Real time nonlinear learning control for robotic manipulator using novel fuzzy neural network

This paper proposes a novel fuzzy-neural network for the control of a robotic manipulator. First, soft computing which is the fusion or combination of fuzzy systems, neural networks and genetic algorithms is studied. Then, by taking advantages of fuzzy systems and neural networks a novel fuzzy-neural network with a general parameter learning algorithm and system structure determination is developed. The general parameter method (GP) is based on GMDH (group methods of data handling). The GP is used for a learning algorithm and the structure determination of the developed fuzzy neural network. The GP is extended to an adaptive genetic algorithm for explanation. The resulting network can easily be implemented with a Hitachi RISK+DSP microprocessor and is fast enough for real time operations. The developed fuzzy neural network is applied to chattering free sliding mode nonlinear control of a robotic manipulator generating equivalent control