The linear models describing the plant are of high order, around a hundred of states. The need of describing the system behavior with sufficient detail contrasts with the so called "curse of dimensionality," that is, the computational difficulties associated with systems of very high dimension. As is known, high dimensionality is also a drawback for the majority of standard linear control schemes. To cope with this problem, models of reduced order are derived, which allow to simplify the design of the controller while giving a sufficiently accurate description of the system. 

A drawback of many of such techniques is that the physical meaning of the state variables is lost in the reduced model. This fact can be particularly relevant when dealing with tokamak models, where both the involved variables (currents, voltages, fluxes, etc.) and the model parameters (resistances, inductances, etc.) do have a specific meaning that can be of paramount importance in the interpretation of the results. 

The model reduction technique based on the Selective Modal Analysis has been devised to reduce the system order maintaining both the most significant part of the plasma dynamics and the physical meaning of the states (currents or fluxes in areas of the machine). Such models have then been employed both in the analysis of the system characteristics and in the design of feedback controllers. 
 
 
 
 
 

Related pubblications

1. A. Beghi, D. Ciscato, and A. Portone, "Model reduction techniques in tokamak modelling," in Proceedings of the 36th IEEE Conference on Decision and Control, San Diego,1997, pp.3691-3696
2. A. Beghi, D. Ciscato, M. Cavinato, and G. Marchiori, "ITER model reduction by Selective Modal Analysis,'' in Proc. of the 20th Symposium on Fusion Technology (B. Beaumont, P. Libeyre, B. de Gentile, and G. Tonon, eds.), vol. 1, (Marseille, France), pp. 507-510, September 1998. 
3. A. Beghi and D. Ciscato, "Aggregation-based model reduction for tokamak control", submitted to IEEE Conf. on Control Appl. 2000.

 

Back