Fast integrators with sensitivity propagation for use in CasADi
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Nov 2022.
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025Differentiable Nonlinear Model Predictive Controlcited by 5
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- 2026Merging model-based control with multi-agent reinforcement learning for multi-agent cooperative teaming strategies4 Jun 2026
- 2025Bayesian Optimization applied for accelerated Virtual Validation of the Autonomous Driving Function30 Jul 2025
- 2025Differentiable Nonlinear Model Predictive Control2 May 2025
- 2025The Mini Wheelbot: A Testbed for Learning-based Balancing, Flips, and Articulated Driving7 Feb 2025