Adaptive neural network control of robot based on a unified objective bound
In the conventional adaptive neural network control of robotic manipulator, the desired position of robot end effector is specified as a point or trajectory. In addition, it is usually difficult to guarantee the transient performance of adaptive neural network control system due to the initializ...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/101658 http://hdl.handle.net/10220/18713 http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6684277&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F87%2F4389040%2F06684277.pdf%3Farnumber%3D6684277 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | In the conventional adaptive neural network control
of robotic manipulator, the desired position of robot end effector
is specified as a point or trajectory. In addition, it is usually
difficult to guarantee the transient performance of adaptive
neural network control system due to the initialization error
of the weight of neural network. In this paper, a new control
formulation is proposed for the adaptive neural network control
of robotic manipulator, which unifies existing neural network
control tasks such as setpoint control, trajectory tracking control
and trajectory tracking control with prescribed performance
bound. The proposed method also includes a new adaptive neural
network control scheme where the objective for the robot end
effector can be specified as a dynamic region, instead of the
desired position or trajectory. The stability of the closed-loop
system is analyzed by using Lyapunov-like analysis. Experimental
results are presented to illustrate the performance of the proposed
approach and the energy-saving property of the proposed neural
network controller with dynamic region. |
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