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Nonlinear model predictive control (NMPC) has become an important tool in the control and optimization of nonlinear systems in a variety of engineering applications. A requirement for a well-performing NMPC implementation is obtaining and maintaining an appropriate mathematical model of the considered system. For linear dynamic systems, developments have been made to incorporate information content...
This paper describes the design and implementation of a model predictive controller (MPC) for a pilot scale binary distillation column containing a mixture of methanol and isopropanol. In a first step experimental data are collected and linear black box models are identified. Secondly, the MPC is configured based on given requirements and restrictions and it is tested offline for several scenarios...
Over the last years, a number of publications were written about Model Predictive Control (MPC) on industrial Programmable Logic Controllers (PLC). They focussed on explicit MPC strategies to provide a fast solution. When sufficient time is available to solve a classic MPC problem, an online solution to the corresponding Quadratic Problem (QP) can be provided. This paper investigates the use of an...
For many applications first-principles nonlinear dynamic models are preferred by practitioners. Parameter estimation for these models is often a non-trivial and time consuming task. The use of optimally designed dynamic inputs can reduce the experimental burden and increase the accuracy of the estimated parameters. Traditionally, piecewise polynomial input sequences are exploited for this purpose...
In this paper we present techniques to optimize periodic stationary states of processes that depend on uncertain parameters. We start with an introduction to approximate robust counterpart formulations and specialize on systems with many uncertain parameters but only a small number of inequality constraints. The presented approximate robust programming formulation has an interesting application for...
This paper presents a convex approach for parameter estimation problems (PEPs) involving parameter-affine dynamic systems. By using the available state measurements, the nonconvex PEP is modified such that a convex approximation is obtained. The optimum delivered by this approximation is subsequently used to linearize the original PEP such that a refined solution is obtained. An assessment of the...
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