The Application Of Recurrent Neural Networks To The Inverse Model Learning Control Of Manipulators

A type of recurrent neural network named second order recurrent neural networkis applied to the inverse model learning control of manipulators. In the traditionalinverse model learning control method, the model of the manipulator is seen as anonlinear static function and approximated by feed forward neural networks. Thedisadvantages of this method are that much of information of the controlled systemhas to be measured, the size of the networks is large and the computation burden isheavy. In this paper the inverse model of the manipulator is seen as a dynamic systemand approximated by recurrent neural networks, so as to downsize the networks,reduce the requisite information and lessen the computation burden. An inverse modellearning control system of manipulators based on second order recurrent neuralnetworks is proposed and simulated to confirm the feasibility and advantages of theapplication of the recurrent neural networks. Simulation experiments show that comparing with the method based on feedforward neural network, the advantages of the method presented in this paper are thatthe recurrent neural network is more efficient in offline approximation of the inversemodel of the manipulator, which implies its online control performance will be better;the control precision is not of much difference;the control signal is continuouswithout jerkiness;the size of the network is smaller and the computation burden islighter;when the controlled object model is changing, the control system is morestable.