Towards a modular software package for embedded optimization

Verschueren, Robin; Frison, Gianluca; Kouzoupis, Dimitris; Duijkeren, Niels van; Zanelli, Andrea; Quirynen, Rien; Diehl, Moritz

doi:10.1016/j.ifacol.2018.11.062

Cited by 67 publications

(42 citation statements)

References 19 publications

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“…All files are compiled using gcc with the -O3 option enabled. We have implemented the simulations on acados, an interface to fast and embedded solvers for nonlinear optimal control and dynamic optimization [16], providing a convenient framework to evaluate various MPC implementations and solver performance. Currently available MPC solvers are qpOASES [17], HPIPM using BLASFEO [18], and HPMPC [19].…”

Section: Resultsmentioning

confidence: 99%

Real-Time Predictive Control for Precision Machining

Liniger¹,

Varano²,

Rupenyan³

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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Precise positioning and fast traversal times are crucial in achieving high productivity and scale in machining. This paper compares two optimization-based predictive control approaches that achieve high performance. In the first approach, the contour error is defined using the global position, the position on the path is inferred through a virtual path parameter, and the cost function combines the corresponding states and inputs to achieve a trade-off between high speed and positioning accuracy. The second approach is based on a local definition of both the error and the progress along the path, and results in a system with a reduced number of states and inputs that enables real-time optimization. Terminal and trust region constraints are required to achieve precise tracking of geometries where a fast or instantaneous change in direction is present. The performance of both approaches using different quadratic programming solvers is evaluated in simulations for geometries that are challenging in machine tools applications.

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Section: Resultsmentioning

confidence: 99%

Real-Time Predictive Control for Precision Machining

Liniger¹,

Varano²,

Rupenyan³

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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“…If the local orientation of the structure is required, the evolution equation for the quaternions (16) needs to be added to equations ( 25) and ( 26), since rotations are not part of the primary variables. Taking gravity as an example of an orientation-dependent forcing term, its contribution to the momentum balance equation ( 25) reads…”

Section: B Rotation Field Approximationmentioning

confidence: 99%

“…This opens the door to efficient NMPC: we exploit the model structure inherited from the intrinsic formulation and an adjoint-based sensitivity analysis to implement the real-time iteration scheme of [15] at low computational cost. It is shown that this approach outperforms NMPC methods which use Automatic Differentiation for sensitivity computations, such as ACADOS [16] or CasADi [17].…”

Section: Introductionmentioning

confidence: 99%

Modal-Based Nonlinear Model Predictive Control for 3-D Very Flexible Structures

Artola

Wynn

Palacios

2022

IEEE Trans. Automat. Contr.

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In this paper a novel NMPC scheme is derived, which is tailored to the underlying structure of the intrinsic description of geometrically exact nonlinear beams (in which velocities and strains are primary variables). This is an important class of PDE models whose behaviour is fundamental to the performance of flexible structural systems (e.g., wind turbines, High-Altitude Long-Endurance aircraft). Furthermore, this class contains the much-studied Euler-Bernoulli and Timoshenko beam models, but has significant additional complexity (to capture 3D effects and arbitrarily large displacements) and requires explicit computation of rotations in the PDE dynamics to account for orientation-dependent forces such as gravity. A challenge presented by this formulation is that uncontrollable modes necessarily appear in any finite dimensional approximation to the PDE dynamics. We show, however, that an NMPC scheme can be constructed in which the error introduced by the uncontrollable modes can be explicitly controlled. Furthermore, in challenging numerical examples exhibiting considerable deformation and nonlinear effects, it is demonstrated that the asymptotic error can be made insignificant (from a practical perspective) using our NMPC scheme and excellent performance is obtained even when applied to a highly resolved numerical simulation of the PDEs. We also present a generalisation of Kelvin-Voigt damping to the intrinsic description of geometrically-exact beams. Finally, special emphasis is placed on constructing a framework suitable for real-time NMPC control, where the particular structure of the underlying PDEs is exploited to obtain both efficient finitedimensional models and numerical schemes.

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“…We implemented our NMPC model in MATLAB with the help of acados [50], an open-source library for fast nonlinear optimal control. This software relies on C code generation for speeding up the computation in real-time applications.…”

Section: Simulation Setupmentioning

confidence: 99%

Predictive control of aerial swarms in cluttered environments

2021

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Classical models of aerial swarms often describe global coordinated motion as the combination of local interactions that happen at the individual level. Mathematically, these interactions are represented with Potential Fields. Despite their explanatory success, these models fail to guarantee rapid and safe collective motion when applied to aerial robotic swarms flying in cluttered environments of the real world, such as forests and urban areas. Moreover, these models necessitate a tight coupling with the deployment scenarios to induce consistent swarm behaviors. Here, we propose a predictive model that combines the local principles of potential field models with the knowledge of the agents' dynamics. We show that our approach improves the speed, order, and safety of the swarm, it is independent of the environment layout, and scalable in the swarm speed and inter-agent distance. Our model is validated with a swarm of five quadrotors that can successfully navigate in a real-world indoor environment populated with obstacles.

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Towards a modular software package for embedded optimization

Cited by 67 publications

References 19 publications

Real-Time Predictive Control for Precision Machining

Real-Time Predictive Control for Precision Machining

Modal-Based Nonlinear Model Predictive Control for 3-D Very Flexible Structures

Predictive control of aerial swarms in cluttered environments

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