Nonlinear Modeling and Simulation of Small Unmanned Aerial Vehicle with Substantial Damage

Blauwe, Hugo De; Johnson, Eric N.

doi:10.2514/6.2009-5694

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…Example damage cases are partial loss of wing surface, loss of control effectiveness, engine failures, etc. This affects the dynamic model on three aspects [23,24]:…”

Section: Simulation Of Severe Structural Damage and Failuresmentioning

confidence: 99%

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Simulation of a high-agility model airplane in the presence of severe structural damage and failures, and design of a dynamic inversion controller

et al. 2011

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The simulation of the aircraft dynamics and behavior of the open loop as well as the closed loop system is a central part in the development of controllers for real flight tests. This paper presents an approach for the systematic development of a nonlinear simulation model in MATLAB/ Simulink for a high-agility model airplane, with a total of 16 different control inputs, together with the design of an appropriate rate command attitude hold (RCAH) controller. The simulation model is capable to represent the behavior of the aircraft, including the dynamics of the motors, the actuators and the sensors, for the nominal case as well as in the case of severe structural damage and failures. The highly sophisticated model of the aircraft is used subsequently for the design of a controller. Dynamic inversion (DI) uses the knowledge of the nonlinear plant dynamics to transform the nonlinear dynamics into a chain of integrators, and after this transformation, a controller can be designed using methods from classical or robust control. A relative degree 1 (RD 1) rate command attitude hold controller, based on dynamic inversion, will be presented together with a semi-analytical approach to select and adjust the appropriate controller gains. The paper is completed by some simulation results encountering severe structural damage and failures. The derived controller can either serve as a stand-alone controller or as a baseline controller for more sophisticated control approaches.

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“…Example damage cases are partial loss of wing surface, loss of control effectiveness, engine failures, etc. This affects the dynamic model on three aspects [23,24]:…”

Section: Simulation Of Severe Structural Damage and Failuresmentioning

confidence: 99%

“…6. Due to space restrictions, the derivation of this layout shall be omitted here, but it can be looked up in [23,24].…”

Section: Dynamic Inversion (Di)mentioning

confidence: 99%

Simulation of a high-agility model airplane in the presence of severe structural damage and failures, and design of a dynamic inversion controller

et al. 2011

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“…Path following of a fixed‐wing aircraft is not a new problem (Beard & McLain, ; Cichella & Xargay, ; Conte, Duranti, & Merz, ; da Silva & de Sousa, ; Jackson, Tisdale, Kamgarpour, Basso, & Hedrick, ). While most traditional work approaches the path following problem using algorithms based on PID control (Rhee, Park, & Ryoo, ), a number of newer techniques that take advantage of machine learning and other methods have also been proposed (Blauwe & Johnson, ; Chowdhary, Johnson, Chandramohan, Kimbrell, & Calise, ; Coates, Abbeel, & Ng, ). This work takes inspiration from path following techniques evaluated by Sujit et al .…”

Section: Related Workmentioning

confidence: 99%

An Automated Emergency Landing System for Fixed‐Wing Aircraft: Planning and Control

Warren

Mejias

Kok

et al. 2015

Journal of Field Robotics

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A number of hurdles must be overcome in order to integrate unmanned aircraft into civilian airspace for routine operations. The ability of the aircraft to land safely in an emergency is essential to reduce the risk to people, infrastructure, and aircraft. To date, few field‐demonstrated systems have been presented that show online replanning and repeatability from failure to touchdown. This paper presents the development of the guidance, navigation, and control (GNC) component of an automated emergency landing system (AELS) intended to address this gap, suited to a variety of fixed‐wing aircraft. Field‐tested on both a fixed‐wing unmanned aerial vehicle (UAV) and Cessna 172R during repeated emergency landing experiments, a trochoid‐based path planner computes feasible trajectories, and a simplified control system executes the required maneuvers to guide the aircraft toward touchdown on a predefined landing site. This is achieved in zero‐thrust conditions with engine forced to idle to simulate failure. During an autonomous landing, the controller uses airspeed, inertial, and GPS data to track motion and maintains essential flight parameters to guarantee flyability, while the planner monitors glide ratio and replans to ensure approach at correct altitude. Simulations show reliability of the system in a variety of wind conditions and its repeated ability to land within the boundary of a predefined landing site. Results from field‐tests for the two aircraft demonstrate the effectiveness of the proposed GNC system in live operation. Results show that the system is capable of guiding the aircraft to close proximity of a predefined keyhole in nearly 100% of cases.

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“…It is a foam built, twin engine aircraft that is -shelf autopilot system (Adaptive Flight FCS 20 © ). GT Twinsta for fault tolerant control system work 16,17,18 . It comes equipped with an ability to inject simulate actuator failures, furthermore GT Twinstar is capable of jettisoning 50% right (starboard) wing while in autonomous flight.…”

Section: A Detection Of Oscillations In Angle Of Attack Due To Asymmmentioning

confidence: 99%

“…The presence of time delay can lead to sustained oscillations and instability in adaptive controllers. To counter this, Calise et al have develop the Adaptive Loop Recovery 16 (ALR) method capable of recovering the phase margin of the baseline non-adaptive design. In this section we demonstrate the effectiveness of the presented method in detecting oscillations in the control system due to injected time delay on the GT Twinstar UAS.…”

Section: B Detection Of Oscillations Due To Injected Time Delaymentioning

confidence: 99%

Frequency Domain Method for Real-Time Detection of Oscillations

Chowdhary

Srinivasan

Johnson

2011

Journal of Aerospace Computing, Information, and Communication

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Because of the growing interest in real-time stability monitoring, there is a need for online methods that can detect off-nominal behavior in control systems. Therefore, we present a realtime frequency domain method for detecting oscillations in control systems. The method uses recursive Fourier transform for representing the measured time domain signal in frequency domain. Oscillations are then detected by monitoring the norm of the Fourier coefficients over a time window of pre-specified size across a range of frequencies that represent off-nominal behavior for the plant. This method is computationally efficient and requires limited memory, hence it can be used for onboard health monitoring of Unmanned Aerial Systems (UAS). Consequently, we demonstrate the feasibility of using this method for real-time detection of oscillation on the Georgia Tech GT Twinstar (UAS).

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Nonlinear Modeling and Simulation of Small Unmanned Aerial Vehicle with Substantial Damage

Cited by 4 publications

References 5 publications

Simulation of a high-agility model airplane in the presence of severe structural damage and failures, and design of a dynamic inversion controller

Simulation of a high-agility model airplane in the presence of severe structural damage and failures, and design of a dynamic inversion controller

An Automated Emergency Landing System for Fixed‐Wing Aircraft: Planning and Control

Frequency Domain Method for Real-Time Detection of Oscillations

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