247 research outputs found
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
Linear tracking MPC for nonlinear systems Part I: The model-based case
We develop a tracking model predictive control (MPC) scheme for nonlinear
systems using the linearized dynamics at the current state as a prediction
model. Under reasonable assumptions on the linearized dynamics, we prove that
the proposed MPC scheme exponentially stabilizes the optimal reachable
equilibrium w.r.t. a desired target setpoint. Our theoretical results rely on
the fact that, close to the steady-state manifold, the prediction error of the
linearization is small and hence, we can slide along the steady-state manifold
towards the optimal reachable equilibrium. The closed-loop stability properties
mainly depend on a cost matrix which allows us to trade off performance,
robustness, and the size of the region of attraction. In an application to a
nonlinear continuous stirred tank reactor, we show that the scheme, which only
requires solving a convex quadratic program online, has comparable performance
to a nonlinear MPC scheme while being computationally significantly more
efficient. Further, our results provide the basis for controlling nonlinear
systems based on data-dependent linear prediction models, which we explore in
our companion paper
A novel constraint-tightening approach for robust data-driven predictive control
In this paper, we present a data-driven model predictive control (MPC) scheme that is capable of stabilizing unknown linear time-invariant systems under the influence of process disturbances. To this end, Willems' lemma is used to predict the future behavior of the system. This allows the entire scheme to be set up using only a priori measured data and knowledge of an upper bound on the system order. First, we develop a state-feedback MPC scheme, based on input-state data, which guarantees closed-loop practical exponential stability and recursive feasibility as well as closed-loop constraint satisfaction. The scheme is extended by a suitable constraint tightening, which can also be constructed using only data. In order to control a priori unstable systems, the presented scheme contains a prestabilizing controller and an associated input constraint tightening. We first present the proposed data-driven MPC scheme for the case of full state measurements, and also provide extensions for obtaining similar closed-loop guarantees in case of output feedback. The presented scheme is applied to a numerical example
- …