This paper works on hybrid force/position control in robotic manipulation and proposes an improved radial basis functional RBF neural network, which is a robust relying on the Hamilton Jacobi Issacs principle of the force control loop. The method compensates uncertainties in a robot system by using the property of RBF neural network. The error approximation of neural network is regarded as an e...

This paper presents a new neural network based controller design for multivariable systems. The proposed controller is designed using radial basis function (RBF) neural network. Weight update equation using classical least mean square principle is derived for the RBF network. The controller generates optimal control signals abiding by constraints, if any, on the control signals. Simulation resu...

A radial basis function (RBF) neural network approach with a fusion of multiple signal candidates in precision motion control is studied in this paper. Sensor weightages are assigned to sensor measurements according to the selector attributes and are approximated using RBF neural network in multi-sensor fusion. A specific application towards precision motion control of a linear motor system usi...

A neural network-based model reference adaptive control approach (MRAC) for ship steering systems is proposed in this paper. For the nonlinearities of ship steering system, performances of traditional adaptive control algorithms are not satisfactory in fact. The presented MRAC system utilizes RBF neural network to approximate the unknown nonlinearities in order to get a high adaptive control pe...

This work presents a control strategy using a network with radial basis function (RBF network) with adaptation in dual mode. The objective of the strategy is to use the approximate capacity of the RBF network to control nonlinear systems with unknown parameters or with uncertainties. The proposed control uses the structure of Model Reference Adaptive Control (MRAC) and a RBF network whose param...

This paper proposes an enhanced RBF network that enhances learning algorithms between input layer and middle layer and between middle layer and output layer individually for improving the efficiency of learning. The proposed network applies ART2 network as the learning structure between input layer and middle layer. And the autotuning method of learning rate and momentum is proposed and applied...

The uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function network (RBF network) as an uncertainty estimator. The ...