Online elicitation of Mamdani-type fuzzy rules via TSK-based generalized predictive control

Many synergies have been proposed between soft-computing techniques, such as neural networks (NNs), fuzzy logic (FL), and genetic algorithms (GAs), which have shown that such hybrid structures can work well and also add more robustness to the control system design. In this paper, a new control architecture is proposed whereby the on-line generated fuzzy rules relating to the self-organizing fuzzy logic controller (SOFLC) are obtained via integration with the popular generalized predictive control (GPC) algorithm using a Takagi-Sugeno-Kang (TSK)-based controlled autoregressive integrated moving average (CARIMA) model structure. In this approach, GPC replaces the performance index (PI) table which, as an incremental model, is traditionally used to discover, amend, and delete the rules. Because the GPC sequence is computed using predicted future outputs, the new hybrid approach rewards the time-delay very well. The new generic approach, named generalized predictive self-organizing fuzzy logic control (GPSOFLC), is simulated on a well-known nonlinear chemical process, the distillation column, and is shown to produce an effective fuzzy rule-base in both qualitative (minimum number of generated rules) and quantitative (good rules) terms.

[1]  David W. Clarke,et al.  Generalized predictive control - Part I. The basic algorithm , 1987, Autom..

[2]  M. Sugeno,et al.  Fuzzy modeling and control of multilayer incinerator , 1986 .

[3]  Mahdi Mahfouf,et al.  Fuzzy Takagi-Sugeno Kang model predictive control for process engineering , 1999 .

[4]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[6]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[7]  Chuen-Chien Lee,et al.  A self‐learning rule‐based controller employing approximate reasoning and neural net concepts , 1991, Int. J. Intell. Syst..

[8]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[9]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[10]  G. Bierman.  Factorization methods for discrete sequential estimation , 1977 .

[11]  David W. Clarke,et al.  Generalized Predictive Control - Part II Extensions and interpretations , 1987, Autom..

[12]  P. R. Bélanger.  On type 1 systems and the clarke-gawthrop regulator , 1983, Autom..

[13]  Derek A. Linkens,et al.  Genetic algorithms for fuzzy control.1. Offline system development and application , 1995 .

[14]  John Anthony Rossiter.  Model-Based Predictive Control , 2003 .

[15]  M. Mahouf.  Generalised Predictive Control and Bioengineering , 1998 .

[16]  Ebrahim H. Mamdani,et al.  A linguistic self-organizing process controller , 1979, Autom..

[17]  Maysam F. Abbod,et al.  Adaptive Fuzzy TSK Model-Based Predictive Control Using a Carima Model Structure , 2000 .

[18]  D. Linkens,et al.  Multi-objective genetic optimisation of GPC and SOFLC tuning parameters using a fuzzy-based ranking method , 2000 .