Control of Unknown Nonlinear Systems with Linear Time-Varying MPC
Abstract
We present a Model Predictive Control (MPC) strategy for unknown input-affine nonlinear dynamical systems. A non-parametric method is used to estimate the nonlinear dynamics from observed data. The estimated nonlinear dynamics are then linearized over time-varying regions of the state space to construct an Affine Time-Varying (ATV) model. Error bounds arising from the estimation and linearization procedure are computed by using sampling techniques. The ATV model and the uncertainty sets are used to design a robust Model Predictive Controller (MPC) which guarantees safety for the unknown system with high probability. A simple nonlinear example demonstrates the effectiveness of the approach where commonly used estimation and linearization methods fail.
Additional Information
© 2020 IEEE. This work was also sponsored by the Office of Naval Research grant ONR-N00014-18-1-2833. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Office of Naval Research or the US government.Attached Files
Submitted - 2004.03041.pdf
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Additional details
- Eprint ID
- 107643
- DOI
- 10.1109/cdc42340.2020.9304441
- Resolver ID
- CaltechAUTHORS:20210121-152558095
- N00014-18-1-2833
- Office of Naval Research (ONR)
- Created
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2021-01-21Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field