Quantum-Assisted Dynamic Optimization for Industrial Control Systems: A Robust Tube-MPC Framework

Abstract

Real-time optimal control of complex hybrid systems-governed by both continuous thermodynamic dynamics and discrete topological switching-presents a formidable Mixed-Integer Nonlinear Programming (MINLP) challenge. Traditional exact solvers often experience severe performance degradation, or “engineering suffocation,” under strict computational time budgets and tightened robust safety constraints. To address this critical bottleneck, we propose the Quantum-Classical Collaborative MINLP Dynamic Optimization (QC-MINLP-DO) framework, which integrates the Quantum Approximate Optimization Algorithm (QAOA) with robust modedependent Tube-MPC. By enforcing a strict dual-timescale separation, the framework isolates continuous worst-case safety from discrete combinatorial exploration. A fast-timescale Tube-MPC guarantees absolute recursive feasibility and physical safety against bounded disturbances. Asynchronously, a costaware meta-scheduler maps a trust-region-bounded pseudo-Boolean surrogate to a sparse Ising Hamiltonian, utilizing the QAOA backend to orthogonally sample high-quality topological candidates. Crucially, a two-tiered Cross-Domain Admissibility Veto Protocol strictly intercepts any unsafe or infeasible proposals prior to execution. Extensive time-domain simulations on a green-hydrogen microgrid demonstrate the framework's efficacy across varying Feasibility Fragmentation Index (FI) regimes. Remarkably, under a strict 1.5-second Time-to-Solution (TTS) budget and extreme FI penalties, the quantumassisted framework completely avoids the choking phenomenon of classical solvers, delivering up to a 15.3x reduction in Non-Update Probability (NUP) while mathematically guaranteeing zero physical constraint violations.