# Risk-averse risk-constrained optimal control

arXiv: https://arxiv.org/abs/1903.06749 · HTML: https://mlsanity.com/paper/1903.06749/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2019.

## Built on by (most cited first, 2 of 2)

- [Distributionally Robust Optimization using Cost-Aware Ambiguity Sets](https://mlsanity.com/paper/2303.09408/) (2023)
- [Interactive Trajectory Planning with Learning-based Distributionally Robust Model Predictive Control and Markov Systems](https://mlsanity.com/paper/2605.07768/) (2026)

## Abstract (arXiv)

Multistage risk-averse optimal control problems with nested conditional risk mappings are gaining popularity in various application domains. Risk-averse formulations interpolate between the classical expectation-based stochastic and minimax optimal control. This way, risk-averse problems aim at hedging against extreme low-probability events without being overly conservative. At the same time, risk-based constraints may be employed either as surrogates for chance (probabilistic) constraints or as a robustification of expectation-based constraints. Such multistage problems, however, have been identified as particularly hard to solve. We propose a decomposition method for such nested problems that allows us to solve them via efficient numerical optimization methods. Alongside, we propose a new form of risk constraints which accounts for the propagation of uncertainty in time.