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American control Conference

作者： Mitra Bahadorian Borislav Savkovic Ray Eaton Tim Hesketh The School of Electrical Engineering and Telecommunications The University of New South Wales The School of Mathematics and Statistics The University of New South Wales

ISBN: (纸本)9781457710957

This work presents a Robust model predictive control (RMPC) approach for trajectory tracking of an Unmanned Ground Vehicle (UGV). In addition to robustness against unknown but bounded disturbances, the controller presented here is also able to deal with constraints on inputs and states due to its formulation as an RMPC. The proposed approach represents an extension of previous model predictive control (MPC) laws based on the concept of constraint restriction (i.e. optimal trajectories via MPC are computed subject to stringent constraints assuming no uncertainty, and a linear local controller ensures that the actual system robustly follows the optimized MPC trajectory). The presented controller carries a low computational complexity overhead, making it attractive for real-time applications. Applying the proposed control approach to the UGV trajectory tracking problem, simulation results demonstrate robust UGV automated trajectory tracking.

关键词： trajectory tracking Unmanned Ground Vehicle distributed model predictive control nonlinear model predictive control Maximum Allowable Concentration model predictive control predictive control real-time application program Constraint

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Hybrid model predictive control Based on Wireless Sensor Feedback: An Experimental Study

Hybrid Model Predictive Control Based on Wireless Sensor Fee...

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Proceedings of the 46th IEEE Conference on Decision and control, Pages 5511-5970

作者： Alberto Bemporad Stefano Di Cairano Erik Henriksson Karl Henrik Johansson Department of Information Engineering Faculty of Engineering University of Sienna Siena Italy The Automatic Control Laboratory School of Electrical Engineering Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781424414970;1424414970

This paper presents the design and the experimental validation of model predictive control (MPC) of a hybrid dynamical process based on measurements collected by a wireless sensor network. The proposed setup is the prototype of an industrial application in which a remote station controls the process via wireless network links. The experimental platform is a laboratory process consisting of four infrared lamps, controlled in pairs by two on/off switches, and of a transport belt, where moving parts equipped with wireless sensors are heated by the lamps. By approximating the stationary heat spatial distribution as a piecewise affine function of the position along the belt, the resulting plant model is a hybrid dynamical system. The control architecture is based on the reference governor approach: the process is actuated by a local controller, while a hybrid MPC algorithm running on a remote base station sends optimal belt velocity set-points and lamp on/off commands over a network link exploiting the information received through the wireless network. A discrete-time hybrid model of the process is used for the hybrid MPC algorithm and for the state estimator.

关键词： Hybrid modes Wireless networks network links predictive control Maximum Allowable Concentration model predictive control State estimation remote base station model checking Infrared lamps Lamps directional function distant station local controller distributed model predictive control

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Economic model predictive control for nonlinear processes incorporating actuator magnitude and rate of change constraints

Economic model predictive control for nonlinear processes in...

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American control Conference

作者： Helen Durand Panagiotis D. Christofides Department of Chemical and Biomolecular Engineering University of California Los Angeles 90095 USA

ISBN: (纸本)9781467386838

In this work, we consider nonlinear systems and develop a formulation of Lyapunov-based economic model predictive control (LEMPC) that incorporates actuator magnitude and rate of change constraints on the calculated control actions. Specifically, we formulate LEMPC including maximum and minimum allowable values for the control actions as well as bounds on the differences between the control actions calculated by the LEMPC and the control actions that would be requested if a stabilizing Lyapunov-based controller were used to control the system. This latter constraint is proven to bound the rate of change of the actual process inputs. It is also proven that an LEMPC formulation incorporating such constraints is feasible and ensures closed-loop stability of the process for a sufficiently small controller sampling period. A chemical process example demonstrates that EMPC with input rate of change constraints can improve process economic performance compared with steady-state operation while limiting actuation energy expended.

关键词： Steady-state Economics Actuators Process control Nonlinear systems Stability analysis Trajectory actuators Nonlinear systems control action Process control change rate trajectory Stability analysis non-linear process economics Steady state Economic models predictive control distributed model predictive control nonlinear model predictive control Constraint

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model predictive control for Continuous-Time Singular Jump Systems with Incomplete Transition Rates

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JOURNAL OF control SCIENCE AND ENGINEERING 2015年第1期2015卷 11-11页

作者： Gu, Xinxin Wen, Jiwei Peng, Li Jiangnan Univ Sch Internet Things Engn Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China

This paper is concerned with model predictive control (MPC) problem for continuous-time Markov Jump Systems (MJSs) with incomplete transition rates and singular character. Sufficient conditions for the existence of a model predictive controller, which could optimize a quadratic cost function and guarantee that the system is piecewise regular, impulse-free, and mean square stable, are given in two cases at each sampling time. Since theMPC strategy is aggregated into continuous-time singularMJSs, a discrete-time controller is employed to deal with a continuous-time plant and the cost function not only refers to the singularity but also considers the sampling period. Moreover, the feasibility of the MPC scheme and the mean square admissibility of the closed-loop system are deeply discussed by using the invariant ellipsoid. Finally, a numerical example is given to illustrate the main results.

关键词： Closed loop systems predictive controllers distributed model predictive control transition rate mean square Maximum Allowable Concentration nonlinear model predictive control sampling time Singular Cost functions predictive control Singularity jump model predictive control

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model predictive control for Pressure Regulation and Surge Prevention in Centrifugal Compressors

Model Predictive Control for Pressure Regulation and Surge P...

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European control Conference

作者： Toufik Bentaleb Alessio Cacitti Sergio De Franciscis Andrea Garulli Dipartimento di Ingegneria dell'Informazione e Scienze Matematiche Universita' degli Studi di Siena Nuovo Pignone Ge Oil & Gas Florence

ISBN: (纸本)9781467371605

This paper proposes a multivariable model predictive control scheme for discharge pressure regulation in centrifugal compressors. The main novelty of the proposed approach is that three control inputs are considered: the rotational speed of the compressor, an anti-surge valve for gas recycle and the inlet guide vane, whose variations allow one to significantly enlarge the operating region of the compressor and hence to enhance the authority of the control system. Surge prevention is achieved by including in the model an output variable accounting for the distance of the operating point from the surge limit. Such distance is defined on a compressor performance map which is invariant to changes in the inlet conditions, and thus its computation requires only standard pressure measurements available from the plant. Numerical simulations show that the proposed control system is able to meet the desired specifications, in the presence of different types of disturbances occurring along the pipeline.

关键词： CENTRIFUGAL COMPRESSOR control systems Pressure regulating model predictive control predictive control Compressors Work area Workstations Pressure measurement distributed model predictive control nonlinear model predictive control rotational speed pipeline Gas recycling inlet guide vanes

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model predictive control of Dimethyl Ether Combustion in a Jet Stirred Reactor under Low Temperature Conditions

Model Predictive Control of Dimethyl Ether Combustion in a J...

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American control Conference

作者： Thomas Lammersen Klaus Stoehr Norbert Peters Dirk Abel Institute of Automatic Control RWTH Aachen University 52074 Aachen Germany Institute for Combustion Technology RWTH Aachen University 52074 Aachen Germany

ISBN: (纸本)9781479901777

This paper deals with the control of instabilities and oscillations during the combustion of dimethyl ether (DME) in a continuous jet stirred reactor (JSR). A model predictive control (MPC) is applied to keep the reactor in a steady state at low temperature combustion conditions. The full chemical state of the system consisting of temperature and species concentration is reconstructed from a temperature and a CH concentration signal by an extended Kalman filter. For prediction within the MPC and the filter, a physical model of the JSR including a full combustion reaction mechanism is presented.

关键词： DME JSR MPC JSR dimethyl ether Maximum Allowable Concentration Low temperature Common Cold Combustion Reactors combustion temperature predictive control Extended Kalman filters distributed model predictive control nonlinear model predictive control Agitation

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model predictive control with integrated experiment design for output error systems

Model predictive control with integrated experiment design f...

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European control Conference

作者： Christian A. Larsson Mariette Annergren Hakan Hjalmarsson Cristian R. Rojas Xavier Bombois Ali Mesbah Per Erik Moden Automatic Control Lab and ACCESS School of Electrical Engineering Delft Center for Systems and Control Delft University of Technology ABB AB Corporate Research

ISBN: (纸本)9781479901890

model predictive control has become an increasingly popular control strategy thanks to the ability to handle constrained systems. Obtaining the required models through system identification is often a time consuming and costly process. Applications oriented experiment design is a means of reducing this effort but is often formulated in terms of the input's spectral properties. Therefore, time domain constraints are difficult to enforce. In this contribution we combine MPC with experiment design to formulate a control problem where excitation constraints are included. The benefits are that time domain constraints are respected while the experiment design criteria are fulfilled. The method is evaluated on a numerical example.

关键词： Experimental Design model predictive control predictive control distributed model predictive control nonlinear model predictive control error system outlet error enforcement Maximum Allowable Concentration System identification numerical example TIME-DOMAIN

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State of Charge Equalization of Battery Modules Using Single-Phase Cascaded Multilevel Converters

State of Charge Equalization of Battery Modules Using Single...

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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Ashkan Pirooz Yousef Firouz Joeri Van Mierlo Electric Engineering and Energy Technology(ETEC) Vrije Universiteit Brussel Brussels Belgium

ISBN: (数字)9781728126586

ISBN: (纸本)9781728126593

This paper aims to use cascaded multilevel converters for connecting several battery modules to a single-phase AC grid or load. Electrical power is exchanged either from storage to AC side to discharge battery modules, or from AC side to the storage to charge battery modules. Charging and discharging operations are performed while battery modules' state of charges get equalized. AC side current is also controlled to follow its reference while high differential-mode dv/dt on load are avoided using adjacent voltage levels assignment. For these purposes, no additional controlling circuit is implemented and only one controlling block is defined to handle AC current reference tracking, and state of charge balancing tasks. model predictive control, as a technique capable of multi-parameter controlling, is used to achieve beforementioned system objectives in a simple, efficient, and scalable manner. MATLAB Simulink simulations demonstrate the feasibility of proposed controlling strategy for this sort of application.

关键词： cascaded multilevel converters battery balancing state of charge model predictive control CHARGE STATES model predictive control predictive control ALTERNATING CURRENT Batteries distributed model predictive control nonlinear model predictive control cascades Converters Electric power

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Capacitor Voltage Imbalance Reduction in Flying Capacitor Modular Multilevel Converters by using model predictive control

Capacitor Voltage Imbalance Reduction in Flying Capacitor Mo...

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Power Electronic and Drive Systems and Technologies Conference (PEDSTC)

作者： Parisa Boodaghi Malidarreh Davood Arab Khaburi Jose Rodriguez Iran University of Science and Technology (IUST) Tehran Iran Universidad Andres Bello

ISBN: (数字)9781728158495

ISBN: (纸本)9781728158501

Multilevel converters are very interesting for medium and high power applications because of their high efficiency and lower distortion harmonic and many other advantages. This paper investigates the effect of finite control set model predictive control (FCS-MPC) on capacitor voltage balancing and load current ripple in a four level three cell flying capacitor (FC) converter and compared with phase shifted carrier pulse width modulation method (PSC-PWM). The FCS-MPC method is presented as a simple strategy that can control multiple variables. The simulation results are used to propose a comparison between two control methods. The results such as current ripple and voltage balancing are displayed in tables for comparison. In this paper simulation is performed using MATLAB/Simulink software.

关键词： capacitors power capacitors predictive control PWM power convertors predictive control Power capacitors Capacitors PWM power convertors current ripple capacitor voltage model predictive control voltage balance Flying distributed model predictive control nonlinear model predictive control

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model predictive control of Nonlinear Stochastic PDEs: Application to a Sputtering Process

Model Predictive Control of Nonlinear Stochastic PDEs: Appli...

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2009 American control Conference (ACC 2009)

作者： Yiming Lou Gangshi Hu Panagiotis D. Christofides Hamilton Sundstraud United Technologies Corporation 2771 North Garey Avenue Pomona CA 91767 USA Department of Chemical and Biomolecular Engineering University of California Los Angeles CA 90095 USA

ISBN: (纸本)9781424445233

In this work, we develop a method for model predictive control of nonlinear stochastic partial differential equations (PDEs) to regulate the state variance, which physically represents the roughness of a surface in a thin film growth process, to a desired level. We initially formulate a nonlinear stochastic PDE into a system of infinite nonlinear stochastic ordinary differential equations (ODEs) by using Galerkin's method. A finite-dimensional approximation is then derived that captures the dominant mode contribution to the state variance. A model predictive control problem is formulated based on the finite-dimensional approximation so that the future state variance can be predicted in a computationally efficient way. The control action is computed by minimizing an objective function including penalty on the discrepancy between the predicted state variance and a reference trajectory, and a terminal penalty. An analysis of the closed-loop nonlinear infinite-dimensional system is performed to characterize the closed-loop performance enforced by the model predictive controller. The model predictive controller is initially applied to the stochastic Kuramoto-Sivashinsky equation (KSE), a fourth-order nonlinear stochastic PDE. Simulation results demonstrate that the proposed predictive controller can successfully drive the norm of the state variance of the stochastic KSE to a desired level in the presence of significant model parameter uncertainties. In addition, we consider the problem of surface roughness regulation in a one-dimensional ion-sputtering process. The predictive controller is applied to the kinetic Monte Carlo model of the sputtering process to successfully regulate the expected surface roughness to a desired level.

关键词： Stochastic predictive controllers predictive control Surface roughness nonlinearity model predictive control Variance penalty closed-loop performance distributed model predictive control nonlinear model predictive control Sputtering

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