Neural network controller is designed to approach the unknown nonlinear dynamics of the robot manipulators, unknown model upper of system uncertainties is not need; Sliding

In this paper, a new neural network enhanced synchronized control approach is proposed for multiple robotic manipulators systems (MRMS) based on leader-follower network

In this paper, an adaptive neural-network controller is proposed to deal with the task space tracking problem of space robot manipulators with uncertain kinematics and

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The paper showed the disturbance estimator based on adaptive RBF neural network combined with the feed- forward correction term, this method is applied for both uncertain nonlinear

Three advanced neural network based control schemes are proposed: radial basis function(RBF) neural network based feedforward-feedback control scheme, RBF based model

The simulation is carried out on a real redundant nonholonomic mobile modular manipulator, which has verified the effectiveness of the dynamic modeling method and the controller

Independent joint control local control with constant gain feedback and optimal linear controllers are designed for the linearized system... Force feedback

Experi- mental results demonstrate that the adaptive RBFNN- based fuzzy sliding mode control is applicable control scheme for trajectory tracking applications of robotic

Dubowsky, S., Desforges, D.T., The Application of Model Referenced Adaptive Control to Robotic Manipulators, Transactions of ASME, Journal of Dynamic Systems, Measurement and

In this research, a radial basis function neural network (RBFNN) is used as an inverse system identifier, and then the network is placed in series with PEMFC

The controller is found by using the tech- nique of feedback linearization, supervisory control and H ∞ control and the parame- ter adaptive laws of the wavelet network are

In this paper, the RBF neural network is used to approximate the gravity term of the dynamic equation, the adaptive control law is designed to adjust the

For the above shortcomings, a radial basis function (RBF) neural network adaptive PID control strategy is put forward for X-Y position platform with uncertainty. Firstly,

The experimental results show that, compared with conventional fault diagnosis methods based on BP neural network and RBF neural network, the method based on RBF Neural

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• Peer-reviewed published human studies (incl. Level I-II prospective studies) • Case reports • Animal studies • Every lot tested in vivo.

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