4 research outputs found
Predictor Design for Altitude Control of a Seaweed Harvester
In this paper, the predictor design, for altitude control of a seaweed harvester, is
investigated. The harvesting system consists of a vessel and a suspended harvester device, the
altitude of which is controlled by a winch. The control approach of Gallieri and Ringwood
(2010), including a feedforward action, which requires a single step disturbance prediction, is
investigated further, focusing on the disturbance prediction, for noisy sensors. The prediction is
performed using AR and ARMA models, identified online, by using the Recursive Least Squared
with Forgetting Factor (RLSFF) algorithm and the Kalman Filter (KF). The dependance
between the error spectrum and the quality of the control is shown, and the prediction performances
are evaluated, using an FFT-based criterion, oriented to the feedforward application.
The control performances are then evaluated, and the results are compared to Gallieri and
Ringwood (2010)
Predictor Design for Altitude Control of a Seaweed Harvester
In this paper, the predictor design, for altitude control of a seaweed harvester, is
investigated. The harvesting system consists of a vessel and a suspended harvester device, the
altitude of which is controlled by a winch. The control approach of Gallieri and Ringwood
(2010), including a feedforward action, which requires a single step disturbance prediction, is
investigated further, focusing on the disturbance prediction, for noisy sensors. The prediction is
performed using AR and ARMA models, identified online, by using the Recursive Least Squared
with Forgetting Factor (RLSFF) algorithm and the Kalman Filter (KF). The dependance
between the error spectrum and the quality of the control is shown, and the prediction performances
are evaluated, using an FFT-based criterion, oriented to the feedforward application.
The control performances are then evaluated, and the results are compared to Gallieri and
Ringwood (2010)
Predictor Design for Altitude Control of a Seaweed Harvester
In this paper, the predictor design, for altitude control of a seaweed harvester, is
investigated. The harvesting system consists of a vessel and a suspended harvester device, the
altitude of which is controlled by a winch. The control approach of Gallieri and Ringwood
(2010), including a feedforward action, which requires a single step disturbance prediction, is
investigated further, focusing on the disturbance prediction, for noisy sensors. The prediction is
performed using AR and ARMA models, identified online, by using the Recursive Least Squared
with Forgetting Factor (RLSFF) algorithm and the Kalman Filter (KF). The dependance
between the error spectrum and the quality of the control is shown, and the prediction performances
are evaluated, using an FFT-based criterion, oriented to the feedforward application.
The control performances are then evaluated, and the results are compared to Gallieri and
Ringwood (2010)
Predictor Design for Altitude Control of a Seaweed Harvester
In this paper, the predictor design, for altitude control of a seaweed harvester, is
investigated. The harvesting system consists of a vessel and a suspended harvester device, the
altitude of which is controlled by a winch. The control approach of Gallieri and Ringwood
(2010), including a feedforward action, which requires a single step disturbance prediction, is
investigated further, focusing on the disturbance prediction, for noisy sensors. The prediction is
performed using AR and ARMA models, identified online, by using the Recursive Least Squared
with Forgetting Factor (RLSFF) algorithm and the Kalman Filter (KF). The dependance
between the error spectrum and the quality of the control is shown, and the prediction performances
are evaluated, using an FFT-based criterion, oriented to the feedforward application.
The control performances are then evaluated, and the results are compared to Gallieri and
Ringwood (2010)