Geology

Control system

Vibration

Agronomy

Biology

Computer science

Astronomy

Quantum mechanics

Oceanography

Engineering

Dead zone

Actuator

Controller (irrigation)

Control (management)

Observer (physics)

Trajectory

Artificial intelligence

Control theory (sociology)

Artificial neural network

Lyapunov function

Electrical engineering

Physics

Control engineering

Nonlinear system

IEEE Transactions on Industrial Informatics

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. Simul...

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