Adaptive Flight Control in the Presence of Limits on Magnitude and Rate

Gaudio, Joseph E.; Annaswamy, Anuradha M.; Bolender, Michael A.; Lavretsky, Eugene

doi:10.48550/arxiv.1907.11913

Cited by 1 publication

(1 citation statement)

References 25 publications

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“…Unlike many RL algorithms, adaptive control techniques excel in the "zero-shot" enforcement of control objectives -that is, in learning to accomplish a task online [5] [30][31] [32]. These adaptive techniques are able to accommodate, in real-time, parametric uncertainties and constraints on the control input magnitude [33][34] and rate [35]. This ability to achieve control goals while accounting for parametric uncertainties in real-time is the strength of adaptive control.…”

Section: Introductionmentioning

confidence: 99%

Online Policies for Real-Time Control Using MRAC-RL

Guha¹,

Annaswamy²

2021

Preprint

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In this paper, we propose the Model Reference Adaptive Control & Reinforcement Learning (MRAC-RL) approach to developing online policies for systems in which modeling errors occur in real-time. Although reinforcement learning (RL) algorithms have been successfully used to develop control policies for dynamical systems, discrepancies between simulated dynamics and the true target dynamics can cause trained policies to fail to generalize and adapt appropriately when deployed in the real-world. The MRAC-RL framework generates online policies by utilizing an inner-loop adaptive controller together with a simulation-trained outer-loop RL policy. This structure allows MRAC-RL to adapt and operate effectively in a target environment, even when parametric uncertainties exists. We propose a set of novel MRAC algorithms, apply them to a class of nonlinear systems, derive the associated control laws, provide stability guarantees for the resulting closed-loop system, and show that the adaptive tracking objective is achieved. Using a simulation study of an automated quadrotor landing task, we demonstrate that the MRAC-RL approach improves upon state-of-the-art RL algorithms and techniques through the generation of online policies.

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Section: Introductionmentioning

confidence: 99%