Neural Network Modeling and Control for 3-axis Horizontal Stabilization Platform

In this study, a novel method combining neural networks with a PID controller to implement a 3-axis stabilization platform was proposed. In the 3-axis stabilization platform, the three servo motor angles corresponding to the plate's roll and pitch angles could not be directly measured. Due to the nonlinear characteristics in actual implementation, directly using a controller was challenging. Therefore, in the proposed study, we used a neural network to learn the relationship between the three servo motor inputs and the roll and pitch angles of the 3-axis stabilization. The neural network provided the servo motor inputs based on the roll and pitch angles measured by sensors. To improve the accuracy and reliability of the data, the measured values were corrected using a Kalman filter, and after data collection, the data was preprocessed for neural network modeling. The trained neural network model was combined with a PID controller to control the three servo motors, maintaining the plate's horizontal position. Experimental results demonstrated the performance comparison across various neural network structures, confirming that the roll and pitch angles of the oscillating plate were controlled by the proposed neural network-based stabilization.