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Sensors 2018, 18(1), 315;

An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models

Department of Electronic Engineering, Technological Educational Institute of Athens, Agiou Spiridonos, 12243 Aigaleo, Greece
School of Chemical Engineering, National Technical University of Athens, Iroon Polytechneiou 9, Zografou, 15780 Athens, Greece
Author to whom correspondence should be addressed.
Received: 7 November 2017 / Revised: 6 January 2018 / Accepted: 18 January 2018 / Published: 22 January 2018
(This article belongs to the Special Issue Soft Sensors and Intelligent Algorithms for Data Fusion)
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This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the fuzzy means (PSO-NSFM) algorithm so that an approximation of the inverse system dynamics is obtained. PSO-NSFM offers models of high accuracy combined with small network structures. Next, the applicability domain concept is suitably tailored and embedded into the proposed control structure in order to ensure that extrapolation is avoided in the controller predictions. Finally, an error correction term, estimating the error produced by the unmodeled dynamics and/or unmeasured external disturbances, is included to the control scheme to increase robustness. The resulting controller guarantees bounded input-bounded state (BIBS) stability for the closed loop system when the open loop system is BIBS stable. The proposed methodology is evaluated on two different control problems, namely, the control of an experimental armature-controlled direct current (DC) motor and the stabilization of a highly nonlinear simulated inverted pendulum. For each one of these problems, appropriate case studies are tested, in which a conventional neural controller employing inverse models and a PID controller are also applied. The results reveal the ability of the proposed control scheme to handle and manipulate diverse data through a data fusion approach and illustrate the superiority of the method in terms of faster and less oscillatory responses. View Full-Text
Keywords: applicability domain; data fusion; intelligent control; neural networks; radial basis function applicability domain; data fusion; intelligent control; neural networks; radial basis function

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Alexandridis, A.; Stogiannos, M.; Papaioannou, N.; Zois, E.; Sarimveis, H. An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models. Sensors 2018, 18, 315.

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