Papers
14
Total Citations
139
H-Index
7
About
Naeim Yousefi Lademakhi is a robotics and control systems researcher whose work sits at the intersection of nonlinear control theory, intelligent learning, and autonomous robotic systems. His research spans two principal domains: the control and estimation of flexible-joint robotic manipulators and the motion planning and control of wheeled mobile robots (WMRs). Among his most significant contributions is his development of observer-based control frameworks for flexible-joint robots, most notably applying the State-Dependent Riccati Equation (SDRE) alongside sliding mode control (SMC) to address the challenges of incomplete state feedback, noise, and actuator-side measurement difficulties — work that has accumulated 24 citations since 2017. His more recent research demonstrates a growing integration of machine learning and neural networks into classical control paradigms, including adaptive robust controllers with intelligent slip estimation (27 citations) and neural network-regulated cost function weighting for optimal controllers. His investigations into Nonlinear Model Predictive Control (NMPC) for obstacle-aware mobile robot navigation and friction model identification in robotic joints further underscore the practical relevance of his contributions. With a growing citation portfolio exceeding 120 cumulative references, Lademakhi's work offers valuable tools for researchers tackling real-world robotic control challenges.
Research Focus
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