关键词: 算法, 过程控制, 模型预测控制, 广义预测控制, 扰动抑制, 约束控制

Abstract: For input and input increment constraints multivariable systems, a weight-varying based multivariable generalized predictive control of diagonal controlled auto-regressive integrated moving average (CARIMA) model was proposed to improve control performance. Because the matrices C and A of a diagonal CARIMA model are chosen to be diagonal, the prediction problem of a multi-input and multi-output (MIMO) process was transformed into generating a set of optimal predictions for a series of multi-input single-output processes, which simplified the predictive controllers and weakened the coupling of outputs in some measure. According to the difference of the model prediction value and the reference trajectory, the weight coefficients of different output tracking errors in cost function were real-time adjusted to further reduce the coupling disturbance among the outputs. The basic idea of weight coefficients adjusting was that when one output prediction was deviating from its reference trajectory at some sampling time point along the predictive horizon, the weight coefficients of all other outputs' tracing errors in the cost function increased according to the square tracing error of that output at that time point, which increased other loops' input increments at the next sampling point to eliminate the possible disturbance caused by that output deviating. Simultaneously, amplitude constraints of inputs and input increments expressions were analyzed. The validity and superiority were demonstrated by comparing simulation results for a Shell heavy oil fractionators control using common multivariable generalized predictive control (MGPC) and disturbance rejection multivariable generalized predictive control of diagonal CARIMA model proposed in the paper.

Key words: algorithm, process control, model-predictive control, generalized predictive control, disturbance rejection, constrained control