Robust Linear Longitudinal Feedback Control of a Flapping Wing Micro Air Vehicle

This paper falls under the idea of introducing biomimetic miniature air vehicles in ambient assisted living and home health applications. The concepts of active disturbance rejection control and flatness based control are used in this paper for the trajectory tracking tasks in the flapping-wing miniature air vehicle (FWMAV) time-averaged model. The generalized proportional integral (GPI) observers are used to obtain accurate estimations of the flat output associated phase variables and of the time-varying disturbance signals. This information is used in the proposed feedback controller in (a) approximate, yet close, cancelations, as lumped unstructured time-varying terms, of the influence of the highly coupled nonlinearities and (b) the devising of proper linear output feedback control laws based on the approximate estimates of the string of phase variables associated with the flat outputs simultaneously provided by the disturbance observers. Numerical simulations are provided to illustrate the effectiveness of the proposed approach

DESIGN AND CONTROL OF A HUMMINGBIRD-SIZE FLAPPING WING MICRO AERIAL VEHICLE

Flying animals with flapping wings may best exemplify the astonishing ability of natural selection on design optimization. They evince extraordinary prowess to control their flight, while demonstrating rich repertoire of agile maneuvers. They remain surprisingly stable during hover and can make sharp turns in a split second. Characterized by high-frequency flapping wing motion, unsteady aerodynamics, and the ability to hover and perform fast maneuvers, insect-like flapping flight presents an extraordinary aerial locomotion strategy perfected at small size scales. Flapping Wing Micro Aerial Vehicles (FWMAVs) hold great promise in bridging the performance gap between engineered flying vehicles and their natural counterparts. They are perfect candidates for potential applications such as fast response robots in search and rescue, environmental friendly agents in precision agriculture, surveillance and intelligence gathering MAVs, and miniature nodes in sensor networks

Control-Oriented Reduced Order Modeling of Dipteran Flapping Flight

Flying insects achieve flight stabilization and control in a manner that requires only small, specialized neural structures to perform the essential components of sensing and feedback, achieving unparalleled levels of robust aerobatic flight on limited computational resources. An engineering mechanism to replicate these control strategies could provide a dramatic increase in the mobility of small scale aerial robotics, but a formal investigation has not yet yielded tools that both quantitatively and intuitively explain flapping wing flight as an "input-output" relationship. This work uses experimental and simulated measurements of insect flight to create reduced order flight dynamics models. The framework presented here creates models that are relevant for the study of control properties. The work begins with automated measurement of insect wing motions in free flight, which are then used to calculate flight forces via an empirically-derived aerodynamics model. When paired with rigid body dynamics and experimentally measured state feedback, both the bare airframe and closed loop systems may be analyzed using frequency domain system identification. Flight dynamics models describing maneuvering about hover and cruise conditions are presented for example fruit flies (Drosophila melanogaster) and blowflies (Calliphorids). The results show that biologically measured feedback paths are appropriate for flight stabilization and sexual dimorphism is only a minor factor in flight dynamics. A method of ranking kinematic control inputs to maximize maneuverability is also presented, showing that the volume of reachable configurations in state space can be dramatically increased due to appropriate choice of kinematic inputs

Modeling and Control of Flapping Wing Micro Aerial Vehicles

abstract: Interest in Micro Aerial Vehicle (MAV) research has surged over the past decade. MAVs offer new capabilities for intelligence gathering, reconnaissance, site mapping, communications, search and rescue, etc. This thesis discusses key modeling and control aspects of flapping wing MAVs in hover. A three degree of freedom nonlinear model is used to describe the flapping wing vehicle. Averaging theory is used to obtain a nonlinear average model. The equilibrium of this model is then analyzed. A linear model is then obtained to describe the vehicle near hover. LQR is used to as the main control system design methodology. It is used, together with a nonlinear parameter optimization algorithm, to design a family multivariable control system for the MAV. Critical performance trade-offs are illuminated. Properties at both the plant output and input are examined. Very specific rules of thumb are given for control system design. The conservatism of the rules are also discussed. Issues addressed include What should the control system bandwidth be vis--vis the flapping frequency (so that averaging the nonlinear system is valid)? When is first order averaging sufficient? When is higher order averaging necessary? When can wing mass be neglected and when does wing mass become critical to model? This includes how and when the rules given can be tightened; i.e. made less conservative.Dissertation/ThesisMasters Thesis Mechanical Engineering 201

Closed-Loop Control of Constrained Flapping Wing Micro Air Vehicles

Micro air vehicles are vehicles with a maximum dimension of 15 cm or less, so they are ideal in confined spaces such as indoors, urban canyons, and caves. Considerable research has been invested in the areas of unsteady and low Reynolds number aerodynamics, as well as techniques to fabricate small scale prototypes. Control of these vehicles has been less studied, and most control techniques proposed have only been implemented within simulations without concern for power requirements, sensors and observers, or actual hardware demonstrations. In this work, power requirements while using a piezo-driven, resonant flapping wing control scheme, Bi-harmonic Amplitude and Bias Modulation, were studied. In addition, the power efficiency versus flapping frequency was studied and shown to be maximized while flapping at the piezo-driven system\u27s resonance. Then prototype hardware of varying designs was used to capture the impact of a specific component of the flapping wing micro air vehicle, the passive rotation joint. Finally, closed-loop control of different constrained configurations was demonstrated using the resonant flapping Bi-harmonic Amplitude and Bias Modulation scheme with the optimized hardware. This work is important in the development and understanding of eventual free-flight capable flapping wing micro air vehicle

Neurobiologically Inspired Control of Engineered Flapping Flight

This article presents a new control approach for engineered flapping flight with many interacting degrees of freedom. This paper explores the applications of neurobiologically inspired control systems in the form of Central Pattern Generators (CPG) to generate wing trajectories for potential flapping flight MAVs. We present a rigorous mathematical and control theoretic framework to design complex three dimensional motions of flapping wings. Most flapping flight demonstrators are mechanically limited in generating the wing trajectories. Because CPGs lend themselves to more biological examples of flight, a novel robotic model has been developed to emulate the flight of bats. This model has shoulder and leg joints totaling 10 degrees of freedom for control of wing properties. Results of wind tunnel experiments and numerical simulation of CPG-based flight control validate the effectiveness of the proposed neurobiologically inspired control approach

A Comparative Framework for Maneuverability and Gust Tolerance of Aerial Microsystems

Aerial microsystems have the potential of navigating low-altitude, cluttered environments such as urban corridors and building interiors. Reliable systems require both agility and tolerance to gusts. While many platform designs are under development, no framework currently exists to quantitatively assess these inherent bare airframe characteristics which are independent of closed loop controllers. This research develops a method to quantify the maneuverability and gust tolerance of vehicles using reachability and disturbance sensitivity sets. The method is applied to a stable flybar helicopter and an unstable flybarless helicopter, whose state space models were formed through system identification. Model-based static H-infinity controllers were also implemented on the vehicles and tested in the lab using fan-generated gusts. It is shown that the flybar restricts the bare airframe's ability to maneuver in translational velocity directions. As such, the flybarless helicopter proved more maneuverable and gust tolerant than the flybar helicopter. This approach was specifically applied here to compare stable and unstable helicopter platforms; however, the framework may be used to assess a broad range of aerial microsystems