Andrei Dorobantu

System Identification for Small, Low-Cost, Fixed-Wing Unmanned Aircraft

This paper describes a practical system identification procedure for small, low-cost, fixed-wing unmanned aircraft. Physical size and cost restrictions limit the sensing capabilities of these vehicles. The procedure is demonstrated on an Ultra Stick 25e, therefore emphasizing a minimum complexity approach compatible with a low-cost inertial sensor. A linear model, obtained from the generic nonlinear equations of motion for aircraft, is used as a basis for system identification. This model is populated with results from a first principles analysis to form a baseline model. Flight experiments are designed using the baseline model and operational constraints to collect informative data. Parameters of the linear model are identified by fitting the model to frequency responses extracted from the data. The parameters are integrated into the nonlinear equations of motion, and both linear and nonlinear models are validated in the time domain. Verification of model accuracy is extended with a sensitivity and resid...

Frequency domain system identification for a small, low-cost, fixed-wing UAV

This paper describes a practical and systematic procedure for modeling and identifying the ight dynamics of small, low-cost, xed-wing uninhabited aerial vehicles (UAVs). The procedure is applied to the Ultra Stick 25e ight test vehicle of the University of Minnesota UAV ight control research group. The procedure hinges on a general model structure for xed-wing UAV ight dynamics derived using rst principles analysis. Wind tunnel tests and simplifying assumptions are applied to populate the model structure with an approximation of the Ultra Stick 25e ight dynamics. This baseline model is used to design informative ight experiments for the subsequent frequency domain system identi cation. The nal identi ed model is validated against separately acquired time domain ight data.

An airborne experimental test platform: From theory to flight

This paper provides an overview of the experimental flight test platform developed by the University of Minnesota Unmanned Aerial Vehicle Research Group. Key components of the current infrastructure are highlighted, including the flight test system, high-fidelity nonlinear simulations, software-and hardware-in-the-loop simulations, and the real-time flight software. Recent flight control research and educational applications are described to showcase the advanced capabilities of the platform. A view towards future expansion of the platform is given in the context of upcoming research projects.

Time-Delay Margin Analysis for an Adaptive Controller

robustness analysis tools are applied to adaptive flight control in this paper. A standard model reference adaptive controller with sigma modification is designed for a linear short-period aircraft model. The resulting nonlinear closed-loop system is governed by polynomial dynamics. The nonlinear analysis algorithms rely on sum-of-squares polynomial optimization to assess the robustness of the adaptive closed-loop system with respect to a time delay. Time-delaymarginsarecomputedforvariouscombinationsofdesignparametersintheadaptivecontrollaw,aswell as in the presence of parametric model uncertainty. Advantages and limitations of the proposed sum-of-squaresbased robustness analysis are presented. Analysis results show a significant promise in the context of recent development in nonlinear robustness analysis. I. Introduction A DAPTIVEcontrolhasthepotentialtoimprovetheperformance and reliability of aircraft systems. Typical adaptive control architectures are inherently nonlinear, which presents a number of challenges. There is currently a lack of tools available to rigorously analyze the robustness and performance of nonlinear control architectures. The inability toverifyrobustnessand performance is a significant roadblock to the implementation of adaptive control architectures on civilian andmilitary aircraft. Thus, the flight control community would greatly benefit from advances in the area of

Robustness Analysis of an L1 Adaptive Controller

NASA’s Generic Transport Model (GTM) is a remote-controlled, 5.5 percent scale commercial aircraft. An L1 adaptive controller was recently designed and ight tested on the GTM. Oscillations in the elevator command were observed at 1.4 to 2 Hertz during the rst ight test in September 2009. In most ight conditions the L1 adaptive controller can be approximated by a linear time-invariant system. Thus linear analysis tools can be used to assess the performance and robustness of the feedback system with the L1 controller. The initial L1 design met the NASA requirement for 60 msec of time delay margin. However, the linear analysis indicates that the margin requirements were insu cient due to inaccurate models that were available at the time of the rst ight test. A revised L1 controller has signi cantly larger margins and demonstrated good performance during subsequent ight tests.

Validating Aircraft Models in the Gap Metric

A framework based on the gap metric is proposed to validate mathematical models of aircraft dynamics using flight data. The approach derives stability margin requirements, and hence is ideally suited to support model-based design and certification of flight control algorithms. This paper shows that the gap metric is an extension of the Theil inequality coefficient: a widely used metric for model validation. The approach is demonstrated on a case study with a small unmanned aircraft.

Optimal waypoint guidance, trajectory design and tracking

This paper first introduces a previously developed and flight tested waypoint guidance method and suggests some improvements, which make it more time optimal. Then - based on the successful flight testing of a trajectory tracking method - it introduces a method, which generates a shortest possible parameterized spatial trajectory based on a series of given waypoints. This trajectory is guaranteed to go through every point, even if they are close to each other. Some official search and rescue paths are generated applying the method. Both waypoint guidance, trajectory generation and tracking are successfully tested using the software-in-the-loop simulation environment and real flight tests of a small unmanned aerial vehicle.

Nonlinear Analysis of Adaptive Flight Control Laws

Adaptive control algorithms have the potential to improve performance and reliability of ight control systems. The application of adaptive control on commercial or military aircraft will require validation and verication of the robustness of these algorithms to modeling errors and uncertainties. Currently, there is a lack of tools to rigorously analyze the performance and robustness of adaptive systems. This paper addresses the development of nonlinear robustness analysis tools for such systems. First a model-reference adaptive controller is derived for an aircraft short-period model. It is noted that the adaptive control law is a polynomial system. Polynomial optimization tools are applied to the closed loop model to assess the performance and robustness of the adaptive control law. Two sets of results are presented in this paper. First, input-output gains are calculated in the presence of model uncertainty to evaluate the performance of the adaptive law. Second, time delay margins are computed for varying parameters in the adaptive law, as well as in the presence of model uncertainty.

Validating uncertain aircraft simulation models using flight test data

Reliability validation of safety-critical flight control systems requires accurate simulation models of the aircraft dynamics. This paper proposes a simple approach to validate such models efficiently based on flight data. A statistical model validation analysis is shown to be equivalent to a robust control analysis for simple linear systems. The analysis is extended to nonlinear systems in conjunction with Monte Carlo simulations to validate an uncertain aircraft simulation model. This approach is demonstrated using the University of Minnesota Unmanned Aerial Vehicle flight test platform.

Analysis of modeling techniques for low-cost actuators

Low-cost actuators are equipped on small uninhabited aerial vehicle platforms to displace the control surfaces and maneuver the aircraft. The control laws, models, and even a complete description of the actuator output are typically unavailable from the manufacturer. This is a concern because many of these aircraft are used to perform flight control research. Models of the actuator system is required in order to accurately run simulations and develop flight controls. The Unmanned Aerial Vehicle Research Group at the University of Minnesota is comparing various approaches to actuator modeling to determine which yields the appropriate fidelity level required in flight control applications. Development time may be reduced when a low fidelity model can be implemented instead of a high fidelity model. This paper compares three types of models: a black box first order equivalent system, a black box second order equivalent system, and a grey box first order equivalent system. These models were constructed based on data recorded from an actuator equipped with sensors that measure angular deflections, angular rates, back electromotive force, and current consumption. Future research will determine the best model to use for flight control research.