Embedded linear model predictive control in field-programmable gate array using register-transfer level implementation and floating-point representation applied to a quadrotor system model

Author(s):  
Alceu Bernardes Castanheira de Farias ◽  
André Murilo ◽  
Renato Vilela Lopes

Model predictive control is increasingly becoming a popular control strategy for a wide range of applications in both industry and academia, mainly motivated by its ability to systematically handle constraints imposed on a system, regardless of its nature. However, this generates high computational demands, limiting the applicability of model predictive control. Field-programmable gate arrays are reconfigurable hardware platforms that allow the parallel implementation of model predictive control, accelerating such algorithms, but most works found in the literature opt to use high-level synthesis tools and fixed-point numeric representation to generate embedded controllers, resulting in faster-designed solutions but not exactly efficient and flexible ones, that can be applied to different scenarios. Regarding such matter, this work proposes the manual implementation (register-transfer level implementation) of linear model predictive control and the usage of floating-point numeric representation applied to a quadrotor system. The initial results obtained using the proposed controller are presented in this article, achieving 29.34 ms of calculation time at 50 MHz for the attitude control of a quadrotor model containing twelve states and four control outputs.

2021 ◽  
Vol 9 (1) ◽  
pp. 1007-1015
Author(s):  
Ahmed G. Mahmoud A. Aziz, Hamdi Ali, Yehia Sayed Mohammed, Ahmed A. Zaki Diab

The current work presents speed, torque and flux control of an induction motor (IM) drive, founded on model predictive control (MPC). Via the MPC techniques, the motor electromagnetic torque and flux linkage are controlled as an internal loop. However, the speed is controlled as the external loop. The internal control loop is founded on finite control set FCS-MPC, and the external control founded on the torque PI controller. The performance of the MPC is tested with various conditions of the drive operation, and the outcomes approve the excellent steady-state and dynamic operation of the system in a wide range of speeds and with torque disturbance.


Author(s):  
Zhi Qi ◽  
Qianyue Luo ◽  
Hui Zhang

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.


2020 ◽  
Vol 8 (4) ◽  
pp. 334-363 ◽  
Author(s):  
Christopher C. Surma ◽  
Martin Barczyk

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.


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

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