Data-Driven Modeling for Dual Retrospectve Cost Adaptive Control
Abstract
We propose to apply principles and techniques of DDDAS to adaptive control of complex systems.In particular, the focus is on dual retrospective cost adaptive control (DRCAC), whichis applicable to command following and disturbance rejection for discrete-time systems. DRCACrequires minimal modeling information, and the objective of this proposal is to developand demonstrate ecient and eective DDDAS-inspired identication methods that can be implementedonline to obtain this modeling information. One of the key challenges is to perform thedata-driven modeling with sucient speed and accuracy that the adaptive control algorithm canobtain the required modeling information. We will develop and demonstrate techniques for thisproblem based on quasi-closed-loop identification methods, where the goal is to determine probingsignals that are minimally disruptive but sufficiently persistent to obtain the required modelinginformation. The concurrent implementation of data-driven modeling and adaptive control is achallenging but important problem for control technology with diverse applications to vehicles andprocesses.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 09, 2018
- Source ID
- FA95501810171
Entities
People
- Dennis S. Bernstein
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Michigan