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- W4290062316 abstract "We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions. DPC is an unsupervised learning-based method for obtaining approximate solutions to explicit model predictive control (MPC) problems. In DPC, the predictive control policy parametrized by a neural network is optimized offline via direct policy gradients obtained by automatic differentiation of the MPC problem. The proposed approach exploits a new form of sampled-data barrier function to enforce offline and online safety requirements in DPC settings while only interrupting the neural network-based controller near the boundary of the safe set. The effectiveness of the proposed approach is demonstrated in simulation." @default.
- W4290062316 created "2022-08-06" @default.
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- W4290062316 date "2022-08-03" @default.
- W4290062316 modified "2023-09-24" @default.
- W4290062316 title "Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach" @default.
- W4290062316 doi "https://doi.org/10.48550/arxiv.2208.02319" @default.
- W4290062316 hasPublicationYear "2022" @default.
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