The United States trucking industry is immense. Employing over three million drivers and traveling to every city in the country. Semi-Trucks travel millions of miles each week and encompass roads that civilians travel on. These vehicles should be safe and allow efficient travel for all. Autonomous vehicles have been discussed in controls for many decades. Now fleets of autonomous vehicles are beginning their integration into society. The ability to create an autonomous system requires domain and system specific knowledge. Approaches to implement a fully autonomous vehicle have been developed using different techniques in control systems such as Kalman Filters, Neural Networks, Model Predictive Control, and Adaptive Control. However some of these control techniques require superb models, immense computing power, and terabytes of storage. One way to circumvent these issues is by the use of an adaptive control scheme. Adaptive control systems allow for an existing control system to self-tune its performance for unknown variables i.e. when an environment changes. In this thesis a LQR error state control system is derived and shown to maintain a magnitude of 15 cm of steady state error from the center-line of the road. In addition a proposed LQR-MRAC controller is used to test the robustness of a lane-keeping control system. The LQR-MRAC controller was able to improve its transient response peak error from the center-line of the road of the tractor and the trailer by 9.47 [cm] and 7.27 [cm]. The LQR-MRAC controller increased tractor steady state error by 0.4 [cm] and decreased trailer steady state error by 1 [cm]. The LQR-MRAC controller was able to outperform modern control techniques and can be used to improve the response of the tractor-trailer system to handle mass changes in its environment.

No / This paper presents an investigation into the challenges in implementing a hard real-time optimal non-stationary system using general regression neural network (GRNN). This includes investigation into the dynamics of the problem domain, discretisation of the problem domain to reduce the computational complexity, parameters selection of the optimization algorithm, convergence guarantee for real-time solution and off-line optimization for real-time solution. In order to demonstrate these challenges, this investigation considers a real-time optimal missile guidance algorithm using GRNN to achieve an accurate interception of the maneuvering targets in three-dimension. Evolutionary Genetic Algorithms (GAs) are used to generate optimal guidance training data set for a large missile defense space to train the GRNN. The Navigation, Constant of the Proportional Navigation Guidance and the target position at launching are considered for optimization using GAs. This is achieved by minimizing the. miss distance and missile flight time. Finally, the merits of the proposed schemes for real-time accurate interception are presented and discussed through a set of experiments. (C) 2012 Elsevier Ltd. All rights reserved.

Attitude control remains one of the top engineering challenges faced by small satellite mission planning and design. Conventional methods for attitude control include propulsion, reaction wheels, magnetic torque coils, and passive stabilization mechanisms, such as permanent magnets that align with planetary magnetic fields. Drawbacks of these conventional attitude control methods for small satellites include size, power consumption, dependence on external magnetic fields, and lack of full control authority. This research investigates an alternative, novel approach to attitude-control method for small satellites, utilizing the noncommutative property of rigid body rotation sequences. Piezoelectric bimorph actuators are used to induce sinusoidal small-amplitude satellite oscillations on two of the satellites axes. While zero net change occurs on these signaled axes, the third axis can develop an average angular rate. This noncommutative attitude control methodology has several advantages over conventional methods, including scalability, power consumption, and operation outside of Earth's magnetic field. This research looks into the feasibility of such a system, and lays the foundation for a simple control system architecture.

As electronics become smaller and more capable, it has become possible to conduct meaningful and sophisticated satellite missions in a small form factor. However, the capability of small satellites and the range of possible applications are limited by the capabilities of several technologies, including attitude determination and control systems. This dissertation evaluates the use of image-based visual attitude propagation as a compliment or alternative to other attitude determination technologies that are suitable for miniature satellites. The concept lies in using miniature cameras to track image features across frames and extracting the underlying rotation. The problem of visual attitude propagation as a small satellite attitude determination system is addressed from several aspects: related work, algorithm design, hardware and performance evaluation, possible applications, and on-orbit experimentation. These areas of consideration reflect the organization of this dissertation. A “stellar gyroscope” is developed, which is a visual star-based attitude propagator that uses relative motion of stars in an imager’s field of view to infer the attitude changes. The device generates spacecraft relative attitude estimates in three degrees of freedom. Algorithms to perform the star detection, correspondence, and attitude propagation are presented. The Random Sample Consensus (RANSAC) approach is applied to the correspondence problem to successfully pair stars across frames while mitigating false-positive and false-negative star detections. This approach provides tolerance to the noise levels expected in using miniature optics and no baffling, and the noise caused by radiation dose on orbit. The hardware design and algorithms are validated using test images of the night sky. The application of the stellar gyroscope as part of a CubeSat attitude determination and control system is described. The stellar gyroscope is used to augment a MEMS gyroscope attitude propagation algorithm to minimize drift in the absence of an absolute attitude sensor. The stellar gyroscope is a technology demonstration experiment on KySat-2, a 1-Unit CubeSat being developed in Kentucky that is in line to launch with the NASA ELaNa CubeSat Launch Initiative. It has also been adopted by industry as a sensor for CubeSat Attitude Determination and Control Systems (ADCS).

Markov processes have been widely studied and used for modeling problems. A Markov process has two main components (i.e., an evolution law and an initial distribution). Markov processes are not suitable for modeling some problems, for example, the problem of predicting a trajectory with a known destination. Such a problem has three main components: an origin, an evolution law, and a destination. The conditionally Markov (CM) process is a powerful mathematical tool for generalizing the Markov process. One class of CM processes, called $CM_L$, fits the above components of trajectories with a destination. The CM process combines the Markov property and conditioning. The CM process has various classes that are more general and powerful than the Markov process, are useful for modeling various problems, and possess many Markov-like attractive properties. Reciprocal processes were introduced in connection to a problem in quantum mechanics and have been studied for years. But the existing viewpoint for studying reciprocal processes is not revealing and may lead to complicated results which are not necessarily easy to apply. We define and study various classes of Gaussian CM sequences, obtain their models and characterizations, study their relationships, demonstrate their applications, and provide general guidelines for applying Gaussian CM sequences. We develop various results about Gaussian CM sequences to provide a foundation and tools for general application of Gaussian CM sequences including trajectory modeling and prediction. We initiate the CM viewpoint to study reciprocal processes, demonstrate its significance, obtain simple and easy to apply results for Gaussian reciprocal sequences, and recommend studying reciprocal processes from the CM viewpoint. For example, we present a relationship between CM and reciprocal processes that provides a foundation for studying reciprocal processes from the CM viewpoint. Then, we obtain a model for nonsingular Gaussian reciprocal sequences with white dynamic noise, which is easy to apply. Also, this model is extended to the case of singular sequences and its application is demonstrated. A model for singular sequences has not been possible for years based on the existing viewpoint for studying reciprocal processes. This demonstrates the significance of studying reciprocal processes from the CM viewpoint.

Biomimetic flapping-wing vehicles have attracted recent interest because of their numerous potential military and civilian applications. In this dissertation is described the design of a multi-agent adaptive controller for such a vehicle. This controller is responsible for estimating the vehicle pose (position and orientation) and then generating four parameters needed for split-cycle control of wing movements to correct pose errors. These parameters are produced via a subsumption architecture rule base. The control strategy is fault tolerant. Using an online learning process, an agent continuously monitors the vehicle's behavior and initiates diagnostics if the behavior has degraded. This agent can then autonomously adapt the rule base if necessary. Each rule base is constructed using a combination of extrinsic and intrinsic evolution. Details of the vehicle, the multi-agent system architecture, agent task scheduling, rule base design, and vehicle control are provided.

This study explores the variable speed operation and shift response of a prototype of a two speed single path CVT rotorcraft driveline system. Here a Comprehensive Variable Speed Rotorcraft Propulsion system Modeling (CVSRPM) tool is developed and utilized to simulate the drive system dynamics in steady forward speed condition. This investigation attempts to build upon previous variable speed rotorcraft propulsion studies by: 1) Including fully nonlinear first principles based transient gas-turbine engine model 2) Including shaft flexibility 3) Incorporating a basic flight dynamics model to account for interactions with the flight control system. Through exploring the interactions between the various subsystems, this analysis provides important insight into the continuing development of variable speed rotorcraft propulsion systems.

This thesis presents the design of an attitude determination system for small satellites that automatically corrects for attitude drift. Existing attitude determination systems suffer from attitude drift due to the integration of noisy rate gyro sensors used to measure the change in attitude. This attitude drift leads to a gradual loss in attitude knowledge, as error between the estimated attitude and the actual attitude increases. In this thesis a Kalman filter is used to complete sensor fusion which combines sensor observations with a projected attitude based on the dynamics of the satellite. The system proposed in this thesis also utilizes a novel sensor called the stellar gyro to correct for the drift. The stellar gyro compares star field images taken at different times to determine orientation, and works in the presence of the sun and during eclipse. This device provides a relative attitude fix that can be used to update the attitude estimate provided by the Kalman filter, effectively compensating for drift. Simulink models are developed of the hardware and algorithms to model the effectiveness of the system. The Simulink models show that the attitude determination system is highly accurate, with steady state errors of less than 1 degree.

Instrumented umanned aerial vehicles represent a new way of measuring turbulence in the atmospheric boundary layer. However, autonomous measurements require control methods with disturbance-rejection and altitude command-following capabilities. Filtered dynamic inversion is a control method with desirable disturbance-rejection and command-following properties, and this controller requires limited model information. We implement filtered dynamic inversion as the pitch controller in an altitude-hold autopilot. We design and numerically simulate the continuous-time and discrete-time filtered-dynamic-inversion controllers with anti-windup on a nonlinear aircraft model. Finally, we present results from a flight experiment comparing the filtered-dynamic-inversion controller to a classical proportional-integral controller. The experimental results show that the filtered-dynamic-inversion controller performs better than a proportional-integral controller at certain values of the parameter.

The obstacle avoidance navigation problem for Unmanned Aerial Vehicles (UAVs) is a very challenging problem. It lies at the intersection of many fields such as probability, differential geometry, optimal control, and robotics. We build a mathematical framework to solve this problem for quadrotors using both a theoretical approach through a Hamiltonian system and a machine learning approach that learns from human sub-experts' multiple demonstrations in obstacle avoidance. Prior research on the machine learning approach uses an algorithm that does not incorporate geometry. We have developed tools to solve and test the obstacle avoidance problem through mathematics.