2017
DOI: 10.1142/s0218488517500088
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Backstepping-Based Recurrent Type-2 Fuzzy Sliding Mode Control for MIMO Systems (MEMS Triaxial Gyroscope Case Study)

Abstract: This paper presents a novel type-2 fuzzy sliding mode control with nonlinear consequent part in fuzzy rules for control of Micro-Electro-Mechanical Systems (MEMS) gyroscope. The MEMS gyroscope consists of the basic mechanical structure, an electronic transducer to excite the system as well as an electronic sensor to detect the change in the mechanical structures modal shape. A nonlinear consequent part recurrent type-2 fuzzy system is used to approximate the conventional sliding mode control (SMC) law. A super… Show more

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Cited by 24 publications
(7 citation statements)
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“…For instance, a new active disturbance rejection scheme is designed for a gyroscope to regulate the output amplitude of the axis in [1]. Asad et al [2] designed a new fuzzy sliding mode strategy with nonlinear part in fuzzy rules for control of MEMS gyroscope. Chu et al [3] derived an adaptive proportional integral derivative global sliding mode controller using neural estimator for a MEMS gyroscope to obtain…”
Section: Introductionmentioning
confidence: 99%
“…For instance, a new active disturbance rejection scheme is designed for a gyroscope to regulate the output amplitude of the axis in [1]. Asad et al [2] designed a new fuzzy sliding mode strategy with nonlinear part in fuzzy rules for control of MEMS gyroscope. Chu et al [3] derived an adaptive proportional integral derivative global sliding mode controller using neural estimator for a MEMS gyroscope to obtain…”
Section: Introductionmentioning
confidence: 99%
“…Computational intelligence is increasingly being developed [11][12][13][14][15]. Neural network-based control systems [16][17][18][19][20], fuzzy system [21][22][23], and fuzzy neural networks [24][25][26][27] have shown good performance. e following discusses some methods of boiler control based on computational intelligence.…”
Section: Introductionmentioning
confidence: 99%
“…Computational intelligence tools in complex systems have performed well in terms of modeling and system identification [10][11][12][13], control and regulation [14][15][16][17], and so on [18][19][20]. Neural networks, fuzzy logic, and evolutionary algorithms have been very efficient in combining model-based methods (control theory) in aerospace systems [21,22].…”
Section: Introductionmentioning
confidence: 99%