Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach
This work addresses safety-critical control for autonomous systems, offering a novel but incremental improvement by combining existing techniques for enhanced robustness.
The paper tackles the problem of ensuring safety in differentiable predictive control by integrating control barrier functions, resulting in a method that maintains safety both offline and online while minimally interrupting the neural network controller near safe set boundaries, as validated through simulation.
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.