Control Policies for a Large Region of Attraction for Dynamically Balancing Legged Robots: A Sampling-Based Approach
| Autor: | Jason Pusey, Steven Farra, Ali Zamani, Jeremy Krause, Pranav A. Bhounsule |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Control and Optimization Computer science General Mathematics Mechanical Engineering Control (management) Sampling (statistics) 02 engineering and technology Trajectory optimization 021001 nanoscience & nanotechnology Attraction Computer Science Applications Periodic function Nonlinear system 020901 industrial engineering & automation Control and Systems Engineering Robustness (computer science) Modeling and Simulation Robot 0210 nano-technology Software |
| Zdroj: | Robotica. 39:107-122 |
| ISSN: | 1469-8668 0263-5747 |
| Popis: | SUMMARYThe 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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