A new method for closed-loop stability prediction in industrial robots. (February 2022)

• Record Type: Journal Article
• Authors: Cvitanic, Toni; Melkote, Shreyes N.
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 73(2022)
• Publication Date: 2022-02
• Subjects: Industrial robot -- Feedback -- Controller gain -- Stability -- Dynamics; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2021.102218
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: The main source of error resulting from real-time correctional commands to an industrial robot is identified to be the structural dynamics of the manipulator. A way of modeling robot dynamics which allows them to be directly incorporated into a closed-loop system model is presented. This model represents real-time corrections by their "equivalent force" by assuming they are achieved with a constant acceleration. A method of using the model to predict closed-loop stability of control systems involving industrial manipulators is presented. The new method alleviates the need to manually tune feedback controllers for industrial manipulators. Abstract: With the demand for higher position accuracy from industrial robots used for precision manufacturing tasks, a common solution approach is to implement closed-loop feedback control using external sensors. Because most industrial robot controllers only allow real-time commands to be specified in the form of Cartesian or joint position offsets, the plant models of these closed-loop systems tend to be very simple in that they assume that the robot executes each input command with minimal or no error. However, real-time motion error can be of the order or larger than the corresponding input commands. Due to the shortcomings of these simplistic models, closed-loop controller gains need to inevitably be tuned manually through trial and error. If the missing components of the simplistic plant models can be identified, closed-loop controller gains can be readily determined efficiently through simulation. In this paper, robot controller delay and robot dynamics are identified as the key missing components, and a new data-driven method for capturing the robot dynamics and a model for closed-loop stability prediction are established. The new model-based method is experimentally evaluated on a six degree-of-freedom (6-DoF) industrial manipulator. It is confirmed that the new method can be used to determine via simulation robot controller gains that ensure closed-loop stability without the need for iterative trial and error experimental gain-tuning.