Temperature decoupling control of double-level air flow field dynamic vacuum system based on neural network and prediction principle

Abstract

Double-level air flow field dynamic vacuum (DAFDV) system is a strong coupling, large time-delay, and nonlinear multi-input–multi-output system. Decoupling and overcoming the impact of time-delay are two keys to obtain rapid, accurate and independent control for two air temperatures in two concatenate chambers of the DAFDV system. A predictive, self-tuning proportional-integral-derivative (PID) decoupling controller based on a modified output–input feedback (OIF) Elman neural model and multi-step prediction principle is proposed for the nonlinearity, time-lag, uncertainty and strong coupling characteristics of the system. A multi-step ahead prediction algorithm is presented for temperature prediction to eliminate the effects of time-delays. To avoid getting into a local optimization, an improved particle swarm optimization is applied to optimize the weights of the OIF Elman neural network during modeling. By using the modified OIF Elman neural network identifier, the DAFDV system is identified and the parameters of PID controller are tuned on-line. The experimental results for two typical cases indicate that the settling times are obviously shorten, steady-state performances are improved and more important is that one temperature no longer fluctuates along the other, which verify the proposed adaptive PID decoupling control is effective.