Closed-loop Subspace Predictive Control for Hammerstein systems

In this paper, a closed loop subspace predictive control algorithm is developed for linear time invariant (LTI) systems with static nonlinearity at the plant input, i.e. for Hammerstein LTI systems (H-SPC). The elaborated method is a data driven approach combining subspace identification with optimal predictive control. Using the closed loop structure of the subspace identification technique an input-output predictor is derived with the estimated Markov parameters and with the identified static input nonlinearity. Identification makes use of the kernel of the data space by least square support vector machinery (LS-SVM). A nominal prediction is then created. The goal of the control is to optimize the associated quadratic performance criteria in a receding horizon fashion. A simulation example is provided that shows the effectiveness of the proposed methodology.