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SSRN 2025年

作者： Hu, Dongyang Liu, Yongqi Dai, Zhongde Sabale, Sandip Kim, Deok-Kee Li, Yiming Xiong, Jian Zheng, Yu Li, Yuping Zhou, Qiongzhi Shen, Jian College of Environment and Resources Xiangtan University Xiangtan411105 China Hunan Brunp Recycling Technology Co. Ltd. Changsha410604 China School of Carbon Neutrality Future Technology Sichuan University Chengdu610065 China Department of Semiconductor Systems Engineering Sejong University Seoul05006 Korea Republic of School of Ecology and Environment Tibet University Lhasa850000 China Beijing Engineering Research Center of Process Pollution Control Division of Environment Technology and Engineering Key Laboratory of Green Process and Engineering Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China

The high concentration of fluoride (F-) ions in contaminated water has become an urgent challenge for public health and environmental safety. In this contribution, we adopted asymmetric flow electrode capacitive deionization (FCDI) technique to remove F- ions, selecting NiCo layered metal oxide/carbon black (NiCo-LMO/CB) and Prussian blue analogs (cobalt hexacyanocobaltate, CoHCC) as the flow anode and cathode materials, respectively. As expected, the NiCo-LMO/CB flow electrode demonstrated excellent performance in terms of F- removal efficiency (68.54%), F- adsorption capacity (13.78 mg/g), and energy consumption (5.39 J/mg) when treating 1000 mg/L NaF solution under 1.2 V cell voltage. Combining electrochemical characterizations with structural analysis, such excellent defluorination performance of NiCo-LMO/CB can be ascribed to both electrostatic attraction and flexible ions intercalation. Hopefully, this work provides new ideas for defluorination of high concentration fluoridated wastewater via adopting asymmetric FCDI. © 2025, The Authors. All rights reserved.

关键词： Deionized water

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Constrained Optimization with Decision-Dependent Distributions

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IEEE Transactions on Automatic control 2025年

作者： Wang, Zifan Liu, Changxin Parisini, Thomas Zavlanos, Michael M. Johansson, Karl H. KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures StockholmSE-10044 Sweden East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy Duke University Department of Mechanical Engineering and Materials Science DurhamNC United States

In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distributions has assumed either the absence of constraints or fixed constraints. This work considers a more general setting where the constraints can also dynamically adjust in response to changes in the decision variables. Specifically, we consider linear constraints and analyze the effect of decision-dependent distributions in both the objective function and constraints. Firstly, we establish a sufficient condition for the existence of a constrained equilibrium point, at which the distributions remain invariant under retraining. Moreover, we propose and analyze two algorithms: repeated constrained optimization and repeated dual ascent. For each algorithm, we provide sufficient conditions for convergence to the constrained equilibrium point. Furthermore, we explore the relationship between the equilibrium point and the optimal point for the constrained decision-dependent optimization problem. Notably, our results encompass previous findings as special cases when the constraints remain fixed. To show the effectiveness of our theoretical analysis, we provide numerical experiments on both a market problem and a dynamic pricing problem for parking based on real-world data. © 1963-2012 IEEE.

关键词： Optimization algorithms

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Reduced Network Cumulative Constraint Violation for Distributed Bandit Convex Optimization under Slater’s Condition

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Ding, Jinliang Cao, Ming Johansson, Karl H. Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201800 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands 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 studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint function. However, the use of the clipping operation renders Slater’s condition (i.e, there exists a point that strictly satisfies the inequality constraints at all iterations) ineffective to achieve reduced network cumulative constraint violation. To tackle this challenge, we propose a new distributed bandit online primal–dual algorithm. If local loss functions are convex, we show that the proposed algorithm establishes 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 total number of iterations and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds, the network cumulative constraint violation bound is reduced to O(T 1−c). In addition, if local loss functions are strongly convex, for the case where strongly convex parameters are unknown, the network regret bound is reduced to O(T1−c), and the network cumulative constraint violation bound is O(T1−c/2) and O(T1−c) without and with Slater’s condition, respectively. For the case where strongly convex parameters are known, the network regret bound is further 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 among the first to establish reduced (network) cumulative constraint violation bounds for (distributed) bandit convex optimization with time-varying constraints under Slater’s condition. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2

关键词： Convex optimization

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Adaptive Pseudo Inverse control for a Class of Nonlinear Asymmetric and Saturated Nonlinear Hysteretic systems

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IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 916-928页

作者： Xiuyu Zhang Ruijing Jing Zhiwei Li Zhi Li Xinkai Chen Chun-Yi Su School of Automation Engineering Northeast Electric Power Universityand also with Jilin Province International Research Center of Precision Drive and Intelligent ControlJilin 132012China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China Department of Electronic and Information Systems Shibaura Institute of TechnologySaitama 337-8570Japan Department of Mechanical and Industrial Engineering Concordia UniversityQCMontreal H3B 1R6Canada

This paper aims at eliminating the asymmetric and saturated hysteresis nonlinearities by designing hysteresis pseudo inverse compensator and robust adaptive dynamic surface control(DSC)***"pseudo inverse"means that an on-line calculation mechanism of approximate control signal is developed by applying a searching method to the designed temporary control signal where the true control signal is *** main contributions are summarized as:1)to our best knowledge,it is the first time to compensate the asymmetric and saturated hysteresis by using hysteresis pseudo inverse compensator because the construction of the true saturated-type hysteresis inverse model is very difficult;2)by designing the saturated-type hysteresis pseudo inverse compensator,the construction of true explicit hysteresis inverse and the identifications of its corresponding unknown parameters are not required when dealing with the saturated-type hysteresis;3)by combining DSC technique with the tracking error transformed function,the"explosion of complexity"problem in backstepping method is overcome and the prespecified tracking performance is *** of stability and experimental results on the hardware-inloop platform illustrate the effectiveness of the proposed adaptive pseudo inverse control scheme.

关键词： Adaptive dynamic surface control adaptive pseudo inverse control asymmetric and saturated hysteresis robust control

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A Hierarchical Dual Decomposition-based Distributed Optimization Algorithm combining Quasi-Newton Steps and Bundle Methods

A Hierarchical Dual Decomposition-based Distributed Optimiza...

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Mediterranean Conference on control and Automation (MED)

作者： Vassilios Yfantis Martin Ruskowski Department of Mechanical and Process Engineering Chair of Machine Tools and Control Systems Technische Universit&#x00E4 t Kaiserslautern Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

This paper presents a hierarchical distributed optimization algorithm based on quasi-Newton update steps. Separable convex optimization problems are decoupled through dual decomposition and solved in a distributed fashion by coordinating the solutions of the subproblems through dual variables. The proposed algorithm updates the dual variables by approximating the Hessian of the dual function through collected subgradient information, analogously to quasi-Newton methods. As the dual maximization problem is generally nonsmooth, a smooth approximation might show poor performance. To this end cutting planes, analogous to bundle methods, are constructed that take the nonsmoothness of the dual function into account and lead to a better convergence behavior near the optimum. The proposed algorithm is evaluated on a large set of benchmark problems and compared to the subgradient method and to the bundle trust method for nonsmooth optimization.

关键词： Automation Benchmark testing Approximation algorithms Convex functions Behavioral sciences Optimization Convergence

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Hierarchical Distributed Model Predictive control based on Dual Decomposition and Quadratic Approximation

Hierarchical Distributed Model Predictive Control based on D...

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Mediterranean Conference on control and Automation (MED)

作者： Vassilios Yfantis Nigora Gafur Achim Wagner Martin Ruskowski Department of Mechanical and Process Engineering Chair of Machine Tools and Control Systems Technische Universit&#x00E4 t Kaiserslautern Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

This paper presents a dual decomposition-based distributed optimization algorithm and applies it to distributed model predictive control (DMPC) problems. The considered DMPC problems are coupled through shared limited resources. Lagrangian duality can be used to decompose an MPC problem, so that each subsystem can compute its individual resource utilization, without sharing information, such as dynamics or constraints, with the other subsystems. The feasibility of the central problem is ensured by the coordination of the subproblems through dual variables which can be interpreted as prices on the shared limited resources. The proposed coordination algorithm makes efficient use of information collected from previous iterations by performing a quadratic approximation of the dual function of the central MPC problem. Aggressive update steps of the dual variables are prevented through a covariance-based step size constraint. The nonsmoothness encountered in dual optimization problems is addressed by the construction of cutting planes, similar to bundle methods for nonsmooth optimization. The cutting planes ensure that the updated dual variables do not lie outside the range of validity of the dual approximation. The proposed algorithm is evaluated on a two-tank system and compared to the standard subgradient method. The results show that the rate of convergence towards the centralized solution can be significantly improved while still preserving privacy between the subsystems through limited information exchange.

关键词： Privacy Heuristic algorithms Prediction algorithms Approximation algorithms Dynamic scheduling Resource management Optimization

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Dynamic path planning and reactive scheduling for a robotic manipulator using nonlinear model predictive control

Dynamic path planning and reactive scheduling for a robotic ...

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Mediterranean Conference on control and Automation (MED)

作者： Nigora Gafur Leo Weber Vassilios Yfantis Achim Wagner Martin Ruskwoski Department of Mechanical and Process Engineering Chair of Machine Tools and Control Systems Technische Universit&#x00E4 t Kaiserslautern Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Operation of robotic manipulators is limited to structured environments and well-defined tasks due to an offline path planning. However, flexible production processes and human-robot collaboration necessitates a real time path planning to allow for replanning a path in changing environments. In this work, we investigate established planning algorithms for their applicability to dynamic path planning problems. We further compare these methods with our approach based on model predictive control. We consider a single manipulator with six degrees of freedom in static and dynamic environments. We investigate three experimental setups and show the advantages of the proposed MPC-ELS approach over more traditional path planning algorithms in terms of several metrics, such as path-length, execution time or trajectory smoothness. In addition, we propose a scheduling algorithm for object allocation to determine an optimal sequence for pick and place tasks with regard to minimum execution time.

关键词： Measurement Scheduling algorithms Heuristic algorithms Production Prediction algorithms Dynamic scheduling Trajectory

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Quantized Distributed Nonconvex Optimization Algorithms with Linear Convergence under the Polyak–Lojasiewicz Condition

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IEEE Transactions on Automatic control 2025年

作者： Xu, Lei Yi, Xinlei Sun, Jiayue Shi, Yang Johansson, Karl H. Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communication capacity, we introduce an encoder–decoder scheme. By integrating it with distributed gradient tracking and proportional integral algorithms, respectively, we then propose two quantized distributed nonconvex optimization algorithms. Assuming the global cost function satisfies the Polyak–Lojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that our proposed algorithms linearly converge to a global optimal point. Moreover, we show that a low data rate is sufficient to guarantee linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical examples. © 1963-2012 IEEE.

关键词： Cost functions

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Distributed Event-Triggered Bandit Convex Optimization with Time-Varying Constraints

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Mathematics Southeast University Nanjing210096 China The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands 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 the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms require that each agent broadcasts its decision to its neighbors at each iteration. To better utilize the limited communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Development of ACS Elements Training Complexes of the Power Equipment for the Increase of Its Reliability 2

Development of ACS Elements Training Complexes of the Power ...

引用

2nd International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2020

作者： Andryushin, A.V. Lunenko, V.S. Militonyan, S.M. National Research University 'MPEI' Department of automated process control systems National Research University 'MPEI' Department of heat power and heat engineering Volzhsky Russia

ISBN: (纸本)9781728156552

The article deals with the current issue of solving problems related to process control. An important role in solving these problems is played by the training process for students and employees of enterprises, which allows simulating the operation of real equipment. Volzhsky branch of Moscow Power Engeneering Institute (MPEI) developed simulators for studying the combination of alternative and renewable sources of electric and thermal energy and a training complex for means of control, automatic regulation and technological protection. The field equipment promotes to visually demonstrate functioning of existing equipment to students, including during laboratory work. The withdrawal of equipment to all modes is not possible for technological reasons and due to the random nature of weather conditions. The simulator of a geothermal heat pump was developed based on the mathematical models of the geothermal heat pump system elements. Comparison of the experimental and simulated values shows sufficient convergence, which indicates the correctness of the mathematical model and allows us to recommend it to practical application. © 2020 IEEE.

关键词： Simulators

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