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第41届中国控制会议

作者： Jianhao Li Xiaowei Jiang Hao Chen Shaoying Wang Xianhe Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System College of Science Binzhou University School of Electrical Engineering and Automation Hubei Normal University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we focus on the tracking performance(TP) limitations of networked control systems(NCS s).The control system considered is a multi-input and multi-output(MIMO) discrete-time network control system with time delay,channel noise,packet dropouts,and logarithmic quantization communication *** modeling the channel,we assumed the channel noise to be additive white Gaussian noise(AWGN) and used a binary random process to model packet dropouts and a logarithmic quantizer to model the quantization *** explicit expression of the TP limitations of NCSs utilizing the two-degree-of-freedom(TDOF) controller is then derived using the frequency-domain approach,coprime decomposition,and Youla parameterization *** results show that the essential characteristics of the plant(locations and directions of nonminimum phase zeros and unstable poles) and network constraints(time delay,packet dropouts,quantization,and noise power spectral density) are closely related to the TP limitations.

关键词： Networked control systems tracking performance limitations packet dropouts logarithmic quantization

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A Novel Bi-Level Temporally-Distributed Mpc Approach: An Application to Green Urban Mobility

SSRN

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SSRN 2023年

作者： Jamshidnejad, Anahita Sun, Dingshan Ferrara, Antonella De Schutter, Bart Department of Control and Operations Delft University of Technology Kluyverweg 1 Delft2629 HS Netherlands Delft Center for Systems and Control Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands Identification and Control of Dynamic Systems Laboratory University of Pavia Italy

Model predictive control (MPC) has been widely used for traffic management, such as for minimizing the total time spent or the total emissions of vehicles. When long-term green urban mobility is considered, the optimization horizon of the MPC problem is significantly larger than the control sampling time, and thus the number of the variables that should be optimized per control time step becomes very *** systems with dynamics that involve nonlinear, non-convex, and non-smooth functions, including urban traffic networks, this results in optimization problems that are computationally intractable in real time. In this paper, we propose a novel bi-level temporal distribution of such complex MPC optimization problems, and we develop two mathematically linked short-term and long-term MPC formulations with small and large control sampling times that will be solved together instead of the original complex optimization problem. The resulting bi-level control architecture is used to solve the two MPC formulations online for real-time control of urban traffic networks with the objective of long-term green mobility. In order to assess the performance of the bi-level control architecture, we perform a case study where a rough version of the model of the urban traffic flow, S-model, is used by the long-term MPC level to estimate the states of the urban traffic networks, and a detailed version of the model is used by the short-term MPC level. The results of the simulations prove the effectiveness (with respect to the objective of control, as well as computational efficiency) of the proposed bi-level MPC approach, compared to state-of-the-art control approaches. © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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Bayesian Methods for the identification of Distribution Networks

Bayesian Methods for the Identification of Distribution Netw...

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IEEE Conference on Decision and control

作者： Jean-Sébastien Brouillon Emanuele Fabbiani Pulkit Nahata Florian Dörfler Giancarlo Ferrari-Trecate Institute of Mechanical Engineering École Polytechnique Fédérale de Lausanne Switzerland Identification and Control of Dynamic Systems Laboratory University of Pavia Italy Automatic Control Laboratory Swiss Federal Institute of Technology (ETH) Switzerland

ISBN: (纸本)9781665436601

The increasing integration of intermittent renewable generation, especially at the distribution level, necessitates advanced planning and optimisation methodologies contingent on the knowledge of the admittance matrix, capturing the topology and line parameters of an electric network. However, a reliable estimate of the admittance matrix may either be missing or quickly become obsolete for temporally varying grids. In this work, we propose a data-driven identification method utilising voltage and current measurements collected from micro-PMUs. More precisely, we first present a maximum likelihood approach and then move towards a Bayesian framework, leveraging the principles of maximum a posteriori estimation. In contrast with most existing contributions, our approach not only factors in measurement noise on both voltage and current data, but is also capable of exploiting available a priori information such as sparsity patterns and known line admittances. Simulations conducted on benchmark cases demonstrate that, compared to other algorithms, our method can achieve greater accuracy.

关键词： Maximum likelihood estimation Renewable energy sources Voltage measurement Current measurement Benchmark testing Bayes methods Topology

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BAYESIAN ERROR-IN-VARIABLES MODELS for the identification of POWER NETWORKS: TECHNICAL REPORT

arXiv

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arXiv 2021年

作者： Brouillon, Jean-Sébastien Fabbiani, Emanuele Nahata, Pulkit Moffat, Keith Dörfler, Florian Ferrari-Trecate, Giancarlo LausanneCH-1015 Switzerland Identification and Control of Dynamic Systems Laboratory University of Pavia Pavia Italy Department of Electrical Engineering and Computer Science UC Berkeley Berkeley United States Zurich Switzerland

The increasing integration of intermittent renewable generation, especially at the distribution level, necessitates advanced planning and optimisation methodologies contingent on the knowledge of the grid, specifically the admittance matrix capturing the topology and line parameters of an electric network. However, a reliable estimate of the admittance matrix may either be missing or quickly become obsolete for temporally varying grids. In this work, we propose a data-driven identification method utilising voltage and current measurements collected from micro-PMUs. More precisely, we first present a maximum likelihood approach and then move towards a Bayesian framework, leveraging the principles of maximum a posteriori estimation. In contrast with most existing contributions, our approach not only factors in measurement noise on both voltage and current data, but is also capable of exploiting available a priori information such as sparsity patterns and known line parameters. Simulations conducted on benchmark cases demonstrate that, compared to other algorithms, our method can achieve significantly greater accuracy. © 2021, CC BY.

关键词： Electric lines

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identification of AC networks via online learning

arXiv

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arXiv 2020年

作者： Fabbiani, Emanuele Nahata, Pulkit de Nicolao, Giuseppe Ferrari-Trecate, Giancarlo Identification and Control of Dynamic Systems Laboratory University of Pavia Pavia Italy Institute of Mechanical Engineering École Polytechnique Fédérale de Lausanne Lausanne Switzerland

The increasing penetration of intermittent distributed energy resources in power networks calls for novel planning and control methodologies which hinge on detailed knowledge of the grid. However, reliable information concerning the system topology and parameters may be missing or outdated for temporally varying electric distribution networks. This paper proposes an online learning procedure to estimate the network admittance matrix capturing topological information and line parameters. We start off by providing a recursive identification algorithm exploiting phasor measurements of voltages and currents. With the goal of accelerating convergence, we subsequently complement our base algorithm with a design-of-experiment procedure which maximizes the information content of data at each step by computing optimal voltage excitations. Our approach improves on existing techniques, and its effectiveness is substantiated by numerical studies on realistic testbeds. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric power transmission networks

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Structure parameter optimized kernel based online prediction with a generalized optimization strategy for nonstationary time series

arXiv

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arXiv 2021年

作者： Guo, Jinhua Chen, Hao Zhang, Jingxin Chen, Sheng Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic Systems Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou350002 China Department of Automation Tsinghua University Beijing100084 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

In this paper, sparsification techniques aided online prediction algorithms in a reproducing kernel Hilbert space are studied for nonstationary time series. The online prediction algorithms as usual consist of the selection of kernel structure parameters and the kernel weight vector updating. For structure parameters, the kernel dictionary is selected by some sparsification techniques with online selective modeling criteria, and moreover the kernel covariance matrix is intermittently optimized in the light of the covariance matrix adaptation evolution strategy (CMA-ES). Optimizing the real symmetric covariance matrix can not only improve the kernel structure's flexibility by the cross relatedness of the input variables, but also partly alleviate the prediction uncertainty caused by the kernel dictionary selection for nonstationary time series. In order to sufficiently capture the underlying dynamic characteristics in prediction-error time series, a generalized optimization strategy is designed to construct the kernel dictionary sequentially in multiple kernel connection modes. The generalized optimization strategy provides a more self-contained way to construct the entire kernel connections, which enhances the ability to adaptively track the changing dynamic characteristics. Numerical simulations have demonstrated that the proposed approach has superior prediction performance for nonstationary time series. Copyright © 2021, The Authors. All rights reserved.

关键词： Radial basis function networks

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Fast Robust Model Predictive control of High-dimensional systems

Fast Robust Model Predictive Control of High-dimensional Sys...

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European control Conference

作者： Lucas C. Foguth Joel A. Paulson Richard D. Braatz Davide M. Raimondo Massachusetts Institute of Technology Identification and Control of Dynamic Systems Laboratory University of Pavia

ISBN: (纸本)9781467371605

In this paper, a fast tube-based algorithm is proposed for robust model predictive control (RMPC) using an uncertain finite step response (FSR) input-output model with time-invariant bounded uncertainty. The use of an FSR model, in place of the high-dimensional state-space model, allows the assurance of good performance while dramatically reducing the online cost of the controller. Bounds on the uncertain FSR coefficients are computed offline from the high-dimensional model or by performing step tests in the plant. Using these bounds, a tube-based RMPC algorithm is employed to guarantee the practical stability of the closed-loop system. The practicality of the algorithm is demonstrated by application to an infinite-dimensional example inspired by the field of tissue engineering.

关键词： Robustness Uncertainty Heuristic algorithms Computational modeling Stability analysis Yttrium Prediction algorithms

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Guaranteed active fault diagnosis for uncertain nonlinear systems

Guaranteed active fault diagnosis for uncertain nonlinear sy...

引用

European control Conference (ECC)

作者： Joel A. Paulson Davide M. Raimondo Rolf Findeisen Richard D. Braatz Stefan Streif Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA University of Pavia Identification and Control of Dynamic Systems Laboratory Italy Otto-von-Guericke-University Magdeburg Institute of Automation Engineering Germany

ISBN: (纸本)9781479947287

An input design method is presented to actively isolate faults for polynomial or rational systems in the presence of unknown-but-bounded uncertainties. For active fault isolation, the input is required to lead to outputs consistent with at most one fault model despite disturbances, measurement noise, and parametric uncertainty. This task is posed in terms of a bilevel optimization problem where the inner program verifies, for a given input, that the outputs are consistent with at most one model, while the outer program determines the minimally harmful input. Because of the nonlinear dynamics, we propose to replace the inner program with a convex relaxation that can be efficiently solved while still guaranteeing fault detection and isolation. The approach is numerically demonstrated on a two-tank system with three fault models.

关键词： Robustness Uncertainty Fault diagnosis Mathematical model Noise Noise measurement Optimization

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A hybrid stochastic-deterministic input design method for active fault diagnosis

A hybrid stochastic-deterministic input design method for ac...

引用

52nd IEEE Conference on Decision and control, CDC 2013

作者： Scott, Joseph K. Roberto Marseglia, G. Magni, Lalo Braatz, Richard D. Raimondo, Davide M. Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA United States Identification and Control of Dynamic Systems Laboratory University of Pavia Italy

ISBN: (纸本)9781467357173

The problem of designing an input to improve the detectability of faults has been addressed previously using both stochastic and deterministic formulations. This article presents a hybrid approach that provides a worst-case guarantee of diagnosis within a time interval [0,N], while maximizing the probability of diagnosis at some earlier time M

关键词： Fault detection

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Hierarchical task allocation for multi-agent systems encoded by stochastic reachability specifications

Hierarchical task allocation for multi-agent systems encoded...

引用

European control Conference (ECC)

作者： Nikolaos Kariotoglou Sean Summers Davide M. Raimondo Automatic Control Laboratory ETH Zurich Identification and Control of Dynamic Systems Laboratory University of Pavia

ISBN: (纸本)9781479901890

We consider the problem of satisfying a set of objectives over a collection of agents. For a single agent, the optimal solution can be obtained via a stochastic reachability framework where optimal control policies come along with a performance metric, defined as the probability of successfully achieving a specified objective. As the number of agents increases, the approach quickly becomes computationally expensive and often intractable. We propose a method which includes an advisory controller that allocates tasks among agents based on their ability of handling individual objectives. This ability is encoded by the stochastic reachability performance metrics. The proposed method is tailored to an autonomous surveillance system composed of pan-tilt-zoom (PTZ) cameras and verified experimentally.

关键词： Cameras Resource management Surveillance Aerospace electronics Robot vision systems Markov processes Multi-agent systems

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