Minimum-time S-curve commands for vibration-free transportation of an overhead crane with actuator limits. (May 2020)

• Record Type: Journal Article
• Authors: Tho, Ho Duc; Kaneshige, Akihiro; Terashima, Kazuhiko
• Is Part Of: Control engineering practice. Volume 98(2020)
• Publication Date: 2020-05
• Subjects: Vibration control -- Crane control -- S-curve -- Input shaping -- Motion planning -- Underactuated system; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2020.104390
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: S-curve commands are ubiquitous in servo drives owing to their simplicity and smoothness. Nevertheless, they need to be adapted for use in flexible systems, where the problem of residual vibration must be addressed. This paper proposes a simple motion planning method for the vibration-free transfer process of an overhead crane using S-curve commands. Based on a position baseline S-curve, which is generated from a bang-off-bang acceleration profile, two approaches are proposed to build the vibration suppression capability. One is an embedding method that injects the essential terminal conditions for vibration-free transportation into the baseline S-curve command without altering its original form. The other is a shaping method inspired from the input shaping technique. In both schemes, the baseline S-curve is parameterized to establish minimum-time optimization problems, in which maximum velocity and maximum acceleration of the actuator are explicitly taken into consideration. The minimum-time solutions are successfully obtained by solving constrained (discrete) nonlinear programs. An online trajectory generation can be realized using the proposed approach. Both simulation and experimental results are given to verify the effectiveness of the proposed method. Highlights: Minimum-time Zero Vibration S-curve commands subject to actuator limits are solved. The class of bang-off-bang inputs are utilized to form (discrete) nonlinear programs. Analytical solutions of the optimization problems can be obtained. Simple, low computational cost, online command generation is feasible. Effectiveness of proposed schemes are verified by both simulations and experiments.