Dynamics identification and control of nonlinear MIMO coupled plant using supervised neural gas and comparison with recurrent neural controller

The dynamics identification and subsequent control of a nonlinear system is not a trivial issue. The application of a neural gas network that is trained with a supervised batch version of the algorithm can produce identification models in a robust way. In this paper, the neural model identifies each...

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Bibliographic Details
Published in:Neural computing & applications 2020-12, Vol.32 (24), p.18123-18142
Main Authors: Machón-González, Iván, López-García, Hilario, Bocos-Barranco, Ignacio
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Back propagation
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Control methods
Controllers
Data Mining and Knowledge Discovery
Image Processing and Computer Vision
MIMO (control systems)
Nonlinear control
Nonlinear systems
Probability and Statistics in Computer Science
Robust control
Transfer functions
Wsom 2017
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Summary:The dynamics identification and subsequent control of a nonlinear system is not a trivial issue. The application of a neural gas network that is trained with a supervised batch version of the algorithm can produce identification models in a robust way. In this paper, the neural model identifies each local transfer function, demonstrating that the local linear approximation can be done. Moreover, other parameters are analyzed in order to obtain a correct modeling. Furthermore, the algorithm is applied to control a nonlinear multi-input multi-output system composed of tanks. In addition, this plant is a coupled system where the manipulated input variables are influencing all the output variables. The aim of the work is to demonstrate that the supervised neural gas algorithm is able to obtain linear models to be used in a state space design scenario to control nonlinear coupled systems and guarantee a robust control method. The results are compared with the common approach of using a recurrent neural controller trained with a dynamic backpropagation algorithm. Regarding the steady-state errors in disturbance rejection, reference tracking and sensitivity to simple process changes, the proposed approach shows an interesting application to control nonlinear plants.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04195-9