<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Adilkhan Salkimbayev</title><link>https://hecatetdm.tech/</link><description>Recent content 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/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><item><title>CV</title><link>https://hecatetdm.tech/cv/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://hecatetdm.tech/cv/</guid><description>&lt;p&gt;&lt;a href="https://hecatetdm.tech/files/cv.pdf"&gt;&lt;strong&gt;Download PDF&lt;/strong&gt;&lt;/a&gt; — my CV (Curriculum vitae).&lt;/p&gt;
&lt;h2 id="education"&gt;Education&lt;/h2&gt;
&lt;p&gt;&lt;strong&gt;Kazakh-British Technical University&lt;/strong&gt; — Automation and Control, 2027&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Completed Theory of Linear/Non-Linear Control Systems, Foundations of Electrical Engineering I/II, PLC Programming;&lt;/li&gt;
&lt;li&gt;GPA 3.58&lt;/li&gt;
&lt;/ul&gt;
&lt;h2 id="research-experience"&gt;Research experience&lt;/h2&gt;
&lt;p&gt;&lt;strong&gt;Self-directed, KBTU&lt;/strong&gt; — Architect · (Nov 2025 - ongoing)&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Produced two papers (one under review at Engineering Applications of Artificial Intelligence, one completed for further submission)&lt;/li&gt;
&lt;li&gt;Utilized Kolmogorov-Arnold Networks for control-theoretic applications&lt;/li&gt;
&lt;li&gt;Designed Model Predictive Control for K. H. Johansson&amp;rsquo;s Quadruple-Tank Process&lt;/li&gt;
&lt;li&gt;Implemented the embedded C code for the NUCLEO-H753ZI motherboard&lt;/li&gt;
&lt;li&gt;Gained experience with Hardware-in-the-Loop methodology&lt;/li&gt;
&lt;li&gt;Produced empirical and theoretical validation for the control architectures&lt;/li&gt;
&lt;li&gt;Gained hands-on experience with Machine Learning and training methodologies&lt;/li&gt;
&lt;li&gt;Familiarized myself with relevant literature and theoretical knowledge&lt;/li&gt;
&lt;li&gt;Gained experience of research iteration under Dr. Khawaja S. Haider&lt;/li&gt;
&lt;li&gt;Currently transitioning to Functional Analysis and Partial Differential Equations&lt;/li&gt;
&lt;/ul&gt;
&lt;h2 id="publications"&gt;Publications&lt;/h2&gt;
&lt;p&gt;Manuscripts in progress — preprints are linked under &lt;a href="https://hecatetdm.tech/projects/"&gt;Projects&lt;/a&gt;.&lt;/p&gt;</description></item></channel></rss>