Model predictive quadrotor control: attitude, altitude and position experimental studies

Alexis, Kostas; Nikolakopoulos, George; Tzes, Anthony

doi:10.1049/iet-cta.2011.0348

Cited by 315 publications

(124 citation statements)

References 27 publications

Supporting

Mentioning

115

Contrasting

Order By: Relevance

“…The altitude control input of the quadrotor shown in figure 2 is thrust which is the multiplication of relative thrust constant to air density with the sum of four motors as written in equation 6. Several algorithms have been studied and applied to quadrotor altitude by previous researchers, for example, the PID algorithm investigated by Lee et al [19], the slidding mode algorithm studied by Gonzalez et al [20], the model predicting control (MPC) algorithm investigated by Alexis et al [21]. From the previous studies, the researchers have used two inputs to design the control block for altitude in quadrotor as in Figure 3(A).…”

Section: Altitude Controlmentioning

confidence: 99%

Hovering Control of Quadrotor Based on Fuzzy Logic

Raharja¹,

Firmansyah²,

Cahyadi³

et al. 2017

IJPEDS

View full text Add to dashboard Cite

Quadrotor is one of rotary wing UAV types which is able to perform a hover position. In order to take off, landing, and hover, it needs controllers. Conventional controllers have been widely applied in quadrotor, yet they have drawbacks namely overshoot. This paper presents attitude and altitude control algorithm in order to obtain a response as quadrotor hovered optimally within minimum overshoot, rise time, and settling time. The algorithm used is Fuzzy Logic Controller (FLC) algorithm with Mamdani method. By using the algorithm, the quadrotor is able to hover with minimum overshoot and maximum rise time. The advantage of the algorithm is that it does not require linearization model of the quadrotor. Keyword:Altitude control Attitude control Fuzzy logic controller Mamdani Quadrotor Copyright © 2017 Institute of Advanced Engineering and Science.All rights reserved. Corresponding Author:Nia Maharani Raharja, Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia. Email: nia.sie13@mail.ugm.ac.id INTRODUCTIONQuadrotor is basically unstable because it is a non-linear model that has many variables with 6 degrees of freedom affected by four actuators. Previous researchers created quadrotor models in dynamics and kinematics represented in two models, namely nonlinear and linear. Quadrotor modeling needs some consideration, namely solid frames, rotor placement on each angle having symmetrical distance, and the position of the center of gravity (COG) is right at the midpoint of the crossing frame arm. Euler-Lagrange and Newton-Euler methods are generally used in aircraft modeling, however, Newton-Euler is more widely used because it is more easily understood.Researches on quadrotor have been widely conducted by previous researchers in the field of control by using conventional traditional controls as practiced by Bouabdallah et al [1] using the configuration Plus for microquadrotor OS4 model weighing 240 grams controlled by PID and LQR control methods by referring to six states of roll, pitch, and yaw based attitude. In designing PID control of non-linear model of quadrotor obtained from the dynamics and kinematics, it is firstly linearized to obtain a state variable in that the stability can be identified.Quadrotor modeling has been successfully developed by Bouabdallah & Siegwart [2] by modeling quadrotor from 6 to 12 states. In addition they modify backstepping algorithm by adding integral then called integral-backstepping. The algorithm is used to control the attitude and altitude, so that it can take off and landing autonomously. The algorithm is also used to control the position of the quadrotor in order to perform fly autonomously and avoid obstacles. With the algorithm, there is no overshoot when it hovers at the desired height but it takes a long raise time.One of the criteria for good performance in the control of hover is a quick or minimum response. However, the rapid response tends to cause oscillation and requires a fairly large control signaling. I...

show abstract

Section: Altitude Controlmentioning

confidence: 99%

Hovering Control of Quadrotor Based on Fuzzy Logic

Raharja¹,

Firmansyah²,

Cahyadi³

et al. 2017

IJPEDS

View full text Add to dashboard Cite

show abstract

“…This trajectory between two successive waypoints even it is desirable to be a line segment is affected by flight dynamic constraints of the UAV 21 .…”

Section: Earthmentioning

confidence: 99%

Path Planning Algorithm based on Arnold Cat Map for Surveillance UAVs

Curiac¹,

Voloşencu²

2015

DSJ

View full text Add to dashboard Cite

During their task accomplishment, autonomous unmanned aerial vehicles are facing more and more threats coming from both ground and air. In such adversarial environments, with no a priori information about the threats, a flying robot in charge with surveilling a specified 3D sector must perform its tasks by evolving on misleading and unpredictable trajectories to cope with enemy entities. In our view, the chaotic dynamics can be the cornerstone in designing unpredictable paths for such missions, even though this solution was not exploited until now by researchers in the 3D context. This paper addresses the flight path-planning issue for surveilling a given volume in adversarial conditions by proposing a proficient approach that uses the chaotic behaviour exhibited by the 3D Arnold's cat map. By knowing the exact location of the volume under surveillance before take-off, the flying robot will generate the successive chaotic waypoints only with onboard resources, in an efficient manner. The method is validated by simulation in a realistic scenario using a detailed Simulink model for the X-4 Flyer quadcopter.

show abstract

“…In this method, a future control sequence is obtained by minimizing a cost function on predicted values of the controlled variable along a finite horizon, typically subjected to constraints (Camacho and Bordons 1998). Applications of MPC to position control of MAVs can be found in Raffo et al (2010), Lopes et al (2011), Alexis et al (2012, and Chen et al (2013). Raffo et al (2010) proposed a control scheme consisting of an unconstrained MPC for position tracking and a non-linear H ∞ controller for attitude stabilization under aerodynamic disturbances and parametric as well as structural uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…Lopes et al (2011) designed a single MPC controller for both position control and attitude stabilization, considering constraints on both the pitch and the roll angles. Alexis et al (2012) proposed a cascade MPC scheme, formulated over a set of piecewise affine models originated from both attitude and translation dynamics. In order to guide an MAV through a desired position trajectory, Chen et al (2013) designed 2 separate MPCs, one for position control and the other for attitude control, the latter considering maximum constraints on the attitude angles.…”

Section: Introductionmentioning

confidence: 99%

A Model Predictive Guidance Strategy for a Multirotor Aerial Vehicle

Prado

Santos

2017

J.Aerosp. Technol. Manag.

View full text Add to dashboard Cite

The present study faces the problem of safely controlling the position trajectory of a multirotor aerial vehicle subjected to a conic constraint on the total thrust vector and a linear convex constraint on the position vector. The problem is solved using a linear state-space model predictive control strategy, whose optimization is made handy by replacing the original conic constraint set on the thrust vector by an inscribed pyramidal space, which renders a linear set of inequalities. The proposed method is evaluated on the basis of Monte Carlo simulations taking into account a random disturbance force. The simulation results show the effectiveness of the method in tracking the commanded trajectory while respecting the constraints. They also predict the effect of both the speed command and the maximum allowed inclination angle on the system performance.

show abstract

Model predictive quadrotor control: attitude, altitude and position experimental studies

Cited by 315 publications

References 27 publications

Hovering Control of Quadrotor Based on Fuzzy Logic

Hovering Control of Quadrotor Based on Fuzzy Logic

Path Planning Algorithm based on Arnold Cat Map for Surveillance UAVs

A Model Predictive Guidance Strategy for a Multirotor Aerial Vehicle

Contact Info

Product

Resources

About