Mohammed J. Mohammed
Papers
1
Total Citations
3
H-Index
1
About
Mohammed J. Mohammed is a researcher advancing intelligent control systems for autonomous robotics, with a focus on bio-inspired optimization and fuzzy logic. His most-cited work, "Design fuzzy neural petri net controller for trajectory tracking control of mobile robot" (2018), introduces a novel Fuzzy Neural Petri Net (FNPN) controller optimized by Particle Swarm Optimization (PSO) to solve path tracking challenges for wheeled mobile robots. By integrating fuzzy reasoning with neural Petri nets, Mohammed’s approach enables precise velocity control and adaptive trajectory planning, addressing fundamental problems in autonomous navigation. Although his citation count is currently modest, his work represents a meaningful step toward more robust, computationally efficient controllers for mobile robotics. Mohammed’s research sits at the intersection of artificial intelligence, control theory, and robotics, offering practical solutions for real-world autonomous systems. His contributions are particularly relevant for students and researchers exploring hybrid intelligent controllers, as they demonstrate how combining multiple AI paradigms can overcome the limitations of traditional control methods in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1