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This work presents a real-time time-delay filtering approach for reference shaping of high precision motion control of vibratory systems. The motion of the system is initiated with a judicious (arbitrary) step command and the acquired motion data is used to estimate the modal parameters in realtime.The modal data is subsequently used to synthesize the subsequent step commands to mitigate the residual vibrations. The proposed control algorithm is tested on a gantry crane structure with a suspended payload. Our method estimates the system parameters based on computer vision while tracking an ArUco fiducial marker which is integral with the payload. Computational efficiency is ensured by using C++ to deploy the algorithm. The goal is to minimize the residual energy at the terminal displacement for rest-to-rest maneuvers of a suspended payload with unknown dynamics. An inertial measurement unit is used to track the pendular angular velocity at the end of the maneuver and is not used in the model identification process.
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Velocity Constrained Time-Optimal Control of a Gantry Crane System
The focus of this paper is on the development of velocity constrained time-optimal control profiles for point-to-point motion of a gantry crane system. Assuming that the velocity of the trolley of the crane can be commanded, an optimal control problem is posed to determine the bang-off-bang control profile to transition the system to the terminal states with no residual vibrations. Both undamped and underdamped systems are considered and the variation of the structure of the optimal control profiles as a function of the final displacement is studied and the collapse and birthing of switches in the control profile are explained. To account for uncertainties in model parameters, a robust controller design is posed and the tradeoff of increase in maneuver time to the reduction of residual vibrations is illustrated.
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- Award ID(s):
- 2021710
- PAR ID:
- 10428442
- Date Published:
- Journal Name:
- 2022 American Control Conference
- Page Range / eLocation ID:
- 3766 to 3770
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/ACC53348.2022.9867701
