Aerial Vehicle Dynamics Research Articles

Design and Longitudinal Dynamics Decoupling Control of a Tilt-Rotor Aerial Vehicle With High Maneuverability and Efficiency

The widespread application of Unmanned Aerial Vehicles (UAVs) brings a higher demand for their interaction capabilities, highlighting the improvements in UAVs’ maneuverability and efficiency. Most existing design schemes of fully-actuated aerial vehicles have disadvantages, such as thrust offset and limited attitude angles. This letter proposes a design and control scheme for a power-efficient Highly-Maneuverable Tilt-rotor (HMT) aerial vehicle. First, an aerial vehicle dynamics configuration composed of three 360-degree tiltable rotors around two parallel pitching axes is designed, bounding the rotor's thrust around the vertical state to reduce the horizontal force offset at large pitch angles and improve the power utilization efficiency. Second, to avoid the Euler angle singularity when the pitch angle is close to ±90 degrees, this letter builds a dynamics model to describe a virtual body mapping the actual body to a close-to-horizontal state. Third, to improve the maneuverability of the aerial vehicle, a longitudinal dynamics decoupling controller is designed to enable the independent control of the position and attitude of the aerial vehicle based on the virtual body concept. Flight tests show that the aerial vehicle can achieve highly-maneuverable omni-directional flight in ±90 degrees pitch angles, which proves the rationality and feasibility of the design and control scheme.

Correction to: A geometric formulation of multirotor aerial vehicle dynamics

Correction to: A geometric formulation of multirotor aerial vehicle dynamics

A geometric formulation of multirotor aerial vehicle dynamics

A geometric dynamic modeling framework for generic multirotor aerial vehicles (MAV), based on a modern Lie group formulation of classical screw theory, is presented. Our framework allows for a broad range of rotor-wing configurations: any number of rotors can be attached in arbitrary configurations to either the body or wings, with the rotors and wings also tiltable. Our framework takes into account all masses and inertias of the MAV body and rotors, and accounts for both rotor thrust forces and moments as well as external aerodynamic and other forces. Compared to existing methods, our Lie group framework possesses several practical advantages useful for applications ranging from design optimization to model identification and trajectory optimization: (1) the dynamic equations can be easily transformed to coordinates of any reference frame; (2) kinematic and mass–inertial parameters can be easily factored from the dynamic equations; (3) exact, closed-form analytic derivatives of the dynamics with respect to the configuration variables are easily derived. We demonstrate our systematic modeling procedure on examples of fixed-tilt, variable-tilt and hybrid MAVs with wings.

A self-organizing multi-model ensemble for identification of nonlinear time-varying dynamics of aerial vehicles

This article presents a novel identification approach which can deal with nonlinear and time-varying characteristics of complex dynamic systems, especially an aerial vehicle in the entire flight envelope. A set of local sub-models are first developed at different operating points of the system, and subsequently a self-organizing multi-model ensemble is introduced to aggregate the outputs of the local models as a single model. The number of employed local models in the proposed multi-model ensemble is optimized using a novel self-organizing approach. Also, wavelet neural networks, which combine both the universal approximation property of neural networks and the wavelet decomposition capability, are used as the local models of the proposed method. In addition, a generalized online sequential extreme learning machine is adopted in the introduced approach to determine the optimal validity function of the local models at each time step. Finally, the introduced self-organizing multi-model ensemble is applied to the NASA Generic Transport Model as a complex nonlinear system to demonstrate the effectiveness of the proposed identification approach. Furthermore, the results obtained from the conventional artificial neural networks are carefully compared with those from the wavelet neural networks, which are employed as the local models of the introduced multi-model ensemble. The simulation results suggest that the introduced wavelet neural network–based self-organizing multi-model ensemble can be used satisfactorily as the prediction model of model-based control systems for long prediction horizons.

Real-Time Hovering Control of Unmanned Aerial Vehicles

In this paper, the design of a controller for the altitude and rotational dynamics is presented. In particular, the control problem is to maintain a desired altitude in a fixed position. The unmanned aerial vehicle dynamics are described by nonlinear equations, derived using the Newton–Euler approach. The control problem is solved imposing the stability of the error dynamics with respect to desired position and angular references. The performance and effectiveness of the proposed control are tested, first, via numerical simulations, using the Pixhawk Pilot Support Package simulator provided by Mathworks. Then, the controller is tested via a real-time implementation, using a quadrotor Aircraft F-450.

Maximizing autonomous performance of fixed-wing unmanned aerial vehicle to reduce motion blur in taken images

In this study, reducing motion blur in images taken by our unmanned aerial vehicle is investigated. Since shakes of unmanned aerial vehicle cause motion blur in taken images, autonomous performance of our unmanned aerial vehicle is maximized to prevent it from shakes. In order to maximize autonomous performance of unmanned aerial vehicle (i.e. to reduce motion blur), initially, camera mounted unmanned aerial vehicle dynamics are obtained. Then, optimum location of unmanned aerial vehicle camera is estimated by considering unmanned aerial vehicle dynamics and autopilot parameters. After improving unmanned aerial vehicle by optimum camera location, dynamics and controller parameters, it is called as improved autonomous controlled unmanned aerial vehicle. Also, unmanned aerial vehicle with camera fixed at the closest point to center of gravity is called as standard autonomous controlled unmanned aerial vehicle. Both improved autonomous controlled and standard autonomous controlled unmanned aerial vehicles are performed in real time flights, and approximately same trajectories are tracked. In order to compare performance of improved autonomous controlled and standard autonomous controlled unmanned aerial vehicles in reducing motion blur, a motion blur kernel model which is derived using recorded roll, pitch and yaw angles of unmanned aerial vehicle is improved. Finally, taken images are simulated to examine effect of unmanned aerial vehicle shakes. In comparison with standard autonomous controlled flight, important improvements on reducing motion blur are demonstrated by improved autonomous controlled unmanned aerial vehicle.

Nonlinear Model Predictive Control for Unmanned Aerial Vehicles

This paper discusses the derivation and implementation of a nonlinear model predictive control law for tracking reference trajectories and constrained control of a quadrotor platform. The approach uses the state-dependent coefficient form to capture the system nonlinearities into a pseudo-linear system matrix. The state-dependent coefficient form is derived following a rigorous analysis of aerial vehicle dynamics that systematically accounts for the peculiarities of such systems. The same state-dependent coefficient form is exploited for obtaining a nonlinear equivalent of the model predictive control. The nonlinear model predictive control law is derived by first transforming the continuous system into a sampled-data form and and then using a sequential quadratic programming solver while accounting for input, output and state constraints. The boundedness of the tracking errors using the sampled-data implementation is shown explicitly. The performance of the nonlinear controller is illustrated through representative simulations showing the tracking of several aggressive reference trajectories with and without disturbances.

A robust nonlinear output feedback control method for limit cycle oscillation suppression using synthetic jet actuators

A synthetic jet actuator-based output feedback control method is presented, which achieves asymptotic limit cycle oscillation regulation in small unmanned aerial vehicle wings, where the dynamic model contains uncertainty and unmodeled external disturbances. In addition, the proposed control method compensates for the parametric uncertainty and nonlinearity inherent in the synthetic jet actuator dynamics. Motivated by the limitations characteristic of small unmanned aerial vehicles, the control method is designed to be computationally inexpensive, eliminating the need for time-varying parameter update laws, function approximators, or other computationally heavy techniques. To this end, a computationally minimal robust-inverse control method is utilized, which is proven to compensate for the uncertainties in both the aerial vehicle dynamics and the synthetic jet actuator dynamics. By endowing the robust-inverse control law with a bank of dynamic filters, asymptotic limit cycle oscillation regulation is achieved using only pitching and plunging displacement measurements in the feedback loop. The result is an asymptotic synthetic jet actuator-based limit cycle oscillation regulation control method, which does not require velocity measurements, adaptive laws, or function approximators in the feedback loop. To achieve the result, a detailed mathematical model of the limit cycle oscillation dynamics is utilized, which includes nonlinear stiffness effects, unmodeled external disturbances, and dynamic model uncertainty, in addition to the parametric uncertainty in the synthetic jet actuator dynamic model. A rigorous Lyapunov-based stability analysis is utilized to prove asymptotic regulation of limit cycle oscillations, and numerical simulation results are provided to demonstrate the performance of the proposed control law.

Unmanned aerial vehicle trajectory planning by an integrated algorithm in a complex obstacle environment

Both the artificial potential field method and direct method for the optimal control problem have shortcomings in terms of effectiveness and computational complexity for the trajectory-planning problem. This paper proposes an integrated algorithm combining the artificial potential field method and direct method for planning in a complex obstacle-rich environment. More realistic unmanned aerial vehicle dynamics equations, which are rarely applied in the traditional artificial potential field method, are considered in this paper. Furthermore, an additional control force is introduced to transcribe the artificial potential field model into an optimal control problem, and the equality/inequality constraints on the description of the shape of the obstacles are substituted by the repulsive force originating from all the obstacles. The Legendre pseudospectral method and virtual motion camouflage are both utilized to solve the modified optimal control problem for comparison purposes. The algorithm presented in this paper improves the performance of solving the trajectory-planning problem in an obstacle-rich environment. In particular, the algorithm is suitable for addressing some conditions that cannot be considered by the traditional artificial potential field method or direct method individually, such as local extreme value points and a large numbers of constraints. Two simulation examples, a single cube-shaped obstacle and a different-shaped obstacle-rich environment, are solved to demonstrate the capabilities and performance of the proposed algorithm.

Dubins path‐based dynamic soaring trajectory planning and tracking control in a gradient wind field

SummaryThis paper studies small unmanned aerial vehicle dynamic soaring for conserving onboard energy to extend flight endurance performance. A novel dynamic soaring path planning approach is proposed using the Dubins path. It enjoys a significant improvement in computational efficiency. In addition, a custom‐built trajectory tracking controller is developed for a nonlinear unmanned aerial vehicle dynamics model to verify the implementation of the proposed path planning approach. Extensive numerical simulations are conducted to demonstrate the effectiveness of the proposed design and development. Copyright © 2016 John Wiley & Sons, Ltd.

An example of quaternion parameterization for dynamical simulations

The dynamical simulation of rigid bodies can be gathered from the classical Newton-Euler differential equations, which commonly make use of the Euler angles parametrization. In this work, the initial value problem associated with motion is presented in terms of quaternion formulation instead of the Euler one. The reason why the quaternion parametrization is proposed lies on the possibility of avoiding singularities that can occur by considering Euler angles. Moreover, the strength of quaternions is represented by the linearity of their formulation, the easiness of their algebraic structure and, overall, on their stability and efficiency. Our proposed application is the mathematical modelling of a small Unmanned Aerial Vehicle dynamics. In particular a multirotor with six blades has been taken into account, its mathematical model is deduced and a comparison between the results obtained by implementing our formulation and the classical one is produced.