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About

Mokhtar Attari is a researcher specializing in robotics control systems, with a particular focus on flexible link manipulators and adaptive intelligent control strategies. His work addresses one of the fundamental challenges in modern robotics: managing the unwanted oscillations and dynamic instabilities that arise when lightweight, flexible robotic arms are deployed in precision applications, including space-based and military systems. Attari's most significant contributions lie in developing hybrid control architectures that combine classical nonlinear control laws derived from Lagrangian dynamics with neural network-based adaptive compensators. His investigations into Cerebellar Model Articulation Controllers (CMAC) and Radial Basis Function Neural Networks (RBFNN) demonstrate a consistent effort to handle both structured and unstructured system uncertainties, producing robust controllers capable of simultaneous joint position, velocity, and tip deflection regulation. These contributions, documented across multiple publications between 2007 and 2012, have collectively attracted citations from researchers working in robotics and control engineering. Beyond manipulator control, Attari has extended his expertise into biomedical instrumentation, contributing to the development of a low-cost sensor array for ankle rehabilitation, reflecting a broader commitment to applying intelligent sensing technologies across robotics and healthcare domains. His body of work offers valuable frameworks for researchers tackling flexible system dynamics and adaptive control design.