Hybrid LQG-Neural Controller for Inverted Pendulum System

TL;DR

This paper introduces a hybrid control system combining neural and LQG controllers, optimized via genetic algorithms, to improve stability and regulation of an inverted pendulum, validated through simulation.

Contribution

It presents a novel hybrid controller that integrates neural and LQG controllers, optimized with genetic algorithms for improved stability and regulation.

Findings

Hybrid controller achieves high regulation quality.

System stability maintained during transients.

Validated on simulation model of inverted pendulum.

Abstract

The paper presents a hybrid system controller, incorporating a neural and an LQG controller. The neural controller has been optimized by genetic algorithms directly on the inverted pendulum system. The failure free optimization process stipulated a relatively small region of the asymptotic stability of the neural controller, which is concentrated around the regulation point. The presented hybrid controller combines benefits of a genetically optimized neural controller and an LQG controller in a single system controller. High quality of the regulation process is achieved through utilization of the neural controller, while stability of the system during transient processes and a wide range of operation are assured through application of the LQG controller. The hybrid controller has been validated by applying it to a simulation model of an inherently unstable system of inverted pendulum.