Papers
17
Total Citations
174
H-Index
7
About
Mohamed Darouach is a control systems researcher whose work spans observer design, robust control, and adaptive learning for nonlinear and robotic systems. His most significant contributions lie in developing advanced state estimation frameworks, particularly for systems with unknown inputs — a notoriously challenging problem in control theory. His pioneering work on functional unknown input observers using the Polytopic Takagi-Sugeno framework has offered constructive, computationally tractable design procedures for complex nonlinear systems, earning nearly 30 citations. His H∞ filtering and dynamic observer methodologies for singular bilinear and Lipschitz nonlinear systems have further strengthened robust estimation theory. Darouach has made enduring contributions to robotics control, with his reduced-order observer-based controllers for robot manipulators — published across the mid-to-late 1990s — accumulating citations that reflect their lasting practical relevance. His more recent bioinspired composite learning control approach, drawing on human motor learning mechanisms to handle discontinuous friction in industrial robots, reflects a creative expansion into intelligent adaptive control. Across a research career spanning nearly three decades, Darouach's cumulative citation record and breadth of contributions — from descriptor system observers to LPV-based SCARA robot control — establish him as a versatile and influential figure in modern control engineering.
Research Focus
Key Achievements
Top Papers
- 1A new polytopic approach for the unknown input functional observer design28 citations · 2017
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- 3Learning from adaptive control under relaxed excitation conditions21 citations · 2019
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- 7H- control of a SCARA robot using polytopic LPV approach14 citations · 2006
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