TThe 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 as an uncertainty estimator. The proposed netw...

multilayer bach propagation neural networks have been considered by researchers. despite their outstanding success in managing contact between input and output, they have had several drawbacks. for example the time needed for the training of these neural networks is long, and some times not to be teachable. the reason for this long time of teaching is due to the selection unsuitable network par...

In adaptive control and system identification the self tuning regulator has wide range of applications. Neural network and artificial intelligence have big role in this area. This paper presents adaptive neural network control based on self tuning regulator (STR) scheme. The paper presents neural network block for on line system identification and discrete PID block controller. Analysis for the...

tthe 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 as an uncertainty estimator. the proposed netw...

A new recurrent neural network approach for on line adaptive control is presented. The resulting control scheme matches both the conventional and neural control methods. Using input-output data, a modified recurrent Elman’s network is trained to model a general non-linear discrete time system. By assuming a linearisation of this neural model a time varying adaptive observer is derived. Therefor...