Modeling and Designing Direct Inverse Control Using Back-propagation Neural Network for Skid Steering Boat Model

This paper discusses about designing a direct inverse control based back-propagation neural network for a skid steer model boat. The boat is modeled into a MIMO system, with port side and starboard propeller as input, while yaw, surge velocity and sway velocity as output. The inverse plant model was created for the controller and tested with an identification model of the plant. The simulation result is the plant output follows the desired output value that is fed to the inverse controller with the normalized mean sum square error for yaw 0.1203, surge velocity 0.4459 and sway velocity 0.1723.