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Learning Feedback Linearization Using Artificial Neural Networks

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Abstract

This paper introduces a novel approach to the design of the feedback linearization called the learning feedback linearization (LFL) method. This method is used to learn the feedback linearization input for a nonlinear plant. The control scheme of the LFL method, which is devised by one artificial neural network (ANN) block, is based on a nonlinear auto regressive moving average model. The LFL method training phase uses not only input-output data pairs of the nonlinear plant but also a generated sequence data obtained from the states of the nonlinear plant. After the training phase of the LFL, a conventional proportional-integral controller is chosen as a linear controller for the feedback linearized closed-loop system. The performance of the developed ANN based LFL method is tested for tracking control problems via mean square error during the training and the test phases. The developed method is applied on both a nonlinear exponential plant and a well-known flexible joint mechanism plant in real-time simulation mode.

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Acknowledgments

This work was supported in part by the Scientific Research Projects Office of İzmir Katip Çelebi University under Grant BAP-2013-1- FMBP-09 and in part by the Scientific and Technological Research Council of Turkey under Grant 114E432.

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Correspondence to Savaş Şahin.

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Şahin, S. Learning Feedback Linearization Using Artificial Neural Networks. Neural Process Lett 44, 625–637 (2016). https://doi.org/10.1007/s11063-015-9484-8

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  • DOI: https://doi.org/10.1007/s11063-015-9484-8

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