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

arXiv: https://arxiv.org/abs/2601.18313 · HTML: https://mlsanity.com/paper/2601.18313/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [OSQP: An Operator Splitting Solver for Quadratic Programs](https://mlsanity.com/paper/1711.08013/) (2017)
- [Diffusion-Based Planning for Autonomous Driving with Flexible Guidance](https://mlsanity.com/paper/2501.15564/) (2025)
- [Cautious Model Predictive Control using Gaussian Process Regression](https://mlsanity.com/paper/1705.10702/) (2017)
- [Receding-horizon Stochastic Model Predictive Control with Hard Input Constraints and Joint State Chance Constraints](https://mlsanity.com/paper/1506.08471/) (2015)
- [Regularity Properties of Optimization-Based Controllers](https://mlsanity.com/paper/2311.13167/) (2023)
- [Structured Hammerstein-Wiener Model Learning for Model Predictive Control](https://mlsanity.com/paper/2107.04247/) (2021)
- [Learning Exactly Linearizable Deep Dynamics Models](https://mlsanity.com/paper/2311.18261/) (2023)
- [Ambiguous Chance-Constrained Binary Programs under Mean-Covariance Information](https://mlsanity.com/paper/1610.00035/) (2016)
- [Scenario Model Predictive Control for Data-based Energy Management in Plug-in Hybrid Electric Vehicles](https://mlsanity.com/paper/2104.07343/) (2021)
- [Moving Obstacle Collision Avoidance via Chance-Constrained MPC with CBF](https://mlsanity.com/paper/2304.01639/) (2023)
- [Modeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control](https://mlsanity.com/paper/2509.19869/) (2025)

## Abstract (arXiv)

This paper proposes a strictly convex chance-constrained stochastic control framework that accounts for uncertainty in control specifications such as reference trajectories and operational constraints. Control inputs and risk allocation are jointly optimized under general, possibly non-Gaussian, uncertainties through sufficient deterministic conditions for probabilistic constraint satisfaction. A decreasing risk regularizer ensures existence and full use of the allowable risk. Under the stated regularity assumptions, the joint solution is unique and locally Lipschitz-continuous, supporting stable numerical optimization. The formulation is extended to learning-based exactly linearizable (EL) models through monotone state and input transformations. A hybrid powertrain example compares deterministic control, equal risk allocation, and optimized risk allocation with and without regularization in terms of charge sustaining, constraint violations, closed-loop cost, and solver time.