Linear Model Predictive Control in the Process Industries

2018 ◽  
pp. 681-708
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Process Industries

A tube-based robust MPC for duty-cycled rotation needle steering systems with bounded disturbances

2021 ◽  
pp. 014233122110430
Author(s):  
Zhi Qi ◽  
Qianyue Luo ◽  
Hui Zhang
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Control Method ◽  
Controller Design ◽  
Control Approach ◽  
Bounded State ◽  
Non Linear ◽  
System States ◽  
Trajectory Tracking Controller

In this paper, we aim to design the trajectory tracking controller for variable curvature duty-cycled rotation flexible needles with a tube-based model predictive control approach. A non-linear model is adopted according to the kinematic characteristics of the flexible needle and a bicycle method. The modeling error is assumed to be an unknown but bounded disturbance. The non-linear model is transformed to a discrete time form for the benefit of predictive controller design. From the application perspective, the flexible needle system states and control inputs are bounded within a robust invariant set when subject to disturbance. Then, the tube-based model predictive control is designed for the system with bounded state vector and inputs. Finally, the simulation experiments are carried out with tube-based model predictive control and proportional integral derivative controller based on the particle swarm optimisation method. The simulation results show that the tube-based model predictive control method is more robust and it leads to much smaller tracking errors in different scenarios.

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 A Survey of the Implementation of Linear Model Predictive Control on FPGAs

2018 ◽  
Vol 51 (20) ◽  
pp. 381-387 ◽  
Author(s):  
Ian McInerney ◽  
George A. Constantinides ◽  
Eric C. Kerrigan
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control

A regularized Newton solver for linear model predictive control

2018 ◽  
Author(s):  
Alexander Malyshev ◽  
Rien Quirynen ◽  
Andrew Knyazev ◽  
Stefano Di Cairano
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control
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