# Fast integrators with sensitivity propagation for use in CasADi

arXiv: https://arxiv.org/abs/2211.01982 · HTML: https://mlsanity.com/paper/2211.01982/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2022.

## Built on by (most cited first, 4 of 4)

- [Differentiable Nonlinear Model Predictive Control](https://mlsanity.com/paper/2505.01353/) (2025)
- [The Mini Wheelbot: A Testbed for Learning-based Balancing, Flips, and Articulated Driving](https://mlsanity.com/paper/2502.04582/) (2025)
- [Bayesian Optimization applied for accelerated Virtual Validation of the Autonomous Driving Function](https://mlsanity.com/paper/2507.22769/) (2025)
- [Merging model-based control with multi-agent reinforcement learning for multi-agent cooperative teaming strategies](https://mlsanity.com/paper/2606.06011/) (2026)

## Abstract (arXiv)

Efficient integrators with sensitivity propagation are an essential ingredient for the numerical solution of optimal control problems. This paper gives an overview on the acados integrators, their Python interface and presents a workflow that allows using them with their sensitivities within a nonlinear programming (NLP) solver interfaced by CasADi. The implementation is discussed, demonstrated and provided as open-source software. The computation times of the proposed integrator and its sensitivity computation are compared to the native CasADi collocation integrator, CVODES and IDAS on different examples. A speedup of one order of magnitude for simulation and of up to three orders of magnitude for the forward sensitivity propagation is shown for an airborne wind energy system model.