Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations

By Moritz Diehl, Hans Georg Bock, Johannes P. Schlöder, Rolf Findeisen, Zoltan K. Nagy, Frank Allgöwer. 2000

712 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/3ceba12ba489f07c4d700c24a7efbb97ea07dba0

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In Control and Systems Engineering

Citations in the corpus

ACADO toolkit—An open‐source framework for automatic control and dynamic optimization (2011, methodology): “This special property is exploited within the ACADO Toolkit by applying the real-time iteration scheme presented in [27, 28].”
Optimization‐based autonomous racing of 1:43 scale RC cars (2015, methodology): “The advantage of this approach is that path planning and path tracking can be combined into one nonlinear optimization problem, which can be solved in real-time by approximating the NLP using local convex QP approximations at each sampling time [21].”
Output feedback stabilization of constrained systems with nonlinear predictive control (2003, methodology): “This assumption does not exclude a piecewise constant (over the sampling interval) parameterization of the input, as often used in NMPC for the numerical solution of the open-loop optimal control problem (3) [28, 29].”
An auto-generated real-time iteration algorithm for nonlinear MPC in the microsecond range (2011, methodology): “Yet a different approach is the nonlinear real-time iteration (RTI) scheme [11,13].”
A General-Purpose Software Framework for Dynamic Optimization (Een algemene softwareomgeving voor dynamische optimalisatie) (2013, background): “Such LTV-OCP with quadratic objective are a structured form of quadratic programs (QP) and originate from a wide class of problem formulations in optimal control, notably in solution algorithms for linear-quadratic model predictive control (MPC) problems [159], linear moving horizon estimation (MHE)”
From linear to nonlinear MPC: bridging the gap via the real-time iteration (2020, methodology): “In this section, we recall the RTI approach fi rst introduced in Diehl et al. (2002).”
Efficient Numerical Methods for Nonlinear MPC and Moving Horizon Estimation (2009, methodology): “Division into preparation and feedback phase: A third ingredient of several NMPC algorithms is to divide the computations in each sampling time into a preparation phase and a feedback phase [16].”
Fast Direct Multiple Shooting Algorithms for Optimal Robot Control (2005, background): “We will now present a specific answer to the online dilemma, the real-time iteration scheme [17, 20].”
Nominal stability of real-time iteration scheme for nonlinear model predictive control (2005, methodology): “A possible approach to take account of the computation time is to predict the state at the time we expect the optimization to be finished and carry out the optimization for this prediction [FA03, CBO00 ], allowing to prove nominal stability; however, this approach may still result in a considerable”
Architectures for distributed and hierarchical Model Predictive Control - A review (2009, neutral): “[10,19].”
A continuation/GMRES method for fast computation of nonlinear receding horizon control (2004, methodology): “For example, one can apply iterative optimization methods utilizing the speciLc structure of the Hessian (Biegler, 2000; Tousain & Bosgra, 2000; Diehl et al., 2002).”
State and Output Feedback Nonlinear Model Predictive Control: An Overview (2003, background): “We do not go into further details here and instead refer to [12, 25, 67].”
An Introduction to Nonlinear Model Predictive Control (2002, background): “Basically two different solution strategies to this optimization problem exist [8, 9, 13, 48, 74]:”