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

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Distribution Systems Reliability and Economic Improvement with Different Electric Energy Storage control Strategies

Distribution Systems Reliability and Economic Improvement wi...

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IEEE Power and Energy Society General Meeting

作者： Yixing Xu Chanan Singh Texas A&M University

ISBN: (纸本)9781457710001

With the rapid development of electric energy storage (EES) technologies, there is a growing interest in integrating these into distribution systems to improve their reliability and economy. Different control strategies of EES have corresponding reliability and economic impacts on the system. In this paper, the reliability and economy of radial distribution system integrated with EES are assessed. The control strategy of utilizing EES as a standby backup energy resource is analyzed. This standby backup control strategy improves system reliability the most but falls short on economy improvement. A novel MPC-based control strategy is presented, which can maximize distribution system economy but could not improve reliability as much as the standby backup control strategy. A hybrid control strategy of balancing the reliability and economy improvement by combining the standby backup control strategy and MPC-based control strategy is presented. The reliability and economic analysis of distribution system integrated with EES using proposed control strategies illustrates the effectiveness of the proposed method. It also illustrates that even with the same EES integrated into the distribution system, different control strategies have very different reliability and economic impact on the system. In order to accurately assess the impact of the implemented control strategies, sequential Monte Carlo Simulation is used and implemented EES control operations are included in the simulation.

关键词： Distribution system Reliability analysis Economy analysis Electric energy storage model predictive control dispensing system control strategy electric energy storage Reliability analysis Economy US ENERGY EXTENSION SERVICE distributed model predictive control nonlinear model predictive control Economic impact

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Deadlock-free scheduling for manufacturing systems based on timed Petri nets and model predictive control

Deadlock-free scheduling for manufacturing systems based on ...

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IEEE Annual Conference on Decision and control

作者： Dimitri Lefebvre GREAH - University Le Havre 75 rue Bellot 76600 Le Havre France

ISBN: (纸本)9781479978878

This paper proposes algorithms that incrementally compute control sequences with near-minimal duration to drive the marking of timed Petri nets from an initial value to a reference one. These algorithms are based on a partial exploration of the net reachability graph with an approach inspired from model predictive control. They include perturbation rejection and forbidden marking avoidance and are suitable to track trajectories when the initial and reference markings are far from each other.

关键词： Petri nets Manufacturing systems model predictive control predictive control control sequences nonlinear model predictive control Avoidance trajectory

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Adaptively constrained stochastic model predictive control applied to security constrained optimal power flow

Adaptively constrained stochastic model predictive control a...

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American control Conference

作者： Frauke Oldewurtel Line Roald Goran Andersson Claire Tomlin Department of Electrical Engineering and Computer Science UC Berkeley USA Power Systems Laboratory Department of Electrical Engineering Swiss Federal Institute of Technology in Zurich (ETHZ) Switzerland

ISBN: (纸本)9781479917730

The number of renewable energy sources in electricity grids is growing and with it the associated uncertainty. For power system operation a good tradeoff between security and cost becomes increasingly important. We consider the problem of including uncertainty from renewable in-feed in a DC security constrained optimal power flow (SCOPF) by formulating chance constraints. The chance-constrained SCOPF is solved repeatedly and can be understood as a chance-constrained model predictive control (MPC) problem, where the time coupling comes from storage in the system and the associated dynamics. The main focus of the paper is to reformulate the problem in a non-conservative way, i.e., to exploit the predefined violation level of the chance constraints based on their empirical closed-loop violations in order to achieve a good tradeoff between operational security and cost. We use and extend a recently proposed adaptive stochastic MPC scheme that starts with a standard conservative chance-constrained reformulation and then adapts the formulation of the constraints online based on the empirical violation level. The proposed approach is demonstrated on a 5-bus network.

关键词： Stochastic model predictive control Chance constraints Adaptive control Closed-loop violation optimal load flow adaptive control predictive control Security nonlinear model predictive control Power grids Renewable energy Cache Memory System Stochastic models tradeoffs

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A block based ALADIN scheme for highly parallelizable direct Optimal control

A block based ALADIN scheme for highly parallelizable direct...

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American control Conference

作者： Dimitris Kouzoupis1 Rien Quirynen Boris Houska Moritz Diehl Department of Microsystems Engineering (IMTEK) University of Freiburg Georges-Koehler-Allee 102 79110 Germany School of Information Science and Technology ShanghaiTech University 319 Yueyang Road Shanghai 200031 China

ISBN: (纸本)9781467386838

nonlinear model predictive control (NMPC) requires the online solution of a nonlinear Optimal control Problem (OCP) at each sampling instant. This paper presents a novel, block based and highly parallelizable algorithm which solves nonlinear OCPs using a recently proposed Augmented Lagrangian based method (ALADIN). The latter employs techniques from standard Sequential Quadratic Programming (SQP) methods within a more parallelizable framework. An implementation tailored to optimal control is proposed where nonlinear Programs (NLPs) are solved approximately and concurrently on each stage while a centralized consensus step is used to update the dual variables of the coupling constraints. The implementation also comprises algorithmic concepts to extend the parallelizability of the consensus step and a blocking technique to accelerate convergence. The performance of the resulting scheme is illustrated using as benchmark example the control of an overhead crane.

关键词： Optimal control Couplings Approximation algorithms Standards Trajectory Optimization predictive control Optimal control predictive control Approximation algorithms Couplings trajectory AAAS gene coupling A-BLOCK CRANE BRIDGE nonlinear model predictive control

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Tube-based Robust model predictive control for a Distributed Parameter System modeled as a Polytopic LPV

Tube-based Robust Model Predictive Control for a Distributed...

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American control Conference

作者： Jawad Ismail Steven Liu Department of Electrical and Computer Engineering Technical University of Kaiserslautern Kaiserslautern Germany

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

Distributed parameter systems (DPS) are formulated as partial differential equations (PDE). Especially, under time-varying boundary conditions, PDE introduce force coupling. In the case of the flexible stacker crane (STC), nonlinear coupling is introduced. Accordingly, online trajectory planning and tracking can be addressed using a nonlinear model predictive control (NMPC). However, due to the high computational demands of a NMPC, this paper discusses a possibility of embedding nonlinearities inside a linear parameter varying (LPV) system and thus make a use of a numerically low-demanding linear MPC. The resulting mismatches are treated as parametric and additive uncertainties in the context of robust tube-based MPC (TMPC). For the proposed approach, most of the computations are carried out offline. Only a simple convex quadratic program (QP) is conducted online. Additionally a soft-constrained extension was briefly proposed. Simulation results are used to illustrate the good performance, closed-loop stability and recursive feasibility of the proposed approach despite uncertainties.

关键词： Distributed parameter systems nonlinear model predictive control time varying model predictive control predictive control Parametric Simulation results

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

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

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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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Negotiation and Learning in Distributed MPC of Large Scale Systems

Negotiation and Learning in Distributed MPC of Large Scale S...

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American control Conference

作者： Valeria Javalera Bernardo Morcego Vicenc Puig Institut de Robotica I Informatica Industrial (CSIC-UPC). Llorens i Artigas 4-6. 08028 Barcelona Spain Universitat Politecnica de Catalunya

ISBN: (纸本)9781424474264

A key issue in distributed MPC control of Large Scale Systems (LSS) is how shared variables among the different MPC controller in charge of controlling each system partition (subsystems) are handled. When these connections represent control variables, the distributed control has to be consistent for both subsystems and the optimal value of these variables will have to accomplish a common goal. In order to achieve this, the present work combines ideas from Distributed Artificial Intelligence (DAI), Reinforcement Learning (RL) and model predictive control (MPC) in order to provide an approach based on negotiation, cooperation and learning techniques. Results of the application of this approach to a small drinking water network show that the resulting trajectories of the levels in tanks (control variables) can be acceptable compared to the centralized solution. The application to a real network (the Barcelona case) is currently under development.

关键词： Cooperative systems Distributed control model predictive control Multi agent Systems Negotiation Reinforcement Learning negotiations decentralised control cooperative systems large-scale systems model predictive control predictive control Learning nonlinear model predictive control SUB-SYSTEM

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Stochastic nonlinear Open-Loop Feedback control with Guaranteed Error Bounds Using Compactly Supported Wavelets

Stochastic Nonlinear Open-Loop Feedback Control with Guarant...

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IEEE Conference on Decision and control

作者： Achim Hekler Martin Kiefel Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Max Planck Institute for Intelligent Systems Tubingen Germany

ISBN: (纸本)9781612848006

In model predictive control, a high quality of control can only be achieved if the model of the system reflects the real-world process as precisely as possible. Therefore, the controller should be capable of both handling a nonlinear system description and systematically incorporating uncertainties affecting the system. Since stochastic nonlinear model predictive control (SNMPC) problems in general cannot be solved in closed form, either the system model or the occurring densities have to be approximated. In this paper, we present an SNMPC framework that approximates the densities and the reward function by their wavelet expansions. Due to the few requirements on the shape and family of the densities or reward function, the presented technique can be applied to a large class of SNMPC problems. For accelerating the optimization, we additionally present an efficient technique, so-called dynamic thresholding, which neglects insignificant coefficients, while at the same time guaranteeing that the optimal control input is still obtained. The capabilities of the proposed approach are demonstrated by simulations and comparisons to a particle-based SNMPC method are conducted.

关键词： Quality control wavelets Reward nonlinear model predictive control control input feedback control Stochastic closed forms Systems modelling Bound

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model predictive control for moored ocean current turbines

Model predictive control for moored ocean current turbines

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American control Conference

作者： Tri D. Ngo Cornel Sultan James H. VanZwieten Nikolaos I. Xiros Ho Chi Minh City University of Technology Vietnam Virginia Tech Blacksburg 24061 USA Florida Atlantic University Boca Raton 33431 USA University of New Orleans LA 70148 USA

ISBN: (纸本)9781509045839

We investigate the feasibility of model predictive control (MPC) in ocean current turbine (OCT) control. A prediction model is obtained from a nonlinear model of a moored OCT via linearization. Three control inputs are assumed, the electromechanical torque applied to the rotor shaft and two blade pitch cyclic controls. The control problem contains is highly constrained, which prompts consideration of MPC. For MPC evaluation, comparisons with another constrained control technique, output variance constrained control, are included, as well as simulations of the linear and nonlinear OCT dynamics when the MPC strategy is applied.

关键词： model predictive control Maximum Allowable Concentration ocean currents predictive control turbines distributed model predictive control nonlinear model predictive control optical coherence tomography Prediction models nonlinear models Standing Orders ROTOR SHAFT

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control of Exhaust Recompression HCCI using Hybrid model predictive control

Control of Exhaust Recompression HCCI using Hybrid Model Pre...

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American control Conference

作者： Anders Widd Hsien-Hsin Liao J. Christian Gerdes Per Tunestal Rolf Johansson Department of Automatic Control Lund University Box 118 SE 221 00 Lund Sweden Department of Mechanical Engineering Stanford University USA Division of Combustion Engines Department of Energy Sciences Lund University

ISBN: (纸本)9781457700811;9781457700804

Homogeneous Charge Compression Ignition (HCCI) holds promise for reduced emissions and increased efficiency compared to conventional internal combustion engines. As HCCI lacks direct actuation over the combustion phasing, much work has been devoted to designing controllers capable of set-point tracking and disturbance rejection. This paper presents results on model predictive control (MPC) of the combustion phasing in an HCCI engine based on a hybrid model formulation composed of several linearizations of a physics-based nonlinear model. The explicit representation of the MPC was implemented experimentally and the performance during set point changes was compared to that of a switched state feedback controller. The hybrid MPC produced smoother transients without overshoot when the set point change traversed several linearizations.

关键词： Hybrid modes Set of points model predictive control gasoline engine predictive control nonlinear model predictive control Recompression Viral Set Point Phase Disturbance rejection overshooting State feedback Combustion EXHAUSTS Standing Orders

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