Mohammed Abu Mallouh
Papers
5
Total Citations
19
H-Index
3
About
Mohammed Abu Mallouh is a researcher specializing in advanced control systems for robotic and pneumatic applications. His work focuses on hybrid force/velocity control, neural network compensation, and adaptive control strategies to overcome nonlinearities in pneumatic and robotic systems. A key contribution is his development of a hybrid controller for pneumatic gantry robots, enabling simultaneous control of contact force and tangential velocity for precise contour tracking tasks like polishing and deburring. His 2008 paper on force/velocity control with neural network compensation, cited 5 times, demonstrates this innovation. He also pioneered a novel adaptive neural network compensator for position control of pneumatic systems (2011, 5 citations), addressing performance limitations caused by nonlinearities. More recently, Abu Mallouh has explored online reinforcement learning control for robotic arms to mitigate friction force variations (2023, 3 citations), showcasing his adaptability to modern AI-driven control methods. His work, spanning from 2007 to 2023, has accumulated over 19 citations, reflecting its relevance in industrial robotics and automation. Abu Mallouh’s research bridges classical control theory with intelligent systems, offering practical solutions for high-precision manufacturing tasks.
Research Focus
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Top Papers
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