JAIPER CORE-VISION V8: Closed-Loop Adaptive Iterative Optimization Framework

Abstract (English) Phases 4A and 4B mark the architectural transition of the JAIPER-CORE ecosystem from an open-loop geometric shaping tool into a Closed-Loop Iterative Learning Control (ILC) framework. The system autonomously iterates over successive excitation signals, evaluating residual physical error and dynamically recalculating optimized waveforms. A multiobjective optimization process combines energy mitigation metrics, peak transient suppression, and signal correlation preservation. Experimental results demonstrate stable operational convergence, reducing the global cost function from 1.2450 to 0.1950 across eight iterations. The optimization process converged toward a stable operational state formally defined as the Instrumental Attractor, where further improvements become constrained by hardware quantization, temporal jitter, and thermal noise rather than by the optimization algorithm itself. This phase establishes the first fully operational closed-loop adaptive architecture within the JAIPER framework and provides the experimental foundation for future adaptive transient control studies. Resumen (Español) Las Fases 4A y 4B marcan la transición arquitectónica del ecosistema JAIPER-CORE desde una herramienta de shaping geométrico en lazo abierto hacia un framework de Control de Aprendizaje Iterativo (ILC) en lazo cerrado. El sistema itera autónomamente sobre señales de excitación sucesivas, evaluando el error físico residual y recalculando dinámicamente formas de onda optimizadas. El proceso de optimización combina métricas de mitigación energética, supresión de sobrepicos transitorios y preservación de correlación de señal. Los resultados experimentales demuestran una convergencia operacional estable, reduciendo la función de costo global desde 1.2450 hasta 0.1950 en ocho iteraciones. El proceso converge hacia un estado operacional estable definido formalmente como Atractor Instrumental, donde las mejoras adicionales quedan limitadas por la cuantización del hardware, el jitter temporal y el ruido térmico, más que por el algoritmo de optimización. Esta fase establece la primera arquitectura adaptativa completamente operativa en lazo cerrado dentro del framework JAIPER y proporciona la base experimental para futuros estudios de control adaptativo de transitorios. Key Results • Closed-loop adaptive optimization successfully implemented. • Global cost reduction: 1.2450 → 0.1950. • Windowed Jaiper coefficient: Jw = 0.9438. • Overshoot reduction: 126.34 → 15.30 arbitrary units. • Stable convergence achieved after eight iterations. • Formal definition of the Instrumental Attractor. • Validation performed using computer-vision-based feedback and DDS waveform synthesis. Figures Included Figure 1 – Closed-Loop Adaptive Architecture Figure 2 – JAIPER CORE-VISION Iteration Engine Figure 3 – Global Cost Convergence Figure 4 – Overshoot Attenuation Figure 5 – Windowed Jaiper Coefficient Evolution Figure 6 – Instrumental Attractor Conceptual Model Methodological Notes The coefficients J and Jw are relative mitigation metrics evaluated within bounded temporal windows and must not be interpreted as absolute reductions of total system energy. Results are constrained by hardware quantization, temporal jitter, optical aliasing, trigger synchronization, DDS bandwidth limitations, ADC/DAC resolution, BMP reconstruction fidelity, and display sampling effects. Correspondence Jaime Perez Davila ORCID: 0009-0002-5231-547X Email: jaipercore@gmail.com WhatsApp: +57 3005422089 Author Note The author is a native Spanish speaker and is available for communication in Spanish. English is used in this document for international accessibility.