Continuous Lyapunov Controller and Chaotic Non-linear System Optimization using Deep Machine Learning

TL;DR

This paper introduces a deep learning-based method for real-time detection and correction of instability in chaotic non-linear systems, demonstrated on complex oscillators to enhance system stability and performance.

Contribution

It presents a novel continuous monitoring and parameter re-calibration approach using deep neural networks for chaotic system stabilization.

Findings

Effective early failure detection in chaotic systems

Successful re-calibration under various dynamic scenarios

Maintains system stability without sacrificing speed or accuracy

Abstract

The introduction of unexpected system disturbances and new system dynamics does not allow guaranteed continuous system stability. In this research we present a novel approach for detecting early failure indicators of non-linear highly chaotic system and accordingly predict the best parameter calibrations to offset such instability using deep machine learning regression model. The approach proposed continuously monitors the system and controller signals. The Re-calibration of the system and controller parameters is triggered according to a set of conditions designed to maintain system stability without compromise to the system speed, intended outcome or required processing power. The deep neural model predicts the parameter values that would best counteract the expected system in-stability. To demonstrate the effectiveness of the proposed approach, it is applied to the non-linear complex combination of Duffing Van der pol oscillators. The approach is also tested under different scenarios the system and controller parameters are initially chosen incorrectly or the system parameters are changed while running or new system dynamics are introduced while running to measure effectiveness and reaction time.