33 research outputs found
Stability and performance in MPC using a finite-tail cost
In this paper, we provide a stability and performance analysis of model
predictive control (MPC) schemes based on finite-tail costs. We study the MPC
formulation originally proposed by Magni et al. (2001) wherein the standard
terminal penalty is replaced by a finite-horizon cost of some stabilizing
control law. In order to analyse the closed loop, we leverage the more recent
technical machinery developed for MPC without terminal ingredients. For a
specified set of initial conditions, we obtain sufficient conditions for
stability and a performance bound in dependence of the prediction horizon and
the extended horizon used for the terminal penalty. The main practical benefit
of the considered finite-tail cost MPC formulation is the simpler offline
design in combination with typically significantly less restrictive bounds on
the prediction horizon to ensure stability. We demonstrate the benefits of the
considered MPC formulation using the classical example of a four tank system
Certification of a class of industrial predictive controllers without terminal conditions
Three decades have passed encompassing a flurry of research and commercial activities in model predictive control (MPC). However, the massive strides made by the academic community in guaranteeing stability through a state- space framework have not always been directly applicable in an industrial setting. This paper is concerned with a priori and/or a posteriori certification of persistent feasibility, boundedness of industrial MPC controllers (i) based on input-output formu- lation (ii) using shorter control than prediction horizon (iii) and without terminal conditions.This work has been supported by FDOC, UGent
Persistently Exciting Tube MPC
This paper presents a new approach to deal with the dual problem of system identification and regulation. The main feature consists of breaking the control input to the system into a regulator part and a persistently exciting part. The former is used to regulate the plant using a robust MPC formulation, in which the latter is treated as a bounded additive disturbance. The identification process is executed by a simple recursive least squares algorithm. In order to guarantee sufficient excitation for the identification, an additional non-convex constraint is enforced over the persistently exciting part
Persistently Exciting Tube MPC
This paper presents a new approach to deal with the dual problem of system identification and regulation. The main feature consists of breaking the control input to the system into a regulator part and a persistently exciting part. The former is used to regulate the plant using a robust MPC formulation, in which the latter is treated as a bounded additive disturbance. The identification process is executed by a simple recursive least squares algorithm. In order to guarantee sufficient excitation for the identification, an additional non-convex constraint is enforced over the persistently exciting part
Analysis and design of model predictive control frameworks for dynamic operation -- An overview
This article provides an overview of model predictive control (MPC)
frameworks for dynamic operation of nonlinear constrained systems. Dynamic
operation is often an integral part of the control objective, ranging from
tracking of reference signals to the general economic operation of a plant
under online changing time-varying operating conditions. We focus on the
particular challenges that arise when dealing with such more general control
goals and present methods that have emerged in the literature to address these
issues. The goal of this article is to present an overview of the
state-of-the-art techniques, providing a diverse toolkit to apply and further
develop MPC formulations that can handle the challenges intrinsic to dynamic
operation. We also critically assess the applicability of the different
research directions, discussing limitations and opportunities for further
researc