Sensorless force estimation and control of Delta robot with limited access interface. Issue 5 (20th August 2018)

• Record Type: Journal Article
• Authors: Cheng, Hongtai; Jiang, Hongfei
• Is Part Of: Industrial robot. Volume 45:Issue 5(2018)
• Page Start: 611
• Page End: 622
• Publication Date: 2018-08-20
• Subjects: Control -- Robotics; Robots, Industrial -- Periodicals; Machinery in the workplace -- Periodicals; 629.892
• DOI: 10.1108/IR-03-2018-0048
• Languages: English
• ISSNs: 0143-991X

Abstract

Abstract : Purpose: Delta robot is a parallel robot specifically designed for high-speed pick and place tasks. However, sometimes they are asked to perform additional assembling and squeezing actions, which is beyond the capability of position-controlled Delta robots. Force sensors may be expensive and add mass to the system. Therefore, the purpose of this paper is to study sensorless force control of Delta robots using limited access interface. Design/methodology/approach: Static force analysis is performed to establish a relation between joint torques and external forces. The joint torques are observed from signals provided by motor drivers. A distributed mass model is proposed to compensate the gravity of upper arms and forearms. To minimize the effect of backlash and nonlinear frictions brought by gearboxes, model parameters are calibrated in two separated modes: "LIFTING" and "LOWERING". Finally, a hybrid force estimation model is built to deal with both cases simultaneously. Surrogate model-based force control law is proposed to increase the force control loop rate and handle the force control problem for discrete position-controlled Delta robots. Findings: The results show that the force estimation model is effective and mode separation can significantly improve the accuracy. The force control laws indeed stabilize the robot in desired states. Originality/value: The proposed solution is based on position-controlled commercial Delta robot and requires no additional force sensor. It is able to extend Delta robots' capability and meet requirements of emerging complex tasks.