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arXiv 2024年

作者： Li, Dongdong Dong, Jiuxiang College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Synthetical Automation of Process Industries Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education Northeastern University Shenyang110819 China

Policy iteration is one of the classical frameworks of reinforcement learning, which requires a known initial stabilizing control. However, finding the initial stabilizing control depends on the known system model. To relax this requirement and achieve model-free optimal control, in this paper, two different reinforcement learning algorithms based on policy iteration and variable damping coefficients are designed for unknown discrete-time linear systems. First, a stable artificial system is designed, and this system is gradually iterated to the original system by varying the damping coefficients. This allows the initial stabilizing control to be obtained in a finite number of iteration steps. Then, an off-policy iteration algorithm and an off-policy Q-learning algorithm are designed to select the appropriate damping coefficients and realize data-driven. In these two algorithms, the current estimates of optimal control gain are not applied to the system to re-collect data. Moreover, they are characterized by the fast convergence of the traditional policy iteration. Finally, the proposed algorithms are validated by simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Damping

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LCA-based Multi-scenario Study on Steel or Aluminum Wheel Hub for Passenger Vehicles

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Procedia CIRP 2023年 116卷 191-196页

作者： Lu Gao Zhihui Wang Yanan Wang Tao Peng Weipeng Liu Renzhong Tang State Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou 310058 China Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province Zhejiang University Hangzhou 310058 China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou 310058 China School of Engineering Zhejiang University City College Hangzhou 310015 China

Lightweighting is one of the enabling techniques for energy saving and carbon reduction in the automotive industry. However, the life cycle performance of lightweight components remains unclear. Wheel hub is an important component, and its trend of replacing steel with aluminum is happening. Based on life cycle assessment and using ReCiPe model, this paper reports a cradle-to-grave study on the environmental impact of steel and aluminum wheel hub in passenger vehicles. Considering multi-scenario in the Chinese market, it is found that when the service life of passenger vehicle is less than 5.6 years, steel wheel hub is more environmental-friendly, and when the service life is less than 8.6 years, steel wheel hub also has more economic advantages. Combining different lightweight methods and different types of vehicles, it is found that structural optimization reduces the environmental impact of per unit weight mostly, process optimization reduces the life cycle environmental impact of wheel hub, and using lightweight steel wheel hub in electric vehicles has more potential in environmental benefits. some suggestions are, 1) to choose wheel hub that matches your actual usage to enjoy more environmental-economic advantages; 2) to prioritize structural-process integrated optimization for lightweight wheel hub; and 3) to use more lightweight components in electric vehicles.

关键词： Life cycle assessment Steel wheel hub Aluminum wheel hub Lightweight methods Scenario study

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Stealthy Attacks on Sampled-Data control and Encryption-Based Countermeasures

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IEEE Transactions on Automatic control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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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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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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A Holistic Approach to Include SiC and Design the Optimal Extrudate Catalyst for Hydrogen Production–Reforming Routes

SSRN

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SSRN 2023年

作者： Alkadhem, Ali M. Tavares, Fernanda Realpe, Natalia Lezcano, Gontzal Yudhanto, Arief Subah, Mohammad Manaças, Vasco Osinski, Jacek Lubineau, Gilles Castaño, Pedro Thuwal23955-6900 Saudi Arabia Thuwal23955-6900 Saudi Arabia Division King Abdullah University of Science and Technology Saudi Arabia Division King Abdullah University of Science and Technology Saudi Arabia Siemens Process Systems Engineering Limited London United Kingdom

Reforming processes are the backbone of hydrogen production routes, given the flexibility of their feedstock, such as methane, carbon dioxide, ammonia, waste plastics, or biomass. Heat transfer is a drawback at the industrial scale, reducing efficiency. We incorporate SiC in the technical extrudate catalyst and develop a holistic approach to optimize and understand the effect of each constituent and its mixtures. We apply Ni-Ce as an active phase, bentonite or kaolin as a binder, alumina as a filler, and carborundum as the heat-transport carrier. We characterize the extrudate catalysts using various techniques, including crushing strength and thermal conductivity. We test the samples in the steam reforming of a model molecule, calculate the kinetics and deactivation, perform a multivariate analysis, and model an industrial reformer. The results lead to optimal catalyst formulations, demonstrating the authentic influence of each constituent at multiple scales: reaction, deactivation, properties, and reactor performance. © 2023, The Authors. All rights reserved.

关键词： Hydrogen production

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

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IEEE Transactions on control of Network systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology 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 solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize 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. © 2014 IEEE.

关键词： Optimization algorithms

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Practice of flow control and smart valves

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Journal of Zhejiang University-Science A(Applied Physics & engineering) 2022年第4期23卷 243-246页

作者： Jin-yuan QIAN Wei WU Min CHENG Jun-hui ZHANG Institute of Process Equipment College of Energy EngineeringZhejiang UniversityHangzhou 310027China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China School of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400044China Institute of Mechatronics and Control Engineering Zhejiang UniversityHangzhou 310027China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China

Introduction The usual metaphors of“heart”and“throat”indi-cate the importance of pumps and valves in industrial systems including petroleum,chemical,metallurgy,aviation,and *** the continuous penetra-tion of the industrial internet,the miniaturization,digi-tization,multi-functionalization,and systemization of valves have become very important(Si et al.,2020;Chang et al.,2021;Pang et al.,2021;Bonilla et al.,2022;Yuan et al.,2022;Zhao et al.,2022).

关键词： smart internet flow

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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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