期刊
2020 59TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC)
卷 -, 期 -, 页码 2258-2263出版社
IEEE
关键词
-
资金
- Office of Naval Research [ONR-N00014-18-1-2833]
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.
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