Nonlinear Dynamic Model of a Fluidized-Bed Steam Generation System

1980 ◽  
Vol 102 (1) ◽  
pp. 202-208 ◽  
Author(s):  
A. Ray ◽  
D. A. Berkowitz ◽  
V. H. Sumaria
Keyword(s):  
Fluidized Bed ◽  
Dynamic Model ◽  
Controller Design ◽  
Digital Simulation ◽  
Steam Generation ◽  
Generation System ◽  
Nonlinear Dynamic Model ◽  
Continuous Time Model ◽  
Design Data ◽  
Time Model

A dynamic model of an atmospheric pressure fluidized-bed steam generation system is presented which allows digital simulation and analytical controller design. The nonlinear, time-invariant, deterministic, continuous-time model is derived in state-space form from conservation relations, empirical correlations and system design data. The model has been verified for steady-state and transient performance with measured data from experimental test runs. Transient responses of several process variables, following independent step disturbances in coal feed rate and air flow, are illustrated.

A simplified dynamic model and control for a multiple launch rocket system

2021 ◽  
pp. 107754632110093
Author(s):  
Bo Li ◽  
Xiaoting Rui ◽  
Guoping Wang
Keyword(s):  
Dynamic Model ◽  
Feedback Linearization ◽  
Controller Design ◽  
Euler Method ◽  
Nonlinear Controller ◽  
Nonlinear Dynamic Model ◽  
Position Accuracy ◽  
System A ◽  
And Control ◽  
Rocket System

Multiple launch rocket system, a type of launching platform used to launch kinetic load to hit the target, is an extremely complicated mechanical system with strong vibration because of the jet force. In this study, a nonlinear dynamic model and vibration control of a multiple launch rocket system are presented to reduce vibration and improve position accuracy. A simplified dynamic model of the multiple launch rocket system is derived using the Newton–Euler method, which facilitates the controller design considering the strong complexity of the multiple launch rocket system. On this basis, the feedback linearization technique is introduced to design a nonlinear controller based on the deduced dynamic model. The simulated and experimental results show that the simplified dynamic model–based control effectively can reduce vibration level of the launching system and make the azimuth and elevation angles reach the desired values with smaller error despite of each rocket’s jet force.

A Mechanistic Dynamic Model of End Milling for Process Controller Simulation

1991 ◽  
Vol 113 (2) ◽  
pp. 176-183 ◽  
Author(s):  
F. M. Kolarits ◽  
W. R. DeVries
Keyword(s):  
Dynamic Model ◽  
Metal Cutting ◽  
Controller Design ◽  
Metal Removal ◽  
End Milling ◽  
Removal Rate ◽  
Digital Simulation ◽  
Surface Geometry ◽  
Cutter Deflection ◽  
On Chip

In an effort to maximize the metal removal rate in end milling while avoiding excessive cutter deflection or breakage, both fixed gain and adaptive controllers have been implemented for on-line feedrate manipulation to maintain a constant cutting force. While such controllers have been able to increase the metal cutting efficiency, they have also exhibited performance problems when large changes in the process dynamics occur. To assist in controller design and evaluation through digital simulation, a new dynamic model of the end milling force response to changes in feedrate and/or spindle speed is presented. This model, based on chip formation mechanics, takes explicitly into account the effect of cutter runout and deflection on the chip load, permits variations in the axial and radial depths of cut to be modeled, and provides surface geometry predictions. Model predictions are shown to correspond well with experimental machining data.

scholarly journals Control-Oriented Modeling of Transcritical Vapor Compression Systems

2004 ◽  
Vol 126 (1) ◽  
pp. 54-64 ◽  
Author(s):  
Bryan P. Rasmussen ◽  
Andrew G. Alleyne
Keyword(s):  
Model Reduction ◽  
Dynamic Model ◽  
Controller Design ◽  
Multiple Time ◽  
Vapor Compression ◽  
Nonlinear Dynamic Model ◽  
Air Conditioning System ◽  
Physical Insight ◽  
Multivariable Controller ◽  
Physical Phenomena

This paper presents a methodology for developing a low order dynamic model of a transcritical air-conditioning system, specifically suited for multivariable controller design. An 11th-order nonlinear dynamic model of the system is derived using first principles. Analysis indicates that the system exhibits multiple time scale behavior, and that model reduction is appropriate. Model reduction using singular perturbation techniques yields physical insight as to which physical phenomena are relatively fast/slow, and a 5th-order dynamic model appropriate for multivariable controller design. Although all results shown are for a transcritical cycle, the methodology presented can easily be extended to subcritical cycles.

An Instrumental Variable Approach for Continuous-Time Identification of an SI Engine Model for Automatic Controller Design

2002 ◽  
Author(s):  
Patrick J. Cunningham ◽  
Matthew A. Franchek
Keyword(s):  
Continuous Time ◽  
Instrumental Variable ◽  
Controller Design ◽  
Identification Algorithm ◽  
Si Engine ◽  
Continuous Time Model ◽  
Time Model ◽  
Idle Speed ◽  
Variable Approach ◽  
Instrumental Variable Approach

Instrumental variable algorithms are popular for their favorable consistency properties and ease of implementation. In this investigation an algorithm for automatic SI engine idle speed controller design uses and instrumental variable approach to perform system identification. The instrumental variable method employed here identifies the coefficients of a continuous-time model from discretely sampled data. This continuous-time model represents the engine dynamics from the bypass air valve voltage to the engine speed. To implements the identification algorithm, filtered derivative estimators approximate engine speed derivatives and an auxiliary model generates instrumental variables. These calculations are performed at each sample instant and are passed to the recursive formulation of the instrumental variable identification algorithm. Identification is performed utilizing reference step response data. A complete consistency analysis of this algorithm is included for this realization of the instrumental variable algorithm. Automatic controller design is completed based on the identified continuous-time model coefficients. As a result, the controller is parameterized based on the model coefficients and matching the transfer function of the idle speed feedback system to an open loop transfer function which represents the desired transient and steady state performance. The controller is implemented via a bumpless transfer process. Experimental results performed on a 4.6L V-8 fuel injected SI engine demonstrate the automated controller design process and the instrumental variable identification algorithm.

Analysis of Tourism Ecology Capacity Based on the Nonlinear Dynamic Model

2009 ◽  
Vol 11 (2) ◽  
pp. 163-168
Author(s):  
Long LV ◽  
Zhenfang HUANG ◽  
Jiang WU
Keyword(s):  
Dynamic Model ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic Model

scholarly journals Development, Modeling and Control of a Dual Tilt-Wing UAV in Vertical Flight

Drones ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 71
Author(s):  
Luz M. Sanchez-Rivera ◽  
Rogelio Lozano ◽  
Alfredo Arias-Montano
Keyword(s):  
Dynamic Model ◽  
Attitude Control ◽  
Test Bench ◽  
Aerodynamic Coefficients ◽  
Nonlinear Dynamic Model ◽  
Modeling And Control ◽  
Drag And Lift ◽  
And Control ◽  
Dynamics Method ◽  
Indoor And Outdoor

Hybrid Unmanned Aerial Vehicles (H-UAVs) are currently a very interesting field of research in the modern scientific community due to their ability to perform Vertical Take-Off and Landing (VTOL) and Conventional Take-Off and Landing (CTOL). This paper focuses on the Dual Tilt-wing UAV, a vehicle capable of performing both flight modes (VTOL and CTOL). The UAV complete dynamic model is obtained using the Newton–Euler formulation, which includes aerodynamic effects, as the drag and lift forces of the wings, which are a function of airstream generated by the rotors, the cruise speed, tilt-wing angle and angle of attack. The airstream velocity generated by the rotors is studied in a test bench. The projected area on the UAV wing that is affected by the airstream generated by the rotors is specified and 3D aerodynamic analysis is performed for this region. In addition, aerodynamic coefficients of the UAV in VTOL mode are calculated by using Computational Fluid Dynamics method (CFD) and are embedded into the nonlinear dynamic model. To validate the complete dynamic model, PD controllers are adopted for altitude and attitude control of the vehicle in VTOL mode, the controllers are simulated and implemented in the vehicle for indoor and outdoor flight experiments.

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