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For most teleoperation tasks, it is desired that the telerobot manipulator follows timely and precisely the reference motion set at the master side. However, the conventional control approach may not guarantee the desired performance when there are dynamic uncertainties, especially when there is a notable variation of the telerobot's payload. In this paper, a neural learning based compensation mechanism...
A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved...
In this paper, a robust neural network based controller is proposed to steer the joint angles of rigid-link robot manipulators to track the desired trajectories asymptotically. The developed control scheme makes use of a two-layer neural network to learn the behaviors of unknown dynamics of robot. Both the estimation error and external disturbances can be effectively counteracted by employing smooth...
A recurrent neural network is proposed to deal with the convex optimization problem. By employing a specific nonlinear unit, the proposed neural network is proved to be convergent to the optimal solution in finite time, which increases the computation efficiency dramatically. Compared with most of existing stability conditions, i.e., asymptotical stability and exponential stability, the obtained finite-time...
A recurrent neural network is proposed to deal with the nonlinear variational inequalities with linear equality and nonlinear inequality constraints. By exploiting the equality constraints, the original variational inequality problem can be transformed into a simplified one with only inequality constraints. Therefore, by solving this simplified problem, the neural network architecture complexity is...
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