Pusher reheating furnace control via fuzzy-neural model predictive control synthesis

2012 ◽  
Author(s):  
Goran Stojanovski ◽  
Mile Stankovski ◽  
Imre J. Rudas ◽  
Juanwei Jing
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Neural Model ◽  
Control Synthesis ◽  
Reheating Furnace ◽  
Fuzzy Neural ◽  
Furnace Control

scholarly journals Fuzzy–neural Model Predictive Control of Multivariable Processes

10.5772/16828 ◽  
2011 ◽  
Author(s):  
Michail Petrov ◽  
Sevil Ahmed ◽  
Alexander Ichtev ◽  
Albena Tanev
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Neural Model ◽  
Fuzzy Neural

PUSHER REHEATING FURNACE CONTROL: A FUZZY-NEURAL MODEL PREDICTIVE STRATEGY

2006 ◽  
Vol 39 (19) ◽  
pp. 165-170 ◽  
Author(s):  
Z.A. Icev ◽  
M.J. Stankovski ◽  
T.D. Kolemishevska ◽  
J. Zhao ◽  
G.M. Dimirovski
Keyword(s):  
Neural Model ◽  
Reheating Furnace ◽  
Fuzzy Neural ◽  
Furnace Control

FUZZY-NEURAL MODEL PREDICTIVE CONTROL OF A BUILDING HEATING SYSTEM

2006 ◽  
Vol 39 (19) ◽  
pp. 69-74 ◽  
Author(s):  
Margarita Terziyska ◽  
Yancho Todorov ◽  
Michail Petrov
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Neural Model ◽  
Heating System ◽  
Fuzzy Neural

ON MODEL PREDICTIVE CONTROL OF QUASI-RESONANT CONVERTERS

2009 ◽  
Vol 18 (07) ◽  
pp. 1167-1183 ◽  
Author(s):  
FARZAD TAHAMI ◽  
MEHDI EBAD
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Models ◽  
Control Strategies ◽  
Sampling Interval ◽  
Control Synthesis ◽  
Resonant Converters ◽  
Control Input ◽  
Model Approximation ◽  
Dynamic Matrix

In this paper, different model predictive control synthesis frameworks are examined for DC–DC quasi-resonant converters in order to achieve stability and desired performance. The performances of model predictive control strategies which make use of different forms of linearized models are compared. These linear models are ranging from a simple fixed model, linearized about a reference steady state to a weighted sum of different local models called multi model predictive control. A more complicated choice is represented by the extended dynamic matrix control in which the control input is determined based on the local linear model approximation of the system that is updated during each sampling interval, by making use of a nonlinear model. In this paper, by using and comparing these methods, a new control scheme for quasi-resonant converters is described. The proposed control strategy is applied to a typical half-wave zero-current switching QRC. Simulation results show an excellent transient response and a good tracking for a wide operating range and uncertainties in modeling.

Output Feedback Control Synthesis for a Helicopter Using Explicit Nonlinear Model Predictive Control, Dynamic Inversion and Extended High Gain Observers

2019 ◽  
Author(s):  
Joohwan Seo ◽  
Jongeun Choi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Time Scale ◽  
Control Design ◽  
High Gain ◽  
Nonlinear Model Predictive Control ◽  
Control Synthesis ◽  
Dynamic Inversion ◽  
Discretization Scheme

Abstract Control design for a helicopter is a challenging problem because of its non-affine inputs, complicated dynamics and it is an under-actuated system. To solve a control problem of the helicopter under model uncertainties and disturbance present environments, an Explicit Nonlinear Model Predictive Control (ENMPC), a dynamic inversion and an Extended High-Gain Observers (EHGO) are combined in a multi-time-scale fashion. The multi-time scaled structrue and the ENMPC provides the framework of the control design, the dynamic inversion deals with non-affine control inputs, and the EHGO estimates the unmeasured states and uncertainties. In addition, a discretization scheme using the saturation and adding low pass filters to the control inputs is presented. Finally, the numerical simulation of a fixed sampling period has been carried out to demonstrate the validity of the proposed multi-time-scale control design and the discretization scheme.

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