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检索条件"主题词=Distributed Model Predictive Control"

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Distribute model predictive control for Large-scale Systems with Multi-Rate Sampling 33

Distribute Model Predictive Control for Large-scale Systems ...

引用

第三十三届中国控制会议

作者： ZHANG Rongchao YU Li LIU Andong ZHANG Wen-an College of Information Engineering Zhejiang University of Technology

ISBN: (纸本)9781479947249

The distributed model predictive control(DMPC)problem is investigated in this paper for large-scale systems with multi-rate sampling,where the sampling rate of each subsystem is *** observer-based distributed controller is designed for the multi-rate large-scale systems by using cooperative DMPC control ***,stability analysis is also presented for the multi-rate large-scale ***,an illustrative example is given to demonstrate the effectiveness of the proposed method.

关键词： distributed model predictive control large-scale systems multi-rate sampling

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Automatic Landing of a UAV using model predictive control for the surveillance of Internal Autopilot's controls

Automatic Landing of a UAV using Model Predictive Control fo...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： de Bonfim Gripp, Juliano A. Sampaio, Ulisses P. Inst Tecnol Aeronaut Sao Jose Dos Campos Brazil

ISBN: (纸本)9781479923762

Recent advancements in processing capabilities have enabled the possibility of use of new computationally costly control methods like the model predictive control (MPC) on real-time applications. On this paper, a framework is proposed for the design of a supervising MPC to be implemented over a simpler, more reliable internal loop PID Autopilot without making any modifications to its structure. The proposed system was able to add new capabilities to the internal loop by making little corrections to its control demands, ensuring the compliance with restrictions. At the end, the system was able to ensure safe landing over many demanding conditions by effectively expanding the internal autopilots states and by enforcing altitude restrictions.

关键词： Automatic pilots Autoland model predictive control predictive control cage set real-time application program distributed model predictive control nonlinear model predictive control Unmanned Aerial Vehicle drone honey bees Internal control Methods Maximum Allowable Concentration

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Execution trace graph analysis of dataflow programs: bounded buffer scheduling and deadlock recovery using model predictive control 8

Execution trace graph analysis of dataflow programs: bounded...

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8th Conference on Design and Architectures for Signal and Image Processing (DASIP)

作者： Casale-Brunet, S. Bezati, E. Mattavelli, M. Canale, M. Janneck, J. W. Ecole Polytech Fed Lausanne EPFL SCI STI MM CH-1015 Lausanne Switzerland Politecn Torino Dipartimento Automat & Informat Turin Italy Lund Univ Dept Comp Sci S-22100 Lund Sweden

ISBN: (纸本)9791092279061

Execution trace graph analysis of dataflow programs has been demonstrated to be an effective way for exploring and optimizing the design space of many core applications. In this work a novel transformation from the execution trace graph to an event driven linear system is proposed. It is also illustrated how the trace space of can be effectively reduced and well known system control techniques can be efficiently used in order to find close to optimal solutions. In particular, the problem of finding a bounded buffer size configuration is proposed and solved using a model predictive controller. Two design examples, a JPEG and an MPEG HEVC decoder have been used to demonstrate the effectiveness of the approach.

关键词： data flow Legal Executions Line graph Trace Linear system optimal solution distributed model predictive control nonlinear model predictive control Bounded model predictive control predictive control

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modelling and model predictive control of oil wells with Electric Submersible Pumps

Modelling and Model Predictive Control of oil wells with Ele...

引用

IEEE Conference on control Applications (CCA)

作者： Pavlov, Alexey Krishnamoorthy, Dinesh Fjalestad, Kjetil Aske, Elvira Fredriksen, Morten Statoil ASA Statoil ASA Res & Dev Ctr NO-3905 Porsgrunn Norway

ISBN: (纸本)9781479974092

In this paper we describe control challenges related to operation of oil wells equipped with Electric Submersible Pumps (ESP) and formalize them in a control problem setting in the language of control system engineers. Then we present a simple dynamic model of an oil well equipped with ESP. This model can be used for controller development. To solve this problem, we propose a model predictive control (MPC) strategy and present experimental results of an MPC controller successfully tested in a large scale test facility with a full scale ESP, live crude oil in an emulated oil well.

关键词： Conferences control systems control systems OIL WELL Submerged pumps OIL FIELD predictive control electrostatic precipitators modeling model predictive control distributed model predictive control nonlinear model predictive control Petroleum crude oil Maximum Allowable Concentration Electricity

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Robust model predictive control of a Laboratory Two-Tank System

Robust Model Predictive Control of a Laboratory Two-Tank Sys...

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American control Conference

作者： Bakosova, Monika Oravec, Juraj Slovak Univ Technol Bratislava Fac Chem & Food Technol Bratislava 81237 Slovakia

ISBN: (纸本)9781479932740

Robust model predictive control was applied for the laboratory two-tank system. The controlled process is modeled as the uncertain polytopic system for the controller design. Two robust model predictive control design strategies were used. The first strategy uses the single Lyapunov function and the second one assumes the parameter-dependent Lyapunov functions. The design problem is converted to the solution of the system of linear matrix inequalities and a simple algorithm is also presented. The controllers designed using both strategies were tested on the laboratory process. The results confirmed that using the parameter-dependent Lyapunov function in the robust model predictive controller design improved the control performance in comparison with the single Lyapunov function based strategy.

关键词： Lyapunov methods Optimization Process control Robustness Symmetric matrices Uncertainty Vectors control applications Robust control Process control Symmetric matrix Liapunov function Lyapunov methods Robust control model predictive control predictive control Robustness distributed model predictive control nonlinear model predictive control design problems control performance TANK SYSTEMS Uncertainty

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model predictive control for LPV System using On-off Actuator with Application to Spacecraft Attitude Maneuver

Model Predictive Control for LPV System using On-off Actuato...

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11th IEEE International Conference on control and Automation (ICCA)

作者： Asakawa, Takeya Kida, Takashi Univ Electrocommun Dept Mech Engn & Intelligent Syst Chofu Tokyo 1828585 Japan

ISBN: (纸本)9781479928378

This paper proposes a model predictive control of linear parameter varying systems driven by non-linear on-off control inputs. We formulate the problem as an integer quadratic programming problem. In order to ensure the closed-loop stability, we newly derive a condition for terminal cost matrices which are obtained by solving linear matrix inequalities. A numerical study is performed to verify the proposed method using a large spacecraft having rotating solar panels.

关键词： Asymptotic stability Cost function Mathematical model Numerical stability predictive control Space vehicles Stability analysis predictive control Space vehicles Numerical stability Cost functions asymptotic stability region Attitude maneuvers linear parameter varying system Stability analysis Mathematical model actuators Spacecraft distributed model predictive control nonlinear model predictive control Solar collectors on-off

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Robust model predictive control of Shimmy Vibration in Aircraft Landing Gears with Probabilistic Uncertainty

Robust Model Predictive Control of Shimmy Vibration in Aircr...

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IEEE Conference on control Applications (CCA)

作者： Hajiloo, Amir Xie, Wen-Fang Concordia Univ Dept Mech & Ind Engn Montreal PQ Canada

ISBN: (纸本)9781479974092

This paper considers robust model predictive control (RMPC) methods for linear parameter varying (LPV) system which has both probabilistic uncertain and time-varying parameters. The parameters are considered to be measured on-line. In this regard, the aircraft landing gear system is considered as a LPV system whose parameters variation can affect both stability and performance. By transforming this system into a convex combination of linear time-invariant vertices form with the tensor-product (TP) model transformation method, the landing gear system is represented as a polytopic linear parameter-varying system. A computationally efficient RMPC law is formulated for this system which guarantees closed-loop robust stability and performance. The control signal is calculated on-line by carrying out the convex optimization involving Linear Matrix Inequalities (LMIs) in MPC. The proposed controller can effectively suppress the shimmy vibration of the landing gear with variable taxiing velocity and wheel caster length, also with the probabilistic uncertain torsional spring stiffness.

关键词： Aircraft Gears Linear matrix inequalities Robustness Uncertainty Upper bound Vibrations Vibration linear matrix inequalities Gears Upper bound Aircraft Aircraft landing gear Uncertainty shimmy Landing gear Robustness model predictive control predictive control distributed model predictive control nonlinear model predictive control spring stiffness

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A Composite model predictive control Strategy for Furnaces

A Composite Model Predictive Control Strategy for Furnaces

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24th Chinese Process control Conference (CPCC)

作者： Zang, Hao Li, Hongguang Huang, Jingwen Wang, Jia Beijing Univ Chem Technol Automat Dept Beijing 100029 Peoples R China

Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control (CMPC) strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively. (C) 2014 Chemical Industry and Engineering Society of China, and Chemical Industry Press. All rights reserved.

关键词： Furnace Tracking nonlinear model predictive control Economic nonlinear model predictive control distributed model predictive control

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Comparison of the Robustness of RNN, MPC, and ANN controller for Residential Heating System 4

Comparison of the Robustness of RNN, MPC, and ANN Controller...

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IEEE Fourth International Conference on Big Data and Cloud Computing (BdCloud)

作者： Javed, Abbas Larijani, Hadi Ahmadinia, Ali Emmanuel, Rohinton Glasgow Caledonian Univ Sch Engn & Built Environm Glasgow G4 0BA Lanark Scotland

ISBN: (纸本)9781479967193

In this paper, a novel random neural network (RNN) controller is proposed to maintain a comfortable indoor environment in a single storey residential building having four rooms fitted with radiators for heating. This controller considers the effect of outside temperature and solar radiations on the building and is capable of maintaining a comfortable indoor environment on the basis of a PMV-based setpoint. The RNN controller is trained with a 30 day dataset from the living room of the building and the performance of the controller is evaluated by testing the controller in all four rooms of the building for 100 days. It is found that the RNN controller is not only capable of maintaining comfortable indoor environment as suggested by PMV-based setpoint but can also adjust the room temperature to a lower setpoint (not included in the training set) required by the user for unoccupied rooms. The RNN controller is further compared with similar artificial neural network (ANN) controller and model predictive control (MPC) controller. The results show that for maintaining comfortable indoor environment, the performance of the RNN controller is approximately equivalent to the MPC controller for the setpoints not covered in the training set, while ANN controller failed to maintain accurate comfortable environment for the operating points not covered in the training phase.

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Robust model predictive control using neural networks

Robust model predictive control using neural networks

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IEEE International Symposium on Intelligent control (ISIC)

作者： Patan, Krzysztof Witczak, Piotr Univ Zielona Gora Inst Control & Computat Engn Zielona Gora Poland Univ Zielona Gora Fac Elect Engn Comp Sci & Telecommun Zielona Gora Poland

ISBN: (纸本)9781479974061

The paper deals with robust model predictive control designed using recurrent neural network. A dynamic neural network is trained to act as the one-step ahead predictor, which is then used successively to obtain k-step ahead prediction of the plant output. Based on the neural predictor, the control law is derived solving a constrained optimization problem. The robustness of the considered predictive scheme is derived using the concept of an error model. Based on the developed robust model, a optimization problem is redefined. Two solutions are portrayed. The first one is to change the cost function in order to consider the robust model of the plant, while the second one is to impose constraints on the process output using derived uncertainty bands.

关键词： control system synthesis neurocontrollers predictive control recurrent neural nets robust control constrained optimization problem control design control law cost function k-step ahead prediction neural predictor one-step ahead predictor recurrent neural network robust model predictive control Neural networks Optimization predictive control predictive models Robustness Solid modeling Uncertainty recurrent neural nets predictive control Cost functions Neural network Neurocontrollers entity modeling control law distributed model predictive control nonlinear model predictive control control system synthesis Prediction models Robust control design control Robustness Constrained optimization

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