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 library, user-friendly interfaces to Matlab and Python, and compatibility with the modeling language of CasADi. acados is free and open-source software released under the permissive BSD 2-Clause license.

acados: a modular open-source framework for fast embedded optimal control

Position and attitude control of multi-rotor aerial vehicles: A survey

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.

Acados — a modular open-source framework for fast embedded optimal control

https://repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/243949/1/j.automatica.2019.108560.pdf

A parallel Newton-type method for nonlinear model predictive control

First principles modeling of physical systems has led to significant technological advances across all branches of science. For nonlinear systems, however, small modeling errors can lead to significant deviations from the true, measured behavior. Even in mechanical systems, where the equations are assumed to be well-known, there are often model discrepancies corresponding to nonlinear friction, wind resistance, etc. Discovering models for these discrepancies remains an open challenge for many complex systems. In this work, we use the sparse identification of nonlinear dynamics (SINDy) algorithm to discover a model for the discrepancy between a simplified model and measurement data. In particular, we assume that the model mismatch can be sparsely represented in a library of candidate model terms. We demonstrate the efficacy of our approach on several examples including experimental data from a double pendulum on a cart. We further design and implement a feed-forward controller in simulations, showing improvement with a discrepancy model.

Learning Discrepancy Models From Experimental Data.

A Highly Parallelizable Newton-type Method for Nonlinear Model Predictive Control

This paper presents a MATLAB software toolkit ParNMPC, which can automatically generate parallel C/C++ code and carry out closed-loop simulation for nonlinear model predictive control (NMPC). ParNMPC is built for shared-memory multi-core processors supporting the OpenMP programming interface. The underlying optimal control problem (OCP) in NMPC is solved based on a highly parallelizable Newton-type method in ParNMPC, which computes the optimal solution iteratively with at most $N$ cores, where $N$ is the number of discretization grids or the prediction horizon in the discrete-time case. ParNMPC provides an easy-to-use environment to formulate the OCP using symbolic representation and to generate customizable and highly optimized parallel C/C++ code automatically. The performance of ParNMPC was demonstrated by swinging up a double inverted pendulum and by controlling a quadrotor.

A Parallel Code Generation Toolkit for Nonlinear Model Predictive Control

Real-time optimisation for nonlinear model predictive control (NMPC) has always been challenging, especially for fast-sampling and large-scale applications. This paper presents an efficient impleme...

ParNMPC – a parallel optimisation toolkit for real-time nonlinear model predictive control

This paper presents a simple iterative method for linear model predictive control (MPC). Approximate value functions requiring only first-order derivatives and incorporating fixed second-order information are used, which leads to a method that splits the MPC problem into subproblems along the prediction horizon, and only the states and costates (Lagrange multipliers corresponding to the state equations) are exchanged between consecutive subproblems during iteration. The convergence is guaranteed under the framework of the majorization-minimization principle. The performance of the proposed method was assessed against both first- and secondorder methods with two numerical experiments. The results indicate that the proposed method can obtain a moderately accurate solution with a small number of inexpensive iterations.

Haoyang Deng

Papers

A parallel Newton-type method for nonlinear model predictive control

A Highly Parallelizable Newton-type Method for Nonlinear Model Predictive Control

A Parallel Code Generation Toolkit for Nonlinear Model Predictive Control

ParNMPC – a parallel optimisation toolkit for real-time nonlinear model predictive control

An Iterative Horizon-Splitting Method for Model Predictive Control*