Efficient interior point methods for multistage problems arising in receding horizon control

By Alexander Domahidi, Aldo U. Zgraggen, Melanie Nicole Zeilinger, Manfred Morari, Colin Neil Jones. IEEE Conference on Decision and Control, 2012

366 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Control and Systems Engineering, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/ff88810996582c1649a07676860546eb5620ea78

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Citations in the corpus

From linear to nonlinear MPC: bridging the gap via the real-time iteration (2020, background): “Recent algorithmic developments have significantly sped up computational times, allowing for the deployment of MPC at outstanding speeds [17, 19, 20, 21, 39].”
Predictive Control for Linear and Hybrid Systems (2017, background): “A good starting point for details on this topic are the references [284, 246, 98, 201, 110, 171, 170].”
HPIPM: a high-performance quadratic programming framework for model predictive control (2020, methodology): “The variant proposed in (Domahidi et al., 2012) is considered, comprising a horizontal chain of M masses connected to each other and to walls using springs, and where the first M − 1 masses are controlled using a force.”
ECOS: An SOCP solver for embedded systems (2013, methodology): “QPs, QCQPs as well as LPs with multistage property can be solved efficiently by FORCES [20], [21].”
Optimization‐based autonomous racing of 1:43 scale RC cars (2015, methodology): “The local QP (14) is solved using FORCES [3], [4].”
An Accelerated Dual Gradient-Projection Algorithm for Embedded Linear Model Predictive Control (2014, methodology): “2275667 QP are available to solve linear MPC problems, mainly active-set methods [4]–[6], interior-point methods [7]–[9], and Newton methods [10].”
CasADi: a software framework for nonlinear optimization and optimal control (2018, methodology): “Other QPs can be solved efficiently with tools such as FORCES [34], qpDUNES [46] and HPMPC [48].”
Computational Complexity Certification for Real-Time MPC With Input Constraints Based on the Fast Gradient Method (2012, methodology): “Dedicated implementations for MPC exploit the problem structure and thus reduce the complexity per iteration from to [26]–[29].”
Trajectory-Tracking Control of Mobile Robot Systems Incorporating Neural-Dynamic Optimized Model Predictive Approach (2016, methodology): “In [43], an approach called fast optimization for real-time control on embedded system (FORCES) was utilized to compute the control inputs for autonomous racing cars, where linear time varying models were obtained to build local approximations of nonlinear optimization in the form of convex quadrati”
A General-Purpose Software Framework for Dynamic Optimization (Een algemene softwareomgeving voor dynamische optimalisatie) (2013, neutral): “[131]), FORCES [72] and qpDUNES [81].”
acados—a modular open-source framework for fast embedded optimal control (2019, methodology): “A big role in bringing MPC to real-time applications is played by the implementation of efficient embedded optimal control methods, giving rise to software packages such as MPT [34] for explicit MPC, qpOASES [19], an active-set solver for quadratic programming (QP), FORCES [14,63], an interior-point”
Multi-Parametric Toolbox 3.0 (2013, methodology): “For highspeed online solutions one should still resort to specially tailored solvers such as FIORDOS [11] and FORCES [5].”