检索结果-内蒙古大学图书馆

作者： Ma, Miaomiao Chen, Hong Liu, Xiangjie Allgöwer, Frank Beijing Key Laboratory of New Technology and System on Measuring and Control for Industrial Process North China Electric Power University No. 2 Beinong Road Huilongguan Changping District Beijing 102206 China Institute for Systems Theory and Automatic Control University of Stuttgart Pfaffenwaldring 9 Stuttgart 70550 Germany Department of Control Science and Engineering Jilin University Campus NanLing Renmin Strasse 5988 Changchun 130025 China

This study suggests a moving horizon H8 control scheme for variable speed wind turbines above the rated wind speed to maintain the output power at the rated value for variable operating points. A constrainedH8 control problem is solved at each sampling time with the update of not only the initial condition but also the prediction model based on different operating points. Closed-loop properties inclusive of L2 disturbance attenuation are discussed in the framework of dissipation theory. The moving horizon H8 control considers the non-linear dynamic aspect of the wind turbine, the turbulent nature of the wind and actuator saturations. Analysis and simulation results for WD34 wind turbine show possible improvements on the output electrical power regulation performance for variable operating points, while respecting actuator saturation limits. © The Institution of Engineering and Technology 2014.

关键词： Wind turbines

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Fast Stochastic Model Predictive control of High-dimensional systems

Fast Stochastic Model Predictive Control of High-dimensional...

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IEEE Annual Conference on Decision and control

作者： Joel A. Paulson Ali Mesbah Stefan Streif Rolf Findeisen Richard D. Braatz Massachusetts Institute of Technology Cambridge MA USA Otto-von-Guericke-University Magdeburg Laboratory for Systems Theory and Automatic Control Institute of Automation Engineering Germany

ISBN: (纸本)9781467360890

Probabilistic uncertainties and constraints are ubiquitous in complex dynamical systems and can lead to severe closed-loop performance degradation. This paper presents a fast algorithm for stochastic model predictive control (SMPC) of high-dimensional stable linear systems with time-invariant probabilistic uncertainties in initial conditions and system parameters. Tools and concepts from polynomial chaos theory and quadratic dynamic matrix control inform the development of an input-output formulation for SMPC with output constraints. Generalized polynomial chaos theory is used to enable efficient uncertainty propagation through the high-dimensional system model. Galerkin projection is used to construct the polynomial chaos expansion for a general class of linear differential algebraic equations (DAEs), so that the SMPC algorithm is applicable to both regular and singular/descriptor systems. The fast SMPC approach is applied for control of an end-to-end continuous pharmaceutical manufacturing process with approximately 8000 states. The on-line computational cost of the proposed probabilistic input-output SMPC algorithm is independent of the state dimension and, therefore, alleviates the prohibitive computational costs of control of uncertain systems with large state dimension.

关键词： SMPC Chaos theory DYNAMIC MATRIX control Predictive control Stochastic models Uncertainty Propagation uncertain systems DYNAMICAL systems

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Optimal Tracking control for a Class of Continuous Time Complex-valued systems Based on Adaptive Dynamic Programming Algorithm

Optimal Tracking Control for a Class of Continuous Time Comp...

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第三十三届中国控制会议

作者： Ruizhuo Song Frank L.Lewis Qinglai Wei Wendong Xiao School of Automation and Electrical Engineering University of Science and Technology Beijing University of Texas at Arlington Research Institute University of Texas at Arlington State Key Laboratory of Synthetical Process Automation Northeastern University The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781479947249

Based on the adaptive dynamic programming(ADP) algorithm,a new optimal tracking control method is proposed for a class of complex-valued nonlinear systems in this *** tracking error is rst de ned,and then the performance index function which is about the tracking error is *** transformation,the complex-valued tracking error system is mapped into *** optimal control method for the tracking error system is *** two neural networks are used to be as the critic network and action network,*** is proven that the closed-loop system is uniformly ultimately bounded based on Lyapunov ***,the simulation study is given to show the effectiveness of the proposed optimal tracking control method for complex-valued nonlinear system.

关键词： Adaptive dynamic programming,optimal control,neural network,complex-valued

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Multirate output feedback control for complex industrial processes in double-layer network environment with RBF performance index

Multirate output feedback control for complex industrial pro...

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World Congress on Intelligent control and automation (WCICA)

作者： Tong Wang Jianbin Qiu Huijun Gao Jialu Fan Tianyou Chai Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China School of Information Bohai University Jinzhou China State Key Laboratory of Synthetical Automation of Process Industries Northeastern University Shenyang China

This paper investigates the output feedback control problem for a class of complex industrial processes in double-layer (upper operation layer and local device layer) network environment with radial basis function (RBF) performance index. First, sampled-data local plants at the device layer with sampling period T d are controlled by local output feedback proportional integral (PI) controllers. Then, the outputs and inputs of local plants are sampled at operation layer with sampling period T u to form the index prediction by using RBF neural networks. In addition, consider the effect of Ethernet between operation and device layer, a Bernoulli random binary distribution is employed to model the packet dropout phenomenon during data transmission. Furthermore, considering the nonlinearity of RBF neural networks, the Lagrange multipliers approach is utilized to solve the real time optimization (RTO) problem at operation layer and an output feedback compensator is designed to regulate the dynamic setpoints. Finally, a rougher flotation process model is employed to demonstrate the effectiveness of the proposed method.

关键词： Indexes process control Optimization Output feedback Symmetric matrices Neurons automation

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Identification of Linear Parameter Varying systems with Missing Output Data Using Generalized Expectation-Maximization Algorithm

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IFAC Proceedings Volumes 2014年第3期47卷 9364-9369页

作者： X. Yang W. Xiong B. Huang H. Gao Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin Heilongjiang 150080 China Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi Jiangsu 214122 China Department of Chemical and Materials Engineering University of Alberta Edmonton Alberta T6G 2G6 Canada Research Institute of Mechatronics and Automation Bohai University Jinzhou 121013 China and Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin Heilongjiang 150080 China

This paper is concerned with the identification problems of linear parameter varying (LPV) systems with randomly missing output data. Since one local linearized model cannot capture the global dynamics of the nonlinear industrial process, the multiple-model LPV model in which the global model is constructed by smoothly weighted combination of multiple local models is considered here. The problem of missing output variables data is commonly encountered in practice. In order to handle the multiple-model identification problems of LPV systems with incomplete data, the local model is taken to have a finite impulse response (FIR) model structure and the generalized expectation-maximization (EM) algorithm is adopted to estimate the unknown parameters of the global LPV model. To avoid the problems of ill-conditioned matrices and high sensitivity of parameters to noise, the prior information on the coefficients of each local FIR model is employed to construct the prior probability of unknown parameters. Then the maximum a posteriori (MAP) estimates of the global model parameters are derived via the generalized EM algorithm. The numerical example is presented to demonstrate the effectiveness of the proposed method.

关键词： Linear parameter varying systems Generalized EM algorithm Missing output data Maximum a posteriori estimates

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Probabilistic Remote Preparation of a Four-Particle Entangled W State for the General Case and for All Kinds of the Special Cases

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Communications in Theoretical Physics 2013年第9期60卷 313-322页

作者：戴宏毅张明张祖荣席在荣 Department of Physics College of ScienceNational University of Defense Technology Department of Automatic Control College of Mechatronics and AutomationNational University of Defense Technology Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of Sciences

We present a protocol for probabilistic remote preparation of a four-particle entangled W state. The quantum channel is composed of two partial entangled four-particle cluster states. We caiculate the total successful probability and total classical communication cost required for the general case and for all kinds of the special cases, respectively. It is shown that for two maximally entangled four-particle cluster states, such a scheme for the general case has the total successful probability of 25% and only consumes the total classical communication of I bit, while this scheme for the special cases under certain conditions can possess successful probability of 50% or 100%, the required classical communication will only be 2 bits or 4 bits. Meantime, we give in detail all unitary transformations for the general case and for all kinds of the special cases, respectively.

关键词： remote state preparation four-particle W state cluster state classical communication cost

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Setpoints Compensation in Industrial processes via Multirate Output Feedback control

Setpoints Compensation in Industrial Processes via Multirate...

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9th Asian control Conference (ASCC)

作者： Yin, Shen Liu, Fangzhou Gao, Huijun Qiu, Jianbin Chai, Tianyou Fan, Jialu Karimi, Hamid Reza Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin 150080 China State Key Laboratory for Synthetical Automation of Process Industries Northeastern University Shenyang 110004 China Department of Engineering University of Agder Grimstad N-4898 Norway

ISBN: (纸本)9781467357692;9781467357678

This paper investigates the setpoints compensation for a class of complex industrial processes. Plants at the device layer are controlled by the local regulation controllers, and a multirate output feedback control approach for setpoint compensation is proposed such that the subsystems can reach the dynamically changed setpoints and the given economic objective can also be tracked via certain economic performance index (EPI). First, a sampled-data multivariable direct output feedback proportional integral (PI) controller is designed to regulate the performance of the subsystems. Second, the outputs and control inputs of the plants at the device layer are sampled at operation layer sampling time to form an EPI. Thus the multirate problem is solved by a lifting method. Third, static setpoints are generated by real time optimization (RTO) and the dynamic setpoints are calculated by the compensator according to the error between the EPI and objective at each operation layer step. Finally, a rougher flotation process model is employed to demonstrate the effectiveness of the proposed method.

关键词： PI control compensation complex networks control system synthesis feedback multivariable control systems optimisation process control sampled data systems

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Solution of a low Prandtl number natural convection benchmark by a local meshless method

Solution of a low Prandtl number natural convection benchmar...

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作者： Kosec, Gregor Šarler, Božidar Laboratory for Parallel and Distributed Computing Jožef Stefan Institute Ljubljana Slovenia Laboratory for Multiphase Processes University of Nova Gorica Nova Gorica Slovenia Laboratory for Advanced Materials Systems Centre of Excellence for Biosensorics Automation and Process Control Solkan Slovenia

Purpose - The purpose of this paper is to present the solution of a highly nonlinear fluid dynamics in a low Prandtl number regime, typical for metal-like materials, as defined in the call for contributions to a numerical benchmark problem for 2D columnar solidification of binary alloys. The solution of such a numerical situation represents the first step towards understanding the instabilities in a more complex case of macrosegregation. Design/methodology/ approach - The involved temperature, velocity and pressure fields are represented through the local approximation functions which are used to evaluate the partial differential operators. The temporal discretization is performed through explicit time stepping. Findings - The performance of the method is assessed on the natural convection in a closed rectangular cavity filled with a low Prandtl fluid. Two cases are considered, one with steady state and another with oscillatory solution. It is shown that the proposed solution procedure, despite its simplicity, provides stable and convergent results with excellent computational performance. The results show good agreement with the results of the classical finite volume method and spectral finite element method. Originality/value - The solution procedure is formulated completely through local computational operations. Besides local numerical method, the pressure velocity is performed locally also, retaining the correct temporal transient. © Emerald Group Publishing Limited.

关键词： Numerical methods

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Outlier Analysis in Set-based Estimation for Nonlinear systems Using Convex Relaxations

Outlier Analysis in Set-based Estimation for Nonlinear Syste...

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European control Conference

作者： Stefan Streif Matthias Karl Rolf Findeisen Laboratory for Systems Theory and Automatic Control Institute for Automation Engineering Otto-von-Guericke-University Magdeburg

ISBN: (纸本)9781479901890

Set-based estimation for nonlinear systems is a useful tool to handle sparse and uncertain data. The tool provides outer bounds on feasible parameter sets and reachable states, as well as provable inconsistency certificates for entire parameter regions. In case of errors in the data such as outliers or incorrect a priori assumptions on variable uncertainties, set-based approaches can, however, lead to poor estimates or even rejection of a consistent model. We present a set-based approach to systematically identify outliers or incorrect variable uncertainty assumptions. The basic idea is to detect outliers by quantifying the influence they have on the inconsistency of an underlying feasibility problem. The results build on a set-based estimation framework that employs convex relaxations. Specifically we derive model consistency measures and sensitivity measures that combine the sensitivity information stored in the Lagrange dual variables. An algorithm is developed that iteratively detects outliers that contribute most to inconsistency. The algorithm terminates once the data and model are no longer proved inconsistent. The approach is illustrated by an example.

关键词： Mathematical model Uncertainty Sensitivity Measurement uncertainty Estimation Data models Analytical models

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Design of Active Inputs for Set-Based Fault Diagnosis

Design of Active Inputs for Set-Based Fault Diagnosis

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American control Conference

作者： Joseph K. Scott Rolf Findeisen Richard D. Braatz Davide M. Raimondo Massachusetts Institute of Technology Institute for Automation Engineering Laboratory for Systems Theory and Automatic Control Otto-von-Guericke University Magdeburg Identification and Control of Dynamic Systems Laboratory University of Pavia

ISBN: (纸本)9781479901777

Effective fault diagnosis depends on the detectability of the faults in the measurements, which can be improved by a suitable input signal. This article presents a deterministic method for computing the set of inputs that guarantee fault diagnosis, referred to as separating inputs. The process of interest is described, under nominal and various faulty conditions, by linear discrete-time models subject to bounded process and measurement noise. It is shown that the set of separating inputs can be efficiently computed in terms of the complement of one or several zonotopes, depending on the number of fault models. In practice, it is essential to choose elements from this set that are minimally harmful with respect to other control objectives. It is shown that this can be done efficiently through the solution of a mixed-integer quadratic program. The method is demonstrated for a numerical example.

关键词： Set-Based Fault Diagnosis measurements guarantee fault diagnosis

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