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Topological Feasibility Guarantees for Differentiable Predictive Control
The paper establishes deterministic feasibility guarantees for differentiable predictive control (DPC), a self-supervised approach for approximating explicit MPC policies. Through topological analysis of the induced reachable safe set, it shows DPC can guarantee feasibility without online safety filters.
Why it matters
Addressing feasibility guarantees for learned control policies extends the applicability of machine learning methods in safety-critical control systems.
Relevant to DT products
- Original headline
- Topological Feasibility Guarantees for Differentiable Predictive Control
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