Rohollah Moghadam
Papers
1
Total Citations
14
H-Index
1
About
Rohollah Moghadam is a leading researcher in intelligent control systems, specializing in optimal adaptive control, neural network-based learning, and nonlinear discrete-time system dynamics. His most-cited work, "Optimal Adaptive Tracking Control of Partially Uncertain Nonlinear Discrete-Time Systems Using Lifelong Hybrid Learning" (2023, 14 citations), introduces a groundbreaking multilayer neural network (MNN) framework for adaptive tracking in affine-form nonlinear systems. Moghadam’s key contribution lies in developing an actor-critic neural network architecture that simultaneously approximates the value function and optimal control policy, enabling lifelong hybrid learning to handle system uncertainties without prior knowledge. This approach significantly advances adaptive control by ensuring robust performance in partially unknown environments, a critical challenge in robotics and autonomous systems. His work has garnered attention for its practical implications in real-time control applications, where system dynamics are often uncertain. Moghadam’s research bridges theoretical rigor with engineering applicability, making him a notable figure in the field of intelligent control and machine learning.
Research Focus
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