Adaptive inferential feed-forward control algorithm and application with reduced model

In the process of industrial production, the system may be affected by many external factors, which can be equivalent to many measurable or immeasurable disturbances. The adaptive inferential feed-forward control algorithm adopts reduced model to design controller which resolves complexity of the adaptive algorithm when the order of model is unknown or too high. Meanwhile, the regularization technique is used to translate the unknown dynamic process into bounded disturbance and the relative dead zone technique is involved to identify parameters of the system, which guarantees bounded stability of the self-tuning control system. Through combining adaptive control, inferential control feed-forward control and adaptive prediction, the algorithm effectively eliminates the influence of measurable and immeasurable disturbances on the system. Finally, the validity and practicability of this algorithm is substantiates by the simulation result of superheated steam temperature control system.