This paper presents backstepping control and backstepping constraint control approaches for a quadrotor unmanned aerial vehicle (UAV) control system. The proposed methods are applied to a Parrot Mambo drone model to control rotational motion along the $x$, $y$, and $z$ axes during hovering and trajectory tracking. In the backstepping control approach, each state of the system controls the previous state and is called “virtual control.” The last state is controlled by the real control input. The idea is to compute, in several steps, a control law that ensures the asymptotic stability of the system. The backstepping constraint control method, based on barrier Lyapunov functions (BLFs), is designed not only to track the desired trajectory but also to guarantee no violation of the position and angle constraints. Symmetric BLFs are introduced in the design of the controller. A nonlinear mathematical model is considered in this study. Based on Lyapunov stability theory, it can be concluded that the proposed controllers can guarantee the stability of the UAV system and the state converges asymptotically to the desired trajectory. To make the control robust, an adaptation law is applied to the backstepping control that estimates the unknown parameters and ensures their convergence to their respective values. Validation of the proposed controllers was performed by simulation on a flying UAV system.

The quaternion is a powerful and common tool to avoid singularity in rotational dynamics in three-dimensional (3D) space. Here it has been particularly used as an alternative to Euler angles and rotation matrix. The application of the quaternion is exercised in quadrotor modeling and control. It changes the dynamics and represents a singularity-free attitude model. Here for the first time (for the best knowledge of authors), the state-dependent differential Riccati equation (SDDRE) control has been implemented on the quaternion-based model of a quadcopter. The proposed control structure is capable of aerobatic flight, and the Pugachev’s Cobra maneuver is chosen to assess the capability of the quaternion-based SDDRE approach. The introduced control simulator is validated by comparison with conventional dynamics based on Euler angles, controlled using a proportional-derivative (PD) controller on a normal regulation flight. The simulator successfully performed the Cobra maneuver and also validated the proposed structure. The more precision in regulation along with lower energy consumption demonstrated the superiority of the introduced approach.

This research deals with the autonomous landing maneuver of a quadrotor unmanned aerial vehicle (UAV) on an unmanned ground vehicle (UGV). It is assumed that the UGV moves independently, and there is no communication and collaboration between the two vehicles. This paper aims at the design of a closed-loop vision-based control system for quadrotor UAV to perform autonomous landing maneuvers in the possible minimum time despite the wind-induced disturbance force. In this way, a fractional-order fuzzy proportional-integral-derivative controller is introduced for the nonlinear under-actuated system of a quadrotor. Also, a feedback linearization term is included in the control law to compensate model nonlinearities. A supervisory control algorithm is proposed as an autonomous landing path generator to perform fast, smooth, and accurate landings. On the other hand, a compound AprilTag fiducial marker is employed as the target of a vision positioning system, enabling high precision relative positioning in the range between 10 and 350 cm height. A software-in-the-loop simulation testbed is realized on the windows platform. Numerical simulations with the proposed control system are carried out, while the quadrotor system is exposed to different disturbance conditions and actuator dynamics with saturated thrust output are considered.

This paper develops a three-dimensional guidance and control algorithm to ensure that a manoeuverable target is preserved by a quadrotor in a long-term tracking scenario. The proposed guidance approach determines the desired altitude of the quadrotor to adjust the field of view (FOV) to the union of two desired trusted and critical regions. The dimensions of the desired trusted region depend on the controller performance that is evaluated by the distance of the target from the center of the FOV. The critical region is a predefined margin around the trusted region that is defined by the operator based on the upper bounds of the quadrotor and target localisation errors. It also depends on the duration and magnitude of the temporal increase in the target velocity compared to the quadrotor velocity. A sufficient condition is provided for the minimum desired altitude of the quadrotor to ensure that the target is maintained in the FOV. Furthermore, a model predictive control (MPC) is employed to preserve the target at the center of the aerial image and the desired altitude determined by the guidance law. Also, the integrals of the position errors are used to achieve null steady-state errors in the presence of wind disturbances. The simulation results show the effectiveness of the proposed approach in preserving the manoeuverable target in the FOV in the presence of the wind, the uncertainty of the target and quadrotor localisation, accelerations estimation errors, and terrain altitude variation.

This work presents a multimode flight framework control scheme for a quadrotor based on the super twisting algorithm. The controller design stages for six flight control modes are presented. The stability proof for each flight mode is carried out by means of Lyapunov functions, while the stability analysis for the complete control scheme, when a transition from one flight mode to another occurs, is demonstrated using the switching nonlinear systems theory. The performance of the proposed framework is shown in a 3D simulation environment considering a forest fire detection task, which takes into account external disturbances.

In this paper, a sliding mode control using a control point concept is proposed for an under-actuated quadrotor. The proposed controller controls the position of the control point, a displaced point from the quadrotor’s geometric center, and the yaw angle. This method solves singularity issues in control matrix inversion and enables the utilization of the multi-input multi-output equation to derive the control inputs. The sliding surface is designed to control four outputs while stabilizing roll and pitch angles. Simulation and experimental results show the effectiveness and robustness of the proposed controller in the tracking of a trajectory under parametric uncertainties.

Quadrotors are unmanned aerial vehicles with many potential applications ranging from mapping to supporting rescue operations. A key feature required for the use of these vehicles under complex conditions is a technique to analytically solve the problem of trajectory planning. Hence, this paper presents a heuristic approach for optimal path planning that the optimization strategy is based on the indirect solution of the open-loop optimal control problem. Firstly, an adequate dynamic system modeling is considered with respect to a configuration of a commercial quadrotor helicopter. The model predicts the effect of the thrust and torques induced by the four propellers on the quadrotor motion. Quadcopter dynamics is described by differential equations that have been derived by using the Newton–Euler method. Then, a path planning algorithm is developed to find the optimal trajectories that meet various objective functions, such as fuel efficiency, and guarantee the flight stability and high-speed operation. Typically, the necessary condition of optimality for a constrained optimal control problem is formulated as a standard form of a two-point boundary-value problem using Pontryagin’s minimum principle. One advantage of the proposed method can solve a wide range of optimal maneuvers for arbitrary initial and final states relevant to every considered cost function. In order to verify the effectiveness of the presented algorithm, several simulation and experiment studies are carried out for finding the optimal path between two points with different objective functions by using MATLAB software. The results clearly show the effect of the proposed approach on the quadrotor systems.

This paper introduces an intuitive and safe command approach for a quadrotor, where inertial and muscular gestures are used for semi-autonomous flight. A bracelet composed of gyroscopes, accelerometers, and electromyographic sensors is used to detect user gestures, then an algorithm is proposed to interpret the signals as flight commands. The main goal is to provide a wearable, easy-to-handle human–machine interface for users a to safely command this kind of vehicles, even for inexpert operators. Safety measures are incorporated in the scheme to further enhance the user’s experience. Experimental tests are performed to validate the proposal.

Designing and testing flight control algorithms for quadrotor UAVs (unmanned aerial vehicles) is not an easy task due to the risk of possible danger and damage during the practical flight. In order to improve the safety and efficiency of the flight control implementation, a low-cost real-time HILS (hardware-in-the-loop simulation) testbed for quadrotor UAVs is developed in this paper. To realize the HILS testbed, a miniature quadrotor is used as the main body, equipped with a micro AHRS (attitude heading reference system) unit and a self-build DSP (digital signal processor) board. The HILS is implemented by using xPC target. A compact PC/104 computer is utilized as the target computer, and a laptop PC is employed as the host computer. A desktop PC is used as flight visualization computer which runs FlightGear and Google Earth to show visual data, such as orientation and flight path of the quadrotor UAV. This testbed can be utilized for simulating various flight control algorithms, without losing safeness and reliableness. To demonstrate the effectiveness of the proposed testbed, a new nonlinear adaptive sliding mode based stabilization control algorithm is developed and verified on the HILS testbed.

This paper presents the design of a controller that allows a four-rotor helicopter to track a desired trajectory in 3D space. To this aim, a dynamic model obtained from Euler-Lagrange equations describes the robot. This model is represented by numerical methods, with which the control actions for the operation of the system are obtained. The proposed controller is simple and presents good performance in face of uncertainties in the model of the system to be controlled. Zero-convergence proof is included, and simulation results show a good performance of the control system.