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region of attraction

Robotica (1)

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Control Policies for a Large Region of Attraction for Dynamically Balancing Legged Robots: A Sampling-Based Approach
Pranav A. Bhounsule, Ali Zamani, Jeremy Krause, Steven Farra, Jason Pusey
Journal:

Robotica / Volume 39 / Issue 1 / January 2021

Published online by Cambridge University Press:

05 May 2020, pp. 107-122
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The popular approach of assuming a control policy and then finding the largest region of attraction (ROA) (e.g., sum-of-squares optimization) may lead to conservative estimates of the ROA, especially for highly nonlinear systems. We present a sampling-based approach that starts by assuming an ROA and then finds the necessary control policy by performing trajectory optimization on sampled initial conditions. Our method works with black-box models, produces a relatively large ROA, and ensures exponential convergence of the initial conditions to the periodic motion. We demonstrate the approach on a model of hopping and include extensive verification and robustness checks.