Self-adjusted decomposition for multi-model predictive control of Hammerstein systems based on included angle

A self-adjusted multi-model decomposition (SAMMD) method is proposed based on included angle (IA) to realize multi-model predictive control (MMPC) of Hammerstein systems. Given an initial value for threshold and a step-size, a balanced decomposition in terms of Measurement of Nonlinearity (MoN) is obtained with an appropriate linear model set to approximate the considered Hammerstein system. Based on the linear model set, a MMPC is designed for set-point tracking and anti-disturbance control using an offline weighting method. Thus, time-consuming tuning of threshold value is largely avoided; reliance on experience is greatly decreased. And the efficiency and quality of decomposition are largely raised. A CSTR process and a Lab-tank system that can be approximated by Hammerstein models are investigated. Simulations illustrate that the proposed SAMMD method is both effective and efficient.