State and Output Feedback Nonlinear Model Predictive Control: An Overview
By Rolf Findeisen, Lars Struen Imsland, Frank Allgöwer. European Journal of Control, 2003
402 citations recorded by the source corpus.
Subfields: Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/788bd3cde31239639f5f2e20375eba8b3fa845e8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Moritz Diehl
- David Q. Mayne
- Frank Allgöwer
- Eric C. Kerrigan
- Rolf Findeisen
- Xiaohua Xia
- Hans Georg Bock
- Gabriele Pannocchia
Citations in the corpus
- Integrator Backstepping using Barrier Functions for Systems with Multiple State Constraints (2005, methodology): “Various techniques have been developed to solve the constrained control problems for nonlinear systems, namely artificial potential field [10], invariance control [11], nonlinear reference governor [12], [13], and Nonlinear Model Predictive Control [14].”
- Offset‐free receding horizon control of constrained linear systems (2005, methodology): “The design of control algorithms able to stabilize constrained linear plants with input and state constraints subject to unknown, but bounded disturbances, has been the subject of several works over the last few decades; a number of excellent surveys are available [1–5], which discuss how the import”
- An application of model predictive control to the dynamic economic dispatch of power generation (2011, methodology): “Two standard examples are studied: the first one of a ten-unit system shows the difference between the OCDD and DED, and possible ramp rate violations, and the second one of a six-unit system shows the convergence and robustness of the MPC solutions, and the comparison with OCDD as well.”
- Nominal stability of real-time iteration scheme for nonlinear model predictive control (2005, background): “The main observation is that the term in (26) provides some robustness with respect to disturbances that might lead to a lower decrease ‐ but no increase ‐ of the value function from time step to time step [SRM97, KT02, FIAF03a , FIAF03b].”
- Invariant approximations of the minimal robust positively Invariant set (2005, background): “Set invariance is fundamental in the synthesis of reference governors [4], [5] and predictive controllers [6]–[9] with guaranteed invariance, stability and convergence properties.”