ESTONIAN ACADEMY
PUBLISHERS
eesti teaduste
akadeemia kirjastus
PUBLISHED
SINCE 1952
 
Proceeding cover
proceedings
of the estonian academy of sciences
ISSN 1736-7530 (Electronic)
ISSN 1736-6046 (Print)
Impact Factor (2021): 1.024
Universal identification and control of industrial manufacturing equipment as a service; pp. 444–452
PDF | 10.3176/proc.2021.4.11

Authors
Verena Tessaro, Axel Vick, Jörg Krüger
Abstract

This paper presents a universal approach of identification and closed-loop control of manufacturing equipment, de- livered through web services using Open Platform Communications United Architecture (OPC UA). Rapid prototyping as well as retrofitting and digitization of legacy systems often need design and application of closed-loop controllers. The analysis and modelling for systems such as energy-conversion or material transport devices is labour-intensive and needs process understanding. Current identification and control toolboxes require systematic preparation of input/output data, modification and tuning of the derived models, also proper design of classic PID controllers. An on-demand service paradigm is applied to allow identification and control with direct access to the controlled system over a network connection. The identified parameters are used to adapt a model predictive controller (MPC), which stabilizes the system and drives trajectories to different operating points. To evaluate the performance of the controllers in terms of stability, accuracy, and time response, several target trajectories and disturbances (signal noise, external physical disturbances, latency in communication) were investigated. The identification service was used to model the linear dynamics of a 6-DOF industrial robot and a laboratory-scale waterworks containing two separately controllable pumps. The robot’s axes and the waterworks’ pumps were successfully controlled with current set-points by using their respective identified state-space models. Simulation and laboratory experiments show promising results for the control of diverse systems with varying time-constants, and imply broad applicability. As a major achievement, this approach enables to efficiently implement system identification and model predictive control in manufacturing.

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