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Single Reference Grid Continuous-Time Formulation for Batch Scheduling

IFAC-PapersOnLine 2019年第1期52卷 832-837页

作者： Hossein Mostafaei Iiro Harjunkoski Process Control and Automation School of Chemical Engineering Aalto University Espoo Finland ABB Corporate Research Ladenburg 68526 Germany

Short term scheduling of multipurpose batch processes has received growing attention over past decades. It concerns the optimal allocation of a set of limited resources to tasks over time in order to enhance the revenue of plants. This paper addresses the short term scheduling of batch processes through a continuous-time mixed integer linear programming (MILP) formulation based on the state-task network (STN) representation that allows to consider multiple intermediate due dates for market requirements. The proposed formulation can be classified as a slot-based approach that views the time horizon as a set of ordered blocks of unknown and variable lengths. Compared to previous similar approaches, it is simpler and leads to a smaller mathematical model without decoupling tasks from units. A few benchmark problems are used to illustrate the computational advantages of the proposed optimization approach.

关键词： Scheduling process operation Continuous time MILP STN

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Graphical Minimax Game and On-Policy Reinforcement Learning for Consensus of Leaderless Multi-Agent Systems *

Graphical Minimax Game and On-Policy Reinforcement Learning ...

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International Conference on control and automation (ICCA)

作者： Wei Dong Chunyan Wang Jinna Li Jianan Wang Key Laboratory of Dynamics and Control of Flight Vehicle within Ministry of Education School of Aerospace Engineering Beijing Institute of Technology Beijing China School of Information and Control Engineering Liaoning Shihua University Fushun China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

In this paper, we study the adaptive optimal consensus control of leaderless multi-agent systems (MASs) with heterogeneous dynamics. First, the consensus control problem is converted into a graphical minimax game problem and the corresponding algebraic Riccati equation (ARE) for each agent is obtained. Then, an on-policy reinforcement learning algorithm is proposed to online learn the optimal control policy without requiring the system dynamics. A certain rank condition is established to guarantee the convergence of the proposed online learning algorithm to the unique solution of the ARE. Finally, the effectiveness of the proposed algorithm is demonstrated through a numerical simulation.

关键词： Games Heuristic algorithms Reinforcement learning Mathematical model Vehicle dynamics Performance analysis Optimal control

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Preface

Advances in Industrial Control

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Advances in Industrial control 2023年 Part F1191卷 vii页

作者： Li, Jinna Lewis, Frank L. Fan, Jialu School of Information and Control Engineering Liaoning Petrochemical University Wanghua District Liaoning Fushun China UTA Research Institute University of Texas at Arlington Fort WorthTX United States State Key Lab of Synthetical Automation for Process Industries Northeastern University Heping District Liaoning Shenyang China

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Output Feedback Based Iterative Learning control with Finite Frequency Range Specifications via a Heuristic Approach for Batch processes with Polytopic Uncertainties ⁎

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IFAC-PapersOnLine 2020年第2期53卷 1397-1402页

作者： Shoulin Hao Tao Liu Wojciech Paszke Hongfeng Tao Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian 116024 China School of Control Science and Engineering Dalian University of Technology Dalian 116024 China Institute of Automation Electronic and Electrical Engineering University of Zielona Góra ul. Szafrana 2 65-516 Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi 214122 China

For robust control and iterative optimization of industrial batch processes with polytopic uncertainties, this paper proposes a robust output feedback based iterative learning control (ILC) design in terms of finite frequency range stability specifications. Robust stability conditions for the closed-loop ILC system along both time and batch directions are first established based on the generalized Kalman-Yakubovich-Popov lemma and linear repetitive system theory. To facilitate the ILC controller design with respect to process uncertainties described in a polytopic form, extended sufficient conditions for the system stability are then derived in terms of matrix inequalities. Correspondingly, a two-stage heuristic approach is developed to iteratively compute feasible ILC controller gains for implementation. An illustrative example is given to demonstrate the effectiveness of the proposed control design.

关键词： Batch processes polytopic uncertainties output feedback iterative learning control finite frequency range design

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Delay and packet-drop tolerant multi-stage distributed average tracking in mean square

arXiv

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arXiv 2020年

作者： Chen, Fei Chen, Changjiang Guo, Ge Hua, Changchun Chen, Guanrong State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110004 China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China Department of Automation Xiamen University Xiamen361005 China Institute of Electrical Engineering Yanshan University Qinhuangdao066004 China Department of Electrical Engineering City University of Hong Kong Hong Kong Hong Kong

This paper studies the distributed average tracking problem pertaining to a discrete-time linear time-invariant multi-agent network, which is subject to, concurrently, input delays, random packet-drops, and reference noise. The problem amounts to an integrated design of delay and packet-drop tolerant algorithm and determining the ultimate upper bound of the tracking error between agents' states and the average of the reference signals. The investigation is driven by the goal of devising a practically more attainable average tracking algorithm, thereby extending the existing work in the literature which largely ignored the aforementioned uncertainties. For this purpose, a blend of techniques from Kalman filtering, multi-stage consensus filtering, and predictive control is employed, which gives rise to a simple yet comepelling distributed average tracking algorithm that is robust to initialization error and allows the trade-off between communication/computation cost and stationary-state tracking error. Due to the inherent coupling among different control components, convergence analysis is significantly challenging. Nevertheless, it is revealed that the allowable values of the algorithm parameters rely upon the maximal degree of an expected network, while the convergence speed depends upon the second smallest eigenvalue of the same network's topology. The effectiveness of the theoretical results is verified by a numerical example. Copyright © 2020, The Authors. All rights reserved.

关键词： Packet loss

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Off-Policy Q-Learning for Anti-Interference control of Multi-Player Systems ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9189-9194页

作者： Jinna Li Zhenfei Xiao Tianyou Chai Frank. L. Lewis Sarangapani Jagannathan School of Information and Control Engineering Liaoning Shihua University Fushun 113001 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China UTA Research Institute the University of Texas at Arlington Arlington TX 76118 USA Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO 65409 USA

This paper develops a novel off-policy game Q-learning algorithm to solve the anti-interference control problem for discrete-time linear multi-player systems using only data without requiring system matrices to be known. The primary contribution of this paper lies in that the Q-learning strategy employed in the proposed algorithm is implemented in an off-policy policy iteration approach other than on-policy learning due to the well-known advantages of off-policy Q-learning over on-policy Q-learning. All of the players work hard together for the goal of minimizing their common performance index meanwhile defeating the disturbance that tries to maximize the specific performance index, and finally they reach the Nash equilibrium of the game resulting in satisfying disturbance attenuation condition. In order to find the solution to the Nash equilibrium, the anti-interference control problem is first transformed into an optimal control problem. Then an off-policy Q-learning algorithm is proposed in the framework of typical adaptive dynamic programming (ADP) and game architecture, such that control policies of all players can be learned using only measured data. Comparative simulation results are provided to verify the effectiveness of the proposed method.

关键词： H ∞ control off-policy Q-learning game theory Nash equilibrium

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Determination the installation efficiency of the evaporative air cooling in the greenhouse by temperature-moisture regime 1

Determination the installation efficiency of the evaporative...

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1st International Conference on Energetics, Civil and Agricultural engineering, ICECAE 2020

作者： Anarbaev, A. Tursunov, O. Zakhidov, R. Kodirov, D. Vakhidov, U. Bozorov, E. Tuhtaeva, G. Babaev, A. Department of Power Supply and Renewable Energy Sources Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Tashkent100000 Uzbekistan School of Mechanical and Power Engineering Shanghai Jiao Tong University Shanghai200240 China Research Institute of Forestry Tashkent111104 Uzbekistan Department of Automation and Control of Technology Process Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Tashkent100000 Uzbekistan Department of Hydrology and Ecology Bukhara Branch of Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Bukhara Uzbekistan

The results of theoretical studies and experiments of the developed exhaust ventilation system with direct evaporative air-cooling with using "wet mats"are given. It has shown sufficient efficiency with increasing quality of flower cutting in the summer months. The effect of moisture on the operation of this system was studied. It is established that the heat absorption capacity of air directly depends on its temperature and relative humidity. It is also determined that the temperature of leaves and flowers in summer is usually 2 ÷ 7 °C greater than the air temperature in the greenhouses. In an experimental greenhouse, according to the results of experiments, the effectiveness of the developed installation for evaporative cooling of its operation was determined. © Published under licence by IOP Publishing Ltd.

关键词： Greenhouses

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control Energy of Directed Networks

Control Energy of Directed Networks

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第三十八届中国控制会议

作者： Jiajun Huang Linying Xiang Fei Chen Department of Automation Xiamen University School of Control Engineering Northeastern University at Qinhuangdao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

This work studies the control energy for directed networks which are exactly controllable. By employing Jordan transformation, we derive a simplified formula to calculate the control energy of directed networks. We show that the control energy of directed networks is a decreasing function of the terminal time, indicating that the more time to control, the less energy to cost. Additionally, we investigate the control energy for the Price network model. We show that for Price networks the control energy is a decreasing function of network heterogeneity and an increasing function of network size.

关键词： control energy Directed network Exact controllability

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Discrete-time distributed average tracking for noisy reference signals

Discrete-time distributed average tracking for noisy referen...

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第三十八届中国控制会议

作者： Changjiang Chen Fei Chen Department of Automation Xiamen University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University School of Control Engineering Northeastern University at Qinhuangdao

We consider the distributed average tracking(DAT) problem for multiple time-varying signals subject to both process and measurement noise under a directed time-varying communication network. In order to attenuate the effect of the noise, we construct a Kalman filter for each agent, based on which we design a discrete-time consensus-based DAT algorithm. It is shown that the proposed algorithm can track the average of the reference signals with small steady-state error. A numerical example is presented to validate the theoretical results.

关键词： Distributed average tracking noise Kalman filter multi-agent system

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A simplified prediction-correction algorithm for time-varying convex optimization

A simplified prediction-correction algorithm for time-varyin...

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第三十八届中国控制会议

作者： Zhicong Lin Fei Chen Linying Xiang Ge Guo Department of Automation Xiamen University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University School of Control Engineering Northeastern University at Qinhuangdao

This paper studies a time-varying unconstrained convex optimization problem under the prediction-correction *** primary objective is to devise a simplified prediction-correction algorithm, which can solve the time-varying unconstrained convex optimization problem effectively without the need to compute the inverse of the Hessian matrix of the cost *** this aim, we propose a simplified prediction step and provide the theoretical analysis on the convergence of the resulting algorithm. The results show that, under suitable conditions, the error bound of our algorithm is O（h）, which is at the same level as those reported in the literature. We demonstrate the validity of the proposed algorithm by a numerical example.

关键词： Convex optimization time-varying optimization simplified prediction-correction

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