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PhD Research
Neural Network Adaptive Controller
Research prototype integrating neural network models of mechanical nonlinearities with adaptive control algorithms for BLDC drives. Experimental validation on a 1-DOF robot arm test bench.
PythonNeural NetworksMPCAdaptive Control
Overview
This is the core experimental platform of my doctoral research. The project combines neural network-based modeling of mechanical systems with adaptive control strategies for BLDC motors.
Research Goals
- Model friction, elasticity, and hysteresis using neural networks
- Integrate these models with adaptive control algorithms
- Compare performance against classical controllers (FOC, PI)
- Validate on a physical 1-DOF robot arm test bench
Current Status
Work in progress — initial simulation results are promising. Experimental validation planned for 2026/2027.