TLMM v4.3: Predictive Adaptive Control under Colored Noise — Extended Fokker–Planck Closure, Identifiability, Proof-of-Contact, and Translational Roadmap

This technical report presents TLMM v4.3, an exploratory mathematical framework for predictive adaptive control under colored noise. The work extends previous TLMM formulations by introducing an extended Fokker–Planck closure in $(E,\xi )$ space, theoretical operable-window narrowing, Kramers escape-based predictive maintenance timing, Fisher-information-based identifiability analysis, and an illustrative proof-of-contact with public CHB-MIT scalp EEG envelope statistics.

The report also provides a translational roadmap toward future adaptive control architectures, while clearly distinguishing the current v4.3 position—qualitative proof-of-contact—from future development stages such as predictive benchmarking, closed-loop control, and adaptive digital models.

This work is theoretical and exploratory. All EEG-related results are qualitative and illustrative only. No clinical, diagnostic, therapeutic, or medical-device claims are made.