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

TL;DR

This paper introduces a differentiable predictive control method that uses control barrier functions to ensure safety and robustness, optimizing neural network policies offline with guarantees during online operation.

Contribution

It presents a novel safety-guaranteed differentiable predictive control framework leveraging control barrier functions for both offline training and online safety enforcement.

Findings

Effective safety guarantees demonstrated in simulation

Neural network policies optimized via automatic differentiation

Safe operation near boundary of the safe set

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.