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

摘要： 针对存在输入和输入增量约束的多变量系统，提出了一种基于变权重的对角CARIMA模型抗扰动约束广义预测控制算法。根据对角CARIMA模型中的A和C矩阵为对角形式的特点，将多输入多输出系统分解为多个多输入单输出系统进行预测和控制，简化了控制器的设计，降低了变量之间的耦合性。根据模型预测值与参考轨迹之间的偏差实时调整目标函数中各输出跟踪误差的权重，达到抑制由耦合而造成回路之间扰动的目的。权重调整的基本原则是，每个输出的预测值跟踪参考轨迹的权重由其他输出在同时刻偏离其参考轨迹的误差平方加权和构成。当某个输出偏离其目标值时，其他输出的控制作用相对增强，避免输出之间的相互扰动，达到抑制扰动的目的。同时，分析了系统输入和输入增量约束的表达形式。利用多变量广义预测控制（MGPC）以及提出的扰动抑制方法，分别对Shell重油分馏问题进行了仿真实验，仿真结果验证了算法的有效性。

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