Online Optimization of Fuzzy Controller for Coke-Oven Combustion Process Based on Dynamic Just-in-Time Learning

To guarantee the control performance of a fuzzy control system for the combustion process in a coke oven, the parameters of the fuzzy controller need to be optimized so that the controller can handle large changes in the operating state of the oven. This paper describes an online optimization method for this purpose. In this method, the distance and angle of the trend of the change are used to select data, and just-in-time learning is used to create a dynamic sample base and to build a radial-basis-function neural-network model of the process. A variable-universe fuzzy logic controller controls the process, and an adaptive differential evolution algorithm optimizes the universe parameters. This enables the controller to adapt to changes in the operating state in a timely fashion. Simulation results demonstrate the effectiveness of the method.

[1]  Jiming Chen,et al.  Data-Driven Modeling Based on Volterra Series for Multidimensional Blast Furnace System , 2011, IEEE Transactions on Neural Networks.

[2]  M. A. Daneshwar,et al.  Identification of a process with control valve stiction using a fuzzy system: A data-driven approach , 2014 .

[3]  Yu. V. Stepanov,et al.  The relation between coke quality and blast-furnace performance , 2007 .

[4]  Juan M. Corchado,et al.  Applying lazy learning algorithms to tackle concept drift in spam filtering , 2007, Expert Syst. Appl..

[5]  Zhi-huan Song,et al.  Online monitoring of nonlinear multiple mode processes based on adaptive local model approach , 2008 .

[6]  Jin-Hua She,et al.  Operating-State-Based Intelligent Control of Combustion Process of Coke Oven , 2008 .

[7]  Andrea Garulli,et al.  Identification of Piecewise Affine LFR Models of Interconnected Systems , 2011, IEEE Transactions on Control Systems Technology.

[8]  Juan Du,et al.  A neuro-fuzzy GA-BP method of seismic reservoir fuzzy rules extraction , 2010, Expert Syst. Appl..

[9]  O. S. Morozov,et al.  Influence of coke quality on blast-furnace performance , 2011 .

[10]  M. Nasir Uddin,et al.  Online Efficiency Optimization of a Fuzzy Logic Controller Based IPMSM Drive , 2009, 2009 IEEE Industry Applications Society Annual Meeting.

[11]  Witold Pedrycz,et al.  Nonlinear context adaptation in the calibration of fuzzy sets , 1997, Fuzzy Sets Syst..

[12]  M. Birattari,et al.  Lazy learning for local modelling and control design , 1999 .

[13]  Witold Pedrycz,et al.  Fuzzy control and fuzzy systems , 1989 .

[14]  Dan-yang Cao,et al.  Variable universe fuzzy expert system for aluminum electrolysis , 2011 .

[15]  Hongxing Li,et al.  Variable universe adaptive fuzzy control on the quadruple inverted pendulum , 2002 .

[16]  Cheng Wu,et al.  A New ANFIS for Parameter Prediction With Numeric and Categorical Inputs , 2010, IEEE Transactions on Automation Science and Engineering.