Dynamic modelling and swing control of a quadrotor with a cable-suspended payload

A quadrotor is a highly nonlinear system due to the presence of aerodynamic factors such as Coriolis and gyroscopic effects when in flight. In meeting todays’ demands, the applications of quadrotors have been extended to include transportation and therefore, the study of Quadrotor Suspended Load (QSL) systems has become equally as important. However, the presence of the suspended load further complicates the quadrotor system as there is strong coupling with the load and excessive load swinging. This is a problem which forms the basis for this work. This project begins by providing a mathematical description of the QSL system using Euler-Lagrange equations as they are much simplified, yet encompass the many factors present during quadrotor operation and subsequently control excessive payload swinging. The main strength of this work is that unlike other previous work, it covers 8 degrees of freedom (8 DOF) in representing the system dynamics. This presents a much more comprehensive and definitive way of describing the quadrotor and payload positions. Input shaping is used as the swing controller as it is more practical and has been used for swing control of other systems. Validation of the swing controller performance is done using MATLAB SIMULINK. Unlike other controllers that require sophisticated algorithms for their implementation, input shaping will be used as a swing controller as it is much simplified in handling excessive load swinging

Dynamic model and ADRC of a novel water-air unmanned vehicle for water entry with in-ground effect

The class of vehicles that can move both in the air and underwater has been of great interest for decades. A novel water-air unmanned vehicle with double quadrotor structure is designed in this study. The air power mechanism works when the vehicle flies in the air, whereas the water power mechanism works when it moves underwater. The water entry process of water-air unmanned vehicle requires accurate attitude and height control, or the vehicle may bounce off or overturn. However, a force resisting its descent known as in-ground effect will affect its stability. The in-ground effect formula of the water entry process is derived by experiments, and the water entry dynamic model is improved at the same time. An active disturbance rejection controller (ADRC) is designed for the control of water entry attitude and height. Experimental results obtained from the comparison of the ADRC and a proportional-integral-derivative (PID) controller show that the ADRC designed in this study is more robust than the PID controller for the internal coupling and external disturbance on the vehicle. Moreover, the ADRC can meet the requirements of rapid attitude adjustment and accurate height control

Backstepping control law application to path tracking with an indoor quadrotor

This paper presents an application of the backstepping control to a path tracking mission using an indoor quadrotor. This study case starts on modeling the quadrotor dynamics in order to design a backstepping control which we applied directly to the Lagrangian dynamic equations. The backstepping control is chosen due to its applicability to this class of nonlinear and under-actuate system. To test the designed control law, a complete quadrotor model identification was performed, using a motion capture system. The procedure used to obtain a good model approximation is presented. Experimental results illustrate the validity of the designed control law, including rich simulations and real indoor flight tests

Asymptotic Vision-Based Tracking Control of the Quadrotor Aerial Vehicle

This paper proposes an image-based visual servo (IBVS) controller for the 3D translational motion of the quadrotor unmanned aerial vehicle (UAV). The main purpose of this paper is to provide asymptotic stability for vision-based tracking control of the quadrotor in the presence of uncertainty in the dynamic model of the system. The aim of the paper also includes the use of ow of image features as the velocity information to compensate for the unreliable linear velocity data measured by accelerometers. For this purpose, the mathematical model of the quadrotor is presented based on the optic ow of image features which provides the possibility of designing a velocity-free IBVS controller with considering the dynamics of the robot. The image features are de ned from a suitable combination of perspective image moments without using the model of the object. This property allows the application of the proposed controller in unknown places. The controller is robust with respect to the uncertainties in the transla- tional dynamics of the system associated with the target motion, image depth and external disturbances. Simulation results and a comparison study are presented which demonstrate the e ectiveness of the proposed approach

Using Actor-Critic Reinforcement Learning for Control of a Quadrotor Dynamics

This paper presents a quadrotor controller using reinforcement learning to generate near-optimal control signals. Two actor-critic algorithms are trained to control quadrotor dynamics. The dynamics are further simplified using small angle approximation. The actor-critic algorithm’s control policy is derived from Bellman’s equation providing a sufficient condition to optimality. Additionally, a smoother converter is implemented into the trajectory providing more reliable results. This paper provides derivations to the quadrotor’s dynamics and explains the control using the actor-critic algorithm. The results and simulations are compared to solutions from a commercial, optimal control solver, called DIDO

Quadcopter stabilization with neural network

UAVs (Unmanned Aerial Vehicle), also known as drones, are becoming attractive in the consumer space due to their relatively low cost and their ability to operate autonomously with minimal human intervention. A user could program the drone with GPS coordinates, and the drone would comply with utmost precision. In order for the drone to operate a preprogrammed flight path, it requires a host of sensors for it to gather data and operate on that data in real time. For instance, a consumer drone typically has obstacle avoidance sensors, a GPS sensor for routing and navigation, and an IMU (Inertial Measurement Unit) for tracking position and orientation. These sensors play a crucial role in both stabilization and navigation of the drone. This report aims to investigate, analyze and understand the complexity involved in designing and implementing an autonomous quadcopter; specifically, the stabilization algorithms. In general, stabilization is achieved using some form of control algorithm. The report covers a popular approach for stabilization (PID Control) found with many open source libraries and contrasts it with an alternative machine learning approach (Neural Networks). Finally, a machine learning based algorithm is implemented and evaluated on a prototype quadcopter, and its results are presented.Electrical and Computer Engineerin