Convex Chance-Constrained Stochastic Control under Uncertain Specifications with Application to Learning-Based Hybrid Powertrain Control

• URL: http://arxiv.org/abs/2601.18313v1
• Date: Mon, 26 Jan 2026 09:49:33 GMT
• Title: Convex Chance-Constrained Stochastic Control under Uncertain Specifications with Application to Learning-Based Hybrid Powertrain Control
• Authors: Teruki Kato, Ryotaro Shima, Kenji Kashima,
• Abstract summary: This paper presents a strictly convex chance-constrained control framework that accounts for uncertainty in control specifications.<n>The proposed method guarantees probabilistic constraint satisfaction while ensuring strict convexity, leading to uniqueness and continuity of the optimal solution.<n>The effectiveness of the proposed approach is demonstrated through model predictive control applied to a hybrid powertrain system.
• Score: 1.9116784879310027
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents a strictly convex chance-constrained stochastic control framework that accounts for uncertainty in control specifications such as reference trajectories and operational constraints. By jointly optimizing control inputs and risk allocation under general (possibly non-Gaussian) uncertainties, the proposed method guarantees probabilistic constraint satisfaction while ensuring strict convexity, leading to uniqueness and continuity of the optimal solution. The formulation is further extended to nonlinear model-based control using exactly linearizable models identified through machine learning. The effectiveness of the proposed approach is demonstrated through model predictive control applied to a hybrid powertrain system.

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