Behzad Farzanegan

About

Behzad Farzanegan is a rising researcher in the fields of nonlinear control systems, adaptive optimal control, and reinforcement learning. His work focuses on developing intelligent control architectures for complex, partially uncertain nonlinear discrete-time systems. A key contribution is his 2023 paper on "Optimal Adaptive Tracking Control of Partially Uncertain Nonlinear Discrete-Time Systems Using Lifelong Hybrid Learning," which has garnered 14 citations. In this work, he introduced a multilayer neural network-based actor-critic framework that enables lifelong learning for optimal tracking, advancing the state of the art in adaptive control. More recently, in 2025, Farzanegan published "Explainable and Safety Aware Deep Reinforcement Learning-Based Control of Nonlinear Discrete-Time Systems Using Neural Network Gradient Decomposition," which has already earned 4 citations. This paper pioneers an explainable deep reinforcement learning approach that ensures safety constraints are respected while maintaining optimal performance, addressing critical challenges in deploying AI-driven control in real-world systems. His research is notable for bridging theoretical rigor with practical safety and interpretability concerns, making him a promising voice in the next generation of control theorists.

Research Focus

Key Achievements

Top Papers

1. 1
   Optimal Adaptive Tracking Control of Partially Uncertain Nonlinear Discrete-Time Systems Using Lifelong Hybrid Learning

   14 citations · 2023
2. 2
   Explainable and Safety Aware Deep Reinforcement Learning-Based Control of Nonlinear Discrete-Time Systems Using Neural Network Gradient Decomposition

   4 citations · 2025

Key Collaborators

Contact & Links