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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Trachte, Jan Gonzalez, Felipe McFadyen, Aaron Univ Stuttgart Inst Flight Mech & Control D-70569 Stuttgart Germany Queensland Univ Technol Fac Sci & Engn Brisbane Qld 4001 Australia Australian Res Ctr Aerosp Automat ARCAA Brisbane Qld 4008 Australia

ISBN: (纸本)9781479923762

In this paper a novel controller for stable and precise operation of multi-rotors with heavy slung loads is introduced. First, simplified equations of motions for the multirotor and slung load are derived. The model is then used to design a nonlinear model predictive controller (NMPC) that can manage the highly nonlinear dynamics whilst accounting for system constraints. The controller is shown to simultaneously track specified waypoints whilst actively damping large slung load oscillations. A Linear-quadratic regulator (LQR) controller is also derived, and control performance is compared in simulation. Results show the improved performance of the nonlinear model predictive control (NMPC) controller over a larger flight envelope, including aggressive maneuvers and large slung load displacements. Computational cost remains relatively small, amenable to practical implementation. Such systems for small Unmanned Aerial Vehicles (UAVs) may provide significant benefit to several applications in agriculture, law enforcement and construction.

关键词： nonlinear model predictive control LOAD Small unmanned aerial vehicles controllers linear quadratic regulators Simulation results nonlinear Dynamics control performance Equations of motion FLIGHT ENVELOPE Heavy

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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 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 strategy for steam turbine rotor stress 10

Nonlinear Model Predictive Control strategy for steam turbin...

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10th International Conference on Applied Energy (ICAE)

作者： Dettori, S. Maddaloni, A. Colla, V Toscanelli, O. Bucciarelli, F. Signorini, A. Checcacci, D. Scuola Super Sant Anna Via Moruzzi 1 I-56124 Pisa Italy Baker Hughes Via F Matteucci 2 I-50127 Florence Italy

The paper proposes a nonlinear model predictive control strategy for the control of steam turbines rotor thermal stresses, which exploits the approximation of the turbine rotor as an infinite cylinder subjected to external convection. The nonlinear model predictive control allows optimizing the control strategy in the long term, by significantly reducing the machine start-up time during the power up ramp. This study proposes two different control strategies: the former one is based on the control of the Heat Transfer Coefficient, correlated to the inlet valve stroke. The latter one is based on the control of Heat Transfer Coefficient and the boiler steam temperature reference. Both strategies achieve good results in shortening the start-up time. The overall approach is validated and currently under development on Programmable Logic controller platforms to the aim of code optimization. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： Rotor Stress control nonlinear model predictive control Power Startup

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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 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 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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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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Robust Adaptive Gain Higher Order Sliding Mode Observer Based control-constrained nonlinear model predictive control for Spacecraft Formation Flying

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 367-381页

作者： Ranjith Ravindranathan Nair Laxmidhar Behera Department of Electrical Engineering Indian Institute of TechnologyKanpur Kanpur-208 016India IEEE

This work deals with the development of a decentralized optimal control algorithm, along with a robust observer,for the relative motion control of spacecraft in leader-follower based formation. An adaptive gain higher order sliding mode observer has been proposed to estimate the velocity as well as unmeasured disturbances from the noisy position measurements.A differentiator structure containing the Lipschitz constant and Lebesgue measurable control input, is utilized for obtaining the estimates. Adaptive tuning algorithms are derived based on Lyapunov stability theory, for updating the observer gains,which will give enough flexibility in the choice of initial ***, it may help to cope with unexpected state jerks. The trajectory tracking problem is formulated as a finite horizon optimal control problem, which is solved online. The control constraints are incorporated by using a nonquadratic performance functional. An adaptive update law has been derived for tuning the step size in the optimization algorithm, which may help to improve the convergence speed. Moreover, it is an attractive alternative to the heuristic choice of step size for diverse operating conditions. The disturbance as well as state estimates from the higher order sliding mode observer are utilized by the plant output prediction model, which will improve the overall performance of the controller. The nonlinear dynamics defined in leader fixed Euler-Hill frame has been considered for the present work and the reference trajectories are generated using Hill-Clohessy-Wiltshire equations of unperturbed motion. The simulation results based on rigorous perturbation analysis are presented to confirm the robustness of the proposed approach.

关键词： Adaptive gain higher order sliding mode observer leader-follower formation nonlinear model predictive control spacecraft formation flying tracking control

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