TY - GEN
T1 - Effect of short-term weather predictions on model predictive trajectory tracking performance of unmanned surface vessels
AU - Armentor, Benjamin
AU - Stevens, Joseph
AU - Madsen, Nathan
AU - Durand, Andrew
AU - Vaughan, Joshua
N1 - Publisher Copyright:
Copyright © 2020 ASME.
PY - 2020
Y1 - 2020
N2 - For mobile robots, such as Autonomous Surface Vessels (ASVs), limiting error from a target trajectory is necessary for effective and safe operation. This can be difficult when subjected to environmental disturbances like wind, waves, and currents. This work compares the tracking performance of an ASV using a Model Predictive Controller that includes a model of these disturbances. Two disturbance models are compared. One prediction model assumes the current disturbance measurements are constant over the entire prediction horizon. The other uses a statistical model of the disturbances over the prediction horizon. The Model Predictive Controller performance is also compared to a PI-controlled system under the same disturbance conditions. Including a disturbance model in the prediction of the dynamics decreases the trajectory tracking error over the entire disturbance spectrum, especially for longer horizon lengths.
AB - For mobile robots, such as Autonomous Surface Vessels (ASVs), limiting error from a target trajectory is necessary for effective and safe operation. This can be difficult when subjected to environmental disturbances like wind, waves, and currents. This work compares the tracking performance of an ASV using a Model Predictive Controller that includes a model of these disturbances. Two disturbance models are compared. One prediction model assumes the current disturbance measurements are constant over the entire prediction horizon. The other uses a statistical model of the disturbances over the prediction horizon. The Model Predictive Controller performance is also compared to a PI-controlled system under the same disturbance conditions. Including a disturbance model in the prediction of the dynamics decreases the trajectory tracking error over the entire disturbance spectrum, especially for longer horizon lengths.
UR - http://www.scopus.com/inward/record.url?scp=85100920596&partnerID=8YFLogxK
U2 - 10.1115/DSCC2020-3316
DO - 10.1115/DSCC2020-3316
M3 - Conference contribution
AN - SCOPUS:85100920596
T3 - ASME 2020 Dynamic Systems and Control Conference, DSCC 2020
BT - Intelligent Transportation/Vehicles; Manufacturing; Mechatronics; Engine/After-Treatment Systems; Soft Actuators/Manipulators; Modeling/Validation; Motion/Vibration Control Applications; Multi-Agent/Networked Systems; Path Planning/Motion Control; Renewable/Smart Energy Systems; Security/Privacy of Cyber-Physical Systems; Sensors/Actuators; Tracking Control Systems; Unmanned Ground/Aerial Vehicles; Vehicle Dynamics, Estimation, Control; Vibration/Control Systems; Vibrations
PB - American Society of Mechanical Engineers
T2 - ASME 2020 Dynamic Systems and Control Conference, DSCC 2020
Y2 - 5 October 2020 through 7 October 2020
ER -