Tracking Control Based on Neural Network for Robot Manipulator

In this paper, an adaptive neural-network-based torque compensator is developed for the trajectory-tracking control of robot manipulators. The overall control structure employs a classical non-linear decoupling controller for actuating torque computation based on an approximated robot dynamic model. To suppress the effects of uncertainties associated with the estimated model, a supplementary neural network algorithm is developed to generate compensation torques. The weight adaptation rule for this neuro-compensator is derived on the basis of the Lyapunov stability theory. Both global system stability and the error convergence can then be guaranteed. Simulation studies on a two-link robot manipulator demonstrate that high performance of the proposed control algorithm could be achieved under severe modelling uncertainties.

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Design of fuzzy-neural-network tracking control with only position feedback for robot manipulator including actuator dynamics

2008 IEEE International Conference on Systems, Man and Cybernetics ◽

10.1109/icsmc.2008.4811644 ◽

2008 ◽

Cited By ~ 4

Author(s):

Rong-Jong Wai ◽

Zhi-Wei Yang

Keyword(s):

Neural Network ◽

Tracking Control ◽

Fuzzy Neural Network ◽

Robot Manipulator ◽

Actuator Dynamics ◽

Position Feedback ◽

Fuzzy Neural

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

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f3005.0810621 ◽

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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A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b3217.1211221 ◽

2021 ◽

Vol 11 (2) ◽

pp. 12-16

Author(s):

Monisha Pathak ◽

◽

Mrinal Buragohain ◽

Keyword(s):

Neural Network ◽

Trajectory Tracking ◽

Tracking Control ◽

Sliding Mode ◽

Robot Manipulator ◽

Rbf Neural Network ◽

Adaptive Controller ◽

Robotic Manipulator ◽

Trajectory Tracking Control ◽

Chattering Effect

In this paper a New RBF Neural Network based Sliding Mode Adaptive Controller (NNNSMAC) for Robot Manipulator trajectory tracking in the presence of uncertainties and disturbances is introduced. The research offers a learning with minimal parameter (LMP) technique for robotic manipulator trajectory tracking. The technique decreases the online adaptive parameters number in the RBF Neural Network to only one, lowering computational costs and boosting real-time performance. The RBFNN analyses the system's hidden non-linearities, and its weight value parameters are updated online using adaptive laws to control the nonlinear system's output to track a specific trajectory. The RBF model is used to create a Lyapunov function-based adaptive control law. The effectiveness of the designed NNNSMAC is demonstrated by simulation results of trajectory tracking control of a 2 dof Robotic Manipulator. The chattering effect has been significantly reduced.

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