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arXiv 2019年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Johansson, Karl H. Chai, Tianyou Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

This paper considers the distributed smooth optimization problem in which the objective is to minimize a global cost function formed by a sum of local smooth cost functions, by using local information exchange. The standard assumption for proving exponential/linear convergence of first-order methods is the strong convexity of the cost functions, which does not hold for many practical applications. In this paper, we first show that the continuous-time distributed primal-dual gradient algorithm converges to one global minimizer exponentially under the assumption that the global cost function satisfies the restricted secant inequality condition. This condition is weaker than the strong convexity condition since it does not require convexity and the global minimizers are not necessary to be unique. We then show that the discrete-time distributed primal-dual algorithm constructed by using the Euler's approximation method converges to one global minimizer linearly under the same condition. The theoretical results are illustrated by numerical simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Continuous time systems

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Distributed bandit online convex optimization with time-varying coupled inequality constraints

arXiv

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arXiv 2019年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Johansson, Karl H. Chai, Tianyou Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

This paper considers the problem of distributed bandit online convex optimization with time-varying coupled inequality constraints. This problem can be defined as a repeated game between a group of learners and an adversary. The learners attempt to minimize a sequence of global loss functions and at the same time satisfy a sequence of coupled constraint functions. The global loss and the coupled constraint functions are the sum of local convex loss and constraint functions, respectively, which are adaptively generated by the adversary. The local loss and constraint functions are revealed in a bandit manner, i.e., only the values of loss and constraint functions at sampled points are revealed to the learners, and the revealed function values are held privately by each learner. We consider two scenarios, one- and two-point bandit feedback, and propose two corresponding distributed bandit online algorithms used by the learners. We show that sublinear expected regret and constraint violation are achieved by these two algorithms, if the accumulated variation of the comparator sequence also grows sublinearly. In particular, we show that O(T θ1 ) expected static regret and O(T 7/4−θ1 ) constraint violation are achieved in the one-point bandit feedback setting, and O(T max{κ,1−κ}) expected static regret and O(T 1−κ/2) constraint violation in the two-point bandit feedback setting, where θ1 ∈ (3/4, 5/6] and κ ∈ (0, 1) are user-defined trade-off parameters. Finally, these theoretical results are illustrated by numerical simulations of a simple power grid example. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

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Linear convergence of first- And zeroth-order primal-dual algorithms for distributed nonconvex optimization

arXiv

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arXiv 2019年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Chai, Tianyou Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Digital Futures Stockholm100 44 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

This paper considers the distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of local cost functions by using local information *** first propose a distributed first-order primal-dual algorithm. We show that it converges sublinearly to a stationary point if each local cost function is smooth and linearly to a global optimum under an additional condition that the global cost function satisfies the Polyak-Lojasiewicz condition. This condition is weaker than strong convexity, which is a standard condition for proving linear convergence of distributed optimization algorithms, and the global minimizer is not necessarily unique. Motivated by the situations where the gradients are unavailable, we then propose a distributed zeroth-order algorithm, derived from the proposed distributed first-order algorithm by using a deterministic gradient estimator, and show that it has the same convergence properties as the proposed first-order algorithm under the same conditions. The theoretical results are illustrated by numerical simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Cost functions

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An Improved Output Feedback MPC scheme for Constrained Linear Systems

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IFAC-PapersOnLine 2017年第1期50卷 15506-15511页

作者： Subramanian, Sankaranarayanan Lucia, Sergio Engell, Sebastian Process Dynamics and Operations Group DortmundTU United States Otto-von-Guericke-University Magdeburg Laboratory for Systems Theory and Automatic Control Institute of Automation Engineering Germany

We propose an output feedback model predictive control (MPC) scheme that is independent of the estimation method employed. The proposed approach is non-conservative by design as the feedback information subject to estimation errors is explicitly considered in the predictions. The proposed scheme is robust to both the plant-model mismatch and the estimation error and avoids the unnecessary conservatism introduced by other existing output feedback MPC approaches in the literature. We achieve this by making use of all the available information at every time-step and formulate a control policy with the assumption that bounds for the estimation error are available at all times. We show that the proposed scheme is recursively feasible and demonstrate its advantages using a double-integrator example. © 2017

关键词： Feedback Errors Estimation Linear systems Model predictive control Predictive control systems Robust control Constrained linear systems Constrained systems Estimation methods Feed back information Multi stage Output feedback Plant model mismatches Robust model predictive control

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A rigorous model for evaluating moving window soft sensors for industrial distillation processes

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Chemical engineering Transactions 2018年 69卷 487-492页

作者： Torgashov, Andrei Skogestad, Sigurd An, Dmitry Process Control Laboratory Institute of Automation and Control Process FEB RAS 5 Radio Str. Vladivostok Russia Department of Chemical Engineering Norwegian University of Science and Technology TrondheimNO-7491 Norway Gazpromneft-Omsk Refinery JSC 1 Pr. Gubkina Omsk644040 Russia

ISBN: (纸本)9788895608662

Traditionally, soft sensors are developed based on measurement data only, but here we consider a moving window (MW) soft sensor (SS) that uses data generated from a calibrated, rigorous model of the distillation columns of an FFC unit at Gazpromneft-Omsk Refinery. The contribution of the paper is that a procedure is developed for MW SS design that incorporates a priori knowledge, which is especially suitable when the training sample is small and contains measurement errors. In addition, we propose a continuous adaptation of all model parameters based on new data, instead of the usual procedure of only updating the bias. The accuracy of the predicted product quality is investigated by calculating the coefficient of determination (R2) and root mean squared error (RMSE) for the test sample. Several approaches were considered, and we found that a constrained optimization approach was superior. The constraints on the model parameters of SSs are derived from a calibrated, rigorous distillation unit model. The improved estimator quality resulted in the successful industrial application of advanced process control (APC) systems. © Copyright 2018, AIDIC Servizi S.r.l.

关键词： Distillation columns

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Adaptive Optimization Deception Attack on Remote State Estimator of Aero-Engine 29

Adaptive Optimization Deception Attack on Remote State Estim...

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第29届中国控制与决策会议

作者： Xin Huang Jiuxiang Dong College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Center of Intelligent ControlNortheastern University

ISBN: (纸本)9781509046584

This paper studies the effect of the deception attacks on the remote state estimator equipped with the chisquared failure *** is assumed that the attacker can monitor and modify all the measurements.A novel adaptive optimization deception attack strategy is proposed,where using the current and previous sensor data,the attackers can ensure themselves to be undetected by the chi-squared monitor while yielding the largest estimation error covariance at the remote state *** the attacker's perspective,the attack is better than the existing other attacks to degrade the system ***,the simulation on the aero-engine model is provided to demonstrate theoretical results.

关键词： Cyber-Physical Systems Aero-Engine Remote State Estimation Cyber Attack Adaptive Optimization

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A Tutorial Review on the Application of C/GMRES-based MPC Algorithm to Automotive Powertrain

A Tutorial Review on the Application of C/GMRES-based MPC Al...

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第37届中国控制会议

作者： Mingxin Kang Jiangyan Zhang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University College of Mechanical and Electronic Engineering Dalian Minzu University State Key Laboratory of Automotive Simulation and Control Jilin University

Receding horizon control(RHC) gains much attention in industry owing to its merits that it is able to tackle explicitly the constrained and multi-variable optimization problem. However, a remarkable drawback of RHC is the heavy commutating load of the on-line solver algorithm, especially for nonlinear optimization problem. Hence, rare implementation can be found in engineering practice with a ‘fast‘ control system. To overcome this issue, some numerical solvers have been developed to improve the real-time performance of the optimization calculation, wherein the C/GMRES(Continuation/General Minimum Residual)algorithm gains bonus on its successful applications on fast control system. This tutorial paper proposes a brief introduction about C/GMRES algorithm, and then focuses on its applications to automotive powertrain control, including engine torque control, speed control, and energy management of hybrid electric vehicle(HEV). The control effects and real-time performance are validated by simulation and bench tests.

关键词： Real-time Optimization C/GMEES Automotive Powertrain Predictive control

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Stability Analysis and Anti-windup Design of Switched Systems with Actuator Saturation

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International Journal of automation and computing 2017年第5期14卷 615-625页

作者： Xin-Quan Zhang Xiao-Yin Li Jun Zhao School of Information and Control Engineering Liaoning Shihua UniversityFushun 113001China School of Foreign Languages Liaoning Shihua UniversityFushun 113001China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The stability analysis and anti-windup design problem is investigated for two linear switched systems with saturating actuators by using the single Lyapunov function approach. Our purpose is to design a switching law and the anti-windup compensation gains such that the maximizing estimation of the domain of attraction is obtained for the closed-loop system in the presence of saturation. Firstly, some sufficient conditions of asymptotic stability are obtained under given anti-windup compensation gains based on the single Lyapunov function method. Then, the anti-windup compensation gains as design variables are presented by solving a convex optimization problem with linear matrix inequality （LMI） constraints. Two numerical examples are given to show the effectiveness of the proposed method.

关键词： Anti-windup switched systems saturating actuators single Lyapunov function domain of attraction linear matrixinequality （LMI）.

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Computing reachable sets for linear discrete systems with bounded input 29

Computing reachable sets for linear discrete systems with bo...

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第29届中国控制与决策会议

作者： Chao Wang Lizhen Shao Guangda Hu Yadong Xu Key Laboratory of Advanced Control of Iron and Steel Process(Ministry of Education ) School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing Department of mathematics Shanghai University

ISBN: (纸本)9781509046584

In this paper,an approximation method of reachable sets for linear discrete systems with bounded input is *** method finds boundary points of reachable sets by solving finite convex optimization *** not only constructs an inner approximation bounding but also an outer approximation *** numerical examples are used to demonstrate the validity of the *** to a recent Lyapunov-Krasovskii functional based approach,our method obtains much tighter bounding sets.

关键词： approximation of reachable sets optimization supporting points normal directions

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A novel fault classification method based on neural network in smart grid

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International Journal of Electrical engineering 2018年第1期25卷 1-7页

作者： Zhang, Tong Sun, Lanxiang Liu, Jianchang Yu, Haibin College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control System CAS Shenyang China University of Chinese Academy of Sciences Beijing China

This paper presents a back propagation (BP) neural network method to identify fault types and phases in the smart grid with renewable sources. A fault classification method based on the BP neural network is used to identify patterns of voltage and current measured with phasor measurement unit (PMU). The voltage and current values are decomposed, and then they are used as the input matrix of the BP neural network, which identifies the fault type and fault phasor. All the common short-circuit faults occurred are analyzed, BP neural network method is proposed for fault classification in a power active distribution network. Simulation results show feasibility and usefulness of the BP neural network method, which provides good classification performances.

关键词： Smart power grids

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