预测控制中的稳态目标优化策略及其应用

实际工业过程中,过程限制或约束的存在以及不可避免的模型失配和干扰的影响,系统很可能达不到期望的设定值,从而存在稳态偏差问题。采用MPC稳态目标计算和动态优化控制两级结构,在每个采样时刻,考虑可测干扰的影响下,重新进行稳态目标计算。稳态目标计算时保证稳态目标与动态MPC计算中的输入速率约束兼容,并根据当前预测输出和测量输出之间偏差的反馈信息校正稳态模型,体现了MPC算法中的反馈思想。以壳牌重油分馏塔标准问题为例进行了仿真,仿真结果表明了该算法的有效性。

Steady-state objective optimization in model predictive control and its application

In the process of industry,the existence of process limitation or constraints and unavoidable model mismatch and disturbance may perturb the plant from the desired target,which will lead to steady state offset.MPC calculation is separated into steady-state and dynamic optimization.Considering the effects of measured disturbance,the steady-state objective is recalculated at each sampling time.The difference between the model prediction and the measured output at the current time is introduced into the model to incorporate feedback and the velocity constraints in steady-state target calculation so as to ensure the steady-state objective is compatible with the velocity constraints in dynamic MPC optimization.Finally,this control strategy is successfully applied to the control of the Shell heavy oil fractionators benchmark problem.