Adaptive neural control using reinforcement learning for a class of robot manipulator

@article{Tang2013AdaptiveNC,
  title={Adaptive neural control using reinforcement learning for a class of robot manipulator},
  author={Li Tang and Yanjun Liu and Shaocheng Tong},
  journal={Neural Computing and Applications},
  year={2013},
  volume={25},
  pages={135-141}
}
In this paper, an adaptive control algorithm is proposed for a class of robot manipulator systems with unknown functions and dead-zone input by using a reinforcement learning scheme. The parameters of the dead zone are supposed to be unknown but bounded. The unknown functions can be approximated based on the neural networks, which is one part of the reinforcement learning scheme, namely an action network. The other part is called critic network which is used to approximate the reinforcement… CONTINUE READING
Highly Cited
This paper has 32 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 15 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 33 references

Mutual Synchronization of Multiple Robot Manipulators with Unknown Dynamics

Journal of Intelligent and Robotic Systems • 2012
View 2 Excerpts

Reinforcement Learning Controller Design for Affine Nonlinear Discrete-Time Systems using Online Approximators

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) • 2012
View 1 Excerpt

Similar Papers

Loading similar papers…