AC-RL: A Framework for Real-Time Control, Learning & Adaptation
Name
guha-anguha-sm-meche-2022-thesis.pdf
Description
Thesis PDF
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938 KB
Format
Adobe PDF
Checksum (MD5)
64e7e9f573ac882ea3600d9d43788807
Author(s)
Guha, Anubhav
Advisor(s)
Annaswamy, Anuradha
Date Issued
September 2022
Publisher
Massachusetts Institute of Technology
Abstract
This paper considers the problem of real-time control and learning in dynamic systems subjected to parametric uncertainties. A combination of Adaptive Control (AC) in the inner loop and a Reinforcement Learning (RL) based policy in the outer loop is proposed such that in real-time the inner-loop model reference adaptive controller contracts the closed-loop dynamics towards a reference system, while the RL in the outerloop directs the overall system towards approximately optimal performance. This AC-RL approach is developed for a class of control affine nonlinear dynamical systems, and employs extensions to systems with multiple equilibrium points, systems with input magnitude constraints, and systems in which a high-order tuner is required for adequate performance. In addition to establishing a stability guarantee with realtime control, the AC-RL controller is also shown to lead to parameter learning with persistent excitation. Numerical validations of all algorithms are carried out using a quadrotor landing task on a moving platform. These results point out the clear advantage of the proposed integrative AC-RL approach.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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