Behzad Farzanegan
Orcid: 0000-0002-8660-2111
According to our database1,
Behzad Farzanegan
authored at least 12 papers
between 2022 and 2025.
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Bibliography
2025
Explainable and Safety Aware Deep Reinforcement Learning-Based Control of Nonlinear Discrete-Time Systems Using Neural Network Gradient Decomposition.
IEEE Trans Autom. Sci. Eng., 2025
Reinforcement Learning-Based Nonlinear Optimal Discrete-Time Control of Power Systems.
Proceedings of the IEEE Conference on Control Technology and Applications, 2025
Multi-Model Safe Neuro-Optimal Output Tracking Control of Autonomous Surface Vessels with Explainable AI.
Proceedings of the IEEE Conference on Control Technology and Applications, 2025
2024
Optimal Adaptive Tracking Control of Partially Uncertain Nonlinear Discrete-Time Systems Using Lifelong Hybrid Learning.
IEEE Trans. Neural Networks Learn. Syst., December, 2024
Data-Driven Distributed Optimal Control Using Neighbourhood Optimization for Nonlinear Interconnected Systems.
J. Optim. Theory Appl., October, 2024
Reinforcement Learning-Based Constrained Optimal Control of Strict-feedback Nonlinear Systems: Application to Autonomous Underwater Vehicles.
Proceedings of the IEEE Conference on Control Technology and Applications, 2024
2023
Continual Reinforcement Learning Formulation for Zero-Sum Game-Based Constrained Optimal Tracking.
IEEE Trans. Syst. Man Cybern. Syst., December, 2023
Continual Learning-based Optimal Output Tracking of Nonlinear Discrete-time Systems with Constraints: Application to Safe Cargo Transfer.
Proceedings of the IEEE Conference on Control Technology and Applications, 2023
Optimal Tracking of Nonlinear Discrete-time Systems using Zero-Sum Game Formulation and Hybrid Learning.
Proceedings of the American Control Conference, 2023
2022
Distributed optimal control for continuous-time nonaffine nonlinear interconnected systems.
Int. J. Control, 2022
A gradient ascent algorithm based on possibilistic fuzzy C-Means for clustering noisy data.
Expert Syst. Appl., 2022
Optimal Adaptive Output Regulation of Uncertain Nonlinear Discrete-time Systems using Lifelong Concurrent Learning.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022