This paper presents two types of nonlinear controllers for an autonomous quadrotor helicopter. One type, a feedback linearization controller involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. The second type involves a new approach to an adaptive sliding mode controller using input augmentation in order to account for the underactuated property of the helicopter, sensor noise, and uncertainty without using control inputs of large magnitude. The sliding mode controller performs very well under noisy conditions, and adaptation can effectively estimate uncertainty such as ground effects.

Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter

This thesis work focused on the study of a quadrotor helicopter. The dynamic system modelling and the control algorithm evaluation were carried out. To test the results, a simulator and a real platform were developed. The Newton-Euler formalism was used to model the dynamic system. Particular attention was given to the group composed of the DC-motor, the gear box and the propeller which needed also the estimation of aerodynamic lift and torque to reach better accuracy. PID control algorithms were compared. The first stage tests were performed on a simulated model where it was easy to evaluate the performance with a mathematical approach. The second stage tests were carried out on the quadrotor platform to evaluate the behavior of the real system. A simulator based on Matlab-Simulink was developed. With this program it was possible to test the accuracy of the model and the robustness of the control algorithms. Furthermore a 3D graphic output and a joystick interface made easier the testability and the observability of the system. A quadrotor platform was developed. The electronics was composed of a Micro Control Unit (MCU) interfaced with several devices: the power supply, the receiving unit, the DC-motor power boards, the Inertial Measurement Unit (IMU), the SONAR and the IR modules. Thanks to these devices and the MCU software, both guided and autonomous flights were possible. (Less)

https://lup.lub.lu.se/student-papers/record/8847641/file/8859343.pdf

Modelling, Identification and Control of a Quadrotor Helicopter

In this paper we describe a vision-based algorithm to control a vertical-takeoff-and-landing unmanned aerial vehicle while tracking and landing on a moving platform. Specifically, we use image-based visual servoing (IBVS) to track the platform in two-dimensional image space and generate a velocity reference command used as the input to an adaptive sliding mode controller. Compared with other vision-based control algorithms that reconstruct a full three-dimensional representation of the target, which requires precise depth estimation, IBVS is computationally cheaper since it is less sensitive to the depth estimation allowing for a faster method to obtain this estimate. To enhance velocity tracking of the sliding mode controller, an adaptive rule is described to account for the ground effect experienced during the maneuver. Finally, the IBVS algorithm integrated with the adaptive sliding mode controller for tracking and landing is validated in an experimental setup using a quadrotor.

Autonomous landing of a VTOL UAV on a moving platform using image-based visual servoing

This paper addresses the stability and tracking control problem of a quadrotor unmanned flying robot vehicle in the presence of modeling error and disturbance uncertainty. The input algorithms are designed for autonomous flight control with the help of an energy function. Adaptation laws are designed to learn and compensate the modeling error and external disturbance uncertainties. Lyapunov theorem shows that the proposed algorithms can guarantee asymptotic stability and tracking of the linear and angular motion of a quadrotor vehicle. Compared with the existing results, the proposed adaptive algorithm does not require an a priori known bound of the modeling errors and disturbance uncertainty. To illustrate the theoretical argument, experimental results on a commercial quadrotor vehicle are presented.

Robust Control of Four-Rotor Unmanned Aerial Vehicle With Disturbance Uncertainty

This paper proposes a new adaptive neural network control to stabilize a quadrotor helicopter against modeling error and considerable wind disturbance. The new method is compared to both deadzone and e-modification adaptive techniques and through simulation demonstrates a clear improvement in terms of achieving a desired attitude and reducing weight drift.

Robust neural network control of a quadrotor helicopter

A new method for adaptive-fuzzy control achieves stabilization of a quadrotor helicopter in the presence of sinusoidal wind disturbance. Techniques traditionally used in adaptive control for robust parameter updates may not be sufficient for fuzzy schemes. In particular, e-modification may result in the fuzzy-membership centers drifting to large values when persistent oscillations are present in the input. These large values can cause control signal chatter, which can be undesirable or even cause instability if they excite unmodeled dynamics. A new method for robust updates is proposed that prevents this drift in the fuzzy membership centers. In the new method, a set of alternate membership function centers guides the adaptation process in order to prevent drift. A Lyapunov-stability proof ensures the uniform ultimate boundedness of all signals. A simulation of a quadrotor helicopter demonstrates the high performance and robust stability of the new method.

Cosmin Coza

Papers

A New Robust Adaptive-Fuzzy Control Method Applied to Quadrotor Helicopter Stabilization