  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.
