Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation

<table>RBF network approximation of <svg height="12.175" id="M192" style="vertical-align:-2.29482pt" version="1.1" viewbox="0 0 9.9624996 12.175" width="9.9624996" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.062,7.675)"><path d="M556 393q0 -39 -36 -106q-42 -78 -185 -279q-47 -66 -81 -108t-117 -135l-112 -26l-8 22q150 90 251 219q-6 136 -39 340q-8 53 -21 53q-6 0 -27 -19.5t-38 -42.5l-16 26q80 111 127 111q23 0 35 -28t20 -90q18 -137 27 -263h2q142 200 142 279q0 24 -14 48q-4 7 5 26
q13 28 43 28q18 0 30 -15.5t12 -39.5z" id="x1D466"></path></g>
</svg>-axis component with adaptive neural sliding compensator.</table>

Journal of Applied Mathematics

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Figure 9

Figure 9: Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation 