A novel weighting method for multi-linear MPC control of Hammerstein systems based on included angle. (September 2018) - Du, Jingjing; Zhang, Lei; Han, QingBang
Abstract: A novel included angle based weighting method is proposed for multi-linear model predictive control (MPC) of Hammerstein systems. It makes full use of the special structure of the Hammerstein models, and thus it is intuitive and simple. Moreover, there is only one tuning parameter and the weights can be calculated offline and stored in a look-up table. Therefore, online computational load is largely reduced. Most important of all, it schedules local controllers properly and effectively. A Lab-tank system which can be modeled into a Hammerstein model is investigated. Comparisons are made among the nonlinearity inversion control method, the proposed weighting method and traditional weighting methods, e.g., Trapezoidal and Gaussian weighting methods. Simulations confirm that the proposed weighting method is superior to traditional methods. Highlights: A novel weighting method is proposed based on included angle. The proposed method is used for multi-linear MPC control of Hammerstein systems. The weights can be calculated offline and computational load are reduced. Complex parameter tuning is avoided in the proposed weighting method. The proposed method has the advantages of traditional methods but avoids their shortcomings.