivariate systems weighted null-space fitting

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Data driven particle size estimation of hematite grinding process using stochastic configuration network with robust technique

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Journal of Central South University 2019年第1期26卷 43-62页

作者： DAI Wei LI De-peng CHEN Qi-xin CHAI Tian-you School of Information and Control Engineering China University of Mining and Technology State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real *** achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares ***,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty *** experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and *** results show that the RSCN has an excellent performance for the PS estimation.

关键词： hematite grinding process particle size stochastic configuration network robust technique M-estimation nonparametric kernel density estimation

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Robust Asynchronous Fuzzy Predictive Fault-Tolerant Tracking control for Nonlinear Multiphase Batch processes with Time-Varying Tracking Trajectories

Research Square

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Research Square 2021年

作者： Li, Hui Wang, Shiqi Shi, Huiyuan Su, Chengli Li, Ping 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

A T-S model-dependent robust asynchronous fuzzy predictive fault-tolerant tracking control scheme is developed for nonlinear multiphase batch processes with time-varying tracking trajectories and actuator faults. Firstly, considering the influence of the mismatch between the previous phase state and the current phase controller during the switching, a T-S fuzzy switching model including the match and mismatch case is established. Depending on the fuzzy switching model, a robust fuzzy predictive fault-tolerant tracking controller is designed by considering the situation whether the model rules correspond to the controller rules. Secondly, using the related theories and methods, the system stability conditions presented by the linear matrix inequality considering the above conditions are provided to guarantee the stability of the system. By solving these stability conditions, the T-S fuzzy control law gain of each phase, the minimum running time of each match case and the maximum running time of each mismatch case are obtained. Then, through the maximum running time, the strategy of the switching signal in advance is adopted to avoid the occurrence of asynchronous switching, so as to ensure that the system can operate stably. Finally, the simulation results verify that the designed controller is effective and feasible. © 2021, CC BY.

关键词： controllers

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Adaptive Neural Network-Based Asynchronous control for Switching Cyber-Physical Systems With Unknown Dead Zone

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IEEE transactions on cybernetics 2025年 PP卷 PP页

作者： Jun Cheng Junhui Wu Huaicheng Yan Dan Zhang Zheng-Guang Wu Ying Zhai Guangxi Normal University School of Mathematics and Statistics Guilin541006 China East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education Shanghai200237 China Zhejiang University of Technology Department of Automation Hangzhou310014 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China

This study investigates the problem of adaptive neural network asynchronous control for switching cyber-physical systems under unknown dead zones. A generalized switching rule, instead of a Markov/semi-Markov process, is utilized to scrutinize the switching behavior of subsystems. This approach characterizes the dynamic nature of sojourn probabilities using single-mode-based sojourn time, aiming to decrease computational load while meeting the demands of real-world scenarios. Considering the intricacies of network environments, the unknown dead zone inputs are considered, which can be effectively implemented via the adaptive neural network-based control law. To counteract the adverse effects of unforeseen information, a saturation-based observer is developed, in which the saturation level is dynamically adjusted with the hope of providing greater flexibility. Utilizing a Lyapunov function that correlates with the detected mode and the system mode, sufficient criteria are established to ensure that the closed-loop system remains bounded in probability. Eventually, the practicality and effectiveness of the proposed control methodology are verified through two simulated examples.

关键词： Markov processes

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Improved quantum bacterial foraging algorithm for tuning parameters of fractional-order PID controller

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Journal of Systems Engineering and Electronics 2018年第1期29卷 166-175页

作者： LIU Lu SHAN Liang DAI Yuewei LIU Chenglin QI Zhidong School of Automation Nanjing University of Science and Technology Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of AutomationJiangnan University

The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant,which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO(IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population *** tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation(PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.

关键词： bacterial foraging algorithm fractional-order quantum rotation gate proportion integration differentiation(PID) servo system

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An Improved Self-Organizing Maps Neural Network Model for Plantar Pressure Image Classification

An Improved Self-Organizing Maps Neural Network Model for Pl...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Zairan Li Dan Wang Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University PR China Wenzhou Polytechnic Wenzhou PR China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

A Self-organizing map neural network combining with classification output layers network was proposed for classifying plantar pressure dataset, which was acquired from foot 3D scan system after features extraction. The single SOM with 10, 15, 20, 30 hidden layers was compared and validated. Subsequently, the classification modeling with SOM combining multiple hidden neural networks was proposed, and results showed that the proposed model performs high effectiveness in plantar pressure dataset and has potential applications on comfort design through last customization.

关键词： Self-organizing feature maps Training Three-dimensional displays Neural networks Manuals Information technology Unsupervised learning

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