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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 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 of Rotation Floating Space Robots for Autonomous Active Debris Removal 22

Nonlinear Model Predictive Control of Rotation Floating Spac...

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22nd International-Federation-of-Automatic-control (IFAC) Symposium on Automatic control in Aerospace (ACA)

作者： Srivastava, Raunak Sah, Roshan Das, Kaushik TCS Res Bengaluru India

ISBN: (纸本)9781713867890

Active Debris removal using robotic manipulators onboard satellites is a promising way of cleaning up the space junk. However, complexities and non-linearities associated with the control of such coupled space-based systems present difficulties in their feasible implementation. Lack of a fixed base arises serious problems in controlling the space manipulators for precision tasks like capture of an orbiting space debris. This paper presents systematic modelling and control approaches for Rotation floating space robots in order to draw a comparison between them while tracking a moving target representing autonomous debris capture. We propose a nonlinear model predictive controller (NMPC) for the space robot in order to design an optimal path that the end-effector can follow while being controlled to capture the target. To the best of the knowledge of the authors, such a controller has not been tested for a Rotation floating space robot before. Further, the current work implements and reviews one of the most commonly used Transpose Jacobian Cartesian (TJC) controller for Rotation floating space robots through the use of Generalized Jacobian Matrix (GJM). The results provide sufficient evidence of the superior performance of the nonlinear model predictive controller over the TJC controller. Finally, the current work also implements the same nonlinear model predictive controller on a more popular state of the art Free floating space robot and compares it with the Rotation floating space robot.

关键词： Rotation Floating Space robot nonlinear model predictive control

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nonlinear model predictive control for Enhanced Path Tracking and Autonomous Drifting Through Direct Yaw Moment control and Rear-Wheel-Steering 16th

Nonlinear Model Predictive Control for Enhanced Path Trackin...

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16th International Symposium on Advanced Vehicle control

作者： Tavo, Gaetano Stanol, Pietro Tavernini, Davide Montanaro, Umberto Tufo, Manuela Fiengo, Giovanni Perlo, Pietro Sorniotti, Aldo Univ Surrey Guildford GU2 7XH Surrey England Univ Sannio I-82100 Benevento Italy Kineton R&D I-80146 Naples Italy Interact Fully Elect VehicleS IFEVS I-10040 Turin Italy Politecn Torino I-10129 Turin Italy

ISBN: (纸本)9783031703942;9783031703928;9783031703911

Path tracking (PT) controllers capable of replicating race driving techniques, such as drifting beyond the limits of handling, have the potential of enhancing active safety in critical conditions. This paper presents a nonlinear model predictive control (NMPC) approach that integrates multiple actuation methods, namely four-wheel-steering, longitudinal tyre force distribution, and direct yaw moment control, to execute drifting when this is beneficial for PT in emergency scenarios. Simulation results of challenging manoeuvres, based on an experimentally validated vehicle model, highlight the substantial PT performance improvements brought by: i) vehicle operation outside the envelope enforced by the current generation of stability controllers;and ii) the integrated control of multiple actuators.

关键词： active safety systems electrified vehicles modelling testing and validation autonomous drifting nonlinear model predictive control

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nonlinear model predictive control in modelica using FMI and Optimization library 16

Nonlinear Model Predictive Control in Modelica using FMI and...

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7th International Workshop on Equation-Based Object-Oriented modeling Languages and Tools (EOOLT)

作者： Seefried, Andreas Pfeiffer, Andreas DLR Oberpfaffenhofen Inst Syst Dynam & Control Oberpfaffenhofen Germany

ISBN: (纸本)9781450342025

In this work-in-progress paper, a currently ongoing development of a generic tool for nonlinear model predictive control is presented. By using an extended interface of FMI 2.0, it is possible to simulate a model that acts as prediction model while the actual system is simulated simultaneously. A trajectory optimization that uses the prediction model provides optimized input control values for the actual system at every sample time. The current work is based on the Optimization library for Dymola and an extended version of FMI 2.0 Co-Simulation. The structure of this approach is explained in detail as well as possible settings and limitations. An example shows the practicability and an outlook for further development is given.

关键词： nonlinear model predictive control optimization FMI FMU Co-Simulation modelica

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nonlinear model predictive control for the industrial BioPower 5 CHP plant 27

Nonlinear model predictive control for the industrial BioPow...

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IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Kortela, Jukka Aalto Univ Dept Chem & Met Engn Sch Chem Engn PL 16100 FI-00076 Aalto Finland

ISBN: (数字)9781665499965

ISBN: (纸本)9781665499965

In the operation of a biopower plant, a crucial role is played by the residual oxygen content in the flue gas. The flue gas oxygen content, depending on the total air supply and on the fuel composition, provides the information needed to estimate the power developed by the combustion. Therefore, in control systems that operate power plants, the flue gas oxygen content is directly measured and represents a key feedback variable. The novel nonlinear model predictive control developed is based on the model of the BioPower 5 CHP plant which also includes the nonlinear model of the flue gas oxygen content. In addition, the fast response is achieved by regulating primary air flow. To verify the model, experiments were performed at a biopower plant, which utilizes BioGrate combustion technology to enable the use of wet biomass fuels with a moisture content as high as 65%. Then the nonlinear model predictive control was tested in the simulated environment. Finally, the results are presented, analyzed, and discussed.

关键词： NMPC nonlinear model predictive control biomass industrial process

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nonlinear model predictive control of Omnidirectional Mobile Robot Formations

Nonlinear Model Predictive Control of Omnidirectional Mobile...

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10th International Conference on Intelligent Autonomous Systems

作者： Kanjanawanishkul, Kiattisin Li, Xiang Zell, Andreas Univ Tubingen Dept Comp Architecture D-72076 Tubingen Germany

ISBN: (纸本)9781586038878

In this paper we focus on solving a path following problem and keeping a geometrical formation. The problem of formation control is divided into a leader agent subproblem and a follower agent subproblem such that a leader agent follows a given path, while each follower agent tracks a trajectory. estimated by using the leader's information. In this paper, we exploit nonlinear model predictive control (NMPC) as a local control law due to its advantages of taking the robot constraints and future information into account. For the leader agent, we propose to explicitly integrate the rate of progression of a virtual vehicle into the local cost function of NMPC. This strategy can overcome stringent in constraints in a path following problem. Our approach was validated by experiments using three omnidirectional mobile robots.

关键词： Omnidirectional mobile robots multi-robot systems formation control nonlinear model predictive control path-following problems

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nonlinear model predictive control based on hyper chaotic diagonal recurrent neural network

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Journal of Central South University 2022年第1期29卷 197-208页

作者： Samira Johari Mahdi Yaghoobi Hamid RKobravi Department of Control Mashhad BranchIslamic Azad UniversityMashhad ***Iran Department of Biomedical Engineering Mashhad BranchIslamic Azad UniversityMashhad ***Iran

nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive ***,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward *** order to improve the convergence of the parameters of the HCDRNN to improve system’s modeling,the extent of chaos is adjusted using a logistic map in the hidden layer.A novel NMPC based on the HCDRNN array(HCDRNN-NMPC)was proposed that the control signal with the help of an improved gradient descent method was *** controller was used to control a continuous stirred tank reactor(CSTR)with hard-nonlinearities and input constraints,in the presence of uncertainties including external *** results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance *** convergence and neglectable prediction error of the neural network(NN),guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.

关键词： nonlinear model predictive control diagonal recurrent neural network chaos theory continuous stirred tank reactor

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nonlinear model predictive control method for carbon fiber angle link weaving machine tension based on neural network 3

Nonlinear model predictive control method for carbon fiber a...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Zhu, Zhihua Wang, Yufeng Tiangong University School of Mechanical Engineering Tianjin China

ISBN: (纸本)9798350341546

In this paper, a neural network-based nonlinear model predictive control strategy for carbon fiber angle link weaving machine tension is proposed. Firstly, the tension nonlinear model considering the opening disturbance is established. Secondly, considering the existence of nonlinear terms in the system, a radial basis function (RBF) neural network is proposed to approximate the nonlinear terms online to improve the control accuracy of the system. A tension nonlinear model predictive control(NMPC) is designed to achieve constant tension control under the driving torque constraint. And compared with the conventional nonlinear model predictive controller, the RBFNMPC improves the time to reach the steady state in the system state variation curve by 0.2s, reduces the overshoot of the three states in the state response curve by 19.1%, 6.1% and 12.3%, respectively, and effectively reduces the time to reach the steady state. © 2023 IEEE.

关键词： carbon fiber Angle loom neural network nonlinear model predictive control tension control

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nonlinear model predictive control Based on Support Vector Machine with Multi-kernel

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Chinese Journal of Chemical Engineering 2007年第5期15卷 691-697页

作者：包哲静皮道映孙优贤 State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou 310027 China

Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.

关键词： nonlinear model predictive control support vector machine with multi-kernel nonlinear system identification kernel function

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