A new approach for neural control of nonlinear discrete dynamic systems

This paper proposes a new approach to control nonlinear discrete dynamic systems, which relies on the identification of a discrete model of the system by a neural network. A locally equivalent optimal linear model is obtained from the neural network model at every operating point the system goes through during the control task. Based on the linear model, a linear state-space control design technique can be used to design a local control action to be applied at that particular operating point. The design procedure is repeated at every operating point of the system during the control task. The proposed approach was applied in three examples, which involved a linear and two nonlinear discrete single-input single-output (SISO) systems. In the first two examples, pole placement was chosen as the linear design technique. This method led to satisfactory tracking of the reference input but a steady-state error was present due to modeling inaccuracies. In the third example, the linear design technique involved an optimal control scheme with an integrator. This method led to satisfactory tracking of the reference input with zero steady-state error.