AC-RL: A Framework for Real-Time Control, Learning & Adaptation
Author(s)
Guha, Anubhav
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Advisor
Annaswamy, Anuradha
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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.
Date issued
2022-09Department
Massachusetts Institute of Technology. Department of Mechanical EngineeringPublisher
Massachusetts Institute of Technology