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A novel strategy to solve communication constraints for formation control of multi-AUVs

Science China(Information Sciences) 2021年第7期64卷 253-255页

作者： Zhenyu GAO Ge GUO Department of Automation Dalian Maritime University School of Control Engineering Northeastern University Qinhuangdao Key Laboratory of Synthetical Automation for Process Industries Northeastern University

Dear editor,Interest in terms of research on formation control of multiple autonomous underwater vehicles (multi-AUVs) has gained considerable attention in recent years [1–5], owing to its extensive applications in d... 详细信息

关键词： Information Systems and Communication Service

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Design of power monitoring system for 33MW DC submerged arc furnace based on PLC 3

Design of power monitoring system for 33MW DC submerged arc ...

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3rd International Conference on Intelligent control, Measurement and Signal processing and Intelligent Oil Field, ICMSP 2021

作者： Cheng, Ying Su, Chang Li, Hong School of Electronic Engineering Xi'An Shiyou University Automation Instrument and Process Control Laboratory Xi an China Jilin Jien Nickel Industry Co. Ltd Pan shi China

ISBN: (纸本)9781665437158

This paper introduces a monitoring system of 33MW submerged arc furnace DC energy saving power supply equipment. The energy saving equipment is equivalent to 36 pulse waves at the side of high voltage grid when running. Three sets of independent 12-pulse thyristor rectifier links are used to supply DC power to 33MW submerged arc furnace through six electrodes. S7-200 Smart PLC and WinCC monitoring configuration software are used to realize the design of the monitoring system, and the monitoring system uses industrial Ethernet and point-to-point interface (PPI) communication protocol to achieve the precise and rapid control of the power supply system of 33MW mining furnace, realizing the digitization and intelligence. The field debugging and application results show that, the monitoring system has a good man-machine interface, high degree of automation, and can run efficiently, stably and safely. © 2021 IEEE.

关键词： Energy conservation

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Global Asymptotic Stability Analysis for Autonomous Optimization

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IEEE Transactions on Automatic control 2025年

作者： Jin, Zhenghong Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore

This paper studies the feedback optimization problem for nonlinear systems. In particular, we assume that the controlled plant admits a global asymptotic stable equilibrium corresponding to each fixed reference input and consider a modified gradient-flow optimizer augmented with a nonlinear perturbation function. With the output map of the controlled plant satisfying a linear growth condition, this paper proves the existence of a perturbation function such that the resulting feedback optimization system is globally asymptotically stable at the desired equilibrium. The proof is based on the seamless integration of tools from the singular perturbation theory, input-to-state stability (ISS), and the nonlinear small-gain theorem. The possibility of extending the proposed approach to other optimizers is also discussed. Three numerical examples are employed to verify the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Perturbation techniques

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Design of Supervisory Model Predictive control for Building HVAC System Subject to Time-Varying Operating Points

Design of Supervisory Model Predictive Control for Building ...

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International Conference on control, automation and Systems ( ICCAS)

作者： Sitthiphat Promchaiwattana David Banjerdpongchai Department of Electrical Engineering Faculty of Engineering Intelligent Control Automation of Process Systems Research Unit Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok Thailand

This paper presents the design of supervisory model predictive control for HVAC systems with consideration of peak demand shaving and thermal comfort. In this work, we analyze the operating points with the thermal comfort requirement. Afterwards, we linearize the nonlinear dynamic model to a linear time-varying model. In particular, we specify that the operating point of the open-loop HVAC system satisfies the thermal comfort condition. This makes the predicted mean vote (PMV) equal to zero or nearly to zero. Subsequently, we calculate the zone temperature, the zone humidity ratio, and the volumetric flow rate. When considering the time-varying disturbance profiles, it appears that the operating points are time-varying. We observe that the coefficient of performance (COP) and the operating points are time-varying which lead to time-varying model. Moreover, we design supervisory control (SC) and model predictive control (MPC) for linear time-varying model. In the SC design, we aim to find the optimal reference zone temperature which is used as the reference signal for MPC. In the MPC design, the objectives are twofold. First, the zone temperature tracks the reference signal. Second, both electrical energy cost and deviation of desired thermal comfort are minimized. The computer simulation shows that MPC of time-varying HVAC system yields the best reference tracking. In addition, it reduces total electrical energy cost the most. We conclude that the optimal temperature reference can be effectively applied to the building HVAC systems while the occupants feel comfortable and the total electrical energy cost is the lowest.

关键词： HVAC Costs Input variables Buildings Predictive models Supervisory control Thermal analysis

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Hazard Analysis of Collaborative automation Systems: A Two-layer Approach based on Supervisory control and Simulation

Hazard Analysis of Collaborative Automation Systems: A Two-l...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Tom P. Huck Yuvaraj Selvaraj Constantin Cronrath Christoph Ledermann Martin Fabian Bengt Lennartson Torsten Kröger Intelligent Process Automation and Robotics Lab Institute of Anthropomatics and Robotics (IAR-IPR) Karlsruhe Institute of Technology India Department of Electrical Engineering Division of Systems and Control Chalmers University of Technology Gothenburg Sweden

Safety critical systems are typically subjected to hazard analysis before commissioning to identify and analyse potentially hazardous system states that may arise during operation. Currently, hazard analysis is mainly based on human reasoning, past experiences, and simple tools such as checklists and spreadsheets. Increasing system complexity makes such approaches decreasingly suitable. Furthermore, testing-based hazard analysis is often not suitable due to high costs or dangers of physical faults. A remedy for this are model-based hazard analysis methods, which either rely on formal models or on simulation models, each with their own benefits and drawbacks. This paper proposes a two-layer approach that combines the benefits of exhaustive analysis using formal methods with detailed analysis using simulation. Unsafe behaviours that lead to unsafe states are first synthesised from a formal model of the system using Supervisory control Theory. The result is then input to the simulation where detailed analyses using domain-specific risk metrics are performed. Though the presented approach is generally applicable, this paper demonstrates the benefits of the approach on an industrial human-robot collaboration system.

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Inspection trolley based on the distance detection obstacle

Inspection trolley based on the distance detection obstacle

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Chinese control and Decision Conference, CCDC

作者： Guicheng Wang Ao Sheng Xiaobin Li Min Zhang Changliang Guan Fengfeng Yin School of Electrical and Electronics Engineering Shanghai Institute of Technology Shanghai China Library Shanghai Institute of Technology Shanghai China Zijin Intelligent Control (Xiamen) Technology Co. Ltd. Xiamen China State Key Laboratory of Process Automation in Mining & Metallurgy and Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

In Industry 4.0 background, unmanned full-process automated production has gradually become a hot topic, and the realization of intelligent and information factory requires the joint cooperation of multiple industries, in which the inspection device that replaces traditional manual work is an inescapable topic. At present, domestic and foreign research on intelligent inspection trolley is focused on obstacle avoidance function based on various sensors. In this context, it is introduced an algorithm based on monocular camera as a sensor, using Python language to access OpenCV library, and utilizing Gaussian filter function and Canny operator-based image contour extraction to complete the distance measurement. The algorithm can achieve the distance measurement and obstacle avoidance function at a lower cost. The test results show that the system can automatically identify the distance of obstacles in the inspection process, and according to the visual information captured by the camera in real time, it can independently generate steering action and collision reminder, and can complete the preliminary requirement of intelligent inspection.

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Lexicographic Multi-objective Order Picking Optimization for Robotic Mobile Fulfillment Systems

Lexicographic Multi-objective Order Picking Optimization for...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Hanying Zhao, Ziyan Liang, Jiaqi Li, Xingyang Liu, Shixin College of Information Science and Engineering Northeastern University Shenyang110819 China Foshan Graduate School of Innovation Northeastern University Foshan China Polytechnique Montréal Département de Mathématiques et Génie Industriel MontréalQCH3T 1J4 Canada Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Shenyang110819 China School of Control Engineering/School Northeastern University at Qinhuangdao Hebei Qinhuangdao066004 China

ISBN: (纸本)9781665410205

In light of advancements in artificial intelligence, the Internet of Things, and mechatronics, robots are increasingly integrated into e-commerce warehouses to enable smart order picking solutions and foster intelligent automation. A robot-assisted order picking process revolutionizes the traditional labor-intensive person-to-goods order picking technology, leading to a goods-to-person (G2P) smart warehouse. Within it, robots transport pods to predefined picking stations, where human pickers retrieve the requested goods from these pods to fulfill customer orders. The allocation of pods to robots and the scheduling of picking operations are key optimization issues in G2P order picking systems. Although they play a key role in improving operational efficiency, existing research has paid limited attention to their joint optimization. This study considers a lexicographic multi-objective optimization problem to shorten the order picking cycles under the premise of optimizing the picking efficiency evaluated by makespan. We build a mixed integer program for the newly proposed problem and develop a matheuristic algorithm by integrating a commodity-order model into a metaheuristic algorithm to solve it. Experimental results show that the proposed method can significantly shorten the total order picking cycles while keeping the minimum makespan. It outperforms a recent state-of-the-art algorithm. This work emphasizes the importance of joint optimization within G2P smart warehouses and reveals the high potential of the proposed method to be used in practice. © 2024 IEEE.

关键词： Integer programming

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

Breaking Limitation of Convergence in Continuous Optimizatio...

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IEEE Conference on Decision and control

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

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

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 ($\mathbf{P}-\mathrm{L}$) 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.

关键词： Upper bound Linear programming Numerical simulation Minimization Convex functions Optimization Convergence Lyapunov methods

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A Norm Optimal Iterative Learning control Approach With Efficient Computation

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

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Data Driven control and Learning Systems (DDCLS)

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

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

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.

关键词： Uncertain systems Uncertainty Optimal control Cost function Numerical simulation Computational efficiency Numerical models

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A Deep Reinforcement Learning-Based Multi-Objective Charging Scheduling Method for Electric Vehicle Charging Station

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IEEE Internet of Things Journal 2025年

作者： Zhao, Qiang Wu, Wenwei Cheng, Ningyu Guo, Ge Han, Yinghua Northeastern University at Qinhuangdao School of Control Engineering Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinghuangdao066004 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China Northeastern University at Qinhuangdao School of Computer and Communication Engineering Hebei Qinhuangdao066004 China

This paper investigates the charging scheduling problem within an Electric Vehicle Charging Station (EVCS), aiming to coordinate multiple charging piles to optimize the charging process. The objectives are to increase the profit for the Charging Station Operator (CSO) and enhance user satisfaction while mitigating significant fluctuations in grid load. However, since these objectives originate from different stakeholders, they are inherently conflicting;a strategy trained using fixed objective weights cannot satisfy all of them simultaneously. Additionally, uncertainties such as vehicle arrival and departure times, power requirements, and grid tariffs complicate the modeling of this process. To address these challenges, this paper reformulates the scheduling process as a Markov Decision process (MDP) and proposes a novel model-free reinforcement learning method. By integrating randomly varying objective preferences into the inputs of agents, a set of policies corresponding to different objective preferences can be learned through a single training session. As a result, agents can execute various policies to satisfy the stakeholders’ needs based on these preferences. Moreover, a sampling adjustment strategy is introduced to improve the uniformity of the final non-dominated solution set and enhance the ability of the agent to explore the objective space. Experiments show that the proposed method can effectively solve the multi-objective problem of cooperative charging within an EVCS. Compared to the baseline method, the proposed method can achieve superior results while simultaneously reducing the computational cost to a significant extent. © 2014 IEEE.

关键词： Deep reinforcement learning

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