{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/147247"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/147247","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"AC-RL: A Framework for Real-Time Control, Learning & Adaptation","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Guha, Anubhav"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["AC-RL: A Framework for Real-Time Control, Learning & Adaptation"]}]}],"canonical_facts":{"dc:contributor.advisor":["Annaswamy, Anuradha"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Mechanical Engineering"],"dc:creator":["Guha, Anubhav"],"dc:date.accessioned":["2023-01-19T18:40:07Z"],"dc:date.available":["2023-01-19T18:40:07Z"],"dc:date.issued":["2022-09"],"dc:description.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. 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