M.A. Christodoulou
Papers
8
Total Citations
84
H-Index
5
About
M.A. Christodoulou’s research focuses on the intersection of nonlinear system identification, neural network control, and robotics. His major contributions lie in developing and analyzing recurrent high-order neural networks (RHONNs) for modeling and controlling unknown dynamical systems. Christodoulou pioneered the use of dynamic neural network structures with inherent stability and convergence properties, enabling “black box” identification of complex systems without prior knowledge. His work on robot identification using dynamical neural networks and robust adaptive control of unknown plants has been foundational, with his most cited paper, “Identification of nonlinear systems using new dynamic neural network structures” (2005), accumulating 41 citations. He also contributed to cooperative robotics, introducing noninverting algorithms for path tracking of two cooperating robot arms that avoid singularities by eliminating the need for inverse kinematics. His research on learning robot contact surface shapes using high-order neural networks further demonstrates his impact in merging neural computation with mechanical systems. With a career spanning from the early 1990s, Christodoulou’s work has shaped modern approaches to neural-network-based control and identification in robotics and nonlinear systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robot identification using dynamical neural networks9 citations · 2002
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- 5Modeling of Robot Dynamics by Neural Networks with Dynamic Neurons6 citations · 1993
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- 8Continuous path planning via a non-inverting parallel algorithm2 citations · 1992