Iterative learning control of singular stochastic distribution model of jet flame temperature field

To improve the performance of controlling jet flame temperature distribution, stochastic distribution control method is adopted to build the model of jet flame temperature field whose parameters are optimizeed using iterative learning control in this research. Firstly, Gaussian type basis functions were used to approximate output probability density function. A singular state-space model for stochastic distribution system was formulated, where the number of independent states was the same as the actual dynamic order of the plant. Thereafter, predictive control algorithm was used to control each batch. After completing the control of each batch, Newton method was used to optimize the parameters of basis function. Finally, simulation results were given with temperature distribution of jet flame as its controlled plant. It is indicated that, through optimizing the modeling parameters of singular stochastic system using iterative learning algorithm, the performance index of stochastic distribution control of flame temperature distribution could be improved effectively.