NEURAL OUTPUT REGULATION FOR A SOLAR POWER PLANT

In this paper the modelling capabilities of a recurrent neural network and the effectiveness and stability of the output regulation control theory are combined. The control structure consists in a neural based indirect adaptive control scheme, being the main goal to provide a viable practical control strategy suitable for real-time implementations. This control scheme was applied to the distributed solar collector field at Plataforma Solar de Almeria, Spain. Experimental results obtained at the solar power plant are presented showing the effectiveness of the proposed approach.

[1]  Jie Huang.  8. Output Regulation for Singular Nonlinear Systems , 2004 .

[2]  Mietek A. Brdys,et al.  Stable adaptive control with recurrent networks , 1997, 1997 European Control Conference (ECC).

[3]  W. Rugh,et al.  Stabilization on zero-error manifolds and the nonlinear servomechanism problem , 1992 .

[4]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[5]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[6]  J. Henriques,et al.  A recurrent neuronal approach for the nonlinear discrete time output regulation , 2000, 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies.

[7]  Kenneth J. Hunt,et al.  Applications of Neural Adaptive Control Technology , 1997 .

[8]  Manuel Berenguel,et al.  Application of a gain scheduling generalized predictive controller to a solar power plant , 1994 .

[9]  M. Berenguel,et al.  Nonlinear neural model-based predictive control of a solar plant , 1997, 1997 European Control Conference (ECC).

[10]  B. Francis.  The linear multivariable regulator problem , 1976, 1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes.

[11]  S. Monaco,et al.  Nonlinear regulation for a class of discrete-time systems , 1993 .

[12]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

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

[14]  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).

[15]  Madan M. Gupta,et al.  Dynamic recurrent neural networks for approximation of nonlinear systems , 1999 .

[16]  J. Henriques,et al.  STATE SPACE NEURAL NETWORKS IN NON-LINEAR ADAPTIVE SYSTEM IDENTIFICATION AND CONTROL , 2001 .

[17]  Kumpati S. Narendra,et al.  Gradient methods for the optimization of dynamical systems containing neural networks , 1991, IEEE Trans. Neural Networks.

[18]  D. Normand-Cyrot,et al.  Minimum-phase nonlinear discrete-time systems and feedback stabilization , 1987, 26th IEEE Conference on Decision and Control.

[19]  A. L. Cardoso,et al.  Fuzzy supervisor and feedforward control of a solar power plant using accessible disturbances , 1999, 1999 European Control Conference (ECC).

[20]  P J Webros.  BACKPROPAGATION THROUGH TIME: WHAT IT DOES AND HOW TO DO IT , 1990 .

[21]  Manolis A. Christodoulou,et al.  Neural adaptive regulation of unknown nonlinear dynamical systems , 1997, IEEE Trans. Syst. Man Cybern. Part B.

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

[23]  Manuel Berenguel,et al.  Fuzzy logic control of a solar power plant , 1995, IEEE Trans. Fuzzy Syst..

[24]  W. Rugh,et al.  An approximation method for the nonlinear servomechanism problem , 1992 .

[25]  E.F. Camacho,et al.  Self-tuning control of a solar power plant with a distributed collector field , 1992, IEEE Control Systems.

[26]  Ronald J. Williams,et al.  Gradient-based learning algorithms for recurrent networks and their computational complexity , 1995 .

[27]  M. Berenguel,et al.  Incremental fuzzy PI control of a solar power plant , 1997 .

[28]  Alexander S. Poznyak,et al.  Nonlinear adaptive trajectory tracking using dynamic neural networks , 1999, IEEE Trans. Neural Networks.