M. Bagherpour
Papers
3
Total Citations
34
H-Index
3
About
M. Bagherpour is a control systems researcher whose work focuses on intelligent, adaptive control strategies for complex nonlinear systems, particularly robotic manipulators. His major contributions lie in the fusion of neural networks and sliding mode control to overcome practical limitations like chattering, a common problem in classical sliding mode controllers. His most cited work, "Adaptive neural network multiple models sliding mode control of robotic manipulators using soft switching" (2005, 15 citations), introduces a novel approach using multiple adaptive RBF neural network models to improve robustness and tracking accuracy. Building on this, he developed an adaptive multi-model CMAC-based supervisory controller for uncertain MIMO systems (2005, 11 citations), demonstrating the versatility of neural-network-driven control in handling multi-input multi-output nonlinearities. His 2006 paper on an adaptive neural network sliding mode controller (8 citations) further refines chattering elimination. Collectively, Bagherpour’s work has advanced the practical implementation of robust, intelligent control for robotic systems, offering solutions that reduce hardware wear and improve precision. His research is foundational for students and engineers working on adaptive control, neural networks, and mechatronics.
Research Focus
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Top Papers
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