Predictive Control of a Solar Power Plant with Neuro-Fuzzy Identification and Evolutionary Programming Optimization

The paper presents an intelligent predictive control to govern the dynamics of a solar power plant system. This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neuro-fuzzy predictive control, can be a better match to govern the system dynamics. In our proposed method, a neuro-fuzzy model identifies the future behavior of the system over a certain prediction horizon while an optimizer algorithm based on EP determines the input sequence. The first value of this sequence is applied to the plant. Using the proposed intelligent predictive controller, the performance of outlet temperature tracking problem in a solar power plant is investigated. Simulation results demonstrate the effectiveness and superiority of the proposed approach.

[1]  Manuel Berenguel,et al.  Neural identification applied to predictive control of a solar plant , 1998 .

[2]  Manuel Berenguel,et al.  ROBUST ADAPTIVE MODEL PREDICTIVE CONTROL OF A SOLAR PLANT WITH BOUNDED UNCERTAINTIES , 1997 .

[3]  Edoardo Mosca,et al.  Adaptive control of a solar energy plant : Exploiting accessible disturbances , 1997 .

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[6]  L. J. Fogel.  The Future of Evolutionary Programming , 1990, 1990 Conference Record Twenty-Fourth Asilomar Conference on Signals, Systems and Computers, 1990..

[7]  Francis J. Doyle,et al.  Nonlinear model predictive control: issues and applications , 2001 .

[8]  Barry Lennox,et al.  Neural network control of a gasoline engine with rapid sampling , 2001 .

[9]  S. Joe Qin,et al.  An Overview of Nonlinear Model Predictive Control Applications , 2000 .

[10]  Andrew J. Mason,et al.  Partition Coefficients, Static Deception and Deceptive Problems for Non-Binary Alphabets , 1991, ICGA.

[11]  Alberto Cardoso,et al.  Supervision and c-Means clustering of PID controllers for a solar power plant , 1999, Int. J. Approx. Reason..

[12]  R. Pickhardt,et al.  Application of a nonlinear predictive controller to a solar power plant , 1998, Proceedings of the 1998 IEEE International Conference on Control Applications (Cat. No.98CH36104).

[13]  Robert Babuska,et al.  Neural and fuzzy modeling in nonlinear predictive control : A comparative study , 1996 .

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

[15]  Kwang Y. Lee,et al.  Boiler-turbine control system design using a genetic algorithm , 1995 .