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Abstract

The aim of this paper is to present a way of ranking the nonlinearities of electrodynamic loudspeakers. For this purpose, we have constructed a nonlinear analytic model which takes into account the variations of the small signal parameters. The determination of these variations is based on a very precise measurement of the electrical impedance of the electrodynamic loudspeaker. First, we present the experimental method to identify the variations of these parameters, then we propose to study theoretically the importance of these nonlinearities according to the input level or the input frequency. We show that the parameter which creates most of the distortions is not always the same and depends mainly on both the input level and the input frequency. Such results can be very useful for optimization of electrodynamic loudspeakers.

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Authors and Affiliations

Romain Ravaud
Guy Lemarquand
Valérie Lemarquand
Tangi Roussel
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Abstract

Model predictive control (MPC) algorithms brought increase of the control system performance in many applications thanks to relatively easily solving issues that are hard to solve without these algorithms. The paper is focused on investigating how to further improve the control system performance using a trajectory of parameters weighting predicted control errors in the performance function of the optimization problem. Different shapes of trajectories are proposed and their influence on control systems is tested. Additionally, experiments checking the influence of disturbances and of modeling uncertainty on control system performance are conducted. The case studies were done in control systems of three control plants: a linear non- minimumphase plant, a nonlinear polymerization reactor and a nonlinear thin film evaporator. Three types of MPC algorithms were used during research: linear DMC, nonlinear DMC with successive linearization (NDMC–SL), nonlinear DMC with nonlinear prediction and linearization (NDMC–NPL). Results of conducted experiments are presented in greater detail for the control system of the polymerization reactor, whereas for the other two control systems only the most interesting results are presented, for the sake of brevity. The experiments in the control system of the linear plant were done as preliminary experiments with the modified optimization problem. In the case of control system of the thin film evaporator the researched mechanisms were used in the control system of a MIMO plant showing possibilities of improving the control system performance.

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Authors and Affiliations

Robert Nebeluk
Piotr Marusak
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Abstract

As nonlinear optimization techniques are computationally expensive, their usage in the real-time era is constrained. So this is the main challenge for researchers to develop a fast algorithm that is used in real-time computations. This work proposes a fast nonlinear model predictive control approach based on particle swarm optimization for nonlinear optimization with constraints. The suggested algorithm divide and conquer technique improves computing speed and disturbance rejection capability, demonstrating its suitability for real-time applications. The performance of this approach under constraints is validated using a highly nonlinear fast and dynamic real-time inverted pendulum system. The solution presented through work is computationally feasible for smaller sampling times and it gives promising results compared to the state of art PSO algorithm
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Authors and Affiliations

Supriya P. Diwan
1
Shraddha S. Deshpande
2

  1. Government College of Engineering, Karad-415124, Maharashtra, India
  2. Walchand College of Engineering, Sangli-416415, Maharashtra, India
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Abstract

The article presents an identification method of the model of the ball-and-race coal mill motor power signal with the use of machine learning techniques. The stages of preparing training data for model parameters identification purposes are described, as well as these aimed at verifying the quality of the evaluated model. In order to meet the tasks of machine learning, additive regression model was applied. Identification of the additive model parameters was performed on the basis of iterative backfitting algorithm combined with nonparametric estimation techniques. The proposed models have predictive nature and are aimed at simulation of the motor power signal of a coal mill during its regular operation, startup and shutdown. A comparative analysis has been performed of the models structured differently in terms of identification quality and sensitivity to the existence of an exemplary disturbance in the form of overhangs in the coal bunker. Tests carried out on the basis of real measuring data registered in the Polish power unit with a capacity of 200 MW confirm the effectiveness of the method.
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Authors and Affiliations

Zofia Magdalena Łabęda-Grudziak
1
ORCID: ORCID
Mariusz Lipiński
2

  1. Warsaw University of Technology, Institute of Automatic Control and Robotics, ul. św. Andrzeja Boboli 8, 02-525 Warsaw, Poland
  2. Institute of Power Systems Automation, ul. Wystawowa 113, 51-618 Wrocław, Poland

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