Iterative Learning Double Closed-Loop Structure for Modeling and Controller Design of Output StochAstic Distribution Control Systems

Jinglin Zhou, Hong Yue, Jinfang Zhang, Hong Wang
2014 IEEE Transactions on Control Systems Technology  
Iterative learning double closed-loop structure for modeling and controller design of output stochastic distribution control systems. IEEE Transactions on Control Systems Technology, 22 (6). Abstract-Stochastic distribution control (SDC) systems are known to have the two-dimensional characteristics regarding time and probability space of a random variables, respectively. A double closed-loop structure, which includes iterative learning (IL) modeling (ILM) and iterative learning control (ILC),
more » ... proposed for non-Gaussian SDC systems. The ILM is arranged in the outer loop, which takes a longer period for each cycle termed as a BATCH. Each BATCH is divided into a modeling period and a number of control intervals, called batches, being arranged in the inner loop for ILC. The output probability density functions (PDFs) of the system are approximated by a radial basis function neural network (RBFNN) model, whose parameters are updated via ILM in each BATCH. Based on the RBFNN approximation of the output PDF, a state-space model is constructed by employing the subspace parameter estimation method. An IL optimal controller is then designed by decreasing the PDF tracking errors from batch to batch. Model simulations are carried out on an 4th-order numerical example to examine the effectiveness of the proposed algorithm. To further assess its application feasibility, a flame shape distribution control simulation platform for a combustion process in a coal-fired gate boiler system is constructed by integrating WinCC interface, Matlab simulation programs and OPC communication together. Simulation study over this industrial simulation platform shows that, the output PDF tracking performance can be efficiently achieved by this double closed-loop IL strategy. Index Terms-Iterative learning (IL), optimal tracking control, probability density function (PDF), radial basis function neural network (RBFNN), stochastic distribution control (SDC), subspace identification, temperature field distribution control.
doi:10.1109/tcst.2014.2306452 fatcat:zhrsidrocnemhjzge5pwjy7imy