Trajectory control based on a modified Elman neural network

We propose a modified Elman neural network (MENN)-based trajectory control scheme. Our approach consists of two stages: a MENN is first employed to identify the nonlinear plant to be controlled, and acts as the dynamical simulator of the plant after identification. The second MENN is then used to control the plant within the desired trajectory by backpropagating the control error through the simulator to update its weights. The application of this proposed method in the trajectory control of the inverted pendulum is illustrated. A simulation experiment demonstrates that our MENN-based trajectory control scheme can perform successful tracking without knowing prior knowledge of the plant.