Exponential stability and suboptimality guarantees for discounted and undiscounted MPC under plant-model mismatch proportional to states and inputs, with uniform robustness over horizon length.
Stability of data-driven Koopman MPC with terminal conditions
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abstract
This paper derives conditions under which Model Predictive Control (MPC) with terminal conditions, using a data-driven surrogate model as a prediction model, asymptotically stabilizes the plant despite approximation errors. In particular, we prove recursive feasibility and asymptotic stability if a proportional error bound holds, where proportional means that the bound is linear in the norm of the state and the input. For a broad class of nonlinear systems, this condition can be satisfied using data-driven surrogate models generated by kernel Extended Dynamic Mode Decomposition (kEDMD) using the Koopman operator. Last, the applicability of the proposed framework is demonstrated in a numerical case study.
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math.OC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Discounted MPC and infinite-horizon optimal control under plant-model mismatch: Stability and suboptimality
Exponential stability and suboptimality guarantees for discounted and undiscounted MPC under plant-model mismatch proportional to states and inputs, with uniform robustness over horizon length.