Adaptive Neuro-Fuzzy Sliding Mode Controller

Adaptive Neuro-Fuzzy Sliding Mode Controller

Sana Bouzaida (Industrial Systems Study and Renewable Energy (ESIER), National Engineering School of Monastir (ENIM), Monastir, Tunisia) and Anis Sakly (Industrial Systems Study and Renewable Energy (ESIER), National Engineering School of Monastir (ENIM), Monastir, Tunisia)
Copyright: © 2018 |Pages: 21
DOI: 10.4018/IJSDA.2018040103


A novel adaptive sliding mode controller using neuro-fuzzy network based on adaptive cooperative particle sub-swarm optimization (ACPSSO) is presented in this article for nonlinear systems control. The proposed scheme combines the advantages of adaptive control, neuro-fuzzy control, and sliding mode control (SMC) strategies without system model information. An adaptive training algorithm based on cooperative particle sub-swarm optimization is used for the online tuning of the controller parameters to deal with system uncertainties and disturbances. The algorithm was derived in the sense of Lyapunov stability analysis in order to guarantee the high quality of the controlled system. The performance of the proposed algorithm is evaluated against two well-known benchmark problems and simulation results that illustrate the effectiveness of the proposed controller.
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1. Introduction

Control theory asks how to influence the behavior of a dynamical system with appropriately chosen inputs so that the system's output follows a desired trajectory or final state. A key notion in control theory is the feedback process: The difference between the actual and desired output is applied as feedback to the system's input, forcing the system's output to converge to the desired output. Feedback control has deep roots in physics and engineering (Azar et al., 2017a; Moysis et al., 2017; Kharola & Patil, 2017; Alain et al., 2017; Mohamed et al., 2017; Joshi & Talange, 2016; Elkady et al., 2016; Ben Hariz & Bouani, 2016).

Recent decades have witnessed many important developments related to the design of nonlinear systems for many practical applications. Several inspiring approaches have been proposed, such as optimal control, nonlinear feedback control, adaptive control, sliding mode control, nonlinear dynamics, chaos control, chaos synchronization control, fuzzy logic control, fuzzy adaptive control, fractional order control, and robust control and their integrations (Azar & Vaidyanathan, 2016; Meghni et al, 2017a, 2017b, 2017c; Ghoudelbourk et al., 2016; Azar & Serrano, 2014, 2015a, 2015b, 2015c, 2015d, 2016a, 2016b, 2017; Azar et al., 2017b, 2017c, 2017d, 2017e; Azar 2010a, 2010b, 2012; Mekki et al., 2015; Vaidyanathan & Azar, 2015a, 2015b, 2015c, 2015d, 2016a, 2016b, 2016c, 2016d, 2016e, 2016f, 2016g, 2017a, 2017b, 2017c, 2018; Grassi et al., 2017; Ouannas et al., 2016a, 2016b, 2017a, 2017b, 2017c, 2017d, 2017e, 2017f, 2017g, 2017h, 2017i, 2017j; Vaidyanathan et al, 2015a, 2015b, 2015c; Wang et al., 2017; Soliman et al., 2017; Tolba et al., 2017a, 2017b; Radwan et al., 2017a, 2017b; Moysis & Azar, 2017; Pham et al., 2017; Munoz-Pacheco et al., 2017).

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