Neural Network-Based Adaptive Control for Dispensing Nozzle Height Compensation

Dispensing nozzle height control is crucial for product quality and production efficiency. Traditional methods struggle with adhesive viscosity changes and environmental temperature fluctuations. This paper proposes an adaptive control strategy based on RBF neural networks. By approximating unknown nonlinear functions and updating weights in real-time, this method effectively compensates for model errors and system uncertainties. Experimental results show that it outperforms traditional PID control in terms of modeling accuracy and robustness, especially when adhesive dynamics change. The study highlights the advantages of neural network - based adaptive control in complex nonlinear systems and offers an effective solution for dispensing nozzle height control.