Adaptive Intelligent Sliding Mode Control of a Dynamic System with a Long Short-Term Memory Structure

Lunhaojie Liu, Wen Fu, Xingao Bian and Juntao Fei
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Lunhaojie Liu: College of IoT Engineering, Hohai University, Changzhou 213022, China
Wen Fu: College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Xingao Bian: College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Juntao Fei: College of IoT Engineering, Hohai University, Changzhou 213022, China

Mathematics, 2022, vol. 10, issue 7, 1-23

Abstract: In this work, a novel fuzzy neural network (NFNN) with a long short-term memory (LSTM) structure was derived and an adaptive sliding mode controller, using NFNN (ASMC-NFNN), was developed for a class of nonlinear systems. Aimed at the unknown uncertainties in nonlinear systems, an NFNN was designed to estimate unknown uncertainties, which combined the advantages of fuzzy systems and neural networks, and also introduced a special LSTM recursive structure. The special three gating units in the LSTM structure enabled it to have selective forgetting and memory mechanisms, which could make full use of historical information, and have a stronger ability to learn and estimate unknown uncertainties than general recurrent neural networks. The Lyapunov stability rule guaranteed the parameter convergence of the neural network and system stability. Finally, research into a simulation of an active power filter system showed that the proposed new algorithm had better static and dynamic properties and robustness compared with a sliding controller that uses a recurrent fuzzy neural network (RFNN).

Keywords: fuzzy neural network; long short-term memory; adaptive sliding mode control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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