On-line motion profile optimization for reciprocating mechanisms

Reciprocating mechanisms are widely used in industry because a complex movement is achieved by a simple rotation of the driven axis. Given the tendency to evolve towards more energy-efficient machines and flexible production, motion profile optimization offers a cost-effective solution as it results in large energy savings without any hardware adaptions. However, the existing optimizers are used off-line because the position-dependent parameters such as load torque and inertia of the system model must be known in advance. When the actual machine differs from the model, or when parameters change during operation due to process flexibility, the off-line determined motion profile is no longer optimal and results in unnecessary energy consumption. This paper therefore presents an on-line approach in which the varying inertia is estimated on the actual machine and used for updating the motion profile. The sliding discrete Fourier transform is proposed for real-time estimation and a gradient-based algorithm combined with Chebyshev polynomials is proposed for on-line optimization. Experimental validation on an industrial pick-and-place unit proves that the presented method leads to similar energy savings as off-line optimizers, but without prior knowledge of the parameters, and is moreover capable of handling mass changes during operation.

Publication year:2022

Keywords:A1 Journal article

Accessibility:Open