Konstantin Yamshanov
Papers
1
Total Citations
8
H-Index
1
About
Konstantin Yamshanov’s research centers on the intersection of robotics, optimal control theory, and neural network modeling, with a particular focus on bridging the gap between theoretical models and real-world robotic applications. His most cited work, “Identification of Neural Network Model of Robot to Solve the Optimal Control Problem” (2021), tackles a fundamental challenge in robotics: constructing accurate mathematical models for control systems. Yamshanov’s key contribution lies in demonstrating how neural networks can serve dual purposes—first, to compute optimal control trajectories, and second, to enable real-time robot navigation by predicting position and correcting sensor data. This work, with 8 citations, underscores his impact on practical robotics, where model fidelity directly affects performance. By emphasizing the reuse of the same model for both planning and execution, he advances the efficiency of autonomous systems. His research is particularly valuable for students and engineers seeking to integrate machine learning into control frameworks, offering a pathway from theoretical optimization to robust, sensor-aware robot behavior.
Research Focus
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Top Papers
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