Suresh Rajendran

Bio: Suresh Rajendran is an academic researcher from Indian Institutes of Technology. The author has contributed to research in topics: Autopilot & Seakeeping. The author has co-authored 1 publications.

Numerical investigation of the manoeuvring motions and trajectory tracking of a KVLCC2 tanker using PID controller in regular waves

Abstract: An autopilot based on a PID control technique is incorporated in a 6DOF unified seakeeping and manoeuvring numerical model of a ship in a seaway. A crude oil carrier (KVLCC2) prototype is used in the study. The surge, sway and yaw are assumed to be slowly varying motions and the heave, roll and pitch respond to the rapidly varying wave forces. The second order wave mean drift forces act on the horizontal motions. For heave, roll and pitch, the Froude-Krylov forces and the restoring are calculated for the exact wetted surface area for each time step. The hull resistance and the control forces are calculated based on an empirical data available in the existing literature. The reference heading is obtained from Line Of Sight (LOS) algorithm using way-points. The effectiveness of autopilot developed based on PID controller is studied in regular waves.

A Model Predictive based Controller (MPC) for the Path Following of a KVLCC2 Tanker in waves

Abstract: This work investigates the performance of different controllers for the heading control and path following of a KVLCC2 tanker ship under wave disturbances. The guidance law is designed based on the Line of Sight (LoS) algorithm. The Maneuvering Modeling Group (MMG) Model of the KVLCC tanker is used to represent the system dynamics, whose linearized model with usual assumptions doesn’t satisfy the straight line stability. An input-output based linear model is obtained by relating the rudder deflection to yaw rate using system identification method, and subsequently used for tuning the controllers. The tuned gains of the controller is then applied to the nonlinear model. The performances of PID and LQR are studied and compared with a linear Model Predictive Controller (MPC). The MPC outperforms the PID and LQR in the presence of external disturbances as well as under calm water conditions.