Model Predictive Control With Integral Compensation for Motion Control of Robot Manipulator in Joint and Task Spaces

Yuhan Chen,Qingsheng Luo,Xiao Luo,Baoling Han,Lijun Qiao

doi:10.1109/access.2020.3001044

Yuhan Chen, Qingsheng Luo + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.3001044

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 39	License type: CC BY 4.0

Affiliation: Beijing Institute of Technology

Abstract

The motion control of robot manipulators is a crucial problem concerning automatically controlled robots. In this work, the model predictive control method with an integral compensation (MPC-I), which compensates for the matched uncertainties due to unmodeled dynamics, is proposed to solve the trajectory tracking problem of robot manipulators in joint and task spaces. First, this paper decouples the joint variables of the robot manipulator using a computed torque control method. The MPC-I method is, thereafter, derived to realize the motion control of the robot manipulators in joint space. To realize the motion control of the robot manipulator in task space, the task space is, thereafter, converted into the joint space, in which the MPC-I method is executed, afterward, to control the robot. Furthermore, an MPC-I variation, in which the inverse kinematics is calculated indirectly, is proposed to achieve the motion control in task space. The novelty of this paper is to propose the MPC-I method and the method of converting task space to joint space with indirect inverse kinematics calculation. The former is suitable for the dynamic control of the robot manipulators in the joint space, and the latter can extend the MPC-I method to dynamic control in task space. To evaluate the performance of the proposed control method, motion control simulations are performed in the task and joint spaces, respectively. Simulation results and comparisons verify the effectiveness of the proposed control approach for the dynamic control of the UR5 robot manipulator.

Highlights

Due to existing demands to improve reliability, accuracy, repeatability, and productivity of industrial processes, the significance of robot technology has been highlighted [1]
The most representative [15] dealt with the robust task space control of electrically driven robot manipulators using voltage control strategy, which is simpler, less computational and requires less feedbacks than
This paper proposes an model predictive control with integral compensation (MPC-I) for the motion control of the robot manipulator that can be used in the joint and task spaces

Summary

INTRODUCTION

Due to existing demands to improve reliability, accuracy, repeatability, and productivity of industrial processes, the significance of robot technology has been highlighted [1]. Y. Chen et al.: MPC-I for Motion Control of Robot Manipulator in Joint and Task Spaces the conventional robust controllers. Reference [29] proposes a combination of MPC and H-infinity control, which is a nonlinear design technique for a robot manipulator. The result of this combination, could not combine the respective advantages of these schemes while trying to avoid their shortcomings. This paper proposes an model predictive control with integral compensation (MPC-I) for the motion control of the robot manipulator that can be used in the joint and task spaces.

PRELIMINARIES

EXPONENTIAL COORDINATE REPRESENTATION OF RIGID-BODY MOTIONS

MPC-I METHOD OF ROBOT MANIPULATORS IN JOINT SPACE

5) CONTROL METHOD OF MOTION IN JOINT SPACE

1: Initialization

SIMULATION AND DISCUSSION

CONCLUSION