Optimal control strategies combined with PSO and control vector parameterization for batchwise chemical process

被引：0
|
作者：
Shi B. [1 ]
Yin Y. [2 ]
Liu F. [1 ]
机构：
[1] Key Laboratory of Advanced Control for Light Industry Process, Ministry of Education, Jiangnan University, Wuxi, 214122, Jiangsu
[2] Department of Mathematics and Statistics, Curtin University, Perth
来源：
Huagong Xuebao/CIESC Journal | 2019年 / 70卷 / 03期
关键词：
Batchwise; Control vector parameterization; Optimal control; Optimization; Process control;
D O I：
10.11949/j.issn.0438-1157.20181140
中图分类号：
学科分类号：
摘要：
As a gradient search algorithm for dynamic optimization of chemical process, the efficiency of control vector parameterization depends on the initial given trajectory deeply. At present, the initial trajectory is usually set at the boundary value or the intermediate value, which does not have enough scientific reason and it affects the convergence speed of the algorithm. To solve this problem, a hybrid strategy combined with particle swarm optimization and control vector parameterization method is proposed in this paper, it uses particle swarm optimization to achieve the value of control variables before employing the method of control vector parameterization to reoptimize the process. The two-layer optimization hybrid strategy improves the convergence speed of the control vector parameterization method and the precision of the particle swarm optimization. The hybrid strategy is applied to two examples of batch chemical process optimization control, and the simulation results show that the algorithm is feasible and effective for solving dynamic optimization problems of chemical process. © All Right Reserved.
引用
收藏
页码：979 / 986
页数：7
相关论文
共 23 条
  • [1] Zhuang X.J., Li H.G., Optimal control of penicillin fermentation process based on genetic algorithm and iterative dynamic programming, Computer and Applied Chemistry, 30, 9, pp. 1051-1054, (2013)
  • [2] Pollard J.P., Sargent R.W.H., Off line computation of optimum controls for a plate distillation column, Automatica, 6, 1, pp. 59-76, (1970)
  • [3] Sargent R.W.H., Sullivan G.R., The development of an efficient optimal control package, Nonlinear and Stochastic Programming, 13, 7, pp. 158-168, (1979)
  • [4] Vassiliadis V.S., Sargent R.W.H., Pantelides C.C., Solution of a class of multistage dynamic optimization problems(Ⅱ): Problems with path constraints, I& EC Research, 33, 9, pp. 2123-2133, (1994)
  • [5] Martin S., Klaus S., Thomas B., Et al., Dynamic optimization using adaptive control vector parameterization, Computers & Chemical Engineering, 29, 8, pp. 1731-1751, (2005)
  • [6] Wang P., Tian X.M., Enhanced control vector parameterization method and its application in dynamic optimization, Control & Decision, 24, 11, pp. 1757-1760, (2009)
  • [7] Chen X., Du W.L., Huaglory T., Et al., Dynamic optimization of industrial processes with nonuniform discretization-based control vector parameterization, IEEE Transactions on Automation Science & Engineering, 11, 4, pp. 1289-1299, (2014)
  • [8] Teo K.L., Jennings L.S., Lee H.W.J., Et al., The control parametrization enhancing transform for constrained optimal control problems, J. Austral. Math. Soc. Sen. B, 40, pp. 314-335, (1999)
  • [9] Li G.D., Liu P., Liu X.G., A control parameterization approach with variable time nodes for optimal control problems, Asian Journal of Control, 18, 3, pp. 976-984, (2016)
  • [10] Binder T., Cruse A., Cruz Villar C.A., Et al., Dynamic optimization using a wavelet based adaptive control, Computers and Chemical Engineering, 24, pp. 1201-1207, (2000)
123