<p>The dissertation research was conducted to examine articles, research, and studies that have been collected in recent years to understand energy management for general aviation airplane pilots. The experiment was broken down into four phases with control and treatment groups which have evaluated the real-world problem of energy management in aviation. The four phases were to fly a flight profile, evaluate the energy state of the airplane within the flight by video, fly the same flight profile again, and a post-flight interview with the pilots. The idea of this experiment was to recognize the lack of understanding in energy management in pilots, build a conceptual model, and lastly verify and validate Phase II of the model by utilizing previous studies and research. Additionally, the three main goals were to assess the ability to interpret energy management, assess the ability to control the aircraft, and lastly, to interview for perception of energy management. The data was collected on the flight training device’s G1000, and the researcher analyzed the data using R, Minitab, Excel, and NVivo. The research provided ideas for creating a future model to evaluate energy management, validated by testing Phase II of the model to understand assessing energy management in real time, and interviewed pilots on their experiences with energy management, identified gaps in general aviation research, and suggested methods of how to facilitate understanding of energy management.</p>

In recent years, significant progress has been made in autonomous robotics and the electrification of transportation, highlighting the growing importance of automation in daily life. Ensuring the safety and sustainability of automated systems necessitates the integration of intelligent algorithms capable of making astute decisions in uncertain circumstances. Autonomous robots possess considerable potential for efficiently performing intricate tasks, but this potential can only be unlocked through intelligent algorithms. Moreover, enhancing the energy efficiency of transportation systems yields extensive benefits for the environment, economy, and society at large. Addressing the urgent challenges of climate change and resource depletion necessitates prioritizing energy efficiency in transportation to construct a more resilient and equitable future. This research delves into the development of bio-inspired neural dynamics, nature-inspired swarm intelligence, fuzzy logic, heuristic algorithms, and optimization techniques for optimal control and navigation of electrified and unmanned ground vehicles. Drawing inspiration from biological systems, this research aims to enhance the performance of robots in dynamic and unstructured environments. The approach encompasses a hybrid bio-inspired method, leveraging the mathematical model of a biological neural system's membrane to facilitate smooth trajectory tracking and bounded velocities for a differential drive robot. Additionally, integration of a Leader-Slime Mold Algorithm (L-SMA) for global path optimization and a modified velocity obstacle (MVO) for local motion planning is pursued. A heuristic algorithm is also devised to enhance decision-making in uncertain and dynamic environments by coordinating actions among the L-SMA path planner, the MVO local motion planner, and the enhanced bio-inspired tracking controller. Furthermore, a real-time optimal predictive controller is proposed to address the energy management challenges of electrified vehicles while improving driveability and comfort. This predictive controller employs a linear parameter-varying model of an electrified vehicle, a custom-designed adaptive cost function, and fuzzy logic to adapt a subset of cost function weights. The integration of fuzzy logic and the adaptive predictive controller yields a convex optimization problem solved in real-time using an active-set solver. To further enhance the energy efficiency of the electrified vehicle, a particle swarm optimization enhanced model predictive controller is suggested as an adaptive cruise controller with superior energy efficiency and safety in vehicle-following scenarios. Through these integrated approaches, the aim is to advance the capabilities of autonomous robotics and electrified transportation systems, thereby contributing to safer, more efficient, and sustainable mobility solutions.