Predictive Control Design for Large Scale Systems

1995 ◽  
Vol 28 (23) ◽  
pp. 47-52
Author(s):  
M.R. Katebi ◽  
M.A. Johnson
Automatica ◽  
1997 ◽  
Vol 33 (3) ◽  
pp. 421-425 ◽  
Author(s):  
M.R. Katebi ◽  
M.A. Johnson

2015 ◽  
Vol 44 (3) ◽  
pp. 247-253
Author(s):  
Branislav Rehak

A control design for a large-scale system using LMI optimization is proposed. The control is designed in a way such that the LQ cost in the case of the decentralized control  does not exceed a certain limit. The optimized quantity are the values of the control gain matrices. The methodology is useful even for finding a decomposition of the system, however, some expert knowledge is necessary in this case. The capabilities of the algorithm are illustrated by two examples.DOI: http://dx.doi.org/10.5755/j01.itc.44.3.6464


2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Koichi Kobayashi ◽  
Kunihiko Hiraishi

We propose computational techniques for model predictive control of large-scale systems with both continuous-valued control inputs and discrete-valued control inputs, which are a class of hybrid systems. In the proposed method, we introduce the notion of virtual control inputs, which are obtained by relaxing discrete-valued control inputs to continuous variables. In online computation, first, we find continuous-valued control inputs and virtual control inputs minimizing a cost function. Next, using the obtained virtual control inputs, only discrete-valued control inputs at the current time are computed in each subsystem. In addition, we also discuss the effect of quantization errors. Finally, the effectiveness of the proposed method is shown by a numerical example. The proposed method enables us to reduce and decentralize the computation load.


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