一种面向输入输出故障的变结构模型预测控制方法

针对输入执行机构故障及输出测量装置故障往往导致MPC（model predictive control）控制器无法实现控制目标的问题，通过对输入稳态与输出稳态关系的分析，提出将存在故障的输入或者输出从控制器的操作变量和被控输出中去除、改变控制器结构的变结构预测控制方法．由于输入故障变结构控制减少了控制器操作变量自由度导致输出稳态误差很大，故根据输出变量优先级重新计算输出设定点以保障重要输出优先满足控制要求．输出故障变结构控制采用结合输入变量稳态值目标跟踪的DMC（dynamic matrix control）算法，避免了输出传感器故障对系统的影响并且保障了被控输出的控制目标可达．利用Shell benchmark重油分馏塔模型仿真验证了本方法的有效性.

Model Predictive Control Method with Variable Structure to Input-output Faults

Input actuator faults and output measurement device faults always lead to poor control performance of model predictive control (MPC). By analysis of the steady-state relationship between input and output, we propose an improved MPC method with variable structure, which removes the faulty input or output variables from the controller. Some high-priority output set points need to be recalculated to meet the control requirements, due to the reduction of input degree caused by the variable structure. The output fault variable structure control used the dynamic matrix control (DMC) algorithm, which ensures integrated input steady-state targets and prevents the system from being influenced by output sensor faults. Simulation results of Shell heavy oil fractionator benchmarks validated the effectiveness of the proposed method.