基于线性二次型高斯基准的多变量预测控制技术经济性能评估  

Economic performance assessment of multivariable model predictive control based on linear quadratic Gaussian benchmark

作　　者：刘詟[1] 苏宏业[1] 谢磊[1] 古勇[1] 

机构地区：[1]浙江大学智能系统与控制研究所工业控制技术国家重点实验室,浙江杭州310027

出　　处：《控制理论与应用》2012年第12期1530-1536,共7页Control Theory & Applications

基　　金：国家自然科学基金资助项目(61134007;60904039;60974100);国家重点基础研究发展计划资助项目(2007CB714006);高等学校学科创新引智计划("111"计划)资助项目(B07031)

摘　　要：由于受控过程参数的漂移及缺乏维护,令采用的控制器性能逐渐降低,需要做经济性能评估,以确保其最佳运行状态.因为目前最小方差评估算法没有考虑控制器的约束条件,对此我们采用线性二次型高斯(linearquadratic Gaussian,LQG)基准的模型预测控制(model predictive control,MPC)双层优化控制结构,将控制和输出的加权值引入上层经济性能指标,通过求解LQG问题获取控制与输出方差关系的离散点集,进一步拟合Pareto最优曲面方程,建立优化命题并求解最优经济指标及设定值.对延迟焦化加热炉的多变量MPC控制进行了性能评估及分析,证明该方法可以改进控制器设计,提高经济效益.Because of the drift in parameter values and the lack of maintenance in the controlled process,the performances of the applied controller gradually deteriorate with time.An economic performance assessment is necessary to give the benefits an evaluation for ensuring system optimal operation status.Since the existing minimum variance control benchmark does not consider restriction conditions of the controller,we propose a two-layer structure model predictive control（MPC） based on the linear quadratic Gaussian（LQG） benchmark,by introducing the weighted input and the weighted output to the upper-layer economic performance index.By solving the LQG problem,we obtain a set of discrete values of the control signal and the output signal for determining their variances and fitting the Pareto optimal surface function.From the formulated propositions,we solve for the optimal economic index and the optimal set-point value.This performance assessment method has been applied to a delayed coking furnace;results show the effectiveness of the proposed approach.

关 键 词：模型预测控制 经济性能评估 LQG基准 Pareto最优曲面 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]