Dissertation Defense Announcement for Amine Taoudi – 05/10/2024 at 3:00 PM

May 2, 2024

Thesis Title: Optimal Control and Navigation of Electrified and Unmanned Ground Vehicles with Bio-inspired and Optimization Approaches
When: 05/10/2024 3:00 PM
Where: Join conversation (microsoft.com) Passcode: fsccfC
Candidate: Amine Taoudi
Degree: Doctor of Philosophy in Electrical and Computer Engineering
Committee Members: Dr. Chaomin Luo, Dr. Randolph F. Follet, Dr. Seundeog Choi, Dr. Masoud Karimi-Ghartemani


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, albeit reliant on 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.