检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Van den Hof, Paul M.J. Shi, Shengling Fonken, Stefanie J.M. Ramaswamy, Karthik R. Hjalmarsson, Håkan Dankers, Arne G. Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands Department of Chemical Engineering Massachusetts Institute of Technology United States ASML Veldhoven Netherlands Division of Decision and Control Systems Royal Institute of Technology Stockholm Sweden Department of Electrical and Software Engineering University of Calgary Canada

When estimating models of a multivariable dynamic system, a typical condition for consistency is to require the input signals to be persistently exciting, which is guaranteed if the input spectrum is positive definite for a sufficient number of frequencies. In this paper it is investigated how such a condition can be relaxed by exploiting prior structural information on the multivariable system, such as structural zero elements in the transfer matrix or entries that are a priori known and therefore not parametrized. It is shown that in particular situations the data-informativity condition can be decomposed into different MISO (multiple input single output) situations, leading to relaxed conditions for the MIMO (multiple input multiple output) model. When estimating a single module in a linear dynamic network, the data-informativity conditions can generically be formulated as path-based conditions on the graph of the network. The new relaxed conditions for data-informativity will then also lead to relaxed path-based conditions on the network graph. Additionally the new expressions are shown to be closely related to earlier derived conditions for (generic) single module identifiability. © 2024, CC BY-NC-ND.

关键词： MIMO systems

来源：评论

学校读者我要写书评

暂无评论

Experimental and numerical study of a gas cyclone with a central filter

引用

Particuology 2022年第4期20卷 47-59页

作者： Zihui Zhang Sijie Dong Kejun Dong Li'an Hou Wenzheng Wang Yi Wei Bo Wang Key Laboratory of Western China's Environmental Systems(Ministry of Education)and Engineering Research Center of Fine Particles Pollution Control Technology and Equipment of Gansu Province College of Earth and Environmental SciencesLanzhou UniversityLanzhou 730000China Centre for Infrastructure Engineering School of EngineeringWestern Sydney UniversityPenrithNSW 2751Australia Xi'an High-Tech Institute Xi'an 710025China Gansu Chemical Industry Research Institute Co. Ltd.Lanzhou 730000China

This paper studies a novel gas cyclone with a cylindrical fiter face installed in the center from the vortex finder to the bottom *** experimental results show that this composite cyclone has a higher collection fficiency and a lower pressure drop than the original *** mechanisms for the improvement are analyzed by both physical experiments and numerical *** measuring dust samples collected at different places it is revealed that the center filter can prevent fine particles from entering the inner vortex and escaping,which accounts for the increase of the collection *** addition,the flow field of the composite cyclone is simulated by computational fluid dynamics and compared with that of the original *** analysis shows that with the filter layer installed,the swirling flow disappears in the vortex finder,which decreases the kinetic energy dissipation and hence lowers the pressure drop.

关键词： Cyclone Filtration Porous media model Collection efficiency Pressure drop

来源：评论

学校读者我要写书评

暂无评论

Imitation Learning of MPC with Neural Networks: Error Guarantees and Sparsification

arXiv

引用

arXiv 2025年

作者： Alsmeier, Hendrik Theiner, Lukas Savchenko, Anton Mesbah, Ali Findeisen, Rolf Control and Cyber-Physical Systems Laboratory Technical University of Darmstadt Germany Dept of Chemical & Biomolecular Engineering University of California Berkeley United States

This paper presents a framework for bounding the approximation error in imitation model predictive controllers utilizing neural networks. Leveraging the Lipschitz properties of these neural networks, we derive a bound that guides dataset design to ensure the approximation error remains at chosen limits. We discuss how this method can be used to design a stable neural network controller with performance guarantees employing existing robust model predictive control approaches for data generation. Additionally, we introduce a training adjustment, which is based on the sensitivities of the optimization problem and reduces dataset density requirements based on the derived bounds. We verify that the proposed augmentation results in improvements to the network’s predictive capabilities and a reduction of the Lipschitz constant. Moreover, on a simulated inverted pendulum problem, we show that the approach results in a closer match of the closed-loop behavior between the imitation and the original model predictive controller. Copyright © 2025, The Authors. All rights reserved.

关键词： Predictive control systems

来源：评论

学校读者我要写书评

暂无评论

Imitation Learning of MPC with Neural Networks: Error Guarantees and Sparsification

Imitation Learning of MPC with Neural Networks: Error Guaran...

引用

IEEE Conference on Decision and control

作者： Hendrik Alsmeier Lukas Theiner Anton Savchenko Ali Mesbah Rolf Findeisen Control and Cyber-Physical Systems Laboratory Technical University of Darmstadt Germany Dept of Chemical & Biomolecular Engineering University of California Berkeley USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

关键词： Training Sensitivity Imitation learning Predictive models Approximation error Prediction algorithms Stability analysis Robustness Data models Predictive control

来源：评论

学校读者我要写书评

暂无评论

Process Monitoring of Operational Cost for Wastewater Treatment Processes Using Variants of ARMA Models Based Soft-sensors 11

Process Monitoring of Operational Cost for Wastewater Treatm...

引用

11th IEEE Data Driven control and Learning systems Conference, DDCLS 2022

作者： Lu, Yu Liu, Yiqi Li, Dong Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Technical University of Denmark Department of Chemical and Biochemical Engineering Lyngby Denmark

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Process monitoring of operation cost index (OCI) is of great importance for wastewater treatment plants (WWTPs), which is not only able to support financial budget, but also to optimize local operation. This paper proposed four variants of auto-regressive and moving average (ARMA), based on recursive least squares algorithm (RLS), ARMA based on recursive extended least squares algorithm (RELS), nonlinear auto-regressive neural network (NARNN), and nonlinear auto-regressive neural network with external input (NARXNN) respectively, to predict the operating cost in WWTPs. The proposed methods were validated in the simulation platform, Benchmark Simulation Model No.2-P (BSM2-P). On account of the strong nonlinearity of the wastewater treatment process, the nonlinear model, like NARXNN, achieved better performance in terms of mean square error (MSE) and correlation coefficient (R). © 2022 IEEE.

关键词： Wastewater treatment

来源：评论

学校读者我要写书评

暂无评论

Machine learning insights in predicting heavy metals interaction with biochar

引用

Biochar 2024年第1期6卷 157-167页

作者： Xin Wei Yang Liu Lin Shen Zhanhui Lu Yuejie Ai Xiangke Wang School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206People’s Republic of China. School of Mathematics and Physics North China Electric Power UniversityBeijing 102206People’s Republic of China. MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environment and Chemical EngineeringNorth China Electric Power UniversityBeijing 102206People’s Republic of China. Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of ChemistryBeijing Normal UniversityBeijing 100875People’s Republic of China.

The use of machine learning(ML)in the field of predicting heavy metals interaction with biochar is a promising field of research,mainly because of the growing understanding of how removal efficiency is affected by characteristic variables,reaction conditions and biochar *** practical application in biochar still faces large challenges,such as difficulties in data collection,inadequate algorithm development,and insufficient ***,the quantity,quality,and representation of data have a large impact on the accuracy,efficiency,and generalizability of machine learning *** this perspective,the present data descriptors,the efficiency of machine learning-aided property and performance prediction,the interpretation of underlying mechanisms and complicated relationships,and some potential ways to augment the data are discussed regarding the interactions of heavy metals with ***,future perspectives and challenges are discussed,and an enhanced model performance is proposed to reinforce the feasibility of a particular perspective.

关键词： Biochar Heavy metals Interaction mechanism Machine learning

来源：评论

学校读者我要写书评

暂无评论

Efficient and Safe Learning-based control of Piecewise Affine systems Using Optimization-Free Safety Filters*

Efficient and Safe Learning-based Control of Piecewise Affin...

引用

IEEE Conference on Decision and control

作者： Kanghui He Shengling Shi Ton Van Den Boom Bart De Schutter Delft Center for Systems and Control Delft University of Technology Delft Netherlands Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

control of piecewise affine (PWA) systems under complex constraints faces challenges in guaranteeing both safety and online computational efficiency. Learning-based methods can rapidly generate control signals with good performance, but rarely provide safety guarantees. A safety filter is a modular method to improve safety for any controller. When applied to PWA systems, a traditional safety filter usually need to solve a mixed-integer convex program, which reduces the computational benefit of learning-based controllers. We propose a novel optimization-free safety filter designed to handle state constraints that involve a combination of polyhedra and ellipsoids. The proposed safety filter only utilizes algebraic and min-max operations to determine safe control inputs. This offers a notable advantage compared with traditional safety filters by allowing for significantly more efficient computation of control signals. The proposed safety filter can be integrated into various function approximators, such as neural networks, enabling safe learning throughout the learning process. Simulation results on a bicycle model with PWA approximation validate the proposed method regarding constraint satisfaction, CPU time, and the preservation of sub-optimality.

关键词： Learning systems Simulation Computational modeling Neural networks Bicycles Safety Computational efficiency Time factors Faces Ellipsoids

来源：评论

学校读者我要写书评

暂无评论

Certainty-Equivalence Model Predictive control: Stability, Performance, and Beyond

arXiv

引用

arXiv 2024年

作者： Liu, Changrui Shi, Shengling De Schutter, Bart Delft Center for Systems and Control Delft University of Technology Delft Netherlands Department of Chemical Engineering Massachusetts Institute of Technology Cambridge United States

Handling model mismatch is a common challenge in model-based controller design, particularly in model predictive control (MPC). While robust MPC is effective in managing uncertainties, its inherent conservatism often makes it less desirable in practice. Certainty-equivalence MPC (CE-MPC), which relies on a nominal model, offers an appealing alternative due to its design simplicity and reduced online computational requirements. Contrary to the existing analysis where MPC has access to the true model, this paper investigates certainty-equivalence MPC for uncertain nonlinear systems with input constraints, here the uncertainty stems from parametric discrepancies between the true model and the nominal model. The primary contributions of the paper are two fold. First, a novel perturbation analysis of the MPC value function is provided, which avoids the common assumption of Lipschitz continuity for cost functions and has broader applicability to topics such as value function approximation, online model adaptation in MPC, and performance-driven MPC design. Besides, a sufficient stability condition and performance analysis of CE-MPC are derived, where the latter quantifies the suboptimality of CE-MPC relative to the infinite-horizon optimal controller with perfect model knowledge. The results provide valuable insights into how the prediction horizon and model mismatch jointly affect the stability and performance. Furthermore, the general results are specialized to linear quadratic control, and the competitive ratio is derived, serving as the first ratio-like performance bound for predictive control of uncertain linear systems with constraints. © 2024, CC BY-NC-ND.

关键词： Predictive control systems

来源：评论

学校读者我要写书评

暂无评论

Stability and Performance Analysis of Model Predictive control of Uncertain Linear systems

Stability and Performance Analysis of Model Predictive Contr...

引用

IEEE Conference on Decision and control

作者： Changrui Liu Shengling Shi Bart De Schutter Delft Center for Systems and Control Delft University of Technology Delft The Netherlands Department of Chemical Engineering Massachusetts Institute of Technology Cambridge United States

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Model mismatch often presents significant challenges in model-based controller design. This paper investigates model predictive control (MPC) for uncertain linear systems with input constraints, where the uncertainty is characterized by a parametric mismatch between the true system and its estimated model. The main contributions of this work are twofold. First, a theoretical performance bound is derived using relaxed dynamic programming. This bound provides a novel insight into how the prediction horizon and modeling errors affect the suboptimality of the MPC controller to the oracle infinite-horizon optimal controller, which has complete knowledge of the true system. Second, sufficient conditions are established under which the nominal MPC controller, which relies solely on the estimated system model, can stabilize the true system despite model mismatch. Numerical simulations are presented to validate these theoretical results, demonstrating the practical applicability of the derived conditions and bounds. These findings offer practical guidelines for achieving desired modeling accuracy and selecting an appropriate prediction horizon in designing certainty-equivalence MPC controllers for uncertain linear systems.

关键词： Linear systems Sufficient conditions Analytical models Adaptation models Uncertainty Predictive models Stability analysis Numerical models Numerical stability Predictive control

来源：评论

学校读者我要写书评

暂无评论

Stability and Performance Analysis of Model Predictive control of Uncertain Linear systems

arXiv

引用

arXiv 2024年

Model mismatch often poses challenges in model-based controller design. This paper investigates model predictive control (MPC) of uncertain linear systems with input constraints, focusing on stability and closed-loop infinite-horizon performance. The uncertainty arises from a parametric mismatch between the true and the estimated system under the matrix Frobenius norm. We examine a simple MPC controller that exclusively uses the estimated system model and establishes sufficient conditions under which the MPC controller can stabilize the true system. Moreover, we derive a theoretical performance bound based on relaxed dynamic programming, elucidating the impact of prediction horizon and modeling errors on the suboptimality gap between the MPC controller and the oracle infinite-horizon optimal controller with knowledge of the true system. Simulations of a numerical example validate the theoretical results. Our theoretical analysis offers guidelines for obtaining the desired modeling accuracy and choosing a proper prediction horizon to develop certainty-equivalent MPC controllers for uncertain linear systems. © 2024, CC BY-SA.

关键词： Predictive control systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：