Vibration Control of a Flexible Robotic Manipulator in the Presence of Input Deadzone

• Published in: IEEE transactions on industrial informatics Vol. 13; no. 1; pp. 48 - 59
• Main Authors: He, Wei, Ouyang, Yuncheng, Hong, Jie
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-02-2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Adaptive Control; Approximation; Artificial neural networks; Computer simulation; Control systems design; Controllers; Feedback control; Flexible manipulators; flexible robotic manipulator; input deadzone; Liapunov direct method; Lumping; Manipulators; Mathematical model; Neural networks; Nonlinear systems; Output feedback; Proportional derivative; Radial basis function; Robot arms; Robotics; Stability analysis; State feedback; Trajectory analysis; Vibration control
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2016.2608739

In this paper, a neural network (NN) controller is designed to suppress the vibration of a flexible robotic manipulator system with input deadzone. The NN aims to approximate the unknown robotic manipulator dynamics and eliminate the effects of input deadzone in the actuators. In order to describe the system more accurately, the model of the flexible manipulator is constructed based on the lumping spring-mass method. Full state feedback NN control is proposed first and output feedback NN control with a high-gain observer is then devised to make the proposed control scheme more practical. The effect of input deadzone is approximated by a radial basis function neural network (RBFNN) and the unknown dynamics of the manipulator is approximated by another RBFNN. The proposed NN control is able to compensate for the estimated deadzone effect and track the desired trajectory. For the stability analysis, the Lyapunov's direct method is used to ensure uniform ultimate boundedness (UUB) of the closed-loop system. Simulations are given to verify the control performance of the NN controllers comparing with the proportional derivative (PD) controller. At last, the experiments are conducted on the Quanser platform to further prove the feasibility and control performance of the NN controllers.