ACADO toolkit—An open‐source framework for automatic control and dynamic optimization

By Boris Houska, Hans Joachim Ferreau, Moritz Diehl. 2011

1,060 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/1d1beab14865cd3691ee873e3fc773760cce9fbc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Control and Systems Engineering

Citations in the corpus

GEKKO Optimization Suite (2018, background): “ACADO [26] is a self-contained toolbox for optimal control.”
qpOASES: a parametric active-set algorithm for quadratic programming (2014, methodology): “Moreover, qpOASES is the default QP solver within the open-source package ACADO Toolkit for automatic con-trol and dynamic optimization [35].”
Cautious Model Predictive Control Using Gaussian Process Regression (2017, neutral): “In addition, there are specialized solvers that exploit the structure of predictive control problems, such as ACADO [37] or FORCES Pro [38], [39], which implements a nonlinear interior point method.”
Nonlinear Model Predictive Control for Integrated Energy-Efficient Torque-Vectoring and Anti-Roll Moment Distribution (2021, positive): “The NMPC algorithms were implemented via the ACADO toolkit [20], which offers a powerful interface for NMPC development, with the following settings: Gauss Newton Hessian approximation, multiple shooting discretization, fourth order implicit Runge Kutta integrator, and qpOASES solver.”
From linear to nonlinear MPC: bridging the gap via the real-time iteration (2020, methodology): “The open-source ACADO Toolkit software (Houska, Ferreau, & Diehl, 2011a) provides a high-level framework for deploying a code-generated RTI approach, including autogenerated integrators with tailored sensitivity propagation (Quirynen, Vukov, Zanon, & Diehl, 2014).”
Model predictive control: Recent developments and future promise (2014, positive): “Houska, Ferreau, and Diehl (2011) have developed software that allows one to define symbolically an optimal control or estimation problem and automatically generates efficient C++ code to implement the solution in an embedded computer.”
PAMPC: Perception-Aware Model Predictive Control for Quadrotors (2018, methodology): “We refer the reader to [20] and [21] for more details on the transcription of the dynamics for optimization.”
Geometrically Constrained Trajectory Optimization for Multicopters (2021, background): “There are various general-purpose solvers that are able to compute high-quality solutions, such as the shooting-based solver ACADO (Houska et al. 2011), and the collocation-based one GPOPS-II (Patterson and Rao 2014).”
An auto-generated real-time iteration algorithm for nonlinear MPC in the microsecond range (2011, methodology): “The ACADO Toolkit is an open-source software tool for automatic control and dynamic optimization [19].”
Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving (2017, methodology): “For example, there are available computationally efficient model predictive control (MPC) methods based on solving nonlinear programming problems [16], and it has been demonstrated that model-predictive-control variants of differential dynamic programming can control very high dimensional nonlinear ”