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.
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Cross-Layer Control Adaptation for Autonomous System Resilience
The last decade has seen tremendous advances
in the transformation of ubiquitous control, computing and
communication platforms that are anytime, anywhere. These
platforms allow humans to interact with machines through
sensing, control and actuation functions in ways not imaginable a
few decades ago. While robust control techniques aim to maintain
autonomous system performance in the presence of bounded
modeling errors, they are not designed to manage large multiparameter
variations and internal component failures that are
inevitable during lengthy periods of field deployment. To address
the trustworthiness of autonomous systems in the field, we
propose a cross-layer error resilience approach in which errors
are detected and corrected at appropriate levels of the design
(hardware-through software) with the objective of minimizing the
latency of error recovery while maintaining high failure coverage.
At the control processor level, soft errors in the digital control
processor are considered. At the system level, sensor and actuator
failures are analyzed. These impairments define the health of the
system. A methodology for adapting the control procedure of the
autonomous system to compensate for degraded system health is
proposed. It is shown how this methodology can be applied to
simple linear and nonlinear control systems to maintain system
performance in the presence of internal component failures.
Experimental results demonstrate the feasibility of the proposed
methodology.
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- Award ID(s):
- 1723997
- PAR ID:
- 10098275
- Date Published:
- Journal Name:
- International On-Line Testing Symposium
- Page Range / eLocation ID:
- 261 to 264
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IOLTS.2018.8474159
