(YIP) MULTI-TIME SCALE STOCHASTIC HYBRID CONTROL- COORDINATED SET-SEEKING ON MANIFOLDS
Abstract
In this project, we propose to develop a new set of tools and methodologies for the analysis and synthesis of multi-time scale feedback control systems. The main focus of the project is on closed-loop dynamics characterized by stochastic hybrid dynamical systems, which are systems that combine continuous-time behaviors and discrete-time random behaviors. Such types of systems are of high-interest to the Air Force due to their relevance in applications such as unmanned aerial vehicles, multi-agent robotic systems, mobile networks, human-machine interactions, and decision making algorithms, among others. The proposed research aims to develop new analytical tools, as well as novel algorithmic architectures. The efforts will be focused on two particular domains, mostly unexplored in stochastic hybrid systems, and which can be tackled via common mathematical tools- singular perturbation and averaging theory. The project, which builds on the PI’s extensive experience on multi-time scale control and hybrid control theory, will fill an important gap in the control theory field, providing the Air Force with a unique set of tools for the synthesis and analysis of advanced control, optimization, and coordination algorithms. The research entails four interrelated research thrusts that provide a balance between the development of analytical tools, and the synthesis of algorithms and controllers- (T1) Developing tools to asses and certify probabilistic stability properties in multi-time scale stochastic hybrid dynamical systems; (T2) Synthesizing novel multi-time scale algorithms able to efficiently steer dynamical systems towards optimal operating sets (set-seeking); (T3) Developing new results on averaging theory for the analysis of systems with oscillatory behaviors; and (T4) synthesizing new stochastic hybrid controllers for the solution of model-free optimization and-or stabilization problems. The outcomes of the project will include a toolbox of analytical and computational tools, as well as numerical validations in systems of interest to the Air Force.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 07, 2023
- Source ID
- FA95502210211
Entities
People
- Jorge Poveda
Organizations
- Air Force Office of Scientific Research
- Regents of the University of Colorado
- United States Air Force