Comparative Study of PID Control and Self-Tuning Neural Network Adaptive Control on an Octarotor for Motion Optimization - Hendi Purnata; Moh Khairudin; Sarwo Pranoto; Galih Mustiko Aji; Nanda Pranandita (Politeknik Negeri Cilacap, 2025)
This study aims to compare the performance between a PID cascade controller and a self-tuning Neural Network adaptive control (PID NN) in controlling an ROV on an octarotor platform with 6 degrees of freedom (DOF), namely Surge, Sway, Heave, Roll, Pitch, and Yaw. The conventional PID control system is used as a proven baseline, while the Neural Network-based adaptive control is applied to adjust parameters in real-time, expected to address the nonlinearity and external disturbances that are difficult to handle by static PID. This study involves an analysis of overshoot, rise time, settling time, and final position for each channel, as well as a comparison of the performance of the two control methods. The results show that PID NN provides faster rise times and lower overshoot in most channels, such as Surge overshoot of 8.3%, rise time of 6.2 seconds, and Sway overshoot of 2.1%, settling time of 90 seconds, compared to PID, which has higher overshoot and longer stabilization times. However, for the Yaw and Heave channels, although PID NN showed larger overshoot and longer settling time, PID was faster in achieving stability. Overall, although PID NN demonstrated superiority in terms of rapid stabilization for Roll and Pitch, further adjustments are needed to optimize Yaw and Heave to achieve faster stabilization without compromising system stability and overall control performance. This study opens opportunities for further development in the field of adaptive control for high-complexity multirotor systems.