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.

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.

A Model Predictive Control Strategy for Lateral and Longitudinal Dynamics in Autonomous Driving

2020 ◽  
Author(s):  
Irfan Khan ◽  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Nicola Amati
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Autonomous Driving ◽  
Maximum Speed ◽  
Reference Trajectory ◽  
Vehicle Speed ◽  
The Road ◽  
Speed Regulation ◽  
Road Geometry

Abstract This paper presents a controller dedicated to the lateral and longitudinal vehicle dynamics control for autonomous driving. The proposed strategy exploits a Model Predictive Control strategy to perform lateral guidance and speed regulation. To this end, the algorithm controls the steering angle and the throttle and brake pedals for minimizing the vehicle’s lateral deviation and relative yaw angle with respect to the reference trajectory, while the vehicle speed is controlled to drive at the maximum acceptable longitudinal speed considering the adherence and legal speed limits. The technique exploits data computed by a simulated camera mounted on the top of the vehicle while moving in different driving scenarios. The longitudinal control strategy is based on a reference speed generator, which computes the maximum speed considering the road geometry and lateral motion of the vehicle at the same time. The proposed controller is tested in highway, interurban and urban driving scenarios to check the performance of the proposed method in different driving environments.

Model Predictive Control of an Electro-Hydraulic Powertrain With Energy Storage

2011 ◽  
Author(s):  
Timothy O. Deppen ◽  
Andrew G. Alleyne ◽  
Kim A. Stelson ◽  
Jonathan J. Meyer
Keyword(s):  
Energy Storage ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Reference Trajectory ◽  
Management Problem ◽  
Hybrid Powertrain ◽  
Engine Operation ◽  
Control Approach ◽  
Hydraulic Hybrid ◽  
Battery Technology

This paper presents a model predictive control approach to solving the energy management problem within a series hydraulic hybrid powertrain. The hydraulic hybrid utilizes a high pressure accumulator for energy storage which has superior power density than conventional battery technology. This makes fluid power attractive for urban driving applications in which there are frequent starts and stops and large startup power demands. Model predictive control was chosen for control design because this technique requires no information about the future drive cycle, which can be highly uncertain in urban settings. The proposed control strategy was validated experimentally using an electro-hydraulic powertrain testbed which includes energy storage. The experimental study demonstrates the controller’s ability to track a reference trajectory while achieving efficient engine operation.

Tracking control of multiple unmanned aerial vehicles incorporating disturbance observer and model predictive approach

2019 ◽  
Vol 42 (5) ◽  
pp. 951-964 ◽  
Author(s):  
Boyang Zhang ◽  
Xiuxia Sun ◽  
Shuguang Liu ◽  
Xiongfeng Deng
Keyword(s):  
Model Predictive Control ◽  
Unmanned Aerial Vehicle ◽  
Predictive Control ◽  
Disturbance Observer ◽  
Control Method ◽  
Differential Evolution Algorithm ◽  
Reference Trajectory ◽  
Control Approach ◽  
Unknown Disturbances ◽  
Aerial Vehicle

This paper studies the disturbance observer-based model predictive control approach to deal with the unmanned aerial vehicle formation flight with unknown disturbances. The distributed control problem for a class of multiple unmanned aerial vehicle systems with reference trajectory tracking and disturbance rejection is formulated. Firstly, a local distributed controller is designed by using the model predictive control method to achieve stable tracking, where the local optimization problem is solved by an adaptive differential evolution algorithm. Then, a feedforward compensation controller is introduced by using a disturbance observer to estimate and compensate disturbances, and improve the ability of anti-interference. Besides, the stability of the proposed composite controller is analyzed as well. Finally, the simulation examples are provided to illustrate the validity of proposed control structure.

scholarly journals Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake

2019 ◽  
Vol 9 (13) ◽  
pp. 2609 ◽  
Author(s):  
Peña Fernández ◽  
Youssef ◽  
Heeren ◽  
Matthys ◽  
Aerts
Keyword(s):  
Body Weight ◽  
Model Predictive Control ◽  
Real Time ◽  
Energy Intake ◽  
Predictive Control ◽  
Reference Trajectory ◽  
Time Model ◽  
Control Approach ◽  
Overweight People ◽  
Mean Square Errors

The number of overweight people reached 1.9 billion in 2016. Lifespan decrease and many diseases have been linked to obesity. Efficient ways to monitor and control body weight are needed. The objective of this work is to explore the use of a model predictive control approach to manage bodyweight in response to energy intake. The analysis is performed based on data obtained during the Minnesota starvation experiment, with weekly measurements on body weight and energy intake for 32 male participants over the course of 27 weeks. A first order dynamic auto-regression with exogenous variables model exhibits the best prediction, with an average mean relative prediction error value of 1.01 ± 0.02% for 1 week-ahead predictions. Then, the performance of a model predictive control algorithm, following a predefined bodyweight trajectory, is tested. Root mean square errors of 0.30 ± 0.06 kg and 9 ± 3 kcal day-1 are found between the desired target and simulated bodyweights, and between the measured energy intake and advised by the controller energy intake, respectively. The model predictive control approach for bodyweight allows calculating the needed energy intake in order to follow a predefined target bodyweight reference trajectory. This study shows a first possible step towards real-time active control of human bodyweight.

scholarly journals Model predictive driver model considering the steering characteristics of the skilled drivers

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401982933 ◽  
Author(s):  
Haobin Jiang ◽  
Huan Tian ◽  
Yiding Hua
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Single Point ◽  
Driver Model ◽  
General Regression Neural Network ◽  
Reference Trajectory ◽  
Model Based ◽  
Vehicle Trajectories ◽  
Steering Characteristics ◽  
General Regression

First, the experienced drivers with good driving skills are used as objects of learning and road steering test data of skilled drivers are collected in this article. To better simulate human drivers, skilled drivers’ steering characteristics are analyzed under different steering conditions. Vehicle trajectories of skilled drivers are fitted by general regression neural network, and the ideal path trajectory is obtained. Second, the model predictive control algorithm is used to build the driver model. According to the requirements of quickly and steadily tracking the track of skilled drivers, vehicle kinematics model is established. The objective function and the corresponding constraint conditions of the driver model based on model predictive control were determined. Finally, numerical simulations results demonstrate that the driver model based on model predictive control can accurately track the reference trajectory of skilled drivers under the four typical steering conditions, and the tracking effect is better than the traditional single-point preview driver model and path tracking method based on a β-spline curve.

Integration of Optimization and Model Predictive Control of an Intensified Continuous Three-Phase Catalytic Reactor

2015 ◽  
Vol 13 (1) ◽  
pp. 51-62 ◽  
Author(s):  
Li Shi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
State Space Model ◽  
Reference Trajectory ◽  
Catalytic Reactor ◽  
Catalytic Reactors ◽  
Three Phase ◽  
Highly Nonlinear ◽  
Steady State Model ◽  
Control Layer

Abstract Intensified continuous three-phase catalytic reactors working in high-pressure and -temperature conditions are particularly effective at coping with mass transfer limitations during three-phase catalytic reactions. They are highly nonlinear, multivariable systems and behave differently from conventional batch, fed-batch or continuous non-intensified reactors. This paper deals with an integration of real-time optimization and model predictive control (RTO–MPC) of an intensified continuous three-phase catalytic reactor. A steady-state model developed by regression method is used in optimization layer and gives the reference trajectory for control layer. At control layer, a linear MPC is proposed based on identified state space model. The performance of RTO–MPC is illustrated by simulation

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