Adaptive Sliding-mode Robust Tracking Control for Robot Manipulator with the Uncertainties

2006 ◽  
Author(s):  
Suolin Duan ◽  
Zhenghua Ma
Keyword(s):  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Robust Tracking ◽  
Robust Tracking Control ◽  
Adaptive Sliding Mode

An Algorithm for Robust Tracking Control of Robots

Robotica ◽  
1991 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
Zoran R. Novaković ◽  
Leon Z˘lajpah
Keyword(s):  
Tracking Control ◽  
Error Control ◽  
Sliding Mode ◽  
Tracking Error ◽  
Lyapunov Theory ◽  
Control Synthesis ◽  
Robust Tracking ◽  
Robust Tracking Control ◽  
Time Simulation ◽  
Inverse Kinematics Problem

SUMMARYBased on the Lyapunov theory, a new principle was developed for synthesizing robot tracking control in the presence of model uncertainties. First, a general Lyapunov-like robust tracking concept is presented. It is then used as a basis for the control algorithm derived via a quadratic Lyapunov function constructed using a sliding mode function (based on the output error). Control synthesis is made in task-space, without any need for solving the inverse kinematics problem, i.e. one does not need to inver the Jacobian matrix. It is also shown that the tracking error becomes close to zero in a settling time which is less than a prescribed finite time. Simulation results are incorporated.

scholarly journals Sliding Mode with Adaptive Control of Robot Manipulator Trajectory Tracking using Neural Network Approximation

2021 ◽  
Vol 10 (6) ◽  
pp. 120
Author(s):  
Monisha Pathak* ◽  
◽  
Dr. Mrinal Buragohain ◽  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Robotic Manipulator ◽  
Adaptive Sliding Mode Control ◽  
Control Approach ◽  
Adaptive Sliding Mode

This paper briefly discusses about the Robust Controller based on Adaptive Sliding Mode Technique with RBF Neural Network (ASMCNN) for Robotic Manipulator tracking control in presence of uncertainities and disturbances. The aim is to design an effective trajectory tracking controller without any modelling information. The ASMCNN is designed to have robust trajectory tracking of Robot Manipulator, which combines Neural Network Estimation with Adaptive Sliding Mode Control. The RBF model is utilised to construct a Lyapunov function-based adaptive control approach. Simulation of the tracking control of a 2dof Robotic Manipulator in the presence of unpredictability and external disruption demonstrates the usefulness of the planned ASMCNN.

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