PD Control of a Manipulator with Gravity and Inertia Compensation Using an RBF Neural Network

Dynamic compensation can improve the accuracy of trajectory tracking for industrial manipulators. For irregularly shape or flexible manipulators, however, it is difficult to measure the position of the center of mass (COM) so that its dynamic model cannot be expressed explicitly. This paper proposes...

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Bibliographic Details
Published in:International journal of control, automation, and systems 2020, Automation, and Systems, 18(12), , pp.3083-3092
Main Authors: Zhang, Yueyuan, Kim, Dongeon, Zhao, Yudong, Lee, Jangmyung
Format: Article
Language:English
Subjects:
Compensation
Control
Control Theory and Applications
Controllers
Degrees of freedom
Dynamic models
Engineering
Flexible manipulators
Inertia
Mechatronics
Neural networks
Optimization
Position measurement
Proportional derivative
Radial basis function
Robot arms
Robotics
제어계측공학
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Summary:Dynamic compensation can improve the accuracy of trajectory tracking for industrial manipulators. For irregularly shape or flexible manipulators, however, it is difficult to measure the position of the center of mass (COM) so that its dynamic model cannot be expressed explicitly. This paper proposes a proportional derivative (PD) controller with radial basis function neural network based gravity and inertia compensation (RBFNN-GIC). The RBFNN is utilized to estimate the gravity disturbance and to enable identification of COM to calculate the compensated inertia term. The proposed strategy based on the dynamic model can be used on any robot arm whose COM, gravity and inertia are difficult to obtain. To demonstrate the optimization and effectiveness of proposed PD controller, comparative experiments between the proposed control scheme and the traditional data-fitting method least mean square method (LMS) are conducted on a 3 degree of freedom (DOF) robotic manipulator.
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-019-0482-x