Iterative learning controllers are a good choice for repetitive trajectory tracking tasks because they do not need identification of a nonlinear system.

Control and Intelligent Systems, Vol. 32, No. 2, 2004 INTELLIGENT LEARNING CONTROLLERS FOR NONLINEAR SYSTEMS USING RADIAL BASIS NEURAL NETWORKS M. Arif,∗ T ...

Abstract: In this paper, an algorithm of a model reference adaptive controller for nonlinear systems based on Radial Basis Function (RBF) Neural Networks ...

This paper concerns conditions for the approximation of functions in certain general spaces using radial-basis-function networks. It has been shown in ...

Methods of designing a radial-basis-function-network-based (RBFN) controller and implementing it for servo controlling mechanical systems are presented.

Feb 19, 2019 · In this paper, an iterative learning radial basis function neural-networks (RBF NN) control algorithm is developed for a class of unknown multi input multi ...

In this work we consider the application of an adaptive neural network control for a class of single input single output non linear systems. The method uses ...

The paper considers intelligent control system architectures for task-level control. The problem is to compute feedforward control for a sequence of control ...

The purpose of RBF neural network learning is to minimize the difference between the control quantity of the system and the output quantity of the neural ...

To improve the tracking stability control of unmanned surface vehicles (USVs), an intelligent control algorithm was proposed on the basis of an optimized ...