Papers
3
Total Citations
23
H-Index
2
About
Dr. Salah Al-Sharhan is a researcher whose work spans the frontiers of control theory, robotics, and computational intelligence. His primary research areas include reinforcement learning for nonlinear systems, trajectory tracking for mobile robots, and the application of computational intelligence to cooperative robotic systems. A standout contribution is his 2023 paper on "Real-time measurement-driven reinforcement learning control approach for uncertain nonlinear systems," which has garnered 19 citations, demonstrating its immediate impact on adaptive control methodologies. In earlier foundational work, Dr. Al-Sharhan proposed a linear time-invariant state feedback operator for nonholonomic mobile robots, addressing a persistent challenge in smooth trajectory tracking on 2D planes. His 2002 study on computational intelligence for joint trajectory generation in multi-joint cooperative robots tackled the complex inverse kinematics problem, showcasing his long-standing commitment to advancing autonomous systems. Through these contributions, Dr. Al-Sharhan has provided practical solutions for real-time control under uncertainty, making his work essential reading for students and researchers in robotics and intelligent control.
Research Focus
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Top Papers
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