Generalized Predictive Control with Two-stage Neural Network Model for Nonlinear Time-delay Systems

For nonlinear systems with time delays, a generalized predictive control method based on fast regression algorithm (FRA) and hat algorithm (BA) is proposed. Aiming at the model mismatch problem of generalized predictive control, a method for a two-stage neural network model is proposed to establish a nonlinear time-delay predictive model. The fast regression algorithm is introduced to establish the initial neural network model, and then the bat algorithm and the gradient descent with momentum arc employed for adjustment of the parameters of the neural network model to obtain the final neural network prediction model. An implicit generalized predictive control (IGPC) is introduced instead of the traditional generalized predictive control (GPC), and this replacement reduces the amount of calculation. The simulation results show that the control performance by our method is better than that via the single neuron PM control method.