MPC to optimise performance in power‐assisted manipulation of industrial objects

Abstract

A 1-DOF power-assist robotic system (PARS) is developed using a linear actuator for vertical manipulation of heavy objects in collaboration with its human user. Human's differential perception of inertia and gravity is considered in the manipulation dynamics, and an admittance-type feedback position control scheme is designed to control the motion of the system. A human manipulates a heavy object with the robot in harmonic motion (the object is repeatedly lifted up and lowered down). A common performance index for the human and the robot for the collaborative task is derived in terms of manipulation velocity and precision. An event-triggered model predictive control (MPC) scheme is designed to maintain optimal performance of the robot and the human through maintaining optimum robot velocity and precision at the events when low performance is observed. Results show that the weightperception-based control helps produce satisfactory collaborative performance, and the MPC is proven effective to maintain optimal performance and improved human-robot interaction. The findings are useful to develop predictive control strategies for human-friendly PARSs for manipulating heavy objects in industries in particular, and for other variable-speed electric drive systems for industrial applications, in general.

Weight-perception-based fixed and variable admittance control algorithms for unimanual and bimanual lifting of objects with a power assist robotic system

“…For multi-DoF manipulation, m 1 , m 2 , and G may be customized for each axis of manipulation. 35 The parameters of the VACA in equation (10) and in Figure 11 should be estimated properly and be as much general as possible, but it is better to customize them properly for each particular application (e.g. each object group, subject group, and unimanual/bimanual manipulation).…”

“…For multi-DoF manipulation, m 1 , m 2 , and G may be customized for each axis of manipulation. 35…”

Calibrating intuitive and natural human–robot interaction and performance for power-assisted heavy object manipulation using cognition-based intelligent admittance control schemes

“…We formulated an MPC taking theoretical and conceptual inspiration from Rawlings and Mayne 36 and Kouvaritakis and Cannon 37 and practical inspiration from Rahman and Ikeura 29 and Rahman et al 35 for the PARS, as shown Figure 10. Mean (n ¼ 10) workload rating scores (out of 100) with standard deviations for the six dimensions of the NASA TLX for the FAC with lightweight and heavy object manipulation.…”

“…27,28 We call this intuitive and natural HRI and performance calibration approach . Our experience suggests that human-centered intelligent controls such as model predictive control (MPC) 29 and VAC 9 can be two good tools to tune this calibration. However, such approach has not been investigated.…”

Dynamic analysis and current calculation of a permanent magnet spherical motor for point‐to‐point motion

“…There are also some problems in the practical applications of PMSM, especially in the aspect of electrifying control based on trajectory planning. At present, the trajectory planning research has been quite mature in the field of robotics, and point-to-point (PTP) motion has a wide range of applications as a common movement [27][28][29]. In the actual electrifying control experiment, electromagnetic torque is very important for the motor.…”