Continuous Lyapunov Controlled Non-linear System Optimization Using Deep Learning with Memory

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.