Computational intelligence-based trajectory scheduling for control of nuclear research reactors

This paper puts forward computational intelligence-based sigmoidal type trajectory scheduling for the control of nuclear research reactors. In order to calculate parameters of the sigmoidal type trajectory, a generator is designed based on artificial neural networks. Data used to train the artificial neural networks have been acquired by utilizing genetic algorithms. The contribution of the proposed trajectory to the reactor control system is investigated. Furthermore, the behaviour of the controller with the proposed trajectory has been tested for various initial and desired power levels, as well as under disturbance. It is seen that the controller can control the system successfully under all conditions within the acceptable error tolerance.

[1]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[2]  Burhanettin Can,et al.  An expert trajectory design for control of nuclear research reactors , 2009, Expert Syst. Appl..

[3]  Burhanettin Can,et al.  Identification and Control of ITU Triga Mark-II Nuclear Research Reactor Using Neural Networks and Fuzzy Logic , 2005, Australian Conference on Artificial Intelligence.

[4]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[5]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[6]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[7]  Gamini Dissanayake,et al.  A neural network-based method for time-optimal trajectory planning , 1998, Robotica.

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

[9]  Jian-Xin Xu,et al.  Enhancing trajectory tracking for a class of process control problems using iterative learning , 2002 .

[10]  R. A. Wright,et al.  Nonlinear controllers for trajectory tracking in batch processes , 1989 .

[11]  John A. Bernard.  Period-Generated Control for Trajectory Tracking , 1991, 1991 American Control Conference.

[12]  John F. MacGregor,et al.  Latent variable MPC for trajectory tracking in batch processes , 2005 .

[13]  Scott Robert Ladd,et al.  Genetic algorithms in C , 1995 .

[14]  Nam Zin Cho,et al.  Design of a nonlinear model-based controller with adaptive PI gains for robust control of a nuclear reactor , 1992 .

[15]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.