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

TL;DR

This paper introduces a convex stochastic control framework that manages uncertainty in control specifications, ensuring probabilistic constraint satisfaction and applying it to hybrid powertrain control with learning-based models.

Contribution

It develops a strictly convex chance-constrained control method that handles non-Gaussian uncertainties and extends to nonlinear models via machine learning.

Findings

Guarantees probabilistic constraint satisfaction.

Ensures unique and continuous optimal solutions.

Demonstrates effectiveness on hybrid powertrain system.

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.