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arXiv · 2208.02319

Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach

Wenceslao Shaw Cortez·Jan Drgona·Aaron Tuor·Mahantesh Halappanavar·Draguna Vrabie
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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.

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Wenceslao Shaw Cortez, Jan Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie. 2022-08-03. Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach. https://arxiv.org/abs/2208.02319

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