Global Control of Reaction Wheel Pendulum Using Artificial Neural Networks and Extended Linearization
DOI:
https://doi.org/10.22517/23447214.13871Keywords:
Global control, Soft commutation, Extended linearization, Energetic models, Artificial Neural NetworksAbstract
In this paper describes the design and simulation of a two-stage hybrid controller for the inverted reaction wheel pendulum (RWP) is presented. In the first stage, the general pendulum arm modeling is performed through a nonlinear model to determine the stored energy in the plant, and using a strategy known as swing up energy regulation, the data required for the training of an artificial neural network are obtained. In the second stage, via a soft switching system, neuronal control is exchanged by a controller based on the extended linearization of the state variables, whereby the permanence of the pendulum in the region of operation is guaranteed. The control strategy proposed shows excellent performance against external disturbance phenomena and ensures the overall operation of the physical system.
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