Dr. Shageer: Run-time assurance via real-time generation of trajectory and transverse dynamics regulation law

Overview

Run-time assurance via real-time generation of trajectory and transverse dynamics regulation law

Abstract

In safety or mission-critical environments run-time assurance is about performing actions with full consciousness of opt-out options and the willingness to exercise such options when they are about to run-out. Autonomy in aerospace systems is one of the most delicate challenges in control theory due to the blatant catastrophic and tragic outcomes of control failures. This talk proposes a new framework for run-time assurance and closed-loop trajectory generation for nonlinear dynamical systems, which enjoys intelligent interaction with complex environments. This framework exploits the transverse dynamics to compute closed-loop control laws for optimal trajectories in complex obstacle environments in real-time. Finally, we will provide a three-state example in a complex obstacle environment for demonstration in which we will use the GuSTO algorithm for the generation of nominal control trajectories.

Brief Biography

Hesham Shageer is an Assistant Research Professor with the collaborative research Center of Excellence for Aeronautics and Astronautics (CEAA), a joint scientific effort between KACST and Stanford University, where he has been Co-PI on multiple projects since 2015. He was appointed to Co-Director of the CEAA in 2018. His research experience includes Adaptive Control Theory and Design Methodologies, System Modeling and Numerical Simulations, as well as Optimal Experimental Design. He received his B.S. (2004), M.S. (2006), and Ph.D. (2013) degrees from the University of Virginia (UVA), all of which in Electrical Engineering and a specialization in Control Systems. While at UVA, he participated on a novel aircraft control design project funded by NASA, in addition to participating in the Solar Decathlon Design Competition. His research interests span multiple domains including Machine Learning Based Adaptive Control Synthesis, Trajectory Optimization for Flying Robots, Autonomous Aircraft System Design, Green Engineering, and Bio-mimicking Design Concepts.