Journal of Engineering Sciences

Author(s): Ganesh E. N.

Affilation(s): Saveetha Engineering College, Kuthambakkam, 600124 Tamil Nadu, Chennai, India

^*Corresponding Author’s Address: [email protected]

Issue: Volume 5; Issue 2 (2018)

Dates:
Paper received: July 13, 2018
The final version of the paper received: September 24, 2018
Paper accepted online: October 3, 2018

Citation:
Ganesh E. N. Kalman Filter Based Controlled Online System Identification / E. N. Ganesh // Journal of Engineering Sciences. – Sumy : Sumy State University, 2018. – Volume 5, Issue 2. – P. E22-E26.

DOI: 10.21272/jes.2018.5(2).e5

Research Area: MECHANICAL ENGINEERING: Computational Mechanics

Abstract. In the development of model predictive controllers a significant amount of time and effort is necessary for the development of the empirical control models. Even if on-line measurements are available, the control models have to be estimated carefully. The payback time of a model predictive controller could be significantly reduced, if a common identification tool would be available which could be introduced in a control scheme right away. In this work it was developed a control system which consists of a neural network (NN) with external recurrence only, whose parameters are adjusted by the extended Kalman filter in real-time. The output of the neural network is used in a control loop to study its accuracy in a control loop. At the moment this control loop is a NN-model based minimum variance controller. The on-line system identification with controller was tested on a simulation of a fed-batch penicillin production process to understand its behaviour in a complex environment. On every signal process and measurements noise was applied. Even though the NN was never trained before, the controller did not diverge. Although it seemed like the on-line prediction of the NN was quite accurate, the real process was not learned yet. This was checked by simulating the process with the NN obtained at the end of the batch. Nevertheless the process was maintained under control near the wanted set-points. These results show a promising start for a model predictive controller using an on-line system identification method, which could greatly reduce implementation times.

Keywords: Kalman filter, neural network, on-line training, variance control.

References:

1. Astrom, K. J., & Wittenmark, B. (1984). Computer Controlled Systems. Prentice-Hall Inc., New Jersey, USA.
2. Haykin, S. (1999). Neural networks: a comprehensive foundation. Prentice-Hall Inc., New Jersey, USA.
3. Julier, S., & Uhlmann, J. K. (1997). A new extension of the Kalman filter to nonlinear systems. Proceedings of the 11th International Symposium on Aerospace/Defence Sensing, Simulation and Controls.
4. Puskorius, G. V., & Feldkamp, L. A. (1991). Decoupled extended Kalman filter training of feedforward layered networks. Proceedings of the International Joint Conference on Neural Networks.
5. Rivals, I., & Personnaz, L. (1998). A recursive algorithm based on the extended Kalman filter for the training of feedforward neural networks. Neurocomputing, Vol. 20(1–3), pp. 279–294.
6. Rodrigues, J. A. D., & Filho, R. M. (1999). Production optimisation with operating constraints for a fed-batch reactor with DMC predictive control. Chemical Engineering Science, Vol. 54(13–14), pp. 2745–2751.
7. Shah, S., & Palmieri, F. (1990). MEKA – A fast, local algorithm for training feedforward neural networks. Proceedings of the International Joint Conference on Neural Networks, pp III-41–45.
8. Scheffer, R., & Filho, R. M. (2000). Training a Recurrent Neural Network by the Extended Kalman Filter as an Identification Tool. Escape-10 Symposium Proceedings, pp. 223–228.
9. Scheffer, R., Filho, R. M. (2001). Process identification of a fed-batch penicillin production process – training with the extended kalman filter. Application of Neural Network and Other Learning Technologies in Process Engineering.
10. Wan, E. A., van der Merwe, R. (2000). The unscented Kalman filter for nonlinear estimation. Proceedings of the Symposium 2000 on Adaptive Systems for Signal Processing, Communication adn Control (AS-SPCC), pp. 153–159.

Full Text