In the building climate control area, the linear model predictive control (LMPC)— nowadays considered a mature technique—benefits from the fact that the resulting optimization task is convex (thus easily and quickly solvable). On the other hand, while nonlinear model predictive control (NMPC) using a more detailed nonlinear model of a building takes advantage of its more accurate predictions an...

A highly nonlinear system controlled by a linear model predictive controller (MPC) may not exhibit a satisfactory dynamic performance. This has led to the development of a number of nonlinear MPC (NMPC) approaches that permit the use of first principles-based nonlinear models. Such models can be accurate over a wide range of operating conditions, but may be difficult to develop for many industr...

This paper presents the development and experimental implementation of an online, fully nonlinear model predictive controller (NMPC) for a gas turbine. The reduced-order, internal model used in the controller was developed from an original high-order, physics-based model using rigorous time scale separation arguments that may be extended to any gas turbine system. A control problem for a protot...

Precise trajectory tracking is a crucial property for Micro Air Vehicles (MAVs) to operate in cluttered environment or under disturbances. In this paper we present a detailed comparison between two state-of-the-art model-based control techniques for MAV trajectory tracking. A classical Linear Model Predictive Controller (LMPC) is presented and compared against a more advanced Nonlinear Model Pr...

In order to effectively implement a good model based control strategy, the combination of different linear models working at various operating regions are mostly utilised since a single model that can operate in that fashion is always a difficult task to develop. This work presents the use of soft computing approaches such as evolutional algorithm called simulated annealing (SA), a genetic algo...

This master thesis project serves to investigate the possibilities of Nonlinear Model Predictive Control (NMPC) using the example of enthalpy control of the BENSON HRSG (heat recovery steam generator) of a combined cycle power plant (CCPP). The general idea of NMPC is to solve an optimization problem, to find the next control action, and this optimization problem is based on a model of the syst...

Keywords: Extended Kalman filter Generalized likelihood ratio method Fault diagnosis and accommodation Failure tolerance Nonlinear model predictive control a b s t r a c t There is growing realization that on-line model maintenance is the key to realizing long term benefits of a predictive control scheme. In this work, a novel intelligent nonlinear state estimation strategy is proposed , which ...

This work presents the development of an unstructured kinetic model incorporating the differing degrees of product inhibition on the kinetic rates of recombinant Zymomonas mobilis fermentation. A mechanism for the inhibition of two competing substrates cellular uptake via membrane transport is proposed. The model was utilized in a nonlinear model predictive control (NMPC) algorithm to control p...

In this paper, a framework for Nonlinear Model Predictive Control (NMPC) that explicitly incorporates the noise influence on systems with continuous state spaces is introduced. By the incorporation of noise, which results from uncertainties during model identification and the measurement process, the quality of control can be significantly increased. Since NMPC requires the prediction of system...

This paper presents a nonlinear model-based predictive controller (NMPC) for trajectory tracking of a four-wheeled omnidirectional mobile robot. Methods of numerical optimization to perform real-time nonlinear minimization of the cost function are used. The cost function penalizes the robot’s position error, the robot’s orientation angle error, and the control effort. Experimental results of th...