Neural Network NARMA Control of a Gyroscopic Inverted Pendulum

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Neural Network NARMA Control of a Gyroscopic Inverted Pendulum
The objective herein is to demonstrate the feasibility of a real-time digital control of an inverted pendulum for modeling and control, with emphasis on nonlinear auto regressive moving average based neural network (NARMA). The plant of interest is a novel Gyroscopic Inverted Pendulum (GIP) device that is nonlinear and open-loop unstable. The GIP balances a pendulum on its free knife-edge base using a flywheel driven by DC motor fixated on the top. In this application, an indirect data-based technique is taken, where a model of the plant is identified on the basis of input-output data and then used in the model-based design of a NARMA controller. The plant under digital PID control with I-adaptation provides initial stability at the beginning of a single layer NARMA neural network training. NARMA models of increasing complexity are used successively to generate input-output data for the training of multilayered NARMA models. In using a NARMA neural network the control laws are nonlinea...
F. Chetouane, S. Darenfed
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2008
Where ENGL
Authors F. Chetouane, S. Darenfed
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