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

共 1663 条记录，以下是121-130 订阅

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nonlinear model predictive control for Regulation of Microalgae Culture in a Continuous Photobioreactor 22

Nonlinear Model Predictive Control for Regulation of Microal...

引用

22nd Mediterranean Conference on control and Automation (MED)

作者： Benattia, S. Tebbani, S. Dumur, D. SUPELEC Syst Sci E3S Automat Control Dept 3 Rue Joliot Curie F-91192 Gif Sur Yvette France

ISBN: (纸本)9781479959013

The objective of this study is to design a nonlinear model predictive controller for a microalgae culture process to regulate the biomass concentration at a chosen setpoint. The optimization problem is discretized and transformed into a nonlinear programming problem, solved by control Vector Parametrization technique. However, the performances of the NMPC usually decrease when the true plant evolution deviates significantly from that predicted by the model. Therefore, a control approach that considers model uncertainty is further considered by adding a system-model error signal which represents the gap between the system output and the model prediction. In order to reduce the influence of measurement noise introduced by sensors and to have a smooth control signal, a penalty term on the control variation is added in the objective function. Finally, the method is validated in simulation and numerical results are given to illustrate the efficiency of the control strategy for setpoint tracking in the presence of parameter uncertainties, measurement noise and light variation.

关键词： Microalgae Droop model nonlinear model predictive control control Vector Parameterization Photobioreactor

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nonlinear model predictive control for Disturbance Rejection in Isoenergetic-isochoric Flash Processes 12

Nonlinear Model Predictive Control for Disturbance Rejection...

引用

12th International-Federation-of-Automatic-control (IFAC) Symposium on Dynamics and control of Process Systems including Biosystems (DYCOPS-CAB)

作者： Ritschel, Tobias K. S. Jorgensen, John Bagterp Tech Univ Denmark Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark Tech Univ Denmark CERE DK-2800 Lyngby Denmark

We present a nonlinear model predictive control (NMPC) algorithm for semi-explicit index-1 stochastic differential-algebraic equations. It is natural to model isoenergetic-isochoric (constant energy-constant volume) flash processes with such equations. The algorithm uses the continuous-discrete extended Kalman filter (EKF) for state estimation, and it uses a single shooting method to solve the involved optimal control problems. It computes the gradients with an adjoint method. The isoenergetic-isochoric flash is also called the UV flash, and it is important to rigorous models of phase equilibrium processes because it is a mathematical statement of the second law of thermodynamics. NMPC algorithms for UV flash processes are therefore relevant to both safe and economical operation of phase equilibrium processes such as flash separation, distillation, two-phase flow in pipes, and oil production. We design the NMPC algorithm for disturbance rejection, and we therefore augment the state vector with the unknown disturbance variables in the continuous-discrete EKF. We present a numerical example of economical NMPC of a UV flash separation process. It involves output constraints and the estimation of an unknown and unmeasured disturbance. The computation time of the NMPC algorithm does not exceed 3.14 s in any of the 5 min control intervals. This indicates that real-time NMPC of UV flash separation processes is computationally feasible. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： nonlinear model predictive control The extended Kalman filter Single-shooting Disturbance rejection Vapor-liquid equilibrium Differential-algebraic equations

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nonlinear model predictive control with Moving Horizon Estimation of a Pendubot System 20

Nonlinear Model Predictive Control with Moving Horizon Estim...

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20th International Conference on Process control (PC)

作者： Gulan, Martin Salaj, Michal Rohal'-Ilkiv, Boris Slovak Univ Technol Bratislava Fac Mech Engn Inst Automat Measurement & Appl Informat Nam Slobody 17 Bratislava 81231 Slovakia

ISBN: (纸本)9781467366274

In this paper we present and investigate a complex control framework based on nonlinear model predictive control (NMPC) to achieve the unstable equilibria, and Moving Horizon Estimation (MHE) to estimate the actual state and parameters of a Pendubot. This fast, under-actuated nonlinear mechatronic system apparently poses a challenging benchmark problem that might benefit from a nonlinear optimization scheme. To overcome the related computational difficulties we make use of the ACADO Code Generation tool allowing to export a highly efficient Gauss-Newton real-time iteration algorithm tailored to the nonlinear model dynamics while respecting imposed constraints. We show simulation results illustrating the overall control performance of the closed-loop system as well as the advantages of the nonlinear MHE-based NMPC scheme.

关键词： nonlinear model predictive control control performance unstable equilibrium Benchmark testing Horizon Moving horizon state estimation Closed loop systems Moving

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nonlinear model predictive control Considering Stochastic and Systematic Uncertainties with Sets of Densities

Nonlinear Model Predictive Control Considering Stochastic an...

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IEEE International Conference on control Applications Part of 2010 IEEE Multi-Conference on Systems and control

作者： Hekler, Achim Lyons, Daniel Noack, Benjamin Hanebeck, Uwe D. Karlsruhe Inst Technol KIT Intelligent Sensor Actuator Syst Lab ISAS Karlsruhe Germany

ISBN: (纸本)9781424453634

In model predictive control, the quality of control is highly dependent upon the model of the system under control. Therefore, a precise deterministic model is desirable. However, in real-world applications, modeling accuracy is typically limited and systems are generally affected by disturbances. Hence, it is important to systematically consider these uncertainties and to model them correctly. In this paper, we present a novel nonlinear model predictive control method for systems affected by two different types of perturbations that are modeled as being either stochastic or unknown but bounded quantities. We derive a formal generalization of the nonlinear model predictive control principle for considering both types of uncertainties simultaneously, which is achieved by using sets of probability densities. In doing so, a more robust and reliable control is obtained. The capabilities and benefits of our approach are demonstrated in real-world experiments with miniature walking robots.

关键词： deterministic model miniature walking robot nonlinear control systems nonlinear model predictive control perturbation perturbation techniques predictive control probability probability density reliable control robust control robust control stochastic systems stochastic uncertainty systematic uncertainty uncertain systems

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nonlinear model predictive control (NMPC) of Fixed Pitch Variable Speed Wind Turbine

Nonlinear Model Predictive control (NMPC) of Fixed Pitch Var...

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IEEE International Conference on Sustainable Energy Technologies

作者： Dang, D. Q. Wang, Y. Cai, W. Nanyang Technol Univ Sch Elect Elect Engn Singapore 639798 Singapore

ISBN: (纸本)9781424418879

Wind power has become one of the most attractive energy systems in several decades due to its rich wind power available and high turn back value than other types of renewable energy systems. The classical wind turbines are normally fixed pitch constant speed which have less power efficient and high dynamics loads that results in reducing life cycle of system. To overcome this disadvantage, modern generation of wind turbine with variable speed is now widely used. However, wind energy conversion system that includes complex combination of aero dynamics and electrical phenomena together with anonymous action of wind speed making control system sophisticated. Despite if a great deal of research has been focus on wind turbines in the literature. In this paper, we present advanced control algorithm nonlinear model predictive control (NMPC) regulating shaft speed to capture as much as possible power and keep it below maximum level at high wind speed. Because of the limitation of power electronics devices, the generation torque is also restricted below maximum value. All these constraints are naturally handled by NMPC algorithm which is promising control theory interested by many researchers resent years. The algorithm is validated by simulation and later is hardware realization of controller.

关键词： Wind Energy Conversion Systems nonlinear model predictive control wind speed variable speed wind turbine Variable speed fixed speed ENERGY SYSTEMS control Theory Power electronics turbines Energy sources

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nonlinear model predictive control via intelligent optimization for maximum wind energy extraction of large-scale wind turbines

Nonlinear model predictive control via intelligent optimizat...

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Chinese Automation Congress (CAC)

作者： Liu, Junbo Song, Dongran Yang, Jian Su, Mei Yang, Yinggang Wang, Li Cent South Univ Sch Automat Changsha Peoples R China

ISBN: (纸本)9781728176871

In this paper, a novel nonlinear model predictive control via intelligent optimization is proposed to extract maximum wind energy for large-scale wind turbines. A single-objective nonlinear function is formulated by using a weighting factor to integrate the extracted wind energy and the generator torque variation within a prediction period of several seconds. Meanwhile, a new Yin-Yang grey wolf optimization algorithm is presented, which can achieve the efficient convergence and global optimum. Simulation results verify the effectiveness and superiority of the proposed nonlinear model predictive control via the new optimization algorithm.

关键词： Wind turbine maximum wind energy extraction nonlinear model predictive control intelligent optimization

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nonlinear model predictive control of Ultra-supercritical Once Through Boiler-turbine Unit

Nonlinear Model Predictive Control of Ultra-supercritical On...

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Chinese Automation Congress (CAC)

作者： Zhang, Fan Shen, Jiong Li, Yiguo Wu, Xiao Southeast Univ Sch Energy & Environm Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781467371896

The coordinated control system (CCS) plays an import role in the operation of ultra-supercritical once-through boiler-turbine unit. To overcome the operating issues of the CCS, such as multivariable coupling, nonlinearity and large-time delay, a collocation method based nonlinear model predictive control is proposed in this paper. The dynamic optimization problem is transcribed into a finite dimensional nonlinear programming problem and solved using interior point-based large scale nonlinear optimization algorithm. Simulation results on a 1000MW ultra-supercritical once-through boiler-turbine unit show the effectiveness of the proposed approach.

关键词： ultral-supercritical boiler-turbine unit coordinated control system nonlinear model predictive control collocation methods

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nonlinear model predictive control of the Tennessee Eastman process

Nonlinear Model Predictive Control of the Tennessee Eastman ...

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American control Conference

作者： Zheng, A Univ Massachusetts Dept Chem Engn Amherst MA 01003 USA

ISBN: (纸本)0780345304

The main purposes of this paper are to illustrate several key issues in the implementation of a conventional nonlinear model predictive control algorithm on a reasonably large industrial process and to test the effectiveness of the nonlinear model predictive control algorithm recently proposed by Zheng for control of large nonlinear systems with constraints. We show why a conventional nonlinear model predictive control algorithm may fail to provide integral control under very reasonable conditions (i.e., integral control is guaranteed if and only if a global solution is implemented and the output horizon is infinite) and illustrate this undesirable behavior through simulations on the Tennessee East man process. In addition to computational advantage, we argue that Zheng's algorithm may be preferred based on robust performance consideration.

关键词： nonlinear model predictive control integral control Tennessee Computer algorithms algorithms

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nonlinear model predictive control of a 5-DoFs Boom Crane 29

Nonlinear Model Predictive Control of a 5-DoFs Boom Crane

引用

29th Mediterranean Conference on control and Automation (MED)

作者： Asani, Zemerart Ambrosino, Michele Cotorruelo, Andres Garone, Emanuele Vrije Univ Brussel Fac Engn Pl Laan 2 B-1050 Brussels Belgium Univ Libre Bruxelles Dept Control Engn & Syst Anal Brussels Belgium

ISBN: (纸本)9781665422581

The goal of this paper is to present a nonlinear model predictive control scheme for boom cranes, one of the most common type of rotary cranes. In this paper we first present a complete mathematical model for this type of cranes where it is possible to control the two rotations of the crane and the cable length. Then, we design a model predictive control law able to drive the load to a desired position while fulfilling mechanical and safety constraints. We finally compare our controller to a partial feedback linearization coupled with a proportional derivative control with gravity compensation applied to the same model.

关键词： Boom cranes Robotics Motion control Under-actuated systems nonlinear model predictive control

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nonlinear model predictive control of an Oxy-Fuel Combustion Boiler for Regulating the Excess Oxygen Percentage in the Flue Gas

Nonlinear Model Predictive Control of an Oxy-Fuel Combustion...

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Annual Conference of the SICE

作者： Haryanto, Ade Hong, Keum-Shik Jeon, Chung Hwan Pusan Natl Univ Sch Mech Engn Pusan South Korea

ISBN: (纸本)9784907764302

In this paper, a nonlinear model predictive control (MPC) of a water-tube boiler system with oxy-fuel combustion process in an electric power plant is investigated. The control objectives are to maintain the water and pressure levels in the drum, the steam temperature in the secondary superheater, and the oxygen percentage in the flue gas. Upon the riser and drum models developed by angstrom strom [5], mathematical (nonlinear) models of the primary and secondary superheaters, attemperator and oxy-fuel combustion process are first developed. The introduced seven state variables are the water volume, steam pressure, and steam volume in the drum, vapor fraction in the riser, temperatures in the primary and secondary superheaters, and attemperator temperature. The five input variables are the feed water flow, steam flow, fuel flow, oxygen flow, and spray flow. The four output variables are the steam pressure and water level in the drum, secondary superheater temperature, and excess of oxygen in the flue gas. Since the water and steam volumes in the drum, vapor fraction in the riser, and the temperatures in the primary superheater and attemperator cannot be directly measured, two nonlinear state estimators (extended Kalman filter and particle filter for comparison purpose) are designed. Simulation results of the designed nonlinear MPC algorithm are provided.

关键词： Water-tube boiler nonlinear model predictive control power plant oxy-fuel combustion extended Kalman filter particle filter

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