Consensus strategies are examined as a possible approach to achieving attitude alignment for a large, close-proximity formation of nanosatellites in low earth orbit (LEO). An attitude-only distributed consensus approach is selected for further consideration due to its comparatively low data transmission requirements. The convergence of a connected network of satellites to the attitude agreement subspace under this control law is shown using a Lyapunov stability approach with a set of idealizing assumptions. A moderate-fidelity simulation demonstrates the performance of the control law under realistic conditions that violate those assumptions. Particular emphasis is placed on the conditions that arise from the limitations of the nanosatellite form factor, namely the low accuracy of sensors and the limited computational resources. The sensitivity of the pointing performance to these factors is characterized, and the control approach is shown to be viable for use in future nanosatellite missions.

The development of atmospheric entry guidance methods is crucial to achieving the requirements for future missions to Mars; however, many missions implement a unique controller which are spacecraft specific. Here we look at the implementation of neural networks as a baseline controller that will work for a variety of different spacecraft. To accomplish this, a simulation is developed and validated with the Apollo controller. A feedforward neural network controller is then analyzed and compared to the Apollo case.

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.

The goal of this thesis is to investigate automatic mass balancing methods for spacecraft attitude dynamics simulators, create a hardware design for a mass balancing system, and assemble the hardware on the Cal Poly Spacecraft Attitude Dynamics Simulator (SADS). Spacecraft attitude dynamics simulators replicate the torque-free environment of space with ground-based hardware. The SADS is mounted on a spherical air bearing, and includes a pyramid of four reaction wheels. The air bearing allows frictionless, unbounded rotation about the vertical axis, and 30 degrees about the horizontal axes. The torque-free configuration of the SADS can be used to test spacecraft attitude control software and hardware. For spacecraft attitude simulators, it is essential to accurately align the center of mass with the center of rotation. Any alignment error will cause a gravitational disturbance torque that quickly saturates actuators. It is necessary to use an automatic mass balancing system if the system's hardware is regularly adjusted, since manual mass balancing methods become prohibitively time consuming. Once balanced, it is also useful if the attitude simulator can measure its inertia tensor for use in control software. To avoid displacing the center of mass, a symmetrical six-sliding-mass balancing system was developed for the SADS, driven by geared DC motors. Several mass balancing algorithms were designed that do not require additional actuators. These algorithms enable mass balancing prior to the integration of reaction wheels, and are intended to eventually be part of a two-stage balancing approach. In the controlled stage, a continuous time controller corrects the gravitational disturbance torque. A moment distribution law for the sliding masses is then used with an FSFB or adaptive controller to align the center of mass and rotation. In the system identification stage, angles and angular rates are measured during phased torque sine sweeps so that the inertia tensor and center of mass position can be estimated. After making a correction to the sliding mass positions, the identification and correction process is repeated until the system is balanced. To be considered 'balanced,' any remaining disturbance torques must be negligible on the time scale of a control system simulation. The horizontal axis positional errors for the center of mass should be on the order of one tenth of a millimeter, and it is generally acceptable for the vertical axis error to be approximately an order of magnitude greater. Since a vertical center of mass offset is generally aligned with the structure of the air bearing, its corresponding gravity disturbance torque is reduced. Since the mass properties of the SADS must be measured regardless, they are a convenient metric for the effectiveness of the balancing system.

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.