acados—a modular open-source framework for fast embedded optimal control
By Robin Verschueren, Gianluca Frison, Dimitris Kouzoupis, Jonathan Frey, N. V. Duijkeren, Andrea Zanelli, B. Novoselnik, Thivaharan Albin, Rien Quirynen, Moritz Diehl. Mathematical Programming Computation, 2019
616 citations recorded by the source corpus.
This paper presents the acados software package, a collection of solvers for fast embedded optimization intended for fast embedded applications. Its interfaces to higher-level languages make it useful for quickly designing an optimization-based control algorithm by putting together different algorithmic components that can be readily connected and interchanged. Since the core of acados is written on top of a high-performance linear algebra library, we do not sacrifice computational performance. Thus, we aim to provide both flexibility and performance through modularity, without the need to rely on automatic code generation, which facilitates maintainability and extensibility. The main features of acados are: efficient optimal control algorithms targeting embedded devices implemented in C, linear algebra based on the high-performance BLASFEO Frison (ACM Transactions on Mathematical Software (TOMS) 44: 1–30, 2018) library, user-friendly interfaces to Matlab and Python, and compatibility with the modeling language of CasADi Andersson (Mathematical Programming Computation 11: 136, 2019). acados is free and open-source software released under the permissive BSD 2-Clause license.
Subfields: Control and Systems Engineering, Hardware and Architecture
View Paper: https://www.semanticscholar.org/paper/988174063b0fa10001eb9844cc4c917a80e84164
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In Control and Systems Engineering
- Gianluca Frison
- Moritz Diehl
- Hans Joachim Ferreau
- Boris Houska
- Alberto Bemporad
- Lars Grüne
- Pierre-Brice Wieber
- Toshiyuki Ohtsuka
Citations in the corpus
- HPIPM: a high-performance quadratic programming framework for model predictive control (2020, methodology): “At the time of writing, HPIPM is employed as a QP solver in several NLP solvers such as acados (Verschueren et al., 2019), Control Toolbox (Giftthaler et al., 2018), MPCC (Liniger et al., 2015) and MATMPC (Chen et al., 2018).”