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

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Guaranteed cost approach for robust model predictive control of uncertain linear systems

Guaranteed cost approach for robust model predictive control...

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American control Conference

作者： Carlos M. Massera Marco H. Terra Denis F. Wolf Institute of Mathmatics and Computer Science University of Sao Paulo Avenida Trabalhador Sao-carlense 400 Brazil Sao Carlos School of Engineering University of Sao Paulo Avenida Trabalhador Sao-carlense 400 Brazil

ISBN: (纸本)9781509045839

In this paper we propose a constrained guaranteed cost robust model predictive controller (GCMPC) for uncertain discrete time systems. This controller was developed based on a quadratic cost functional and guarantee robustness with respect to quadratically bound uncertainties. Such a class of problems is currently intractable by Min-Max Robust model predictive controllers without polytopic approximations of the uncertainties. The proposed technique is computationally more efficient then an enumeration-based approach and requires only a Quadratically Constrained Quadratic Problem (QCQP) optimization, whereas LMI-based GCMPC approaches require a Semi-Definite Programming (SDP) optimization.

关键词： Linear system nonlinear model predictive control predictive controllers Plant uncertain Uncertainty model predictive control predictive control Robustness Quadratic discrete time systems

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model predictive control for dynamic positioning system of ships with unknown time-varying disturbances and actuator constraint

Model predictive control for dynamic positioning system of s...

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International Conference on Information Science and Technology (ICIST)

作者： Guibing Zhu Jialu Du Yongchao Liu College of Information Science and Technology Dalian Maritime University Dalian China

This paper proposes a disturbance compensating model predictive control (DCMPC) strategy for the dynamic positioning (DP) system of ships with unknown time-varying environmental disturbances and actuator saturation. The proposed strategy can make the position and heading of the dynamically positioned ship converge to the desired values without any priori knowledge about environmental disturbances. The extended state observer (ESO) is introduced to estimate the unknown environmental disturbances. Based on ESO, the DCMPC algorithm is proposed to deal with the unknown disturbances. Finally, the simulations are carried out on a dynamically positioned ship and simulation results show the effectiveness of the proposed control strategy.

关键词： disturbance compensating dynamically positioned ships extended state observer model predictive control Ships model predictive control predictive control distributed model predictive control nonlinear model predictive control Observers Dynamic positioning control strategy Unknown actuators time varying European standardisation body DP system

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Simultaneous Optimization of Dispatching and Routing for OHT Systems via Hybrid System modeling

Simultaneous Optimization of Dispatching and Routing for OHT...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Ryosuke Nakamura Kenji Sawada Seiichi Shin Kenji Kumagai Hisato Yoneda Department of Mechanical Engineering and Intelligent Systems The University of Electro-Communications Murata machinery Ltd

ISBN: (纸本)9781479902255

This paper proposes a new static simultaneous optimization of dispatching and conflict-free routing for unidirectional Overhead Hoist Transport (OHT) systems. Our objective is to minimize the entire transportation time where the given transportation tasks are finished. A hybrid system modeling is applied for OHT system that is constituted by the OHT vehicles at the lower level and governed by the upper level scheduler. In the proposed modeling, it is clarified that the simultaneous optimization problems are recast as a binary integer linear problem by using model predictive control. This paper presents a numerical example and discusses limitation of proposed modeling.

关键词： Overhead Hoist Transport system Simultaneous optimization Dispatching Routing Hybrid system model predictive control Dispatching hybrid systems Network routing model predictive control predictive control distributed model predictive control nonlinear model predictive control modeling

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Non-linear Moving Horizon State Estimation and control for the Superfluid Helium Cryogenic Circuit at the Large Hadron Collider

Non-linear Moving Horizon State Estimation and Control for t...

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IEEE Annual Conference on Decision and control

作者： Rafal Noga Cesar de Prada Toshiyuki Ohtsuka Enrique Blanco Juan Casas University of Valladolid 47011 Valladolid Spain Kyoto University Yoshida-honmachi Sakyo-ku Kyoto 606-8501 Japan European Organization for Nuclear Research CERN CH-1211 Geneve 23 Switzerland

ISBN: (纸本)9781467360890

We consider the problem of temperature stabilization of the Large Hadron Collider's (LHC) superconducting magnets, cooled with superfluid helium 4 (He II). The temperature dynamics is highly non-linear, exhibiting inverse response, variable dead-times, strong couplings between process variables and strong saturation effects. Multiple operational constraints must be respected. We present a prototype application of a hybrid Non-linear Moving Horizon State Estimation - Luen-berger observer (MHSE-LO) and Non-linear model predictive control (NMPC). It is based on: 1) a detailed, low computing cost, first principles model of the temperature dynamics and 2) a simple optimization approach tailored for systems with stiff dynamics. The prototype control system has been tested at the LHC, demonstrating excellent, robust performance.

关键词： Moving horizon state estimation superfluidity Hadron colliders nonlinear model predictive control Robust control control systems response variable Standing Orders Process variables

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A New Fast NMPC Scheme for Parallel Kinematic Manipulators: Design and Real-Time Experiments

A New Fast NMPC Scheme for Parallel Kinematic Manipulators: ...

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Signal, control and Communication (SCC), International Conference on

作者： Rihab Kouki Ahmed Chemori Faouzi Bouani LR11ES20 Laboratoire Analyse Conception et Commande des Systémes Université de Tunis El Manar Ecole Nationale d’Ingénieurs de Tunis Tunis Tunisie Laboratoire d’Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) Univ. Montpellier - CNRS Montpellier France

ISBN: (数字)9781728135960

ISBN: (纸本)9781728135977

This paper adresses the design of a new extension of fast nonlinear model predictive control (NMPC) for parallel manipulators. The developed controller is based on a parameterized NMPC, a fast gradient solver and a proportional derived controller (PD). The main motivation behind the proposed approach is to improve the tracking performance of fast parallel manipulators and reduce the computation time per control iteration. This control scheme is faster, in terms of computing time, than the classical NMPC and ensures the robustness of the resulting closed-loop system. To demonstrate the effectiveness of the proposed controller, real-time experiments are performed on a 4-DOF parallel kinematic manipulator called VELOCE.

关键词： computational time Manipulators Fasting visual tracking Parallel Lines kinematics nonlinear model predictive control controllers

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predictive Energy Management Strategy for Fully Electric Vehicles based on Hybrid model predictive control

Predictive Energy Management Strategy for Fully Electric Veh...

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American control Conference

作者： Shuwei Zhang Yugong Luo Keqiang Li Junmin Wang Department of Mechanical and Aerospace Engineering The Ohio State University Columbus OH 43210 USA State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing 100084 China

ISBN: (纸本)9781509045839

Limited travelling range is a major concern for fully electric vehicles (FEVs), and enlarging the total driving range is a main research topic of FEVs. Recently, with the development of intelligent transportation systems, traffic information can be available for the vehicle control systems, like terrain profile and velocity and acceleration of preceding vehicles. With such information, energy-efficient control for FEVs could further prolong the total driving range. In this paper, a hybrid model predictive control method is introduced to control an FEV considering the road terrain profile ahead and preceding vehicle movement information. With the vehicle-to-vehicle wireless communication technology, the movement of preceding vehicle can be predicted in each control cycle. While keeping a suitable distance from the preceding vehicle, the driving energy consumption is optimized. Simulation results show that this method has a better energy saving performance than the traditional methods under various driving conditions even with communication packet losses.

关键词： fully electric vehicles energy efficient control hybrid model predictive control all-electric Hybrid modes vehicle Energy management control period distributed model predictive control nonlinear model predictive control predictive control Packet loss control systems FEV gene Forced Expiratory Volume

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Hybrid model predictive control for Optimizing Gestational Weight Gain Behavioral Interventions

Hybrid Model Predictive Control for Optimizing Gestational W...

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American control Conference

作者： Yuwen Dong Daniel E. Rivera Danielle S. Downs Jennifer S. Savage Diana M. Thomas Linda M. Collins Control Systems Engineering Laboratory (CSEL) School for Engineering of Matter Transport and Energy Arizona State University Tempe AZ USA Exercise Psychology Laboratory Department of Kinesiology Penn State University University Park PA USA Center for Childhood Obesity Research and the Department of Nutritional Sciences Penn State University University Park PA USA Department of Mathematical Sciences Montclair State University Montclair NJ USA The Methodology Center and Department of Human Development and Family Studies Penn State University University Park PA USA

ISBN: (纸本)9781479901777

Excessive gestational weight gain (GWG) represents a major public health issue. In this paper, we pursue a control engineering approach to the problem by applying model predictive control (MPC) algorithms to act as decision policies in the intervention for assigning optimal intervention dosages. The intervention components consist of education, behavioral modification and active learning. The categorical nature of the intervention dosage assignment problem dictates the need for hybrid model predictive control (HMPC) schemes, ultimately leading to improved outcomes. The goal is to design a controller that generates an intervention dosage sequence which improves a participant's healthy eating behavior and physical activity to better control GWG. An improved formulation of self-regulation is also presented through the use of Internal model control (IMC), allowing greater flexibility in describing self-regulatory behavior. Simulation results illustrate the basic workings of the model and demonstrate the benefits of hybrid predictive control for optimized GWG adaptive interventions.

关键词： Hybrid modes Behavior Therapy predictive control Intervention nonlinear model predictive control

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Hybrid model predictive control for Sequential Decision Policies in Adaptive Behavioral Interventions

Hybrid Model Predictive Control for Sequential Decision Poli...

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American control Conference

作者： Yuwen Dong Sunil Deshpande Daniel E. Rivera Danielle S. Downs Jennifer S. Savage Control Systems Engineering Laboratory (CSEL) School for Engineering of Matter Transport and Energy Arizona State University Tempe AZ USA Exercise Psychology Laboratory Department of Kinesiology Penn State University University Park PA USA Center for Childhood Obesity Research and the Department of Nutritional Sciences Penn State University University Park PA USA

ISBN: (纸本)9781479932757

control engineering offers a systematic and efficient method to optimize the effectiveness of individually tailored treatment and prevention policies known as adaptive or "just-in-time" behavioral interventions. The nature of these interventions requires assigning dosages at categorical levels, which has been addressed in prior work using Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes. However, certain requirements of adaptive behavioral interventions that involve sequential decision making have not been comprehensively explored in the literature. This paper presents an extension of the traditional MLD framework for HMPC by representing the requirements of sequential decision policies as mixed-integer linear constraints. This is accomplished with user-specified dosage sequence tables, manipulation of one input at a time, and a switching time strategy for assigning dosages at time intervals less frequent than the measurement sampling interval. A model developed for a gestational weight gain (GWG) intervention is used to illustrate the generation of these sequential decision policies and their effectiveness for implementing adaptive behavioral interventions involving multiple components.

关键词： Hybrid modes Behavior Therapy nonlinear model predictive control Self tuning Policies predictive control Sequential

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model predictive control of Thermal Storage for Demand Response

Model Predictive Control of Thermal Storage for Demand Respo...

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American control Conference

作者： Kevin J. Kircher K. Max Zhang Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca NY 14850

ISBN: (纸本)9781479917730

Buildings with thermal storage use it mainly to shift cooling loads. Ice or chilled water is produced when electricity prices are low and stored to provide cooling when prices are high. While this price-based load shifting has value for power system operators, buildings with thermal storage could provide more direct grid services by reacting to demand charges and demand response calls. In this paper, we consider the problem of cooling a building under these incentives. The context is a New York City office building with passive and active thermal storage, subject to Consolidated Edison's (ConEd's) default rate plan for large commercial buildings. This rate plan includes a three-tiered demand charge and hourly energy prices determined by the system operator's day-ahead dispatch. We also model a ConEd demand response program, and consider the thermal comfort of building occupants. The problem is formulated in the language of stochastic optimal control and solved approximately using model predictive control (MPC). Extending previous work on MPC of thermal storage, which has focused on dynamic energy prices, we include the full set of economic incentives directly in the stage and terminal costs. Simulations of the hottest day of 2013 demonstrate the value of realistic economic modeling. They also highlight an interesting tension between the various incentives, which all compete for shiftable load.

关键词： heat storage Demand-Responsive model predictive control Buildings Demand Charges incentives predictive control Load shifting Energy costs stochastic optimal control chilled water nonlinear model predictive control Prices SUPPORT FRAME

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A model predictive control Approach to Attitude Stabilization and Trajectory Tracking control of a 3D Universal Joint Space Robot with an Initial Angular Momentum

A Model Predictive Control Approach to Attitude Stabilizatio...

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IEEE Conference on Decision and control

作者： Tatsuya Kai Faculty of Information Science and Electrical Engineering Kyushu University JAPAN

ISBN: (纸本)9781612848006

In this paper, we deal with two types of control problems for a 3D space robot of two rigid bodies with an initial angular momentum. In our previous work, we studied a near-optimal control approach to the state transition control problem for the 3D space robots with initial angular momenta. However, for the approach, we need huge computation amount and other control purposes cannot be treated. In order to overcome these disadvantages, in this paper we apply model predictive control to the attitude stabilization control problem and the trajectory tracking control problem for a 3D universal joint space robot with an initial angular momentum. As a results of simulations, we can see that both attitude stabilization control and trajectory tracking control for the 3D space robot are accomplished by the model predictive control approach. Moreover, it can be also confirmed that this approach considerably reduces computation amount and achieves real-time control of the 3D space robot.

关键词： aerospace robotics trajectory tracking predictive control attitude stabilisation Joint space tracking control Rigid bodies Real time control Standing Orders nonlinear model predictive control Three Dimensions Robot

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