Modelling and optimal control system design for quadrotor platform – an extended approach

Zawiski, Radosław; Błachuta, Marian

doi:10.2478/bpasts-2014-0058

Cited by 12 publications

(12 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In quadrotor configuration, there are three main categories of forces and torques as follows: gravitational force, gyroscopic effect, and the force and torques produced by the rotors thrust. The popular dynamic equations of quadrotor are found in [19,20]. These equations are neglected influence of the ground effect, because most of missions operate at high altitude.…”

Section: System Modelingmentioning

confidence: 99%

PSO-based online estimation of aerodynamic ground effect in the backstepping controller of quadrotor

Keshavarzian

Daneshjou

2020

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

Stability of landing and forward flight of unmanned aerial vehicles near the ground is one of the challenging problems in the quadrotor control system. In real-time operation, when the rotary-wing vehicle flies close to the ground, the thrust increases are called ground effect. According to the flight and environmental conditions and the quality of ground surface, the aerodynamic ground effect is varied continuously. In this paper, a backstepping controller is designed and adjusted by using particle swarm optimization. Controller adjustment is referred to as the process of online estimation of the ground effect in various conditions. The stability analysis is performed and guaranteed by Lyapunov theory. The simulation results indicate that the proposed method is highly practical and capable of tracking the desired trajectory appropriately.

show abstract

Section: System Modelingmentioning

confidence: 99%

PSO-based online estimation of aerodynamic ground effect in the backstepping controller of quadrotor

Keshavarzian

Daneshjou

2020

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…Compared to optimal LQR attitude controller as presented in Case 2 and previous model-based attitude controller design in [3][4][5][6][7][8][9][10][11], the proposed robust optimal controller is robust against multiple uncertainties and it does not require an accurate model in order to achieve the desired tracking performances. In addition, the proposed controller can improve the steady-state and dynamic attitude tracking performances by introducing the disturbance observer to estimate the total disturbance acting on the system.…”

Section: Case 3: Proposed Robust Optimal Attitudementioning

confidence: 99%

“…However, the performances of the PID controller will be degraded under the effects of nonlinear dynamics and coupling. Therefore, many modelbased control methods have been extensively studied to restrain the influence of nonlinearities and coupling in dynamic model of multirotor MAVs such as a flatness-based nonlinear control method [3], a dynamic inversion control method [4], a hierarchical control method [5], a nonlinear control method using nested saturations [6], a backsteppingbased control method [7], a model predictive controller (MPC) [8], a quaternion-based nonlinear feedback controller [9], an optimal LQR controller [10], and a backstepping-based inverse optimal controller [11]. Actually, those controllers require an accurate model of hexarotor in order to restrain the effects of nonlinear dynamics and coupling.…”

Section: Introductionmentioning

confidence: 99%

Robust Optimal Attitude Controller for MIMO Uncertain Hexarotor MAVs: Disturbance Observer-Based

Salim

Derawi

Zamzuri

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper proposes a robust optimal attitude control design for multiple-input, multiple-output (MIMO) uncertain hexarotor micro aerial vehicles (MAVs) in the presence of parametric uncertainties, external time-varying disturbances, nonlinear dynamics, and coupling. The parametric uncertainties, external time-varying disturbances, nonlinear dynamics, and coupling are treated as the total disturbance in the proposed design. The proposed controller is achieved in two simple steps. First, an optimal linear-quadratic regulator (LQR) controller is designed to guarantee that the nominal closed-loop system is asymptotically stable without considering the total disturbance. After that, a disturbance observer is integrated into the closed-loop system to estimate the total disturbance acting on the system. The total disturbance is compensated by a compensation input based on the estimated total disturbance. Robust properties analysis is given to prove that the state is ultimately bounded in specified boundaries. Simulation results illustrate the robustness of the disturbance observer-based optimal attitude control design for hovering and aggressive flight missions in the presence of the total disturbance.

show abstract

“…The images fusion task appears when one has to solve problems which one encounters when we use image processing systems in such applications as: image recognition [9,10,11,12], an unmanned aerial vehicle [13,14] used for image acquisition [15,16,17,18], object tracking [19,20,21] or images fusion [22,23].…”

Section: Introductionmentioning

confidence: 99%

Dynamical Pattern Vector in Pattern Recognition with the Use of Thermal Images

Kuś

2016

MATEC Web of Conferences

View full text Add to dashboard Cite

Abstract. The goal of the following paper was to develop the methodology of object tracking in adverse conditions. Suddenly appearing clouds, fog or smoke could be the examples of atmospheric conditions. We used thermal and visible images in each moment during object tracking. We computed the pattern vectors of the tracked object on the basis of the visual and thermal images separately. The pattern vector and current feature vector for an image of a given type are used to compute the distance between the object pattern vector and feature vector calculated for a given location of the aperture. It is calculated for both: the visual and thermal image. The crux of the proposed method was the algorithm of selection which distance (for visual or thermal image) was used for object tracking. It was obtained by multiplying the values of the distances between a pattern vector and current feature vector by some coefficients (different for thermal and visual images). The values of these coefficients depended on the usefulness of a given type of an image for pattern recognition. This usefulness was defined by the variability of the particular pixels in the image which is represented by calculating gradient in the image. On top of that, this study presented the examples of the object recognition by means of the developed method.

show abstract

Modelling and optimal control system design for quadrotor platform – an extended approach

Cited by 12 publications

References 23 publications

PSO-based online estimation of aerodynamic ground effect in the backstepping controller of quadrotor

PSO-based online estimation of aerodynamic ground effect in the backstepping controller of quadrotor

Robust Optimal Attitude Controller for MIMO Uncertain Hexarotor MAVs: Disturbance Observer-Based

Dynamical Pattern Vector in Pattern Recognition with the Use of Thermal Images

Contact Info

Product

Resources

About