Neural network implementation of heuristic rule based nonlinear control

In this paper, we present a rule-based control scheme for the cart-inverted pendulum system. The control task is to swing up the pendulum mounted on a cart and stabilize both the cart and pendulum by applying forces to the cart. Through the solution of this specific control problem, we try to illustrate a heuristic neural control approach with task decomposition, control rule extraction and neural-net rule implementation as its basic elements. Specializing to the pendulum problem, the global control task is decomposed into sub-tasks, namely, pendulum positioning and cart positioning. Accordingly, three separate neural sub-controllers are designed to cater to the sub-tasks and their coordination. The simulation result is included to show the actual performance of the controller.<<ETX>>

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