Convergence and Stability Analysis of the Extended Infinite Horizon Model Predictive Control
Luz A. Alvarez, Diego F. de Bernardini, Christophe Gallesco

TL;DR
This paper rigorously analyzes the convergence and stability of extended and zone control MPC methods, providing mathematical proofs applicable to general gain matrices and any input horizon, enhancing theoretical understanding of MPC stability.
Contribution
It offers a comprehensive mathematical analysis of the convergence and stability of extended and zone control MPC, applicable to general gain matrices and arbitrary input horizons.
Findings
Proofs based on geometric and algebraic tools
Applicable to systems with non-regular gain matrices
Valid for any input horizon m
Abstract
Model Predictive Control (MPC) is a popular technology to operate industrial systems. It refers to a class of control algorithms that use an explicit model of the system to obtain the control action by minimizing a cost function. At each time step, MPC solves an optimization problem that minimizes the future deviation of the outputs which are calculated from the model. The solution of the optimization problem is a sequence of control inputs, the first input is applied to the system, and the optimization process is repeated at subsequent time steps. In the context of MPC, convergence and stability are fundamental issues. A common approach to obtain MPC stability is by setting the prediction horizon as infinite. For stable open-loop systems, the infinite horizon can be reduced to a finite horizon MPC with a terminal weight computed through the solution of a Lyapunov equation. This paper…
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Taxonomy
TopicsAdvanced Control Systems Optimization
