Application of spike neural network for stabilizing pendulum in the nonlinear formulation

Abstract

The article describes the solution to the problem of stabilizing a nonlinear system using machine learning methods. Neural networks are one of the promising directions in this area. The article describes a model of spiking neural network, which differs from previous generations of networks by its similarity to biological neurons. A pendulum on an elastic foundation was chosen as a dynamic system for the study. The input layer of the neural network is the so-called sensory neuron, and information about the deviation of the pendulum from the equilibrium position was received on it. The Leaky Integrate-and-Fire model of the spiking neural network was used. The article shows the process of stabilization of a pendulum on an elastic foundation. The closed system was built and a method for a numerical solution was implemented. Two configurations of control functions have been considered. It is shown that the time required to bring the system into a steady equilibrium state depends on the choice of the control function.