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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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Monitoring Wastewater Treatment processes Using a Maximum Likelihood-Based Narx Neural Network

SSRN

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SSRN 2023年

作者： Liu, Wentao Xiong, Weili Sun, Wenxin Chen, Hongtian Key Laboratory of Advanced Process Control for Light Industry Ministry of Education School of Internet of Things Engineering Jiangnan University Wuxi214122 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

Wastewater treatment processes (WWTPs) contribute significantly to water recycling and energy conservation, because their failures can lead to the potential risk of adverse environmental impacts. WWTPs are acknowledged to be largely nonlinear systems with a series of biological reactors. Additionally, the presence of process uncertainties and measurement noises in WWTPs makes it difficult for the purpose of fault detection. This paper focuses on the data-driven realization of fault detection for sludge bulking and toxic impact in WWTPs. The fault detection model employs NARXbased neural networks to construct the residual generator through the maximum likelihood approach. The maximum likelihood-based NARX neural network can optimize both the model parameters and covariance matrix related to stochastic noises, based on which the model delivers more robustness performance against uncertainties than the existing work. Therefore, the nonlinearity and unknown system uncertainties of WWTPs can be processed simultaneously. The superiority and effectiveness of the maximum likelihood-based NARX neural network are validated through the benchmark simulation model no.1, where the successful detection rates for detecting sludge bulking faults and toxic impact faults are proved to be significantly higher than the traditional methods. © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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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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Robust Event-Triggered control of Nonlinear Systems Under a Global Sector-Bound Condition

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Guidance, Navigation and control 2022年第2期2卷 59-79页

作者： Pengpeng Zhang Tengfei Liu 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

This paper studies an event-tniggered control problem for nonlinear systems subject to both external disturbancoes and dy namic *** is assumed that the system satisfies a global sector bound *** avold infnitely fast samplng,a novel eventriggred sampling mechanism is propoeed,which use8 not only the measuned system state but also an estimation of the inluence of the *** the propoeed design,the intersampling intervals an be lower bounded by a poeitive constant,and it is independent of botb external disturbances and dynamie ***,the doeedl loop event-tniggered system i proved to be input-torstate stable with repect to the extemal *** smalgain techmigues are;used for the stability analysis of the dloeeil-bop system.

关键词： Event-triggered control external disturbance dynamic uncertainty input-to-state stability

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Classification on point-cloud of shoe-last curvature using weight-updated boosting based ensemble learning 9

Classification on point-cloud of shoe-last curvature using w...

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9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

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

Optimizing the curvature shape of shoe last is an important research content and a promising direction in current shoe-last design. The emergence of plantar pressure imaging method provided a new technical mean for shoe-last design. The point-cloud dataset was acquired from the plantar pressure experiments which may be used for generating comfort shoe-last curvature, and the key zones on last curvature may be classified and has potential benefit to the designers to produce comfort shoe. In this paper ensemble learning technologies are applied for classifying those points through the proposed weights-updated algorithms. The proposed weights-updated algorithm with boosting operation performed high effectiveness by comparing to k-NN, random forest (RF) and Bayers (NB) in classification accuracy. The proposed boosting based ensemble learning method will be potential applied in interactive design of industry and be beneficial to produce comfort shoes. © 2020 IEEE.

关键词： Learning systems

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In-situ controller Autotuning by Bayesian Optimization for Closed-loop Feedback control of Laser Powder Bed Fusion process

arXiv

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arXiv 2024年

作者： Kavas, Barış Balta, Efe C. Tucker, Michael R. Krishnadas, Raamadaas Rupenyan, Alisa Lygeros, John Bambach, Markus Department of Mechanical and Process Engineering ETH Zürich Zürich8092 Switzerland Control and Automation Group Inspire AG Zürich8005 Switzerland Department of Information Technology and Electrical Engineering Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland ZHAW Centre for Artificial Intelligence Zürich University of Applied Sciences Switzerland

Open-loop control of laser powder bed fusion (LPBF) additive manufacturing (AM) has enabled the industrial production of complex and high-criticality parts for aerospace, power generation, medical, transportation, and other industries. This approach relies on static parameter sets obtained through extensive experimentation and a priori simulation on analog parts, with the hope that they remain stable and defect-free once transferred to the production parts. Closed-loop control of LPBF has the potential to enhance process stability further and reduce defect formation in the face of complex thermal histories, stochastic process noise, hardware drift, and unexpected perturbations. The controllers can be classified based on the spatial and temporal scales in which they operate, designated as layer-to-layer and in-layer controllers. However, the performance and effectiveness of controllers largely depend on the tuning of their parameters. Traditionally, controller tuning has been a manual, expertise-driven process that does not guarantee optimal controller performance and is often constrained by the non-transferability of settings between different systems. This study proposes the use of Bayesian Optimization (BO), a sample-efficient algorithm, to automate the tuning of an in-layer controller by leveraging the layer-to-layer repetitive nature of the LPBF process. Two alternative approaches are introduced: online tuning, which adjusts parameters iteratively during the process, and offline tuning, conducted in a representative setup such as laser exposures on a bare metal plate. The proposed methods are experimentally implemented on an in-layer PI controller and the performance of the resulting tuned controllers is investigated on two different wedge geometries that are prone to overheating. The results demonstrate that BO effectively tunes controllers using either method, where both significantly reduced overheating in controlled wedge specimens compared to those uncontro

关键词： controllers

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Q-Learing-Based Multi-Rate Optimal control and Its Application

Q-Learing-Based Multi-Rate Optimal Control and Its Applicati...

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Chinese control and Decision Conference, CCDC

作者： Zhenxing Xia Wei Dai School of Information and Control Engineering China University of Mining and Technology Xuzhou State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (纸本)9781665478977

In this paper, adaptive dynamic programming (ADP) algorithm, with lifting technology, is develop to solve the multi-rate optimal control problem for discrete-time linear systems. We make use of the lifting technology to convert the multi-rate sample control problem to the single-rate one in the uniform cycle. The propose a Q-Leaming based approach to learn the optimal regulator by a value iteration (VI) algorithm. First, a class of continuous-time (CT) linear system with multi-timescale is considered. Then, the convergence of a Q-Learning based algorithm is given. It is proven that the iterative cost function precisely converges to the optimal value, and the control input also converges to the optimal values. Finally, HIL system for grinding process is given to illustrate the effective performance of the proposed method.

关键词： Linear systems Regulators Q-learning System dynamics Heuristic algorithms Optimal control process control

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Robust Iterative Learning control Design for a Class of Uncertain Batch processes

Robust Iterative Learning Control Design for a Class of Unce...

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European control Conference (ECC)

作者： Robert Maniarski Wojciech Paszke Hongfeng Tao Eric Rogers Institute of Automation Electronic and Electrical Engineering University of Zielona Góra Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi China School of Electronics and Computer Science University of Southampton Southampton UK

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper investigates the problem of designing robust iterative learning control laws for discrete-time batch processes with norm-bounded parameter uncertainties. A law of proportional-differential type is designed to achieve robust convergence of the tracking error in the batch-to-batch direction. It is shown that the design problem can be written as a two-dimensional system. Then, the recently developed non-conservative conditions for (structural) stability analysis for a linear Roesser model are used. The conditions for the existence and computation of the required control law matrices are linear matrix inequality-based. Finally, comparative simulation results show the effectiveness of the new design.

关键词： Uncertain systems Analytical models Simulation Computational modeling Batch production systems Europe Linear matrix inequalities

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Discrete-time Formation control With Attitude Alignment: A Geometric Approach under Ergodic Products

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IEEE Transactions on control of Network Systems 2025年

作者： Li, Zhen Tang, Yang Zhang, Wenbing Fan, Yongqing Huang, Tingwen Xi-an University of Posts and Telecommunications School of Automation Xian710121 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Yangzhou University Department of Mathematics Jiangsu225002 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

Assuming agents possess sensing capabilities and dynamics relative to their body coordinate frames, this paper addresses a displacement-based strategy for discrete-time formation control through attitude synchronization. Unlike the Eulerian approximation, the first challenge lies in developing discrete-time models that capture both position and attitude motions, thereby complementing existing continuous-time systems with a discretized version. Then, two leader-following control protocols are designed, incorporating a jointly-connected topology to relax the connectivity requirements. The analysis of these systems often relies on the Lyapunov function, the existence of which may be hard to guarantee. Consequently, geometric properties of several polytopes are explored under the dynamic topologies and time-varying parameters. By extending the ergodicity results to leader-following scenarios, these properties are characterized as positively invariant sets. This invariance, which is independent of the existence of the Lyapunov function or the solvability of its associated criteria, provides a beneficial tool for analyzing time-variant systems. Utilizing this invariance and some mathematical techniques, the desired formation shape can be achieved in the cases where the attitudes of the followers synchronize with the leader. Finally, a numerical simulation validates the results. © 2014 IEEE.

关键词： Lyapunov functions

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Research on Data Encryption in Lightweight Embedded Web Server 10

Research on Data Encryption in Lightweight Embedded Web Serv...

引用

10th International Conference on control, automation and Information Sciences, ICCAIS 2021

作者： Li, Pengfei Qu, Junsuo Shi, Haonan Xi'an University of Posts Telecommunications School of Automation Xi'an China Xi'an University of Posts Telecommunications Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an China Xi'an University of Posts Telecommunications School of Communication and Information Engineering Xi'an China

ISBN: (纸本)9781665440295

From the perspective of reducing the configuration complexity of IoT device parameters and improving data security, a lightweight embedded Web server is designed based on the TencentOS tiny operating system to reduce the hardware requirements of the embedded Web server design. In order to improve the security and transmission reliability of IoT monitoring data, a mechanism for data encryption is proposed. Embedded Elliptic Curve Cryptography (ECC) in Flash inside the chip to ensure the security of key data during transmission. The data monitoring platform receives and decrypts the received data, and stores the data into a database server after processing. The experimental results show that the security of data transmission and reached the expected goals. © 2021 IEEE.

关键词： Internet of things

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