Recurrent neural network modeling and learning control of flexible plates by nonlinear handling system

Proposes a trajectory control method for a flexible plates handling system with unknown parameters and joint friction. First a recurrent neural network (RNN) learns the dynamics model of the flexible plate handled by a robotic manipulator. Next, the authors obtain the feedfoward control input based on the RNN model using the proposed learning control method. The authors applied this repetitive method to both linear system and nonlinear system control. Coulomb friction is considered at the joint as the nonlinear effect. Simulation examples are conducted to show effectiveness of the proposed method