<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Projects on Adilkhan Salkimbayev</title><link>https://hecatetdm.tech/projects/</link><description>Recent content in Projects on Adilkhan Salkimbayev</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 24 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://hecatetdm.tech/projects/index.xml" rel="self" type="application/rss+xml"/><item><title>Symbolic Kolmogorov-Arnold Networks as a Constant-Time Alternative to Linear Time-Varying Model Predictive Control for Embedded Control of Non-Minimum Phase Systems</title><link>https://hecatetdm.tech/projects/project-one/</link><pubDate>Fri, 24 Apr 2026 00:00:00 +0000</pubDate><guid>https://hecatetdm.tech/projects/project-one/</guid><description>Model Predictive Control (MPC) is the gold standard for constrained multi-variable control, yet its computational burden often precludes deployment on low-cost embedded hardware. As a primary contribution to artificial intelligence, this work proposes distilling complex control policies into a symbolic Kolmogorov-Arnold network (KAN), creating a computationally efficient, explicit neural controller.</description></item><item><title>Robust Adaptive Kolmogorov-Arnold Neural Control</title><link>https://hecatetdm.tech/projects/project-two/</link><pubDate>Thu, 16 Apr 2026 00:00:00 +0000</pubDate><guid>https://hecatetdm.tech/projects/project-two/</guid><description>Designed an adaptive control architecture achieving a significant speedup and reduction of upwards of 1.5x in actuator Total Variation compared to Linear Time-Varying Model Predictive Control. Features rigorous experimental validation and proof of Lyapunov stability by ISS/GUUB and LaSalle-Yoshizawa Theorem.</description></item></channel></rss>