# Predictive Control with Learning-Based Terminal Costs Using Approximate Value Iteration

@article{MorenoMora2022PredictiveCW, title={Predictive Control with Learning-Based Terminal Costs Using Approximate Value Iteration}, author={Francisco Moreno-Mora and Lukas Beckenbach and Stefan Streif}, journal={ArXiv}, year={2022}, volume={abs/2212.00361} }

: Stability under model predictive control (MPC) schemes is frequently ensured by terminal ingredients. Employing a (control) Lyapunov function as the terminal cost constitutes a common choice. Learning-based methods may be used to construct the terminal cost by relating it to, for instance, an inﬁnite-horizon optimal control problem in which the optimal cost is a Lyapunov function. Value iteration, an approximate dynamic programming (ADP) approach, refers to one particular cost approximation…

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