scholarly journals Parameters Online Detection and Model Predictive Control during the Grain Drying Process

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Lihui Zhang ◽  
Helei Cui ◽  
Hongli Li ◽  
Feng Han ◽  
Yaqiu Zhang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Constant Velocity ◽  
Variable Temperature ◽  
Structural Characteristics ◽  
Cross Flow ◽  
System Control ◽  
Drying Process ◽  
Grain Drying ◽  
Set Up

In order to improve the grain drying quality and automation level, combined with the structural characteristics of the cross-flow circulation grain dryer designed and developed by us, the temperature, moisture, and other parameters measuring sensors were placed on the dryer, to achieve online automatic detection of process parameters during the grain drying process. A drying model predictive control system was set up. A grain dry predictive control model at constant velocity and variable temperature was established, in which the entire process was dried at constant velocity (i.e., precipitation rate per hour is a constant) and variable temperature. Combining PC with PLC, and based on LabVIEW, a system control platform was designed.

scholarly journals Economic Multiple Model Predictive Control for HVAC Systems - A Case Study for a Food Manufacturer in Germany

2018 ◽  
Author(s):  
Tobias Heidrich ◽  
Jonathan Grobe ◽  
Henning Meschede ◽  
Jens Hesselbach
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Air Conditioning ◽  
Variable Temperature ◽  
Conveyor Belt ◽  
Electricity Price ◽  
Multiple Model ◽  
Air Conditioning System ◽  
Discrete State ◽  
Temperature And Humidity

The following paper describes an economical, multiple model predictive control (EMMPC) for an air conditioning system of a confectionery manufacturer in Germany. The application consists of a packaging hall for chocolate bars, in which a new local conveyor belt air conditioning system is used and thus the temperature and humidity limits in the hall can be significantly extended. The EMMPC calculates the optimum energy or cost humidity and temperature set points in the hall. For this purpose, time-discrete state space models and an economic objective function with which it is possible to react to flexible electricity prices in a cost-optimised manner are created. A possible future electricity price model for Germany with a flexible EEG levy was used as a flexible electricity price. The flexibility potential is determined by variable temperature and humidity limits in the hall, which are oriented towards the comfort field for easily working persons, and the building mass. The building mass of the created room model is used as a thermal energy store. Considering electricity price and weather forecasts as well as internal, production plan-dependent load forecasts, the model predictive controller directly controls the heating and cooling register and the humidifier of the air conditioning system.

Linear model predictive control for vision-based UAV pursuit

2020 ◽  
Vol 8 (4) ◽  
pp. 334-363 ◽  
Author(s):  
Christopher C. Surma ◽  
Martin Barczyk
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
High Energy ◽  
Aerial Videography ◽  
Combined Control ◽  
Communication Devices ◽  
Laser Systems ◽  
Commercial Off The Shelf ◽  
Set Up

This article develops and implements a vision-based unmanned aerial vehicle (UAV)-to-UAV pursuit system using a commercial off-the-shelf Parrot AR.Drone 2.0 quadrotor. This technology is intended as a countermeasure to rogue drones carrying out activities such as flying in restricted airspace, performing unauthorized aerial videography, transporting contraband and other criminal activities, or being used as improvised weapons. The proposed approach offers benefits over other current solutions, such as wide-area radio-frequency jamming that interferes with regular communication devices or high-energy military laser systems that are expensive and time consuming to set up. A linear dynamics model of the AR.Drone 2.0 vehicle stabilized by its onboard feedback control system is derived, and its parameters are experimentally identified. A linear model predictive control is developed to track specified flight trajectories, then implemented and validated in hardware flight tests. Detection and ranging of the target UAV from the pursuer UAV’s onboard monocular camera are performed using the YOLO v2 convolutional neural network algorithm. The combined control and vision design is implemented in hardware and tested quantitatively in flight experiments.

scholarly journals Lactate-Based Model Predictive Control Strategy of Cell Growth for Cell Therapy Applications

2020 ◽  
Vol 7 (3) ◽  
pp. 78
Author(s):  
Kathleen Van Beylen ◽  
Ali Youssef ◽  
Alberto Peña Fernández ◽  
Toon Lambrechts ◽  
Ioannis Papantoniou ◽  
...  
Keyword(s):  
Cell Growth ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Feeding Strategy ◽  
A Priori ◽  
Process Models ◽  
Feeding Strategies ◽  
Cell Culture Process ◽  
Reference Trajectory ◽  
Set Up

Implementing a personalised feeding strategy for each individual batch of a bioprocess could significantly reduce the unnecessary costs of overfeeding the cells. This paper uses lactate measurements during the cell culture process as an indication of cell growth to adapt the feeding strategy accordingly. For this purpose, a model predictive control is used to follow this a priori determined reference trajectory of cumulative lactate. Human progenitor cells from three different donors, which were cultivated in 12-well plates for five days using six different feeding strategies, are used as references. Each experimental set-up is performed in triplicate and for each run an individualised model-based predictive control (MPC) controller is developed. All process models exhibit an accuracy of 99.80% ± 0.02%, and all simulations to reproduce each experimental run, using the data as a reference trajectory, reached their target with a 98.64% ± 0.10% accuracy on average. This work represents a promising framework to control the cell growth through adapting the feeding strategy based on lactate measurements.

scholarly journals Dead Band Zone Model Predictive Control of Cut Tobacco Drying Process

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 157781-157792
Author(s):  
Angang Chen ◽  
Zhengyun Ren ◽  
Zhiping Fan ◽  
Xue Feng
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Zone Model ◽  
Drying Process ◽  
Dead Band ◽  
Cut Tobacco
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