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American control Conference

作者： B. M. Doekemeijer S. Boersma L. Y. Pao J. W. van Wingerden Delft Center for Systems and Control (DCSC) Faculty of Mechanical Engineering Delft University of Technology Department of Electrical Computer and Energy Engineering (ECEE) University of Colorado Boulder (CU)

ISBN: (纸本)9781509045839

Currently, wind farms typically rely on greedy control, in which the individual turbine's structural loading and power are optimized. However, this often appears suboptimal for the whole wind farm. A promising solution is closed-loop wind farm control using state feedback algorithms employing a dynamic model of the flow. This control method is a novelty in wind farms, and has potential to provide a temporally optimal control policy accounting for time-varying inflow conditions and unmodeled dynamics, both often neglected in current methods. An essential building block for state feedback control is a state estimator (observer) that reconstructs the system states for the dynamic model using a small number of measurements. As computational efficiency is critical in real-time control, lower-fidelity models are proposed to be used. In this work, WindFarmObserver (WFObs) is introduced, which is a state estimator relying on the WindFarmSimulator (WFSim) model and an Ensemble Kalman Filter (EnKF). The states of WFSim form the two-dimensional flow field in a wind farm at hub height. WFObs is tested in a two-turbine setup using a high-fidelity simulation model. With a realistic sensor setup where only 1.1% of the to-be-estimated states are measured, WFObs reduces the RMS error by 21% compared to open-loop simulation of WFSim, at a low computational cost of 0.76 s per timestep, a factor 10~2 faster than the common Extended Kalman Filter.

关键词： Wind farms Kalman filtering ROOT-MEAN-SQUARE ERRORS State estimation state feedback control unmodeled dynamics Flow models Two dimensional flow Wind fields Computational efficiency

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Random asynchronous iterations in distributed coordination algorithms

arXiv

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arXiv 2018年

作者： Chen, Yao Xia, Weiguo Cao, Ming Lü, Jinhu Department of Computer Science Southwestern University of Finance and Economics Chengdu611130 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China Faculty of Science and Engineering ENTEG University of Groningen Groningen9747 AG Netherlands School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

Distributed coordination algorithms (DCA) carry out information processing processes among a group of networked agents without centralized information fusion. Though it is well known that DCA characterized by an SIA (stochastic, indecomposable, aperiodic) matrix generate consensus asymptotically via synchronous iterations, the dynamics of DCA with asynchronous iterations have not been studied extensively, especially when viewed as stochastic processes. This paper aims to show that for any given irreducible stochastic matrix, even non-SIA, the corresponding DCA lead to consensus successfully via random asynchronous iterations under a wide range of conditions on the transition probability. Particularly, the transition probability is neither required to be independent and identically distributed, nor characterized by a Markov chain. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Computing the projected reachable set of switched affine systems: An application to systems biology

arXiv

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arXiv 2017年

作者： Parise, Francesca Valcher, Maria Elena Lygeros, John Laboratory for Information and Decision Systems MIT CambridgeMA United States Department of Information Engineering University of Padova Italy Automatic Control Laboratory ETH Zurich Switzerland

A fundamental question in systems biology is what combinations of mean and variance of the species present in a stochastic biochemical reaction network are attainable by perturbing the system with an external signal. To address this question, we show that the moments evolution in any generic network can be either approximated or, under suitable assumptions, computed exactly as the solution of a switched affine system. Motivated by this application, we propose a new method to approximate the reachable set of switched affine systems. A remarkable feature of our approach is that it allows one to easily compute projections of the reachable set for pairs of moments of interest, without requiring the computation of the full reachable set, which can be prohibitive for large networks. As a second contribution, we also show how to select the external signal in order to maximize the probability of reaching a target set. To illustrate the method we study a renown model of controlled gene expression and we derive estimates of the reachable set, for the protein mean and variance, that are more accurate than those available in the literature and consistent with experimental data. Copyright © 2017, The Authors. All rights reserved.

关键词： Stochastic systems

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Security analysis of control system anomaly detectors

Security analysis of control system anomaly detectors

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American control Conference

作者： David Umsonst Henrik Sandberg Alvaro A. Cardenas Department of Automatic Control in the School of Electrical Engineering at the KTH Royal Institute of Technology 10044 Stockholm Sweden Department of Computer Science in the Erik Jonsson School of Engineering at the University of Texas at Dallas 75080 Richardson United States of America

ISBN: (纸本)9781509045839

Two anomaly detectors for control systems are analyzed with respect to their sensitivity to malicious data injection attacks. A stateless anomaly detector based on the current residual signal is compared to a cumulative sum detector. The worst-case impact of a stealthy time-limited data injection attack is characterized for both detectors by a non-convex optimization problem and compared to determine which detector limits the impact the most. We prove that the problem can be solved by means of a set of convex optimization problems. Simulations verify that finding the right configuration for the cumulative sum is crucial to limit the worst-case attack impact more than with a stateless anomaly detector.

关键词： control systems analyzed detector anomaly Standing Orders control systems residual signal

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Optimal decentralized control of large scale systems

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control Theory and Technology 2016年第4期14卷 287-313页

作者： Xiao Qi SHI Daniel E. DAVISON Raymond KWONG Edward J. DAVISON Systems Control Group Department of Electrical and Computer Engineering University of Toronto Ontario Canada MSS 3G4 Department of Electrical and Computer Engineering University of Waterloo Ontario Canada N2L 3G1

This paper presents a new optimized decentralized controller design method for solving the tracking and disturbance rejection problems for large-scale linear time-invariant systems, using only low-order decentralized controllers. To illustrate the type of results which can be obtained using the new optimized decentralized control design method, the control of a large flexible space structure is studied and compared with the standard centralized LQR-observer controller. The order of the resultant decentralized controller is much smaller than that of the standard centralized LQR-observer controller. The proposed controller also has certain fail-safe properties and, in addition, it can be five orders of magnitude more robust than the standard LQR-observer controller based on their real stability radii. The new decentralized controller design method is applied to a large flexible space structure system with 5 inputs and 5 outputs and of order 24.

关键词： Decentralized control multivariable control PID control robustness optimization large-scale systems flexiblestructures

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Distributed Continuous-Time Algorithm for Robust Resource Allocation Problems using Output Feedback

Distributed Continuous-Time Algorithm for Robust Resource Al...

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American control Conference

作者： Xianlin Zeng Peng Yi Yiguang Hong Key Laboratory of Systems and Control Institute of Systems Science Chinese Academy of Sciences Beijing 100190 China Department of Electrical and Computer Engineering University of Toronto Canada

ISBN: (纸本)9781509045839

This paper proposes a novel distributed continuous-time algorithm for the resource allocation problem with uncertainty parameters, which is a robust optimization problem. The considered objective function is the sum of local convex functions assigned to agents in a multi-agent network, with private set constraints and global inequality constraint involving uncertain parameters. Each agent only knows its local objective function, local constraint set, and neighbor information. We propose a novel continuous-time distributed subgradient-based algorithm with projected output feedback to solve the optimization problem. Finally, we show that the algorithm is able to find the optimal solution under some mild conditions.

关键词： Output feedback Resource utilization inequality constraint algorithms management of resources Constraint Set Objective function NETWORKING AGENT Convex function Distribute Agents optimal solution

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Observer-based fault detection for affine nonlinear systems with disturbances and uncertainties 35

Observer-based fault detection for affine nonlinear systems ...

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第35届中国控制会议

作者： HAN Huayun YANG Ying LI Linlin Chen Yu State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University School of Automation and Electrical Engineering University of Science and Technology Beijing the Institute for Automatic Control and Complex Systems (AKS) University of Duisburg-Essen Beijing Institute of Astronautics System Engineering

ISBN: (纸本)9781509009107

This paper is concerned with the integrated design schemes of L observer-based fault detection（FD） systems for affine nonlinear processes with disturbances and uncertainties,*** this end,a so called L observer-based FD scheme is studied ***,the integrated design approaches for nonlinear systems with disturbances and uncertainties are addressed,*** the end,examples are given to illustrate the effectiveness of the proposed approaches.

关键词： Robust Fault Detection Observer Design Affine Nonlinear systems

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Current control based on super-Twisting algorithm with time delay estimation for a five-phase induction motor drive

Current control based on super-Twisting algorithm with time ...

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2017 IEEE International Electric Machines and Drives Conference, IEMDC 2017

作者： Kali, Yassine Rodas, Jorge Saad, Maarouf Gregor, Raul Benjelloun, Khalid Doval-Gandoy, Jesus Department of Electrical Engineering École Mohammadia d'Ingénieurs Mohammed v University Rabat Morocco Laboratory of Power and Control Systems Facultad de Ingeniería Universidad Nacional de Asunción Luque2060 Paraguay Department of Electrical Engineering École de Technologie Supérieure MontrealQCH3C 1K3 Canada Applied Power Electronics Technology Research Group Universidad de Vigo Vigo36310 Spain

ISBN: (纸本)9781509042814

Multiphase induction motor drives have gained popularity due to their fault tolerance and better power/current distribution per phase which are very attractive for industrial applications. Current control strategies with the estimation of unmeasurable variables (typically rotor variables) has been recently proposed for multiphase induction drive. This paper proposes a novel current control based on super-Twisting algorithm with time delay estimation for a five-phase induction motor drives. This method considers the estimation of rotor variables and disturbances, and also ensures the stability by using Lyapunov theory. Simulation results are provided to show the effectiveness of the proposed method and also to compare with other controllers such as a proportional-integral and a model-based predictive controllers. © 2017 IEEE.

关键词： Sliding mode control

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Information-theoretic attacks in the smart grid

arXiv

引用

arXiv 2017年

作者： Sun, Ke Esnaola, Iñaki Perlaza, Samir M. Vincent Poor, H. Dept. of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom Lyon France Dept. of Electrical Engineering Princeton University PrincetonNJ08544 United States

Gaussian random attacks that jointly minimize the amount of information obtained by the operator from the grid and the probability of attack detection are presented. The construction of the attack is posed as an optimization problem with a utility function that captures two effects: firstly, minimizing the mutual information between the measurements and the state variables;secondly, minimizing the probability of attack detection via the Kullback-Leibler divergence between the distribution of the measurements with an attack and the distribution of the measurements without an attack. Additionally, a lower bound on the utility function achieved by the attacks constructed with imperfect knowledge of the second order statistics of the state variables is obtained. The performance of the attack construction using the sample covariance matrix of the state variables is numerically evaluated. The above results are tested in the IEEE 30-Bus test system. Copyright © 2017, The Authors. All rights reserved.

关键词： Information theory

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Efficient implementation of Randomized MPC for miniature race cars

Efficient implementation of Randomized MPC for miniature rac...

引用

2016 European control Conference, ECC 2016

作者： Carrau, Jesus Velasco Liniger, Alexander Zhang, Xiaojing Lygeros, John Department of Automatic Systems' Engineering Universitat Politècnica de València Spain Automatic Control Laboratory ETH Zurich Switzerland

ISBN: (纸本)9781509025916

This paper describes a data-driven Randomized Model Predictive control (MPC) approach toward autonomous racing of miniature cars. The main challenge in autonomous racing is to drive as fast as possible, without actually exiting the track. Designing such controllers is a challenging task, since (i) the models of the cars are not entirely accurate, and (ii) the time to compute the control inputs is limited to tens of milliseconds. The current practice is to resort to standard MPC, where the safety-related constraints are tightened manually. In this paper, we propose an alternative method based on Stochastic MPC, which naturally introduces robustness by design. We approximate the Stochastic MPC problem by means of randomization, implemented as a sparse Randomized MPC problem. We show that the resulting quadratic program can be solved in the range of milliseconds. Moreover, the sparse Randomized MPC formulation can be interpreted as a standard MPC problem whose constraints are, based on collected data, automatically tightened. We show experimentally that the proposed controller can outperform currently implemented controllers. Our results suggest that Randomized MPC is an attractive alternative to standard controllers, due to its ability to introduce robustness without sacrificing performance. © 2016 EUCA.

关键词： Model predictive control

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