An evolutionary approach to discovering execution mode boundaries for adaptive controllers

Adaptive controllers enable cyber-physical systems, such as autonomous robots, to manage uncertain conditions during execution. However, there is a limit to the range of conditions that can be handled by a given controller. When this limit is exceeded, a controller might fail to respond as expected, not only rendering it ineffective but possibly putting the entire system at risk. In this paper, we describe a method based on evolutionary search for automatically enhancing, and discovering the boundaries of, a given adaptive controller. Collectively, these boundaries define an execution mode for that controller. Explicit specification of mode boundaries facilitates the development of decision logic that determines, based on system state and sensed conditions, when to switch to a different execution mode and typically a different controller, such as one for providing fail-safe operation. To evaluate the proposed approach, we apply it to a robotic fish propelled by a flexible caudal fin that is governed by a model-free adaptive controller. Experimental results demonstrate that this approach is effective in characterizing a controller's ability to adapt to environmental dynamics, including physical damage to the robot itself.