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- W142099026 abstract "The objective herein is to demonstrate the feasibility of a real-time digital control of an in verted 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 flywhe el driven by DC motor fixated on the top. In this application, an indirect data-based technique is ta ken, where a model of the plant is identified on the bas is 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 stab ility 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 nonlinear and online adaptation of the model is possible to c apture un-modeled or time-varying dynamics. Such an environment provides for experimentation, data collection, system identification and real time con trol strategy implementation." @default.
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- W142099026 date "2008-01-01" @default.
- W142099026 modified "2023-09-26" @default.
- W142099026 title "Neural Network NARMA Control of a Gyroscopic Inverted Pendulum" @default.
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