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About

N. Sadati is a researcher whose work lies at the intersection of adaptive control, neural networks, and robotic systems. His key contributions focus on developing intelligent control strategies for complex, uncertain, and multi-agent robotic environments. Sadati is perhaps best known for pioneering adaptive neural network multiple models sliding mode control for robotic manipulators (15 citations), a method that elegantly solves practical implementation issues of classical controllers. He has also made significant strides in adaptive multi-model CMAC-based supervisory control for uncertain MIMO systems (11 citations), offering robust solutions for nonlinear dynamics. In the realm of legged locomotion, Sadati has tackled the challenging problem of stabilizing periodic orbits for running robots, including both planar and three-dimensional monopedal designs (9 and 2 citations). His work on optimal control of multiple-arm robotic systems using gradient methods (7 citations) and output feedback adaptive decentralized control for cooperative robots (3 citations) further demonstrates his commitment to advancing coordination and autonomy in robotics. Through these contributions, Sadati has established himself as a thoughtful engineer dedicated to making robots more adaptive, stable, and cooperative in the face of real-world uncertainty.