An Industrial Implementation of a Model Based Control Strategy

1995 ◽  
pp. 683-700
Author(s):  
J. L. Figueroa ◽  
O. E. Agamennoni ◽  
G. W. Barton ◽  
J. A. Romagnoli ◽  
J. B. Lear
Keyword(s):  
Control Strategy ◽  
Model Based Control ◽  
Model Based ◽  
Industrial Implementation

An adaptive non-model-based control strategy for smart structures vibration suppression

2013 ◽  
Author(s):  
Matteo Morlacchi ◽  
Ferruccio Resta ◽  
Francesco Ripamonti ◽  
Gisella Tomasini
Keyword(s):  
Control Strategy ◽  
Vibration Suppression ◽  
Smart Structures ◽  
Model Based Control ◽  
Model Based

scholarly journals A novel model-based control strategy for aerobic filamentous fungal fed-batch fermentation processes

2017 ◽  
Vol 114 (7) ◽  
pp. 1459-1468 ◽  
Author(s):  
Lisa Mears ◽  
Stuart M. Stocks ◽  
Mads O. Albaek ◽  
Benny Cassells ◽  
Gürkan Sin ◽  
...  
Keyword(s):  
Control Strategy ◽  
Batch Fermentation ◽  
Fed Batch ◽  
Model Based Control ◽  
Fermentation Processes ◽  
Model Based ◽  
Fed Batch Fermentation ◽  
Novel Model

scholarly journals Model-Based Control Strategy for Autonomous Vehicle Path Tracking Task

2020 ◽  
Vol 12 (1) ◽  
pp. 35-45
Author(s):  
Ahmad Reda ◽  
József Vásárhelyi
Keyword(s):  
Control Strategy ◽  
Control Strategies ◽  
Autonomous Vehicle ◽  
Steering System ◽  
Model Based Control ◽  
Model Based ◽  
Common Control ◽  
Vehicle Path ◽  
And Performance ◽  
Robust Systems

AbstractDespite the advanced technologies used in recent years, the lack of robust systems still exists. The automated steering system is a critical and complex task in the domain of the autonomous vehicle’s applications. This paper is a part of project that deals with model-based control strategy as one of the most common control strategies. The main objective is to present the implementations of Model Predictive Control (MPC) for an autonomous vehicle steering system in regards to trajectory tracking application. The obtained results are analysed and the efficiency of the use of MPC controller were discussed based on its behaviour and performance.

scholarly journals Model-based Control Strategy Research for the Hydrogen System of Fuel Cell

2021 ◽  
Vol 54 (10) ◽  
pp. 67-71
Author(s):  
Cheng Li ◽  
Xiaowei Li ◽  
Weihai Jiang
Keyword(s):  
Fuel Cell ◽  
Control Strategy ◽  
Strategy Research ◽  
Model Based Control ◽  
Model Based ◽  
Hydrogen System

A Model-Based Control Strategy for Upper Limb Exosuits

2021 ◽  
pp. 339-343
Author(s):  
N. Lotti ◽  
F. Missiroli ◽  
M. Xiloyannis ◽  
L. Masia
Keyword(s):  
Upper Limb ◽  
Control Strategy ◽  
Model Based Control ◽  
Model Based

Evaluation of model-based control strategy based on generated setpoint schedules for NH4–N removal in a pilot-scale A2/O process

2012 ◽  
Vol 203 ◽  
pp. 387-397 ◽  
Author(s):  
H.S. Kim ◽  
Y.J. Kim ◽  
S.P. Cheon ◽  
G.D. Baek ◽  
S.S. Kim ◽  
...  
Keyword(s):  
Control Strategy ◽  
Pilot Scale ◽  
Model Based Control ◽  
Model Based ◽  
N Removal

scholarly journals Combustion phasing modeling and control for compression ignition engines with high dilution and boost levels

2018 ◽  
Vol 233 (7) ◽  
pp. 1834-1850 ◽  
Author(s):  
Wenbo Sui ◽  
Carrie M Hall
Keyword(s):  
Steady State ◽  
Control Strategy ◽  
Internal Combustion Engines ◽  
Integral Model ◽  
Loop Control ◽  
Model Based Control ◽  
Model Based ◽  
Crank Angle Degree ◽  
Crank Angle ◽  
Phasing Control

Because fuel efficiency is significantly affected by the timing of combustion in internal combustion engines, accurate control of combustion phasing is critical. In this paper, a nonlinear combustion phasing model is introduced and calibrated, and both a feedforward model–based control strategy and an adaptive model–based control strategy are investigated for combustion phasing control. The combustion phasing model combines a knock integral model, burn duration model, and a Wiebe function to predict the combustion phasing of a diesel engine. This model is simplified to be more suitable for combustion phasing control and is calibrated and validated using simulations and experimental data that include conditions with high exhaust gas recirculation fractions and high boost levels. Based on this model, an adaptive nonlinear model–based controller is designed for closed-loop control, and a feedforward model–based controller is designed for open-loop control. These two control approaches were tested in simulations. The simulation results show that during transient changes, the CA50 (the crank angle at which 50% of the mass of fuel has burned) can reach steady state in no more than five cycles and the steady-state errors are less than ±0.1 crank angle degree for adaptive control and less than ±0.5 crank angle degree for feedforward model–based control.

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