Minimal-learning-parameter technique based adaptive neural sliding mode control of MEMS gyroscope

DOI10.1155/2017/6019175zbMath1373.93082OpenAlexW2737575686WikidataQ59142924 ScholiaQ59142924MaRDI QIDQ1674913

Pengchao Zhang, Bin Xu

Publication date: 26 October 2017

Published in: Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2017/6019175

zbMATH Keywords

neural networkLyapunov approachadaptive neural sliding mode controlclosed-loop stability analysisMEMS gyroscopesminimal-learning-parameter technique

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Stabilization of systems by feedback (93D15) Neural networks for/in biological studies, artificial life and related topics (92B20) Lyapunov and other classical stabilities (Lagrange, Poisson, (L^p, l^p), etc.) in control theory (93D05) Variable structure systems (93B12)

Related Items (10)

Event-triggered control for the stabilization of probabilistic Boolean control networks ⋮ Composite learning sliding mode control of flexible-link manipulator ⋮ Adaptive neural network control of serial variable stiffness actuators ⋮ Neural learning control of flexible joint manipulator with predefined tracking performance and application to Baxter robot ⋮ Inertial forces and torques acting on a spinning annulus ⋮ Vibration control of an axially moving system with restricted input ⋮ Boundary robust adaptive control of a flexible Timoshenko manipulator ⋮ Prescribed chattering reduction control for quadrotors using aperiodic signal updating ⋮ Robust Actuator‐Fault‐Tolerant Control System Based on Sliding‐Mode Observer for Thrust‐Vectoring Aircrafts ⋮ Fuzzy wavelet neural control with improved prescribed performance for MEMS gyroscope subject to input quantization

Cites Work

• Dynamic learning from adaptive neural control of uncertain robots with guaranteed full-state tracking precision
• Composite learning sliding mode control of flexible-link manipulator

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