Convex Chance-Constrained Stochastic Control under Uncertain Specifications with Application to Learning-Based Hybrid Powertrain Control
This work addresses control challenges in systems like hybrid powertrains, offering a method to handle uncertainty in specifications, but it appears incremental as it builds on existing chance-constrained and learning-based control approaches.
The paper tackles the problem of stochastic control under uncertain specifications by proposing a convex chance-constrained framework that guarantees probabilistic constraint satisfaction and strict convexity, demonstrating its effectiveness in a hybrid powertrain system with model predictive control.
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.