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

• URL: http://arxiv.org/abs/2208.02319v1
• Date: Wed, 3 Aug 2022 19:24:44 GMT
• Title: Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach
• Authors: Wenceslao Shaw Cortez, Jan Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie
• Abstract summary: We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees. DPC is an unsupervised learning-based method for obtaining approximate solutions to explicit model predictive control (MPC) problems.
• Score: 3.617866023850784
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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