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Fixed-Time Anti-Disturbance Average-Tracking for Multi-Agent Systems Without Velocity Measurements

Journal of Systems Science & Complexity 2024年第3期37卷 985-1002页

作者： LI Yuling LIU Chenglin ZHANG Ya CHEN Yangyang Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Institute of AutomationJiangnan UniversityWuci 214122China School of Automation Southeast UniversityNanjing 210096China

In this paper,the authors tackle a fixed-time average-tracking problem for multi-agent systems which do not require velocity measurements and are affected by external mismatched and matched *** objective requires the considered agents with second-order dynamics to follow the average value of several time-varying reference signals,each of which is acquired by only one *** of all,the average value is estimated by using a novel distributed average estimator in a fixed ***,an original observer is created to provide estimated values in a fixed time for external mismatched and matched disturbances as well as unmeasured velocities of ***,the authors propose a novel fixed-time anti-disturbance average-tracking protocol with the aid of the observer and a sliding mode *** the end,the feasibility of theoretical analysis is illustrated by some simulation experiments.

关键词： Average-tracking control fixed-time algorithm fixed-time observer mismatched distur-bances multi-agent systems velocity measurements

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Improved High Order Model-Free Adaptive Iterative Learning control with Disturbance Compensation and Enhanced Convergence

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Computer Modeling in Engineering & Sciences 2023年第1期134卷 343-355页

作者： Zhiguo Wang Fangqing Gao Fei Liu Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Institute of AutomationJiangnan UniversityWuxi214122China

In this paper,an improved high-order model-free adaptive iterative control(IHOMFAILC)method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization *** method adds the differential of tracking error in the criteria function to compensate for the effect of the random ***,a high-order estimation algorithmis used to estimate the value of pseudo partial derivative(PPD),that is,the current value of PPD is updated by that of previous *** the rapid convergence of the maximumtracking error is not limited by the initial value of *** convergence of the maximumtracking error is deduced in *** method can track the desired output with enhanced convergence and improved tracking *** examples are used to verify the convergence and effectiveness of the proposed method.

关键词： Pseudo partial derivative enhanced convergence tracking error disturbance compensation

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A Fusion Kalman Filter and UFIR Estimator Using the Influence Function Method

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 709-718页

作者： Wei Xue Xiaoli Luan Shunyi Zhao Fei Liu IEEE the Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Institute of AutomationJiangnan University

In this paper,the Kalman filter(KF)and the unbiased finite impulse response(UFIR)filter are fused in the discrete-time state-space to improve robustness against *** avoid the problem where fusion filters may give up some advantages of UFIR filters by fusing based on noise statistics,we attempt to find a way to fuse without using noise *** fusion filtering algorithm is derived using the influence function that provides a quantified measure for disturbances on the resulting filtering outputs and is termed as an influence finite impulse response(IFIR)*** main advantage of the proposed method is that the noise statistics of process noise and measurement noise are no longer required in the fusion process,showing that a critical feature of the UFIR filter is *** numerical example and a practice-oriented case are given to illustrate the effectiveness of the proposed *** is shown that the IFIR filter has adaptive performance and can automatically switch from the Kalman estimate to the UFIR estimates according to operating ***,the proposed method can reduce the effects of optimal horizon length on the UFIR estimate and can give the state estimates of best accuracy among all the compared methods.

关键词： Fusion filter influence function Kalman filter(KF) robustness unbiased finite impulse response(FIR)

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Reinforcement learning for optimal tracking of large-scale systems with multitime scales

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Science China(Information Sciences) 2023年第7期66卷 5-29页

作者： Jinna LI Hao NIE Tianyou CHAI Frank L.LEWIS School of Information and Control Engineering Liaoning Petrochemical University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University UTA Research Institute University of Texas at Arlington

This paper aims to solve an optimal tracking control(OTC) problem of large-scale systems with multitime scales and coupled subsystems using singular perturbation(SP) theory and reinforcement learning(RL) techniques. A considerable contribution of this paper is the development of a data-driven SP-based RL method for the OTC of unknown large-scale systems with multitime scales. To achieve this, a multitime scale tracking problem was decomposed into a linear quadratic tracker problem for slow subsystems and a dynamical game problem for fast subsystems using the SP theory. Then, the distributed composite feedback controllers were found using a distributed off-policy integral RL algorithm that uses only measured data from the system in real time. Thus, the operational index can follow its prescribed target value via an approximately optimal approach. Theoretical analysis and proof are presented to demonstrate that the sum of the performances of reduced-order subsystems is approximately equal to the performance of the original large-scale system. Finally, numerical and practical examples are provided to validate the effectiveness of the proposed method.

关键词： reinforcement learning singular perturbation multitime scales data-driven control

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Self-triggered control for linear systems based on hierarchical reinforcement learning with RNNs 24

Self-triggered control for linear systems based on hierarchi...

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3rd International Conference on Computer, Artificial Intelligence and control Engineering, CAICE 2024

作者： Chen, Wei Wan, Haiying Luan, Xiaoli Liu, Fei Key Laboratory of Advanced Process Control for Light Industry Institute of Automation Jiangnan University Wuxi China

ISBN: (纸本)9798400716942

In this research, we introduce a novel model-free self-triggered control strategy for linear systems, employing a hierarchical reinforcement learning approach. This strategy establishes a dual-Actor Critic hierarchical architecture, utilizing two neural networks that guide each other's decision-making to simulate the triggering and control strategies in self-triggered control. The triggering strategy, serving as the top-level policy, aims to find the maximum triggering interval that maintains system stability based on the initial control strategy. The control strategy, as the bottom-level policy, under the guidance of the triggering strategy, takes the optimization of control performance considering the impact of triggering intervals as a sub-goal. This approach reduces the exploration space of the bottom-level policy and accelerates the reinforcement learning process. In the implementation of the dual-Actor Critic algorithm, the Critic network employs recurrent neural networks to capture the temporal dependencies of expected rewards, thereby enhancing the efficiency and accuracy in approximating the Q-function. The efficacy of our proposed method is demonstrated through a series of numerical simulations. Theory of computation∼Theory and algorithms for application domains∼Machine learning theory∼Reinforcement learning∼Sequential decision making. © 2024 ACM.

关键词： Federated learning

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Data-based prediction of the correction roll intermesh in industrial hot-dip galvanizing lines

Data-based prediction of the correction roll intermesh in in...

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2023 IEEE Conference on control Technology and Applications, CCTA 2023

作者： Marko, Lukas Kugi, Andreas Steinboeck, Andreas Tu Christian Doppler Laboratory for Intelligent Process Control for High-Quality Steel Products Automation and Control Institute Wien Austria Tu Automation and Control Institute Wien Austria

ISBN: (纸本)9798350335446

In the continuous hot-dip galvanizing process, the final zinc coating thickness is heavily influenced by the distance between the strip surface and the gas wiping dies which blow off excess zinc. To this end, establishing a flat, non-curved strip is a key prerequisite to minimize coating thickness deviations and inhomogeneities. A common way to influence the lateral strip profile is to introduce a controlled plastic deformation of the strip at the guiding rolls in the zinc bath. This motivates a data-based study of the optimal roll intermesh value of the zinc bath rolls to achieve a flat (zero-crossbow) strip profile during the galvanizing process. Based on this analysis, a data-based algorithm for the online prediction of the optimal roll intermesh value at the head end of each strip is developed and implemented in an industrial processing line. The prediction results from 1045 strips demonstrate the feasibility and accuracy of the proposed prediction algorithm. © 2023 IEEE.

关键词： Forecasting

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Feedforward control of flat hybrid automata: a behavioral systems theory approach 63

Feedforward control of flat hybrid automata: a behavioral sy...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Zahn, Frederik Kleinert, Tobias Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Karlsruhe Germany Rwth Aachen University Chair of Process Control Engineering Aachen Germany

ISBN: (纸本)9798350316339

Differential flatness has been defined in the literature for continuous time dynamical systems and for discrete time systems. We define flatness of automata from the perspective of behavioral systems theory, and synthesize a flatness definition for hybrid automata. These new definitions allow for feedforward trajectory generation for flat hybrid automata. Additionally, we provide necessary and sufficient conditions of hybrid automaton flatness for different types of continuous state constraints and switching conditions, and how to compute whether a hybrid automaton is flat. We illustrate feedforward trajectory generation based on our findings for a robotic manipulator example. © 2024 IEEE.

关键词： Continuous time systems

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Graph-guided masked autoencoder for process anomaly detection

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process Safety and Environmental Protection 2024年 186卷 1345-1357页

作者： Jia, Mingwei Xu, Danya Yang, Tao Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical Engineering National Tsing Hua University Hsinchu300044 Taiwan

Developing an accurate and reliable anomaly detection model is of great significance for safe operation in the process industry. To minimize false positives, it is crucial to accurately model the intricate topological and nonlinear connections among variables. In this study, a process anomaly detection method based on graph-guided masked autoencoder (GGMAE) is proposed by introducing the concept of the graph to the process industry. GGMAE first constructs a topology graph according to the process flowchart. Then, the patch and mask mechanism forces GGMAE to learn the process intrinsic information to reconstruct the input according to the variable topological relationship and temporal characteristics. Additionally, Kullback-Leibler divergence is used as the loss to ensure that the distribution of input and output is consistent. Experiments on two publicly available anomaly detection benchmarks demonstrate the superiority of GGMAE over existing methods. Visualization of the results demonstrates the physically compliant reconstruction logic. © 2024 The Institution of Chemical Engineers

关键词： Anomaly detection

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Model-Free H2/H∞ Predictive control for Discrete-Time System via Q-Learning 13

Model-Free H2/H∞ Predictive Control for Discrete-Time Syste...

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

作者： Lin, Yihong He, Peng Wan, Haiying Liu, Zhuangyu Luan, Xiaoli Liu, Fei Institute of Automation Jiangnan University Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Wuxi214122 China

ISBN: (纸本)9798350361674

This paper presents a model-free H2/H∞ Q-learning predictive control strategy for linear discrete-time systems. To design predictive controller with the system measured states, a policy iteration solution algorithm is employed to approximate the control inputs. Specifically, the developed algorithm is formulated in the form of linear matrix inequalities, designed to stabilize the system with mixed H2/H∞ control performance. Additionally, to improve robust performance under system disturbance variations, we introduce the receding horizon optimization into the Q-learning based predictive control. Finally, simulation results demonstrate the effectiveness of the proposed approach. © 2024 IEEE.

关键词： H2/H∞ performance index Model-free predictive control Q-learning Receding horizon optimization Unknown discrete-time systems

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Bayesian Filtering for High-Dimensional State-Space Models With State Partition and Error Compensation

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1239-1249页

作者： Ke Li Shunyi Zhao Biao Huang Fei Liu Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Institute of AutomationJiangnan UniversityWuxi 214122China Department of Chemical and Materials Engineering University of AlbertaEdmonton AB T6G 2G6Canada

In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally *** paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear *** high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense *** that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state ***,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing *** results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.

关键词： filtering estimation error

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