Nonlinear Model Predictive Horizon for Optimal Trajectory Generation

Younes, Younes Al; Barczyk, Martin

doi:10.3390/robotics10030090

Cited by 14 publications

(23 citation statements)

References 26 publications

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“…Among the techniques that can be carried on board vehicles we can cite the gradient method, the quadratic programming method sequential or the particle swarm method. Some techniques are based also on the approximation of certain signals by their Taylor series expansion [36]- [40].…”

Section: Nonlinear Model Predictive Controlmentioning

confidence: 99%

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Towards Optimization of Energy Consumption of Tello Quadrotor with MPC Model Implementation

Benotsmane¹,

Vásárhelyi²

2022

Preprint

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For a decade, the studies of dynamic control for unmanned aerial vehicles took a large interest, where drones as a useful technology in different areas were always suffering from several issues like instability-high energy consumption of batteries - inaccuracy of tracking targets. Different approaches are proposed for dealing with the non-linearity issues which present the most important features of this system. This paper describes our focus on the most common control strategies, known as model predictive control MPC, by developing a model based on the sensors embedded in our Tello quadrotor used for indoor purposes. The original controller of Tello quadrotor is supposed to be a slave, where the designed model predictive controller is created in MATLAB and imported to another embedded system, considered as a master; the objective of this model is to track the reference trajectory, almost keeping the stability of the system and ensure the low energy consumption. In the first part, a profound description of the modelling process of a dynamic model for drones is presented, explaining the design of MPC controller with both linear and non-linear strategies built in MATLAB. In the final part, simulation and results are discussed regarding its behaviour and performance, highlighting the MPC model's important role on drones' energy consumption profile.

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Section: Nonlinear Model Predictive Controlmentioning

confidence: 99%

“…𝑋𝑋 𝑖𝑖 = [ 𝑥𝑥 𝑖𝑖 𝑦𝑦 𝑖𝑖 𝑧𝑧 𝑖𝑖 0 0 0 0 0 0 0 0 0] 𝑇𝑇 , (39) 𝑢𝑢 𝑖𝑖 = [𝑚𝑚𝑚𝑚 0 0 0] 𝑇𝑇 (40) After the linearization calculation we get the following four matrices for the state space representation:…”

mentioning

confidence: 99%

Towards Optimization of Energy Consumption of Tello Quadrotor with MPC Model Implementation

Benotsmane¹,

Vásárhelyi²

2022

Preprint

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“…The influence of forces (Petrescu, 2022) is actually what determines the dynamic, real operation of all mechanical processes, including robots. In this way, it is possible to control the trajectory of robots and/or spacecraft (or drones) (Alpers, 2021;Caruso et al, 2021;Ebel et al, 2021;Thompson et al, 2021;Vatsal and Hoffman, 2021;Al Younes and Barczyk, 2021;Pacheco-Gutierrez et al, 2021;Stodola et al, 2021;Raviola et al, 2021;Medina and Hacohen, 2021;Malik et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

The Study of the Dynamics of a Cebâşev Manipulator

Petrescu¹

2022

American Journal of Engineering and Applied Sciences

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The paper presents a new method for determining the dynamic parameters of a Cebâşev conveyor mechanism, solvable in two steps. In the first step, the classic method of preserving the kinetic energy of the entire mechanism is used to determine the rotating mass of the Cebâşev mechanism denoted by J*, this being the dynamic rotating mass of the entire mechanism reduced to the driving element 1 (of the crank type; classical rotating mass is called the mechanical or mass moment of inertia). In the second step (with the help of a new method), the dynamic angular speed of the Cebâşev mechanism's crank is directly determined, by using the mechanism's kinetic energy conservation a second time. This eliminates the need to use a more laborious classical method in the second step, such as differential Newton, Lagrange of type 1, Laplace transform, or Fourier, a finite difference method.

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“…This work introduces a completely new model design for linear and nonlinear MPC controllers intended to be used for indoor applications with the Tello drone. Compared to previous works [27][28][29][30], this paper also provides a methodology for designing MPC controllers considering the real system parameters, the limitations of linear MPC, and approaches in order to increase the energy efficiency.…”

mentioning

confidence: 99%

“…The aim of this section is to generate a linear state-space representation from the nonlinear model written in Equations ( 29) and (30). The nonlinear model is linearized around an equilibrium point X e ("hover position"), and linearization is performed on a simplified model, where small oscillations are considered as sin(angle) = angle and cos(angle) = 1.…”

mentioning

confidence: 99%

Towards Optimization of Energy Consumption of Tello Quad-Rotor with Mpc Model Implementation

Benotsmane

Vásárhelyi

2022

Energies

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For the last decade, there has been great interest in studying dynamic control for unmanned aerial vehicles, but drones—although a useful technology in different areas—are prone to several issues, such as instability, the high energy consumption of batteries, and the inaccuracy of tracking targets. Different approaches have been proposed for dealing with nonlinearity issues, which represent the most important features of this system. This paper focuses on the most common control strategy, known as model predictive control (MPC), with its two branches, linear (LMPC) and nonlinear (NLMPC). The aim is to develop a model based on sensors embedded in a Tello quad-rotor used for indoor purposes. The original controller of the Tello quad-rotor is supposed to be the slave, and the designed model predictive controller was created in MATLAB. The design was imported to another embedded system, considered the master. The objective of this model is to track the reference trajectory while maintaining the stability of the system and ensuring low energy consumption. The case study in this paper compares linear and nonlinear model predictive control (MPC). The results show the efficiency of NLMPC, which provides more promising results compared to LMPC. The comparison concentrates on the energy consumption, the tracked trajectory, and the execution time. The main finding of this research is that NLMPC is a good solution to smoothly track the reference trajectory. The controller in this case processes faster, but the rotors consume more energy because of the increased values of control inputs calculated by the nonlinear controller.

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Nonlinear Model Predictive Horizon for Optimal Trajectory Generation

Cited by 14 publications

References 26 publications

Towards Optimization of Energy Consumption of Tello Quadrotor with MPC Model Implementation

Towards Optimization of Energy Consumption of Tello Quadrotor with MPC Model Implementation

The Study of the Dynamics of a Cebâşev Manipulator

Towards Optimization of Energy Consumption of Tello Quad-Rotor with Mpc Model Implementation

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