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

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model predictive Self-healing control Scheme for Dual Active Bridge Converter

Model Predictive Self-healing Control Scheme for Dual Active...

引用

Workshop on Smart Grid and Renewable Energy (SGRE)

作者： Mohsen Hosseinzadehtaher Ahmad Khan Matthew W. Baker Mohammad B. Shadmand Power Electronics & Autonomous Systems Research Group Kansas State University Manhattan KS USA

ISBN: (数字)9781728129600

ISBN: (纸本)9781728129617

This paper presents a model predictive self-healing control (MPSC) of dual active bridge (DAB) converter for battery energy storage system (BESS) application. The main feature of the proposed MPSC scheme is an autonomous corrective action at the prediction stage in comparison to conventional model predictive control (MPC) approach. This feature is required in particular for predictive controlled DAB converter to ensure and validate the predicted AC link inductor's current n-step ahead in horizon of time. The proposed MPSC for DAB converter doesn't require substantial tuning effort in comparison to conventional control schemes for DAB converters. The MPSC cost function is developed based on the desired phase shift between the primary and secondary sides' voltages of the DAB transformer. The self-healing loop utilizes the feasible range of power transfer in conjunction with the AC link inductor's voltage status i.e. profile, to validate the predicted current and phase shift for the cost function. The AC link inductor voltage profile in DAB converter could cause high error in predicted current, thus the proposed self-healing loop autonomously corrects potential error in current prediction. The theoretical concepts are validated via several case studies for the considered BESS application. The performance and design effort of the controller is compared with conventional PI-based control schemes for DAB.

关键词： Dual active bridge converter model predictive control Self-healing control Autonomous control Audio recording autonomous control Phase shift Multiprotocol Serial controller Converters AC link model predictive control distributed model predictive control nonlinear model predictive control predictive control design effort

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Power Assist System with Motion Estimation Using Constrained predictive Functional control

Power Assist System with Motion Estimation Using Constrained...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Takahiko Mori Department of Electronic Control Engineering Gifu National College of Technology

ISBN: (纸本)9781479902255

Recently, many types of control methods using electromyogram signals have been proposed for power-assist robots or electric prosthesis. However, they do not easily adapt to unknown environment or conditions. Therefore, we propose a new power-assist system using model predictive control (MPC) that can realize a stable power-assist and predict the future human fingers touque and the future trajectory in real time. Our power-assist system is composed of a combination of an MPC system based on an expansion plant model and a predictive functional control system based on a plant model.

关键词： Power-Assist Motion Estimation model predictive control predictive Functional control Electric Prosthesis motion estimation predictive functional control model predictive control plant models control Methods predictive control distributed model predictive control nonlinear model predictive control

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Execution time certification for gradient-based optimization in model predictive control

Execution time certification for gradient-based optimization...

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IEEE Annual Conference on Decision and control

作者： Pontus Giselsson Department of Automatic Control LTH Lund University

ISBN: (纸本)9781467320658

We consider model predictive control (MPC) problems with linear dynamics, polytopic constraints and quadratic objective. The resulting optimization problem is solved by applying an accelerated gradient method to the dual problem. The focus of this paper is to provide bounds on the number of iterations needed in the algorithm to guarantee a prespecified accuracy of the dual function value and the primal variables as well as guaranteeing a prespecified maximal constraint violation. The provided numerical example shows that the iteration bounds are tight enough to be useful in an inverted pendulum application.

关键词： model predictive control predictive control iterative methods execution time certification distributed model predictive control nonlinear model predictive control Bound BIFUNCTIONAL

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model predictive control OF FEED FLOW REVERSAL IN A REVERSE OSMOSIS DESALINATION PROCESS

MODEL PREDICTIVE CONTROL OF FEED FLOW REVERSAL IN A REVERSE ...

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2009 American control Conference (ACC 2009)

作者： Alex R. Bartman Charles W. McFall Panagiotis D. Christofides Yoram Cohen Department of Chemical and Biomolecular Engineering University of California Los Angeles CA 90095

ISBN: (纸本)9781424445233

model-predictive control algorithms are applied to a high capacity reverse osmosis (RO) membrane desalination process simulation that utilizes feed flow-reversal in order to prevent and/or reverse scale crystal formation on the membrane surface. A dynamic non-linear model which incorporates feed concentration and membrane properties is used for simulation and demonstration of optimally controlled feed flow reversal. Before flow reversal can take place on a high capacity RO plant, the flow into the membrane unit must be carefully reduced to eliminate the risk of membrane module damage and unnecessary energy consumption. A cost-function is formulated for the transition between the normal high flow steady-state operating point to a low flow steady-state operating point where it is safe to reverse the flow direction. Open-loop and closed-loop simulations demonstrate non-linear model-predictive control strategies that induce transition from the high-flow to low-flow steady-states in an optimal way.

关键词： Workstations Desalination REFLUX Reverse osmosis low flow High Flow Current direction nonlinear model predictive control Fodder distributed model predictive control predictive control input concentration Membrane properties membrane Steady state

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Fault tolerant control using Gaussian processes and model predictive control

Fault tolerant control using Gaussian processes and model pr...

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Conference on control and Fault-Tolerant Systems

作者： Jan M. Maciejowski Xiaoke Yang Department of Engineering University of Cambridge

ISBN: (纸本)9781479928569

Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control.

关键词： fault-tolerant control Gaussian processes model predictive control predictive control distributed model predictive control nonlinear model predictive control Aircraft control footwall of a fault

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Experimental Research on model predictive control of 3-level 4-leg Flying Capacitor Converter Operating as Shunt Active Power Filter

Experimental Research on Model Predictive Control of 3-level...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Kamil Antoniewicz Marek Jasinski Marian P. Kazmierkowski Mariusz Malinowski Institute of Control and Industrial Electronics Warsaw University of Technology Koszykowa 75 00-662 Warsaw

ISBN: (纸本)9781479917631

In this paper model predictive control (MPC) with finite control states set (FS) is proposed as the new control approach for 3-level 4-leg Flying Capacitor Converter (FCC) operating as a Shunt Active Power Filter (SAPF). The main concerns and solutions regarding the algorithm development and experimental studies are described. The first is addressed to the modeling of the 4-wire SAPF and 3-level 4-leg FCC. The second relates to the control of Flying Capacitors voltages, which is performed outside the prediction loop. The issue regarding undesirable transitions of switching states is described and the solution to eliminate it is shown. Also, an analysis of possible delays on laboratory model is performed and the compensation method is presented. Finally, the control performance was tested experimentally on the 10 kVA FCC laboratory model and the results are shown in the last section.

关键词： active power filter compensation method Flying Capacitors model predictive control predictive control power filters Federal Communications Commission (FCC) convener operations shunt Shunts (electrical) distributed model predictive control nonlinear model predictive control control performance Solutions

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Combined probabilistic and set-based uncertainties for a stochastic model predictive control of island energy systems

Combined probabilistic and set-based uncertainties for a sto...

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American control Conference

作者： Julia Sachs Andreas Gienger Oliver Sawodny Institute for System Dynamics University of Stuttgart Postbox 80 11 40 70550 Germany

ISBN: (纸本)9781467386838

Microgrids comprising of diesel generators, storage devices and renewable sources present an effective approach for an economic energy supply to rural areas. This paper presents the development of a stochastic model predictive control (SMPC) with the analytical calculation of chance constraints for the combination of set-based and probabilistic uncertain data. The stochastic uncertainties resulting from changes in load demand and fluctuating power from renewables are directly included in the stochastic optimization problem. To account for these disturbances the presented approach assumes probabilistic constraints on the battery state of charge which can be analytically calculated based on the continuous probability distribution of the uncertainties. The additional set-based uncertainties on the load demand and PV power forecast imply further limitations on the probabilistic constraints. The performance of the SMPC is enhanced by evaluating the probability distribution and probabilistic constraints over several time steps as well as using a relaxation technique to assure feasibility. The presented SMPC approach is used for an efficient optimization of the microgrid operation while reducing the complexity of the operational problem compared to other approaches and satisfying all operation constraints. Simulation studies using a real-world application have been performed to showcase the performance of the proposed method.

关键词： Uncertainty Optimization Probabilistic logic Generators Stochastic processes Batteries Mathematical model Probabilistic logic Markov chain Batteries dynamos SMPC Uncertainty Battery charging status Mathematical model Relaxation Techniques distributed model predictive control nonlinear model predictive control predictive control Stochastic Processes Stochastic models Probability distribution

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Simultaneous control of safety constraint sets and process economics using economic model predictive control

Simultaneous control of safety constraint sets and process e...

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American control Conference

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

ISBN: (纸本)9781467386838

Maintaining safe operation of chemical processes is of paramount importance in process systems and control engineering, and is ideally achieved while maximizing profit. It has long been argued that process safety is fundamentally a process control problem, yet few research efforts have attempted to integrate process safety and control. Economic model predictive control (EMPC) has attracted significant attention recently due to its ability to optimize process operation from an economic perspective. However, there is very limited work on the problem of integrating safety considerations with EMPC. Motivated by the above considerations, this work presents an EMPC methodology that adjusts in real-time the size of the safety sets in which the process state should reside in order to ensure safe process operation and feedback control of the process state while optimizing economics via time-varying process operation. Recursive feasibility and closed-loop stability are established for a sufficiently small EMPC sampling period. The proposed method is demonstrated with a chemical process example.

关键词： Safety Process control Economics Level set Lyapunov methods Chemical processes Chemicals Chemical processes process state safety Process control Lyapunov methods Consumer safety economics Level set simultaneous control chemical compounds Economic models distributed model predictive control nonlinear model predictive control Constraint Set predictive control

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model predictive control of Energy Systems with Hybrid Storage

Model Predictive Control of Energy Systems with Hybrid Stora...

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IEEE Power and Energy Society General Meeting

作者： Felix Rafael Segundo Sevilla Chan Park Valerijs Knazkins Petr Korba Power Systems and Smart Grid Lab at the Zurich University of Applied Science ZHAW

ISBN: (纸本)9781509041695

In this work an algorithm to control the power flow of an electric power system with two integrated energy storage systems is investigated. The power system under consideration consists of a conventional distribution feeder that supplies the power to satisfy the customers' demand, a set of photovoltaic (PV) panels that also contribute to the power generation, one unit of Lithium-Ion battery storage for the intra-day use and a combined power-to-gas (PtG) and gas-to-power installation that converts the power excess in the summertime into hydrogen and injects power back to the system in the wintertime. The proposed control algorithm is based on model predictive control tailored for the energy system under investigation. To demonstrate the performance of the proposed control, a set of synthetic PV and demand profiles representing future conditions in Switzerland were created and used as input data to the system model. The synthesized generation and consumption data span a whole year of operation. A number of detailed simulations performed in the framework of the study reported in this paper demonstrate the effectiveness of the proposed control algorithm and provided invaluable insights into the optimum operation of the complex integrated power system.

关键词： Energy storage systems Electric power system Optimal power flow model predictive control Self-consumption Power-to-gas power systems optimal load flow model predictive control predictive control ENERGY SYSTEMS distributed model predictive control nonlinear model predictive control Cache Memory System energy storage Energy storage systems Computer algorithms self-consumption optimum operation Power flow Energy sources

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model predictive control for Optimal Portfolios with Cointegrated Pairs of Stocks

Model Predictive Control for Optimal Portfolios with Cointeg...

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IEEE Annual Conference on Decision and control

作者： Yuji Yamada James A. Primbs Faculty of Business Sciences University of Tsukuba Department of Systems Engineering University of Texas at Dallas

ISBN: (纸本)9781467320658

In this paper, we demonstrate a model predictive control (MPC) approach to constructing an optimal portfolio consisting of multiple spreads of cointegrated pairs of stocks. It is shown that a conditional mean-variance (MV) optimization problem with any given prediction horizon may be solved efficiently when the spreads of stocks follow a vector autoregressive (VAR) model. Based on the solution to the conditional MV problem, we can apply an MPC strategy that calculates the conditional MV optimal portfolio with a given prediction horizon at each rebalancing period. We also perform out-of-sample simulations using empirical stock price data from Japan, and examine the effects of the length of the prediction horizon, rebalance intervals, and transaction costs.

关键词： Spread portfolio Cointegrated pairs model predictive control Conditional mean-variance optimization Empirical simulations predictive control stocks model predictive control distributed model predictive control nonlinear model predictive control rebalance Horizon Conditional Maximum Allowable Concentration

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