Minimal-learning-parameter technique based adaptive neural sliding mode control of MEMS gyroscope. (English) Zbl 1373.93082
Summary: This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics,a neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.
MSC:
| 93B12 | Variable structure systems |
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 93D05 | Lyapunov and other classical stabilities (Lagrange, Poisson, \(L^p, l^p\), etc.) in control theory |
| 93D15 | Stabilization of systems by feedback |
Keywords:
adaptive neural sliding mode control; MEMS gyroscopes; minimal-learning-parameter technique; neural network; closed-loop stability analysis; Lyapunov approachReferences:
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