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Title: ALERA: Accelerated Reinforcement Learning Driven Adaptation to Electro-Mechanical Degradation in Nonlinear Control Systems Using Encoded State Space Error Signatures
The successful deployment of autonomous real-time systems is contingent on their ability to recover from performance degradation of sensors, actuators, and other electro-mechanical subsystems with low latency. In this article, we introduce ALERA, a novel framework for real-time control law adaptation in nonlinear control systems assisted by system state encodings that generate an error signal when the code properties are violated in the presence of failures. The fundamental contributions of this methodology are twofold—first, we show that the time-domain error signal contains perturbed system parameters’ diagnostic information that can be used for quick control law adaptation to failure conditions and second, this quick adaptation is performed via reinforcement learning algorithms that relearn the control law of the perturbed system from a starting condition dictated by the diagnostic information, thus achieving significantly faster recovery. The fast (up to 80X faster than traditional reinforcement learning paradigms) performance recovery enabled by ALERA is demonstrated on an inverted pendulum balancing problem, a brake-by-wire system, and a self-balancing robot.
Authors:
Award ID(s):
1723997
Publication Date:
NSF-PAR ID:
10175563
Journal Name:
ACM transactions on intelligent systems and technology
Volume:
10
Issue:
4
Page Range or eLocation-ID:
1-25
ISSN:
2157-6912
Sponsoring Org:
National Science Foundation
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