Parameter Design for Predictive Control with Adaptive Disturbance Model

The step model widely used to estimate the unmeasured output disturbance in MPC at present has limited disturbance rejection performance. Adaptive disturbance model can estimate the disturbance dynamics better and improve the ability of disturbance rejection. Parameter design of the controller has great impact on the control performance. The disturbance rejection strategy of disturbance adaptation predictive control (DMCA) is analyzed in the paper, as well as the effects of controller parameters on system dynamic performance, robustness and disturbance rejection ability. In addition, design methods for parameters such as disturbance prediction horizon, orders of time series model and filter factor for output error are researched and then experience guidelines for the parameter design are summarized. Simulation results show that DMCA can decrease the integral of absolute value of error criterion for the controlled variable by 45% than Dynamic Matrix Control (DMC). The optimization design of controller parameters improves DMCA's ability of predicting and rejecting disturbance further.