Optimal Control Surface Mixing of a Rhomboid-Wing Unmanned Aerial Vehicle

2017 ◽  
Vol 54 (3) ◽  
pp. 1035-1046
Author(s):  
E. Miles ◽  
B. A. Broughton
Keyword(s):  
Optimal Control ◽  
Unmanned Aerial Vehicle ◽  
Control Surface ◽  
Surface Mixing ◽  
Aerial Vehicle

Optimal Control of a Multirotor Unmanned Aerial Vehicle Based on a Multiphysical Model

2020 ◽  
Author(s):  
Nicolas Michel ◽  
Zhaodan Kong ◽  
Xinfan Lin
Keyword(s):  
Optimal Control ◽  
Unmanned Aerial Vehicle ◽  
Operation Time ◽  
Rigid Body Dynamics ◽  
System Level ◽  
Optimal Velocity ◽  
Rotor Aerodynamics ◽  
Time Optimal ◽  
Aerial Vehicle ◽  
Level Model

Abstract Electric multirotor aircraft with vertical-take-off-and-landing capabilities are emerging as a revolutionary transportation mode. This paper studies optimal control of a multirotor unmanned aerial vehicle based on a system-level multiphysical model. The model considers aerodynamics of the rotor-propeller assembly, electro-mechanical dynamics of the motor and motor controller, and rigid-body dynamics of the vehicle, as control based on a system-level model incorporating all these dynamics and their coupling is missing in literature. A forward flight operation is considered for time-optimal and energy-optimal control, as well as battery voltages of 25 V and 21 V. Energy-optimal control is shown to reduce the energy required for the operation by 38.5% at 25 V, while reducing the battery voltage increases the minimum operation time by 19.8%. The energy-optimal cruise velocity is also examined, demonstrating that the optimal velocity predicted without considering rotor aerodynamics uses 35.2% more energy per meter travelled than is required at the true optimal velocity.

AERODYNAMIC OF UITM'S BLENDED-WING-BODY UNMANNED AERIAL VEHICLE BASELINE-II EQUIPPED WITH ONE CENTRAL VERTICAL RUDDER

2015 ◽  
Vol 75 (8) ◽  
Author(s):  
Wirachman Wisnoe ◽  
Rizal E.M. Nasir ◽  
Ramzyzan Ramly ◽  
Wahyu Kuntjoro ◽  
Firdaus Muhammad
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Cfd Simulation ◽  
Aerodynamic Characteristics ◽  
Control Surface ◽  
Wind Tunnel Experiments ◽  
Aerodynamic Parameter ◽  
Aerial Vehicle ◽  
Computational Fluid Dynamics Cfd ◽  
Blended Wing Body ◽  
Lift And Drag

In this paper, a study of aerodynamic characteristics of UiTM's Blended-Wing-Body Unmanned Aerial Vehicle (BWB-UAV) Baseline-II in terms of side force, drag force and yawing moment coefficients are presented through Computational Fluid Dynamics (CFD) simulation. A vertical rudder is added to the aircraft at the rear centre part of the fuselage as yawing control surface. The study consists of varying the side slip angles for various rudder deflection angles and to plot the results for each aerodynamic parameter. The comparison with other yawing control surface for the same aircraft obtained previously are also presented. For validation purpose, the lift and drag coefficients are compared with the results obtained from wind tunnel experiments. 

scholarly journals Formation of unmanned aircraft trajectory when flying around prohibited areas

2022 ◽  
pp. 47-53
Author(s):  
A. A. Lobaty ◽  
A. Y. Bumai ◽  
A. M. Avsievich
Keyword(s):  
Optimal Control ◽  
Unmanned Aerial Vehicle ◽  
Center Of Mass ◽  
Unmanned Aircraft ◽  
Control Synthesis ◽  
Initial Stage ◽  
Aerial Vehicle ◽  
Aircraft Trajectory ◽  
Flight Route ◽  
Definition Of

Considered the problem of flying over restricted areas by an unmanned aerial vehicle (UAV), which have various shapes and restrictions, set on the basis of the international airspace classification system for aviation in accordance with the Chicago Convention and the recommended principles for the formation of forbidden zones, rules for creating a flight route along forbidden zones and actions in case of border violations of restricted areas. The problem of analytical synthesis of the control acceleration of an unmanned aerial vehicle (UAV) is solved during its flight along a route passing along the boundaries of the forbidden zone of a given shape, along a given trajectory, which consists of subsequent segments located at the same height relative to the earth’s surface, in a given coordinate system. The optimal control synthesis problem is solved as an analytical definition of the optimal control of a linear non-stationary system based on the quadratic quality functional. A mathematical model of UAV motion in the horizontal plane is proposed, in the form of a system of ordinary differential equations in the Cauchy form. A law for measuring the control acceleration of the UAV’s center of mass is obtained on the basis of specifying the minimized quality functional and the corresponding constraints, which is a feature of the considered method of solving the problem. The proposed quality functional takes into account the parameters of coordinates and speed of the UAV, which correspond to the given points in the airspace, which characterize the necessary trajectory for flying around the restricted area. The derived mathematical dependences make it possible to implement them on board a UAV and minimize energy costs when guiding a UAV moving through specified points in space. Computer modeling of the derived analytical results, mathematical dependencies representing the optimal trajectory of the UAV flight along the boundaries of the forbidden zone, as well as the corresponding processes of changing the control acceleration and speed of the UAV movement was carried out, which made it possible to draw conclusions about the efficiency of the proposed method and the feasibility of its further use as a basis. for the initial stage of the synthesis of the UAV control system.

Dubins Vehicle Tracking of a Target With Unpredictable Trajectory

2011 ◽  
Author(s):  
Ross Anderson ◽  
Dejan Milutinovic´
Keyword(s):  
Optimal Control ◽  
Markov Chain ◽  
Random Walk ◽  
Cost Function ◽  
Unmanned Aerial Vehicle ◽  
Bellman Equation ◽  
Control Policy ◽  
Vehicle Tracking ◽  
Dubins Vehicle ◽  
Aerial Vehicle

Motivated by a fixed-speed, fixed-altitude Unmanned Aerial Vehicle (UAV), we seek to control the turning rate of a planar Dubins vehicle that tracks an unpredictable target at a nominal standoff distance. To account for all realizations of the uncertain target kinematics, we model the target motion as a planar random walk. A Bellman equation and an approximating Markov chain that is consistent with the stochastic kinematics is used to compute an optimal control policy that minimizes the expected value of a cost function based on the nominal distance. Our results illustrate that the control can further be applied to a class of continuous, smooth trajectories with no need for further computation.

scholarly journals Optimal control and state estimation for unmanned aerial vehicle under random vibration and uncertainty

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1264-1271 ◽  
Author(s):  
Mohammad Abdulrahman Al-Mashhadani
Keyword(s):  
Optimal Control ◽  
State Estimation ◽  
Unmanned Aerial Vehicle ◽  
Random Vibration ◽  
Large Scale ◽  
A Priori ◽  
Number Of Factors ◽  
Aerial Vehicle ◽  
Vibration Noise ◽  
Complicated Task

In the past decade, many approaches that attempted to solve the problem of optimal control and parameter estimation of an unmanned aerial vehicle with a priori uncertain parameters simply implied two ways to solve such problem. First, by the formation of optimal control based on a refined mathematical model of the unmanned aerial vehicle, and second, by using the estimation and identification methods of the model parameter of the unmanned aerial vehicle based on measured data from flight tests. However, the identification of the dynamic parameters of the unmanned aerial vehicle is a complicated task because of a number of factors such as random vibration noise, disturbance, and uncertainty of the sensor measurements. Due to the influence of random vibration noise, the problem of correlated vibration noises and uncertainty has encountered inevitably, and the accuracy of the state estimation for unmanned aerial vehicle is degraded. This study concentrates on the optimal control and state estimation for the unmanned aerial vehicle under the combination of both random vibration noise and uncertainty collected by the sensors. The effects of random vibrations at various stages of a large-scale flight that are a priori uncertain require the inclusion of identification algorithms in the optimal control loop. The results showed that the method used in the analysis had been able to provide accurate estimations.

Automatic Landing Controller of Unmanned Aerial Vehicle

2013 ◽  
Vol 677 ◽  
pp. 442-448
Author(s):  
Chart Rithirun ◽  
Pitikhate Sooraksa
Keyword(s):  
Mathematical Model ◽  
Unmanned Aerial Vehicle ◽  
Control Strategies ◽  
Control Surface ◽  
Simulink Simulation ◽  
Automatic Landing ◽  
Control Scheme ◽  
Aerial Vehicle ◽  
Fuzzy Pd ◽  
Soft Control

This paper presents the soft control strategies for automatic landing of Unmanned Aerial Vehicle and simulation the result of controller. The soft controller parameters can be modify and show off the results response of control surface of Unmanned Aerial Vehicle which can fly to the desirable waypoints along the flight plan, one may freely select a control scheme to stabilize and perform the target tracking with robustness. The main control system of Unmanned Aerial Vehicle is developed from Fuzzy PD+I controller with auto-tuning gain parameters and the simulation is carried out by Matlab/Simulink simulation program including with Aerosim toolbox software. The model of Unmanned Aerial Vehicle for simulation in this paper is selected the model of Aerosonde UAV from Aerosonde PTY LTD., which is developed mathematical model by Unmanned Dynamics.

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