<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive) multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF) motions of a drillship over the drilling point at the sea bottom. The designed neural controller contains a set of neural nets that determine values of its parameters chosen on the basis of two measured auxiliary signals. These are the ship’s current forward speed measured with respect to water and the systematically calculated difference between the course angle and the sea current (yaw angle). Four different methods for synthesis of multivariable modal controllers are used to obtain source data for training the neural controller with parameters reproduced by neural networks. Neural networks are designed on the basis of 3650 modal controllers obtained with the use of the pole placement technique after having linearized the model of LF motions made by the vessel at its nominal operating points in steady states that are dependent on the speciﬁed yaw angle and the sea current velocity. The ﬁnal part of the paper includes simulation results of system operation with a neural controller along with conclusions and ﬁnal remarks.</p></abstract>

The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive) multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF) motions of a drillship over the drilling point at the sea bottom. The designed neural controller contains a set of neural nets that determine values of its parameters chosen on the basis of two measured auxiliary signals. These are the ship’s current forward speed measured with respect to water and the systematically calculated difference between the course angle and the sea current (yaw angle). Four different methods for synthesis of multivariable modal controllers are used to obtain source data for training the neural controller with parameters reproduced by neural networks. Neural networks are designed on the basis of 3650 modal controllers obtained with the use of the pole placement technique after having linearized the model of LF motions made by the vessel at its nominal operating points in steady states that are dependent on the speciﬁed yaw angle and the sea current velocity. The ﬁnal part of the paper includes simulation results of system operation with a neural controller along with conclusions and ﬁnal remarks.

Design of a multivariable neural controller for control of a nonlinear MIMO plant

Faculty of Electrical Engineering West Pomeranian University of Technology in Szczecin, 26 Kwietnia 10, 71-126 Szczecin, Poland

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive) multivariable neural controller for control of a...

Design of a multivariable neural controller for control of a nonlinear MIMO plant

Published Online: Jun 26, 2014

Page range: 357 - 369

Received: Apr 29, 2013

Accepted: Feb 21, 2014

DOI: https://doi.org/10.2478/amcs-2014-0027

Keywords
MIMO multivariable control systems, nonlinear systems, neural control

© 2014 Stanisław Bańka et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Design of a multivariable neural controller for control of a nonlinear MIMO plant

Published Online: Jun 26, 2014

Page range: 357 - 369

Received: Apr 29, 2013

Accepted: Feb 21, 2014

DOI: https://doi.org/10.2478/amcs-2014-0027

KeywordsMIMO multivariable control systems, nonlinear systems, neural control

© 2014 Stanisław Bańka et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
MIMO multivariable control systems, nonlinear systems, neural control