A hierarchical neuro-fuzzy mrac of a robot in flexible manufacturing environment

Abstract EN

In one hand, the Model Reference Adaptive Control (MRAC) architecture has been widely used in linear adaptive control field.

The control objective is to adjust the control signal in a stable manner so that the plant’s output asymptotically tracks the reference model’s output.

The performance will depend on the choice of a suitable reference model and the derivation of an appropriate learning scheme.

While in the other hand, clusters analysis has been employed for many years in the field of pattern recognition and image processing.

To be used in control the aim is being to find natural groupings among a set of collected data.

The mean-tracking clustering algorithm is going to be used in order to extract the input-output pattern of rules from applying the suggested control scheme.

These rules will be learnt later using the widely used Multi-layer perceptron neural network to gain all the benefits offered by those nets.

A hierarchical neuro-fuzzy MRAC is suggested to control robots in a flexible manufacturing system.

This proposed controller will be judged for different simulated cases of study to demonstrate its capability in dealing with such a system.