Iterative optimal control based on support vector machine modeling within the Bayesian evidence framework
In the paper, an iterative method is presented to the optimal control of batch processes. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is powerful for the problems characterized by small samples, nonlinearity, high dimension and local minima, support vector regression models are developed for the optimal control of batch processes where end-point properties are required. The model parameters are selected within the Bayesian evidence framework. Based on the model, an iterative method is used to exploit the repetitive nature of batch processes to determine the optimal operating policy. Numerical simulation shows that the iterative optimal control can improve the process performance through iterations.
iterative optimal control, support vector machine (SVM), Bayesian evidence framework.
李赣平、阎威武、邵惠鹤
展开 >
Department of Automation, Shanghai Jiaotong University, Shanghai 200030, P. R. China
iterative optimal control, support vector machine (SVM), Bayesian evidence framework.
NETL
NSTL
万方数据
