A review of modelling and analysis of morphing wings

Morphing wings have a large potential to improve the overall aircraft performances, in a way like natural flyers do. By adapting or optimising dynamically the shape to various flight conditions, there are yet many unexplored opportunities beyond current proof-of-concept demonstrations. This review discusses the most prominent examples of morphing concepts with applications to two and three-dimensional wing models. Methods and tools commonly deployed for the design and analysis of these concepts are discussed, ranging from structural to aerodynamic analyses, and from control to optimisation aspects. Throughout the review process, it became apparent that the adoption of morphing concepts for routine use on aerial vehicles is still scarce, and some reasons holding back their integration for industrial use are given. Finally, promising concepts for future use are identified

PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.Comment: This paper has been accepted for publication in Information Science Journal 201

Design and Modeling of Smartphone Controlled Vehicle

While many have worked on the transition phases of more popular hybrid aerial vehicle configurations, In this paper, we explore a novel multi-mode hybrid Unmanned Aerial Vehicle (UAV). Due to its expanded flying range and adaptability, hybrid aerial vehicles—which integrates two or more operating configurations—have become more and more widespread. The stages of transition between these modes are reasonably important whether there are two or more flight forms present. Whereas numerous have worked on the early stages of more widely used hybrid aerial vehicle types, in this paper a brand-new multi-mode hybrid UAV will be investigated. In order to fully exploit the vehicle's propulsion equipment and aerodynamic surfaces in both a horizontal cruising configuration and a vertical hovering configuration, we combine a tailless fixed-wing with a four-wing monocopter. By increasing construction integrity over the whole operational range, this lowers drag and wasteful mass when the aircraft is in motion in both modes. The transformation between the two flight states can be carried out in midair with just its current flying actuators and sensors. Through a ground controller, this vehicle may be operated by an Android device

DECENTRALIZED ROBUST NONLINEAR MODEL PREDICTIVE CONTROLLER FOR UNMANNED AERIAL SYSTEMS

The nonlinear and unsteady nature of aircraft aerodynamics together with limited practical range of controls and state variables make the use of the linear control theory inadequate especially in the presence of external disturbances, such as wind. In the classical approach, aircraft are controlled by multiple inner and outer loops, designed separately and sequentially. For unmanned aerial systems in particular, control technology must evolve to a point where autonomy is extended to the entire mission flight envelope. This requires advanced controllers that have sufficient robustness, track complex trajectories, and use all the vehicles control capabilities at higher levels of accuracy. In this work, a robust nonlinear model predictive controller is designed to command and control an unmanned aerial system to track complex tight trajectories in the presence of internal and external perturbance. The Flight System developed in this work achieves the above performance by using: 1 A nonlinear guidance algorithm that enables the vehicle to follow an arbitrary trajectory shaped by moving points; 2 A formulation that embeds the guidance logic and trajectory information in the aircraft model, avoiding cross coupling and control degradation; 3 An artificial neural network, designed to adaptively estimate and provide aerodynamic and propulsive forces in real-time; and 4 A mixed sensitivity approach that enhances the robustness for a nonlinear model predictive controller overcoming the effect of un-modeled dynamics, external disturbances such as wind, and measurement additive perturbations, such as noise and biases. These elements have been integrated and tested in simulation and with previously stored flight test data and shown to be feasible

Aircraft Modeling and Simulation

Various aerodynamics, structural dynamics, and control design and experimental studies are presented with the aim of advancing green and morphing aircraft research. The results obtained with an in-house CFD code are compared and validated with those of two NASA codes. The aerodynamical model of the UAS-S45 morphing wing as well as the structural model of a morphing winglet are presented. A new design methodology for oleo-pneumatic landing gear drop impact dynamics is presented as well as its experimental validation. The design of a nonlinear dynamic inversion (NDI)-based disturbance rejection control on a tailless aircraft is presented, including its validation using wind tunnel tests