Dynamic feedback controller of Euler angles and wind parameters estimation for a quadrotor unmanned aerial vehicle (original) (raw)

Dynamic Feedback Control for a Quadrotor Unmanned Aerial Vehicle

A nonlinear kino-dynamic model for a quadrotor unmanned aerial vehicle is presented with a comparison of state parametrizations based on Euler angles and positions states. The first method, called partial state representation, emphasizes the control of roll, pitch and yaw angles rather than the second one is dedicated to drive translational motions of the UAV. The system is presented as two cascaded parts, the first one for the rotational motion and the last for translations. A dynamic feedback control is applied to linearize the closed loop first part leading a controllable and decoupled subsystem. Performance and robustness of the proposed controller are analysed in simulation.

Dynamical Modeling and Controlof Quadrotor

2016

In this paper a dynamical modeling of a quadrotor is discussed with different frame of references.The state equations are derived using gyroscopic and aerodynamics effects. The motor transfer function is derived from theoretical equations of DC motor with an operational delay. Furthermore a control strategy is presented using Proportional Derivative (PD) Controller for the attitude and trajectory control of the Quadrotor (UAV). Finally, simulation results for the PD controller are generated on MATLAB/ Simulink platform by utilizing the dynamical model of Quadrotor (UAV) .Feedback sensor noise is added to the model output to make simulations more realistic. The simulation results confirm that the Quadrotor (UAV) is following the desired trajectory with a maximum error deviation of 0.2 meters.

Modelling and Control of Quadrotor Control System using MATLAB/Simulink

International Journal of Science and Engineering Applications, 2018

Generally, Quadrotor type Unmanned Aerial Vehicles are unstable in nature, so to stabilize it, the controller is used. This paper observes the PD controller that make use of UAV to control the adjust of quadrotor UAV even as in the air. The gain parameters of the PD controller, the proportional gain Kp and the derivative gain Kd are apply to be stable and good performance. Unmanned aerial vehicles (UAV) are becoming increasingly common and span a huge range of size and shape. After integrating PD controllers into the systems, quadcopter settling time of roll, pitch and yaw system. Simulations result and comparison of X, Yand Yaw control techniques are presented at the end of this paper. This controller monitors the controlled process variable, and compares it with the reference or set point. The difference between actual and desired value of the process variable, called the error signal. Error is applied as feedback to generate a control action to bring the controlled process variable to the same value as the set point. Control action in which the output is proportional to a linear combination of the input and the time rate of change of input.

Quadrotor control: modeling, nonlinearcontrol design, and simulation

2015

In this work, a mathematical model of a quadrotor’s dynamics is derived, usingNewton’s and Euler’s laws. A linearized version of the model is obtained, andtherefore a linear controller, the Linear Quadratic Regulator, is derived. Afterthat, two feedback linearization control schemes are designed. The first one isthe dynamic inversion with zero dynamics stabilization, based on Static FeedbackLinearization obtaining a partial linearization of the mathematical model.The second one is the exact linearization and non-interacting control via dynamicfeedback, based on Dynamic Feedback Linearization obtaining a total linearizationof the mathematical model. Moreover, these nonlinear control strategiesare compared with the Linear Quadratic Regulator in terms of performances.Finally, the behavior of the quadrotor under the proposed control strategies isobserved in virtual reality by using the Simulink 3D Animation toolbox.

Dynamic Feedback Controller of an Unmanned Aerial Vehicle

The research interest in UAVs (Unmanned Aerial Vehicles) has grown in the past few years, comprising a wide range of applications. New technologies on MEMS (Micro-Electro-Mechanical Systems) sensors and signal processing techniques have been also strong influences on this type of research. This work is dedicated to the dynamic modeling and control implementation of a quadrotor UAV using linear control techniques for position tracking via image, ultrasonic, compass, acceleration and gyroscopic sensors, applying pole placement control technique. Simulation on the performance of the controller with a Kalman Filter is presented.

DYNAMIC MODELING AND CONTROL OF QUADROTOR VEHICLE

The control of Unmanned Aerial Vehicles (UAVs) is a very challenging field of research especially for Vertical Take-Off and Landing (VTOL) vehicles or aircrafts for their numerous advantages over the traditional airplanes and due to the rapid advances that were made in this field with the development of light weight Micro-Electromechanical System (MEMS) sensors it has become possible to build an autonomous model for a light weight quadrotor and to develop various controls for it. This paper focuses on the mathematical model of a quadrotor vehicle. A CAD model has been built for estimating mass and inertial properties of the physical model. Finally a PID controller for the proposed model is introduced then a Simulink model has been implemented for estimating the response of flight dynamics.

Dynamic modeling and control of a Quadrotor using linear and nonlinear approaches

2014

With the huge advancements in miniature sensors, actuators and processors depending mainly on the Micro and Nano-Electro-Mechanical-Systems (MEMS/NEMS), many researches are now focusing on developing miniature flying vehicles to be used in both research and commercial applications. This thesis work presents a detailed mathematical model for a Vertical Takeoff and Landing (VTOL) type Unmanned Aerial Vehicle(UAV) known as the quadrotor. The nonlinear dynamic model of the quadrotor is formulated using the Newton-Euler method, the formulated model is detailed including aerodynamic effects and rotor dynamics that are omitted in many literature. The motion of the quadrotor can be divided into two subsystems; a rotational subsystem (attitude and heading) and a translational subsystem (altitude and x and y motion). Although the quadrotor is a 6 DOF underactuated system, the derived rotational subsystem is fully actuated, while the translational subsystem is underactuated. The derivation of the mathematical model is followed by the development of four control approaches to control the altitude, attitude, heading and position of the quadrotor in space. The first approach is based on the linear Proportional-Derivative-Integral (PID) controller. The second control approach is based on the nonlinear Sliding Mode Controller (SMC). The third developed controller is a nonlinear Backstepping controller while the fourth is a Gain Scheduling based PID controller. The parameters and gains of the forementioned controllers were tuned using Genetic Algorithm (GA) technique to improve the systems dynamic response. Simulation based experiments were conducted to evaluate and compare the performance of the four developed control techniques in terms of dynamic performance, stability and the effect of possible disturbances.

Quadrotor flight stability system with Routh stability and Lyapunov analysis

AIP Conference Proceedings, 2016

UAV (Unmanned Aerial Vehicle) can fly autonomously or be controlled remotely by a pilot. Quadrotor as one type of UAV has been widely implemented in various needs. Its system design has a lot of control techniques involved. The design starts with the physical modeling. Quadrotor physical modeling is modeling based on the laws of physics as a theory and mathematical modeling of physical interpretation. The problem arises when actual plants are not fit with mathematical models that are used as the control design before. Such discrepancy arises because of external interference, plant parameters, and dynamics models that are nonlinear. If control systems are not designed to deal with non-linear interference, it is difficult to us to maintain quadrotor flight. Therefore, we need control methods that can be applied to linear and nonlinear systems. Routh Stability can be used to generate PID (Proportional Integral and Derivative) constants as a linear control method by using a Ziegler-Nichols. Lyapunov as a method of non-linear control method offers distinct advantages over other control methods. Lyapunov second method is further implemented by a control technique that gives a good effect. So the PID and Lyapunov method can make quadrotor approaching the stationary state.

Comparison and Implementation of Control Strategies for a Quadrotor

2017

This paper presents the comparison and implementation of state estimation and control strategies for the attitude of a quadrotor. We’ve started by developing a mathematical model for the attitude of the device using quaternions representation of attitude alongside the matrix algebra. Based on this model, we’ve proposed three different control strategies based on PID control, Feedback Linearization and Backstepping control. The controllers were implemented in the Crazyflie 1.0, an open-source development platform by Bitcraze. The results were compared to the built-in control system and an improvement could be verified. Keywords— Quadrotor, state estimation, Kalman filter, attitude control.