ADAPTIVE RADIAL-BASIS FUNCTION NEURAL NETWORK-BASED PID CONTROL FOR MARINE DIESEL ENGINE SPEED

Abstract

This paper proposes an adaptive control system for regulating the speed of marine main diesel engines based on a radial-basis function neural network (RBF) and proportional-integral-derivative (PID) control. The RBF is utilized to approximate the nonlinear dynamics of the diesel engine, while the PID controller is designed to regulate the engine speed. The proposed control system is capable of handling the uncertainties and disturbances inherent in the marine environment, such as changes in load demand and sea conditions. The adaptive nature of the RBF enables it to adapt to changes in the system dynamics, making the control system robust and able to maintain optimal performance over a wide range of operating conditions. The effectiveness of the proposed control system is demonstrated through simulations conducted using the MATLAB-Simulink program. The results demonstrate that the proposed adaptive neural network integrated PID control system outperforms traditional PID control and other existing control strategies in terms of response speed and robustness.