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About

Sheroz Khan is a researcher whose work sits at the intersection of robotics, intelligent control systems, and biomedical engineering. His primary research areas include rehabilitation robotics, brain-computer interfaces (BCI), and the application of meta-heuristic optimization algorithms for advanced robotic control. Khan has made significant contributions to the development of upper-limb rehabilitation exoskeletons, notably optimizing PID controllers using bio-inspired algorithms such as the Artificial Bee Colony and Firefly algorithms to improve system performance under noisy conditions. His work on model predictive control for rehabilitation robots has garnered attention, with his most cited paper accumulating 9 citations. Khan has also explored the integration of electroencephalogram (EEG) signals with robotic manipulators, aiming to create assistive technologies for paralyzed individuals and those with conditions like ALS. More recently, his research has advanced into deep reinforcement learning for controlling cable-driven parallel robots, reflecting a forward-looking approach to complex control challenges. Through these efforts, Khan is helping to shape more responsive, intelligent, and accessible robotic systems for both industrial and therapeutic applications.