Elements overview and a commercial uav electric drive model

Arm mounted unmanned aerial vehicles provide more feasible and attractive solution to manipulate objects in remote areas where access to arm mounted ground vehicles is not possible. In this research, an under-actuated quadrotor unmanned aerial vehicle model equipped with gripper is utilized to grab objects from inaccessible locations. A dual control structure is proposed for controlling and stabilization of the moving unmanned aerial vehicle along with the motions of the gripper. The control structure consists of model reference adaptive control augmented with an optimal baseline controller. Although model reference adaptive control deals with the uncertainties as well as attitude controlling of unmanned aerial vehicle, baseline controller is utilized to control the gripper, remove unwanted constant errors and disturbances during arm movement. The proposed control structure is applied in 6-degree-of-freedom nonlinear model of a quadrotor unmanned aerial vehicle equipped with gripper having (2 degrees of freedom) robotic limb; it is applicable for the simulations to desired path of unmanned aerial vehicle and to grasp object. Moreover, the efficiency of the presented control structure is compared with optimal baseline controller. It is observed that the proposed control algorithm has good transient behavior, better robustness in the presence of continuous uncertainties and gripper movement involved in the model of unmanned aerial vehicle.

In this study, a quantitative radar cross section (RCS) analysis of different unmanned aerial vehicle (UAV) models were accomplished by means of a series of RCS simulations. The simulations were carried out by high-frequency RCS simulation and analysis tool called PREDICS. To quantify the RCS features of the UAV model, both the anglevariation and frequency-variation simulations for all polarization excitations were performed. The results of the simulations suggested that RCS values were dramatically varying with respect to look angle with some special angles providing the large values of RCS. Generally, the RCS values of the UAV model was increasing with frequency as expected. A quantitative radar detection range analyses were also accomplished to assess the visibility of both the military-type and civil-type UAV models. The outcome of these studies has suggested that large-size UAV model can be easily detected by a high-sensitive radar on the ranges of tens of kilometers while these numbers reduce to a few kilometers for a civilian UAV model that is much smaller than the its military counterpart.

Nowadays, synthetic environments are considered a powerful tool to perform system testing. The use of virtual experimentation means results in a cost-effective option when facing large and/or complex system testing campaigns. Simulation-based testing reduces resources use, eliminates risks of failure on real experimentation and increments the safety level, especially when working with UAS/RPAS. Moreover, the use of simulation leads to a reduction of development costs and time to market. This work presents a set of simulation tools for UAV (Unmanned Aerial Vehicles) and UGV (Unmanned Ground Vehicles) systems that have been developed in the framework of the FP7 EC-SAFEMOBIL project. They are intended to be used as a tool to perform validation tests before real experimentation. The EC-SAFEMOBIL project is devoted to the development of sufficiently accurate motion estimation and control methods and technologies in order to reach higher levels of reliability and safety to enable unmanned vehicle deployment in a broad range of applications (landing on mobile platform, cooperative surveillance, etc.). These simulation tools allow testing the cited methods in a synthetic environment, using the exactly same estimation and control algorithms in the virtual world as those implemented for real systems. The comprehensive developed simulation environment has required the implementation of an optimized communication middleware, to provide flexibility, adaptability (allowing the addition or modification of control algorithms or UGVs, UAVs models, etc.) and scalability in order to fulfil the different needs of the specific scenarios. The development of a communication framework called ANIMO based on RTI implementation of DDS (Data Distribution Service) decouples the communication between modules or entities (UAV or UGV models, simulation core, etc.) from the simulation itself, and enables real-time communication of heterogeneous systems.

In this work, we study the current coupled to a simplified Unmanned Aerial Vehicle (UAV) model using a dual computational and experimental approach. The simplified surrogate structure reduced the computational burden and facilitated the experimental measurement of the coupled currents. For a practical system, a wide range of simulations and measurements must be performed to analyze the induced current variations with respect to properties of the incident excitation waveform, such as the frequency, angle of incidence, and polarization. To simplify this analysis, Characteristic Mode Analysis (CMA) was used to compute the eigen-currents of the UAV model and predict where and under which RF excitation conditions the coupled current is maximized. We verified these predictions using direct experimental measurement of the coupled currents. The presented simulations and measurements show the usefulness of CMA for studying electromagnetic coupling to practical systems.

The dynamic model of the 6-DOF fixed-wing unmanned aerial vehicle (UAV) is non-affine in the input and the solution of the associated nonlinear control and stabilisation problem is a non-trivial task. In this article, a novel nonlinear optimal control method is applied to the above-noted model of the 6-DOF fixed-wing UAV. First, it is proven that the dynamic model of the fixed-wing aircraft is differentially flat. Next the state–space model of the fixed-wing UAV undergoes approximate linearisation around a temporary operating point that is recomputed at each time-step of the control method. The linearisation is based on Taylor series expansion and on the associated Jacobian matrices. For the linearised state–space model of the fixed-wing UAV, a stabilising optimal (H-infinity) feedback controller is designed. This controller stands for the solution of the nonlinear optimal control problem under model uncertainty and external perturbations. To compute the controller's feedback gains, an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed nonlinear optimal control approach achieves fast and accurate tracking of setpoints under moderate variations of the control inputs and a minimum dispersion of energy by the propulsion and steering system of the UAV.

The goal is set to adjust the model of unmanned aerial vehicle (UAV) using experimental data for identification and statistical analysis of experimental model completeness. The suggested statistical approach to evaluating multifactor uncertainty-resistant design solutions for an experimental model of unmanned aerial vehicle reflects the validity and completeness of solution for adjusting aerodynamic UAV models using experimental data.

Purpose The purpose of this paper is to give reviews on the platform modeling and design of a controller for autonomous vertical take-off and landing (VTOL) tilt rotor hybrid unmanned aerial vehicles (UAVs). Nowadays, UAVs have experienced remarkable progress and can be classified into two main types, i.e. fixed-wing UAVs and VTOL UAVs. The mathematical model of tilt rotor UAV is time variant, multivariable and non-linear in nature. Solving and understanding these plant models is very complex. Developing a control algorithm to improve the performance and stability of a UAV is a challenging task. Design/methodology/approach This paper gives a thorough description on modeling of VTOL tilt rotor UAV from first principle theory. The review of the design of both linear and non-linear control algorithms are explained in detail. The robust flight controller for the six degrees of freedom UAV has been designed using H-infinity optimization with loop shaping under external wind and aerodynamic disturbances. Findings This review will act as a basis for the future work on modeling and control of VTOL tilt rotor UAV by the researchers. The development of self-guided and fully autonomous UAVs would result in reducing the risk to human life. Civil applications include inspection of rescue teams, terrain, coasts, border patrol buildings, police and pipelines. The simulation results show that the controller achieves robust stability, good adaptability and robust performance. Originality/value The review articles on quadrotors/quadcopters, hybrid UAVs can be found in many literature, but there are comparatively a lesser amount of review articles on the detailed description of VTOL Tilt rotor UAV. In this paper modeling, platform design and control algorithms for the tilt rotor are presented. A robust H-infinity loop shaping controller in the presence of disturbances is designed for VTOL UAV.

The multi-DOF dynamic model of unmanned aerial vehicles (UAVs) is a highly nonlinear one and its control can be performed again with (i) global linearization control methods, (ii) local linearization control methods and (iii) Lyapunov analysis-based methods. In approach (i) the dynamic model of the UAV is transformed into an equivalent linear description through the application of a change of variables (diffeomorphisms). In (ii) the nonlinear model of the UAV is decomposed into local linear models for which linear feedback controllers are designed and next the aim is to select the feedback control gains so as to assure the global asymptotic stability of the control loop. In (iii) the objective is to define an energy function for the UAV (Lyapunov function) and to demonstrate that through suitable selection of the feedback control the first derivative of the energy function is always negative and thus the global stability of the control loop is assured. The latter approach is particularly suitable for model-free control of UAVs and takes the form of adaptive control methods. This chapter analyzes the aforementioned control approaches for UAVs and proves global asymptotic stability for all considered control approaches (i) to (iii). The robustness of the aforementioned control methods to model uncertainty and external perturbations is confirmed. Besides elaborated nonlinear filtering approaches are developed that allow for accurate estimation of the state vector of the UAVs through the processing of measurements coming from a limited number of sensors. In particular this chapter treats the following topics: (a) Control of UAVs based on global linearization methods, (b) Control of UAVs based on approximate linearization methods.

The vertical take-off and landing (VTOL) of unmanned aerial vehicles (UAVs) is extensively employed in various sectors. To ensure adherence to design specifications and mission requirements, it is vital to verify flight control and system performance using an accurate dynamic model specific to UAV configuration. Traditionally, engineers follow a sequential approach in UAV design, which involves multiple design iterations comprising CAD drawings, material collection, fabrication, flight tests, system identification, modifications, dynamic model extraction, checking if the results meet requirements, and then repeating the process. However, as UAVs become larger, heavier, and more enduring to meet complex system demands, the costs and time associated with each design iteration of creating a new UAV escalate exponentially. The bare-airframe dynamics of the UAV are crucial for engineers to design a controller and validate handling quality and performance. This paper proposes a novel method to accurately predict the dynamic model of the bare airframe for quadrotor UAVs without physically constructing them in the real world. The core concept revolves around converting the quadrotor UAV design from CAD software into a UAV model within an X-Plane simulator. Leveraging the CIFER software’s two key features—frequency domain system identification and parametric model fitting—the unstable bare-airframe dynamics are extracted for both the UAV model in X-Plane and a real-world DJI 450 UAV with the same physical configuration. This paper provides essential parameters and guidance for constructing a 92% high-fidelity dynamic model of the given UAV configuration in X-Plane. The flight test results demonstrate excellent alignment with the simulation outcomes, instilling confidence in the effectiveness of the proposed method for designing and validating new UAVs. Moreover, this approach significantly reduces the time and cost associated with the traditional design process, which requires an actual build of the UAV and many flight tests to verify the performance.

The rapid development and diversified application modules of unmanned aerial vehicles (UAVs) have boosted economic development. According to Teal Group, a renowned aerospace analysis company, non-military UAV production will increase to $14.5 billion in 2028. The purpose of this study is collecting estimates of the future development and application modules of UAVs, to which related industries and users can refer. Hopefully, this study can benefit the development of the local UAV industry. With the popularity of UAVs, there arises UVA operation-related risk worth our concern. Taiwan's Civil Aeronautics Administration has placed emphasis on formulation of regulations and risk management in relation to UAV application. Although there are third party liability insurance products available on the market, they fail to cover the financial loss incurred upon UAV users. In view of this fact, this study also takes into concern current UAV third party insurance and related risk management. Users are expected to conduct risk management and reduce loss.

Control-oriented UAV highly feasible trajectory planning: A deep learning method

The construction of a six-degree-of-freedom (6-DOF) model for the composite motion of the actual mechanical structure (defined as an all-true composite motion model) of unmanned aerial vehicles (UAVs) is a prerequisite for achieving stable control of rotorcraft UAVs. Therefore, this paper proposes a construction approach for a nonlinear 6-DOF model of quadrotor and dual-rotor coaxial UAVs based on all-true composite motion. Two types of attitude–altitude control systems for rotorcraft based on a self-optimizing intelligent proportional–integral–derivative (PID) control method are constructed. Three-dimensional geometric models of the two rotorcraft types, incorporating their physical characteristics, are built. The attitude responses to different pulse width modulation (PWM) inputs are tested, thereby verifying the accuracy of the all-true composite model and analyzing the stability of the two types of UAVs. Furthermore, two types of attitude–altitude control inner loop controllers are designed, and the intelligent PID control algorithm is used to optimize the control parameters. Further verification of the robustness of the optimized parameters is carried out, and the designed attitude controllers are verified via experiment using a turntable. The simulation and experimental results show that the proposed all-true composite motion model and controller design method can accurately simulate the dynamic characteristics of the two types of UAVs and maintain stable attitude control, thus providing a valuable reference for the accurate attitude control of rotorcraft UAVs based on all-true composite motion.

Modeling of unmanned aerial vehicle (UAV) with system identification is very important in terms of its model-based effective control. The modeling of UAV is required for aircraft crashes, analyzing autonomous aircrafts, preventing external disturbances, pre-flight analysis. However, since UAV has nonlinear inherent dynamics including inherent chaoticity and fractality, it becomes difficult to obtain a mathematical model under external disturbance. In this study, some of the inherent nonlinear dynamics of UAV are linearized and the model of UAV is obtained by system identification approaches under external disturbance. The linearized lateral dynamics of a fixed wing UAV is used in this study. Further, the flight motion equations applied to fixed wing UAV have been utilized for obtaining the coefficients of lateral model for straight and level flight. The roll angles are calculated using transfer functions for aileron, rudder and deflections inputs. The autoregressive exogenous (ARX), autoregressive moving average with exogenous (ARMAX) and output error (OE) parametric system identification approaches are performed to estimate UAV lateral dynamic system response as using empirical input-output data sets. The accuracy of parametric model estimation and model degrees are compared for different external disturbance effects.

Aiming at the limitation of the traditional four-dimensional (4-D) trajectory-prediction model of unmanned aerial vehicles (UAV), a 4-D trajectory combined prediction model based on a genetic algorithm is proposed. Based on historical flight data and the UAV motion equation, the model is weighted dynamically by a genetic algorithm, which can predict UAV trajectory and the time of entering the protection zone instantly and accurately. Then, according to the number of areas where the tangent line of the current trajectory point intersects with the collision area, alarm area, alert area, and the time of entering the protection zone, the UAV’s behavior intention can be estimated. The simulation experiments verify the dangerous behaviors of UAV under different danger levels, which provides reference for the subsequent maneuvering strategies.

The thorough research and the exploration in the calculation algorithm and a mathematical model of small unmanned aerial vehicle (UAV) obstacle avoidance and path planning in the static scene were thoroughly researched and explored. The mathematical model of small UAV obstacle avoidance and path planning in the static scene was established. Combined with the small UAV flight performance and the minimum turn radius, a path planning algorithm for small UAV based on Dubins path was proposed. Finally, the algorithm was proved to be effective through the design and simulation verification.