Aircraft modeling and simulation has become increasingly important in the aviation world. Simulations allow for safer and more economical training prior to flight testing. In this project, a three degree-of-freedom control simulation coded in a MATLAB environment is used to assess and simulate the dynamic stability of group three unmanned aircraft system. By calculating, evaluating, and simulating the static and dynamic stability derivatives of the aircraft, this project will allow for the estimation of the handling qualities of an unmanned aircraft system. This can help mitigate risks that come along with altering the mass and aerodynamic properties of an aircraft, therefore creating a safer environment for flight testing.

We present new adaptive control algorithms that address the problem of rejecting sinusoids with known frequencies that act on an unknown asymptotically stable linear time-invariant system. To achieve asymptotic disturbance rejection, adaptive control algorithms of this dissertation rely on limited or no system model information. These algorithms are developed in discrete time, meaning that the control computations use sampled-data measurements. We demonstrate the effectiveness of algorithms via analysis, numerical simulations, and experimental testings. We also present extensions to these algorithms that address systems with decentralized control architecture and systems subject to disturbances with unknown frequencies.

A thermal is a column of warm rising air triggered by differential heating on the ground. In recent studies UAVs were programmed to exploit this free atmospheric energy from thermals to improve their range and endurance. Researchers had successfully flown UAVs autonomously with thermal soaring method. Most research involved some form of flight simulation. Improvements to the aircraft and thermal models for simulation purpose would enable researchers to better design their UAVs and explore any potential flaws in their designs. An aircraft simulation with a thermal environment was created in Horizon Simulation Framework, a modeling and verification framework that was developed by Cal Poly Space Technologies and Applied Research laboratory. The objective of this study is to enhance the fidelity of existing modeling and simulation methods on autonomous thermal soaring, and to advance and demonstrate the capabilities of Horizon Simulation Framework through such implementation. The geometry of a small remote controlled glider was used in this simulation. Aerodynamic prediction programs DATCOM+ and AVL were used to obtained stability and control derivatives for this glider. The induced roll effect caused by the asymmetric vertical velocity distribution of a thermal was included in the aerodynamic roll moment calculation. The autonomous guidance algorithm for the glider included a turn logic which would determine the correct turn direction for the glider when a thermal is detected. The thermal model developed in this thesis included the capabilities to vary the time dependent location, height, radius, and vertical velocity characteristics of naturally occurring thermals.

In-flight simulation allows one aircraft to simulate the dynamic response of another aircraft. A control system designed to give RASCAL, a JUH-60A Black Hawk helicopter based at Moffett Field, CA, in-flight simulation capabilities has been designed, optimized and validated in this research. A classical explicit model following control system with a frequency dependent feedback controller was used. The frequency dependent controller allows model following of the attitude in the short term and the velocity in the long term. Controller gains were optimized using a high order, linearized model of UH-60 dynamics. Non-linear simulations of the control laws were performed, first on a desktop computer based simulation, then in the RASCAL development facility, a hardware-in-the-loop simulator. Comparing quantitative results of the non-linear simulations with the results of the optimization using the linearized model ensured that the control system designed with the linearized model was valid in non-linear environments. Finally, a piloted evaluation in the hardware-in-the-loop simulator was performed to obtain qualitative information on the behavior of the control laws.

Applied System Identification for a Four Wheel Reaction Wheel Platform By Seth Franklyn Silva At the California Polytechnic State University, San Luis Obispo there is a four-wheel reaction wheel pyramidal simulator platform supported by an air-bearing. This simulator has the current capability to measure the wheel speeds and angular velocity of the platform, and with these measurements, the system identification process was used to obtain the mass properties of this simulator. A handling algorithm was developed to allow wireless data acquisition and command to the spacecraft simulator from a “ground” computer allowing the simulator to be free of induced torques due to wiring. The system identification algorithm using a least squares estimation scheme was tested on this simulator and compared to theoretical analysis. The resultant principle inertia about the z-axis from the experimental analysis was 3.5 percent off the theoretical, while the other inertias had an error of up to 187 percent. The error is explained as noise attributed to noise in the measurement, averaging inconsistencies, low bandwidth, and derivation of accelerations from measured data.

The aim of this effort is to implement a nonlinear flight control architecture, specifically flight path control via command filtered backstepping, for use in AME UAS's Fury® 1500 unmanned flying wing aircraft. Backstepping is a recursive, control-effort minimizing, constructive design procedure that interlaces the choice of a Lyapunov function with the design of feedback control. It allows the use of certain plant states to act as intermediate, virtual controls, for others breaking complex high order systems into a sequence of simpler lower-order design tasks. Work herein is a simplified implementation based on publications by Farrell, Sharma, and Polycarpou. Online approximation is not applied, however command filtering along with two variants of control allocation is. This minimal approach was done to mitigate risk, as adaptation could be added in future work to this baseline. Command filtering assures that control inputs generated meet magnitude, rate, and bandwidth constraints for states and actuators as well as provides command derivatives that reduce computation. Two different forms of control allocation were implemented, the simplest a least-squares pseudo-inverse and the second an optimal quadratic programming method. Two Simulink based simulations successfully flew AME's Fury® 1500 UAS: a nominal case with fully operational actuators and a failure case with an actuator stuck at -10°. Coordinated flight for both cases with outer loop tracking was achieved for a demanding autopilot task of simultaneously varying heading and flight-path angle commands, ±60° and ±10° respectively, for a constant airspeed command of 135 ft/s. Command signals were generated were achievable due to the command filter implementation.

Faults and failures in system components are the two main reasons for the instability and the degradation in control performance. In recent decades, fault-tolerant control (FTC) approaches were introduced to improve the resiliency of the control system against faults and failures. In general, FTC techniques are classified into two major groups: passive and active. Passive FTC systems do not rely on the fault information to control the system and are closely related to the robust control techniques while an active FTC system performs based on the information received from the fault detection and isolation (FDI) system, and the fault problem will be tackled more intelligently without affecting other parts of the system. This dissertation technically reviews fault and failure causes in control systems and finds solutions to compensate for their effects. Recent achievements in FDI approaches, and active and passive FTC designs are investigated. Thorough comparisons of several different aspects are conducted to understand the advantages and disadvantages of different FTC techniques to motivate researchers to further developing FTC, and FDI approaches. Then, a novel active FTC system framework based on online FDI is presented which has significant advantages in comparison with other state of the art FTC strategies. To design the proposed active FTC, a new FDI approach is introduced which uses the artificial neural network (ANN) and a model based observer to detect and isolate faults and failures in sensors and actuators. In addition, the extended Kalman filter (EKF) is introduced to tune ANN weights and improve the ANN performance. Then, the FDI signal combined with a nonlinear dynamic inversion (NDI) technique is used to compensate for the faults in the actuators and sensors of a nonlinear system. The proposed scheme detects and accommodates faults in the actuators and sensors of the system in real-time without the need of controller reconfiguration. The proposed active FTC approach is used to design a control system for three different applications: Unmanned aerial vehicle (UAV), load frequency control system, and proton exchange membrane fuel cell (PEMFC) system. The performance of the designed controllers are investigated through numerical simulations by comparison with conventional control approaches, and their advantages are demonstrated.

The control of multiple unmanned aerial vehicles (UAVs) in a swarm or cooperative team scenario has been a topic of great interest for well over a decade, growing steadily with the advancements in UAV technologies. In the academic community, a majority of the studies conducted rely on simulation to test developed control strategies, with only a few institutions known to have nurtured the infrastructure required to propel multiple UAV control studies beyond simulation and into experimental testing. With the Cal Poly UAV FLOC Project, such an infrastructure was created, paving the way for future experimentation with multiple UAV control systems. The control system architecture presented was built on concepts developed in previous work by Cal Poly faculty and graduate students. An outer-loop formation flight controller based on a virtual waypoint implementation of potential function guidance was developed for use on an embedded microcontroller. A commercially-available autopilot system, designed for fully autonomous waypoint navigation utilizing low cost hardware and open source software, was modified to include the formation flight controller and an inter-UAV communication network. A hardware-in-the-loop (HIL) simulation was set up for multiple UAV testing and was utilized to verify the functionality of the modified autopilot system. HIL simulation results demonstrated leader-follower formation convergence to 15 meters as well as formation flight with three UAVs. Several sets of flight tests were conducted, demonstrating a successful leader-follower formation, but with relative distance convergence only reaching a steady state value of approximately 35 +/- 5 meters away from the leader.

Benefits of developing missions with multiple formation flying spacecraft as an alternative to a traditional monolithic vehicle are becoming apparent. In some cases, these missions can lower cost and increase flexibility among other situational advantages. However, there are various limitations that are imposed by these missions that are centered on the concept of maintaining the necessary formation. One such limitation is that of the propulsion system required for each spacecraft. To mitigate the complexity and mass of the onboard propulsion, the pairing of electromagnetic actuators and differential drag to replace the functionality of a propulsive system is investigated. By using COTS magnetorquer boards to command satellite orientation, a scenario in which two 3U CubeSats are initially deployed from the ISS NanoRacks at an altitude of 400 km. They are then commanded to achieve a relative separation of 1 km and hold the spacing to demonstrate the capability of formation flight. The scenario was simulated through the MATLAB/Simulink platform and the magnitude of the necessary command torques were determined. By comparison to the ISIS magnetorquer board, the necessary command torques seem relatively high than compared to what the actuator is capable of. The ISIS board may supply ~5e-6 Nm of torque while the mission requires as much as 3e-3 Nm at times. However, by extending the settling time of the control law at the expense of absolute orientation control, the control torques necessary to carry out the simulated mission are well within the bounds of the ISIS magnetorquer boards as well as other COTS boards. With this alteration, mission feasibility is determined. It should be noted that further analysis should be conducted regarding concerns with CubeSat detumble to further confirm feasibility.

In addition to serving as a brief overview of aspects relevant to reduced-order modeling (in particular balanced-state and modal techniques) as applied to structural finite element models, this work produced tools for visualizing the relationship between the modes of a model and the states of its balanced representation. Specifically, error contour and mean error plots were developed that provide a designer with frequency response information absent from a typical analysis of a balanced model via its Hankel singular values. The plots were then used to analyze the controllability and observability aspects of finite element models of an illustrative system from a modal perspective -- this aided in the identification of computational artifacts in the models and helped predict points at which to halt the truncation of balanced states. Balanced reduced-order reference models of the illustrative system were implemented as part of a direct adaptive control algorithm to observe the effectiveness of the models. It was learned that the truncation point selected by observing the mean error plot produced the most satisfactory results overall -- the model closely approximated the dominant modes of the system and eliminated the computational artifacts. The problem of improving the performance of the system was also considered. The truncated balanced model was recast in modal form so that its damping could be increased, and the settling time decreased by about eighty percent.