Modeling and sliding mode-based attitude tracking control of a quadrotor UAV with time-varying mass

Attitude control of a 3-DoF quadrotor platform using a linear quadratic integral differential game approach

In this study, a linear quadratic integral differential game approach is applied to regulate and track the Euler angles for a quadrotor experimental platform using two players. One produces commands for each channel of the quadrotor and another generates the worst disturbance based on the mini-maximization of a quadratic criterion with integral action. For this purpose, first, the attitude dynamics of the platform are modeled and its parameters are identified based on the Nonlinear Least Squares Trust-Region Reflective method. The performance of the proposed controller is evaluated for regulation and tracking problems. The ability of the controller is also examined in the disturbance rejection. Moreover, the influence of uncertainty modeling is studied on the obtained results. Then, the performance of the proposed controller is compared with the classic Proportional Integral Derivative, Linear Quadratic Regulator, and Linear Quadratic Integral Regulator. The results demonstrate the effectiveness of the Game Theory on the Linear Quadratic Regulator approach when the input disturbance occurs.

Adaptive path following control for miniature unmanned aerial vehicle confined to three-dimensional Dubins path: From take-off to landing

This study proposes a method for resolving the challenge of controlling miniature fixed-wing unmanned aerial vehicles (MAVs) along a predetermined three-dimensional (3D) Dubins path while using models with uncertainty and when experiencing external wind disturbances. We provide a multilayered structure that incorporates both guiding and control at the same time. In the guidance layer, a modified vector-field-based approach is presented to enable the MAV to follow a 3D Dubins path, including the takeoff, cruise, and landing processes with three different types of route segments. Then, an adaptive sliding model controller is used for the analysis and management of both wind disturbances and system uncertainties. Finally, both simulated scenarios and in-flight trials demonstrate the applicability of the methodology and the efficiency of the proposed approach.

Adaptive Sliding Mode Attitude Control of Quadrotor UAVs Based on the Delta Operator Framework

In this paper, a novel adaptive sliding-mode control algorithm is proposed for the attitude control of quadrotor unmanned aerial vehicles (UAVs) under the delta operator framework. First, the delta operator technique is used to discretize the attitude control systems of a quadrotor UAV. Then, based on the linear matrix inequality technique, a linear sliding surface is designed to ensure the asymptotical stability of the quadrotor UAV attitude control system during the sliding motion process. Second, by the estimated external disturbance using a radical basis function (RBF) neural network, an adaptive sliding-mode attitude controller is designed such that the states of the quadrotor UAV attitude systems can be driven towards the desired sliding surface, and thus the attitude control objective of the qudarotor UAV is achieved. Compared with the traditional adaptive sliding-mode control algorithm, the proposed adaptive sliding-mode control algorithm can effectively realize the attitude control of a quadrotor UAV subject to strong disturbances and couplings. Finally, comparisons of the simulation results verify the effectiveness and superiority of the control algorithm proposed in this paper.

Sliding Mode Fault Tolerant Control for a Quadrotor with Varying Load and Actuator Fault

In this paper, an adaptive sliding mode fault-tolerant control scheme based on prescribed performance control and neural networks is developed for an Unmanned Aerial Vehicle (UAV) quadrotor carrying a load to deal with actuator faults. First, a nonsingular fast terminal sliding mode (NFTSM) control strategy is presented. In virtue of the proposed strategy, fast convergence and high robustness can be guaranteed without stimulating chattering. Secondly, to obtain correct fault magnitudes and compensate the failures actively, a radial basis function neural network-based fault estimation scheme is proposed. By combining the proposed fault estimation strategy and the NFTSM controller, an active fault-tolerant control algorithm is established. Then, the uncertainties caused by load variation are explicitly considered and compensated by the presented adaptive laws. Moreover, by synthesizing the proposed sliding mode control and prescribed performance control (PPC), an output error transformation is defined to deal with state constraints and provide better tracking performance. From the Lyapunov stability analysis, the overall system is proven to be uniformly asymptotically stable. Finally, numerical simulation based on a quadrotor helicopter is carried out to validate the effectiveness and superiority of the proposed algorithm.