具有输入增量分辨率的多变量预测控制策略
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摘要
工业现场中不可避免地会出现噪声和不可测扰动等不利因素,造成控制器的控制变量波动,不利于加以控制和实现设备的长期使用.同时,对于现场中需要控制动作较大的阀门,在面对较小的控制动作时,无法准确跟踪控制.针对上述两种问题,结合当前工业控制目标为设定点和区间混合类型,引入动态增量阈值和静态增量阈值作为输入分辨率.当约束多变量系统通过QP规划算法求解出最优控制律后,对于动态阈值,若控制律超过阈值,则将此控制律添加到输入上,反之则忽视此控制律;对于静态阈值,若控制律超过阈值,则将此控制律添加到输入上,反之将控制律累加,直到累加值超出阈值后采用累加的控制律.仿真结果验证了所提策略能够有效保证控制变量的稳定,解决大控制动作阀门的跟踪控制问题.
Unfavorable factors such as noise and unmeasurable disturbances are inevitable in the industrial field, which cause the fluctuations in the control variables of the controller and are not conducive to the actual control and long-term use of the equipment. At the same time, for valves that require large control action, it is impossible to accurately track and control when faced with small control action. For the above two problems, combined with the current industrial control target as the hybrid type with set point and interval, the dynamic incremental threshold and the static incremental threshold are introduced as the input resolution. When the constrained multi-variable system solves the optimal control law through the QP planning algorithm, for the dynamic threshold, if the control law exceeds the threshold, the control law is added to the input, otherwise the control law is ignored. For the static threshold, If the control law exceeds the threshold, the control law is added to the input, otherwise the control law is accumulated until the accumulated value exceeds the threshold, and then the accumulated control law is used. The simulation verifies that the strategy effectively guarantees the stability of the manipulated variable and solves the tracking control problem of the large control action valve.
Unfavorable factors such as noise and unmeasurable disturbances are inevitable in the industrial field, which cause the fluctuations in the control variables of the controller and are not conducive to the actual control and long-term use of the equipment. At the same time, for valves that require large control action, it is impossible to accurately track and control when faced with small control action. For the above two problems, combined with the current industrial control target as the hybrid type with set point and interval, the dynamic incremental threshold and the static incremental threshold are introduced as the input resolution. When the constrained multi-variable system solves the optimal control law through the QP planning algorithm, for the dynamic threshold, if the control law exceeds the threshold, the control law is added to the input, otherwise the control law is ignored. For the static threshold, If the control law exceeds the threshold, the control law is added to the input, otherwise the control law is accumulated until the accumulated value exceeds the threshold, and then the accumulated control law is used. The simulation verifies that the strategy effectively guarantees the stability of the manipulated variable and solves the tracking control problem of the large control action valve.
引文
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[3] Richalet J, Rault A, Testud J L, et al. Medel predictiveheristic control:Applications to industrial processes[J]. Automatica, 1978, 14(5):413-428.
[4] Cuter C R, Ramaker B L. Dynamic matrix control—A computer control algorithm[C]. The 86th AIChE National Meeting. Houston:IEEE, 1979.
[5] Clarke D W, Mohtadi C. Properties of generalized predictive control[J]. Automatica, 1989, 25(6):859-873.
[6]席裕庚,李德伟,林姝.模型预测控制—–现状与挑战[J].自动化学报, 2013, 39(3):222-236.(Xi Y G, Li D W, Lin S. Model predictive control—Status and challenges[J]. Acta Automatica Sinica, 2013,39(3):222-236.)
[7] Darby M L, Nikolaou M. MPC:Current practice and challenges[J]. Control Engineering Practice, 2012, 20(4):328-342.
[8] Neshasteriz A R, Sedigh A K, Sadjadian H. Generalized predictive control and tuning of industrial processes with second order plus dead time models[J]. J of Process Control, 2010, 20(1):63-72.
[9] Salsbury T, Mhaskar P, Qin S J. Predictive control methods to improve energy efficiency and reduce demand in buildings[J]. Computers and Chemical Engineering,2013, 51:77-85.
[10] Wang Y, Zisser H, Dassau E, et al. Model predictive control with learning-type set-point:Application to artificial pancreaticβ-cell[J]. Aiche J, 2010, 56(6):1510-1518.
[11] Wang Y, Valdés-González H, Vyhmesiter E, et al. Model predictive control of semiautogenous mills(sag)[J].Minerals Engineering, 2014, 64(1):92-96.
[12] Xu Z H, Zhu Y C, Han K, et al. A multi-iteration pseudo-linear regression method and an adaptive disturbance model for MPC[J]. J of Process Control,2010, 20(4):384-395.
[13]韩恺,赵均,朱豫才,等.一种在线辨识扰动模型的MPC控制器及其在溶剂脱水塔装置中的应用[J].化工学报, 2008, 59(7):1657-1664.(Han K, Zhao J, Zhu Y C, et al. MPC with on-line disturbance model estimation and its application to PTA solvent dehydration tower[J]. CIESC J, 2008, 59(7):1657-1664.)
[14]金鑫,池清华,刘康玲,等.对角CARIMA模型抗扰约束广义预测控制[J].化工学报, 2014, 65(4):1310-1316.(Jin X, Chi Q H, Liu K L, et al. Disturbance rejection constraints generalized predictive control of diagonal CARIMA model[J]. CIESC J, 2014, 65(4):1310-1316.)
[15] Li L J, Song J Q, Zhang X X, et al. Model deficiency diagnosis and improvement via model residual assessment in MPC[J]. Industrial&Engineering Chemistry Research, 2017, 56(42):12151-12162.
[16]邹涛,李海强,丁宝苍,等.多变量预测控制系统稳态解的相容性与唯一性分析[J].自动化学报, 2013,39(5):519-529.(Zou T, Li H Q, Ding B C, et al. Compatibility and uniqueness analysis of steady state solution for multi-variable predictive control system[J]. Acta Automatic a Sinica, 2013, 39(5):519-529.)