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About

M. Ertugrul is a researcher specializing in intelligent control systems, with a particular focus on the integration of neural networks and sliding mode control (SMC) for robotic applications. His work addresses one of the most persistent challenges in advanced robotics: developing robust controllers that can operate effectively without requiring complete knowledge of system dynamics and parameters — a condition rarely met in real-world environments. Ertugrul's most influential contribution, "Neuro Sliding Mode Control of Robotic Manipulators" (2000), has garnered 188 citations, establishing him as a notable voice in the field of neuro-adaptive control. His research systematically explores how neural networks can be harnessed to compute equivalent control signals in sliding mode frameworks, effectively eliminating the problematic chattering phenomenon and improving trajectory tracking performance in robotic manipulators, including SCARA-type systems. Beyond neural-SMC hybridization, Ertugrul has contributed innovative gain adaptation strategies using Lyapunov-based design and MIT rules, further enhancing the practical viability of sliding mode controllers. Collectively, his body of work — spanning the late 1990s through early 2000s — has helped lay important groundwork for intelligent, model-free robotic control, making his research valuable reading for students and engineers working at the intersection of machine learning and control theory.