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arXiv 2023年

作者： Shi, Shengling Tsiamis, Anastasios De Schutter, Bart Department of Chemical Engineering Massachusetts Institute of Technology United States Delft Center for Systems and Control Delft University of Technology Netherlands Automatic Control Laboratory ETH Zurich Switzerland Delft Center for Systems and Control Delft University of Technology Netherlands

This work analyzes how the trade-off between the modeling error, the terminal value function error, and the prediction horizon affects the performance of a nominal receding-horizon linear quadratic (LQ) controller. By developing a novel perturbation result of the Riccati difference equation, a novel performance upper bound is obtained and suggests that for many cases, the prediction horizon can be either 1 or +∞ to improve the control performance, depending on the relative difference between the modeling error and the terminal value function error. The result also shows that when an infinite horizon is desired, a finite prediction horizon that is larger than the controllability index can be sufficient for achieving a near-optimal performance, revealing a close relation between the prediction horizon and controllability. The obtained suboptimality performance bound is applied to provide novel sample complexity and regret guarantees for nominal receding-horizon LQ controllers in a learning-based setting. We show that an adaptive prediction horizon that increases as a logarithmic function of time is beneficial for regret minimization. Copyright © 2023, The Authors. All rights reserved.

关键词： controllability

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2024 China Automation Congress, CAC 2024

作者： Wu, Yibo Li, Shuai Li, Zijun Zhu, Shijie Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Hongta Liaoning Tobacco Co. Ltd Shenyang China College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China

ISBN: (纸本)9798350368604

As industrial systems expand in scale and production processes grow more complex, the safety and quality of products have become increasingly important. Consequently, fault detection and diagnosis have taken on greater significance. Traditional fault detection and diagnosis methods rely heavily on mathematical modeling, while data-driven approaches such as Partial Least Squares (PLS) have gained prominence with advancements in information and automation technologies. The impact of faults on product quality and Variable Operating Conditions (VOC) bring challenges to the industrial applications of fault detection and diagnosis methods. This review summarizes recent data-driven methods for quality-related and VOC scenarios. Firstly, we summarized recent quality-related fault detection and diagnosis methods centered around PLS. Then, we categorized recent fault detection and diagnosis researches for VOC scenarios and highlighted representative studies. Following this, we delved into specific scenarios to explore quality-related fault detection and diagnosis for VOC scenarios. Finally, on the basis of the remaining issues and challenges in quality-related fault detection and diagnosis for VOC scenarios, we discussed and envisioned future research directions. © 2024 IEEE.

关键词： Failure analysis

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Approximate Dynamic Programming for Constrained Piecewise Affine systems with Stability and Safety Guarantees

arXiv

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arXiv 2023年

作者： He, Kanghui Shi, Shengling van den Boom, Ton De Schutter, Bart Delft Center for Systems and Control Delft University of Technology Delft Netherlands Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA United States

Infinite-horizon optimal control of constrained piecewise affine (PWA) systems has been approximately addressed by hybrid model predictive control (MPC), which, however, has computational limitations, both in offline design and online implementation. In this paper, we consider an alternative approach based on approximate dynamic programming (ADP), an important class of methods in reinforcement learning. We accommodate non-convex union-of-polyhedra state constraints and linear input constraints into ADP by designing PWA penalty functions. PWA function approximation is used, which allows for a mixed-integer encoding to implement ADP. The main advantage of the proposed ADP method is its online computational efficiency. Particularly, we propose two control policies, which lead to solving a smaller-scale mixed-integer linear program than conventional hybrid MPC, or a single convex quadratic program, depending on whether the policy is implicitly determined online or explicitly computed offline. We characterize the stability and safety properties of the closed-loop systems, as well as the sub-optimality of the proposed policies, by quantifying the approximation errors of value functions and policies. We also develop an offline mixed-integer linear programming-based method to certify the reliability of the proposed method. Simulation results on an inverted pendulum with elastic walls and on an adaptive cruise control problem validate the control performance in terms of constraint satisfaction and CPU time. © 2023, CC BY.

关键词： Predictive control systems

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State-action control barrier functions: Imposing safety on learning-based control with low online computational costs

arXiv

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arXiv 2023年

作者： He, Kanghui Shi, Shengling van den Boom, Ton De Schutter, Bart Delft Center for Systems and Control Delft University of Technology Delft Netherlands The Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA United States

Learning-based control with safety guarantees usually requires real-time safety certification and modifications of possibly unsafe learning-based policies. The control barrier function (CBF) method uses a safety filter containing a constrained optimization problem to produce safe policies. However, finding a valid CBF for a general nonlinear system requires a complex function parameterization, which in general makes the policy optimization problem difficult to solve in real time. For nonlinear systems with nonlinear state constraints, this paper proposes the novel concept of state-action CBFs (SACBFs), which do not only characterize the safety at each state but also evaluate the control inputs taken at each state. SACBFs, in contrast to CBFs, enable a flexible parameterization, resulting in a safety filter that involves a convex quadratic optimization problem, which significantly alleviates the online computational burden. We propose a learning-based approach to synthesize SACBFs. The effect of learning errors on the effectiveness of SACBFs is addressed by constraint tightening and introducing a new concept called contractive-set CBFs. This ensures formal safety guarantees for the learned CBFs and control policies. Simulation results on an inverted pendulum with elastic walls validate the proposed CBFs in terms of constraint satisfaction and CPU time. © 2023, CC BY.

关键词： Constrained optimization

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Data-driven model predictive control for vehicle path-tracking using a recurrent neural network

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IFAC-PapersOnLine 2024年第29期58卷 367-372页

作者： Jian Yi Xialai Wu Ling Lin Jiabin Qin Junghui Chen *Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou University Huzhou 313000 China **Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou University Huzhou 313000 China ***Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou University Huzhou 313000 China ****Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou University Huzhou 313000 China *****Department of Chemical Engineering Chung-Yuan Christian University Taoyuan Taiwan 32023 Republic of China

Path tracking control is crucial for autonomous vehicle driving. Traditional nonlinear model predictive control (NMPC) for path tracking demands extensive computation, making real-time implementation challenging. This paper introduces a Fast Model Predictive control (FMPC) method for vehicle path tracking, utilizing a recurrent neural network (RNN) with symmetric saturating linear transfer functions (SSL-RNN) in the hidden layer. The proposed approach leverages the SSL-RNN model to accurately capture vehicle dynamics. Consequently, the optimal control problem in MPC is reformulated as a mixed integer linear programming problem, facilitating swift solutions. Simulation experiments validate the proposed method's efficacy. Compared to traditional vehicle mechanism model-based MPC and RNN-based NMPC, our FMPC demonstrates superior accuracy in path tracking and significantly enhances controller solution efficiency.

关键词： model predictive control mixed integer linear programming symmetric saturating linear transfer function recurrent neural network vehicle path tracking

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Graphene-nickel nitride hybrids supporting palladium nanoparticles for enhanced ethanol electrooxidation

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Journal of Energy Chemistry 2021年第4期30卷 48-54页

作者： Tong Wu Xiao Wang Ahmet Emrehan Emre Jinchen Fan Yulin Min Qunjie Xu Shigang Sun Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power Shanghai Engineering Research Center of Energy-Saving in Heat Exchange SystemsShanghai University of Electric PowerShanghai 200090China Department of Chemical Engineering and Biointerfaces Institute University of MichiganAnn Arbor 48109MIUnited States Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China State Key Laboratory of Physical Chemistry of Solid Surfaces Department of ChemistryCollege of Chemistry and Chemical EngineeringXiamen UniversityXiamen 361005FujianChina

Electrocatalysts for ethanol oxidation reaction(EOR)are generally limited by their poor durability because of the catalyst poisoning induced by the reaction intermediate carbon monoxide(CO).Therefore,the rapid oxidation removal of CO intermediates is crucial to the durability of EOR-based ***,in order to effectively avoiding the catalyst CO poisoning and improve the durability,the graphene-nickel nitride hybrids(AG-Ni_(3)N)were designed for supporting palladium nanoparticles(Pd/AG-Ni_(3)N)and then used for ethanol *** density functional theory(DFT)calculations demonstrated the introduction of AG-Ni_(3)N depresses the CO absorption and simultaneously promotes the adsorption of OH species for CO oxidation *** fabricated Pd/AG-Ni_(3)N catalyst distinctively exhibits excellent electroactivity with the mass catalytic activity of 3499.5 m A mg^(-1) on EOR in alkaline media,which is around 5.24 times higher than Pd/C(commercial catalyst).Notably,the Pd/AG-Ni_(3)N hybrids display excellent stability and durability after chronoamperometric measurements with a total operation time of 150,000 s.

关键词： Graphene Pd nanoparticles Nickel nitride Ethanol electrooxidation

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Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat control

arXiv

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arXiv 2023年

作者： Lin, Runze Luo, Yangyang Wu, Xialai Chen, Junghui Huang, Biao Xie, Lei Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 2G6 Canada Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou313000 China Department of Chemical Engineering Chung-Yuan Christian University Taoyuan32023 Taiwan

The Organic Rankine Cycle (ORC) is widely used in industrial waste heat recovery due to its simple structure and easy maintenance. However, in the context of smart manufacturing in the process industry, traditional model-based optimization control methods are unable to adapt to the varying operating conditions of the ORC system or sudden changes in operating modes. Deep reinforcement learning (DRL) has significant advantages in situations with uncertainty as it directly achieves control objectives by interacting with the environment without requiring an explicit model of the controlled plant. Nevertheless, direct application of DRL to physical ORC systems presents unacceptable safety risks, and its generalization performance under model-plant mismatch is insufficient to support ORC control requirements. Therefore, this paper proposes a Sim2Real transfer learning-based DRL control method for ORC superheat control, which aims to provide a new simple, feasible, and user-friendly solution for energy system optimization control. Experimental results show that the proposed method greatly improves the training speed of DRL in ORC control problems and solves the generalization performance issue of the agent under multiple operating conditions through Sim2Real transfer. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Tracking performance limitations of MIMO discrete-time networked control systems with multiple constraints

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Science China(Information Sciences) 2023年第8期66卷 267-268页

作者： Xiaowei JIANG Bin ZHANG Xiangyong CHEN Huaicheng YAN School of Automation China University of Geosciences Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation and Electrical Engineering Linyi University Key Laboratory of Smart Manufacturing in Energy Chemical Process of the Ministry of Education

Dear editor,Networked control systems(NCSs) have attracted widespread attention in some fields because they offer the advantages of reduced cabling, greater resource sharing,and ease of installation and maintenance [1]. However, in an unreliable network environment, network constraints can significantly limit the performance of NCSs.

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User-Centric Bandwidth Allocation in Network Slicing via Dueling DRL

User-Centric Bandwidth Allocation in Network Slicing via Due...

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Computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Yitian Wang Kangxu Tian Hui Wang Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Information Engineering Shenyang University of Chemical Technology Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

Network slicing plays a pivotal role in 5 G and next-generation communication systems to support diversified service requirements. This paper investigates bandwidth allocation in a multi-base station, multi-user network where users probabilistically request network slicing services. We formulate a dynamic resource optimization problem to maximize user satisfaction, accounting for stochastic service demands and heterogeneous quality-of-service requirements. An Adaptive Hierarchical Dueling Deep Q-Network (AH-Dueling DQN) algorithm is proposed to address the NP-hard mixed-integer programming nature of this problem, leveraging deep reinforcement learning to handle high-dimensional state-action spaces. Simulation results demonstrate the algorithm’s effectiveness in reducing users’ latency while improving the throughput.

关键词： Network slicing Heuristic algorithms Simulation Stochastic processes Quality of service Channel allocation Deep reinforcement learning Throughput Resource management Optimization

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