Application Of Model Reference Adaptive Control Research Articles

Dynamic positioning systems (DPS) comprise the deployment of active propulsion to maintain the position and heading of a vessel. Several sensors are used to measure the actual position of the floating body, while a control algorithm is responsible for the calculation of forces to be delivered by each propeller, in order to counteract all environmental forces, such as wind, waves and current loads. The controller cannot directly compensate motions in the sea waves frequency range, since they would require an enormous amount of power to be attenuated, possibly causing damage to the propeller system. That is the reason why a filtering algorithm is to be put in place to separate high-frequency components from the low-frequency ones, which are, then, fed into the control loop. Usual commercial systems apply Kalman filtering technique to perform such task, due to the smaller phase-lag introduced in the control loop compared to conventional low-pass filters. The Kalman filter draws on a model of the system to be controlled, which, in turn, depends on an unknown parameter, related to the wave frequency. Adaptive filtering is called upon with a view to perform an on-line estimation of such parameter. Most control algorithms, however, rely on fixed gains, thus making it possible for a noticeable performance degradation to take place in some situations, as those associated to mass variation during a loading operation. This paper presents the application of model-reference adaptive control (MRAC) technique to DPS's, cascaded with the commonly used adaptive Kalman filter. The model of a dynamically-positioned shuttle tanker exposed to waves and current is employed to highlight the advantages of the adaptive controller compared to commonplace fixed-gain controllers.