A Novel Neuro-Fuzzy Model-Based Run-to-run Control for Batch Processes with Uncertainties
In this paper, a run-to-run control with neuro-fuzzy model updating mechanism is developed. This strategy features the ability to learn from previous batches to obtain iteratively the optimal control profile and adjust the neuro-fuzzy model parameters. In addition, an updating algorithm guaranteeing the global convergence of the weights of the model is developed based on the Lyapunov approach. As a result, model uncertainties can be handled. Simulation results show that by updating the model from batch to batch, the control profile converges to the corresponding suboptimal one in the subsequent batches.
batch processes neuro-fuzzy system Run-to-run control global convergence
JIA Li SHI Jiping SONG Yang CHIU Min-Sen
Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Faculty of Engineering, National University of Singapore, Singapore
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
5813-5818
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）