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About

Hamid Toshani is a researcher whose work sits at the intersection of robotics, control theory, and neural networks. His primary research areas include the kinematic control of redundant manipulators, multi-agent consensus tracking, and the application of neural-optimiser-based sliding mode control. Toshani’s most significant contribution is his pioneering work on real-time inverse kinematics for redundant manipulators, where he introduced a Lyapunov-based approach combining neural networks and quadratic programming. This paper, published in 2014, has garnered 80 citations, underscoring its influence in the field of robotic control. He further advanced the field by proposing an optimal sliding-mode control technique using projection recurrent neural networks to solve tracking consensus problems in multi-agent systems, a work that has earned 11 citations. Additionally, his 2011 study on kinematic control of a seven-degree-of-freedom robot manipulator, addressing joint limits and obstacle avoidance through radial-basis function neural networks, laid foundational groundwork for real-time robotic motion planning. Toshani’s research is notable for its practical focus on real-time, constraint-aware control, making his methods highly applicable to modern robotics and autonomous systems. His work continues to inspire advancements in neural-optimised control for complex, multi-agent environments.