Over the past several decades, a considerable interest has been devoted to problems involving signals and systems that depend on more than one variable. 2-D signals and systems have been studied in relation to several modern engineering fields such as process control, multi-dimensional (m-D) digital filtering, image enhancement, image deblurring, signal processing, etc. Among the major results developed so far concerning the 2-D signals and systems, 2-D digital filters are investigated as a description in frequency domain or as a convolution of the input and the unit impulse response. Its great potential for practical applications in the 2-D image and signal processing has been shown [64][65]. On the other hand, a technically very important range of 2-D problems exist which require feedback control [44]. 2-D control has previously been approached from a predominantly systems theoretical point of view. This has two main branches, seen in [60][72], taking an input-output transfer function approach and a state-space approach, respectively. 2-D state-space models, however, have attracted a lot of interest due to its advantage of providing a simple and intuitive research method for 2-D signals and systems. Although it appears something like the one-dimensional (1-D) state space model, there exist some essential differences between them. Therefore, 2-D state-space representations have been extensively studied theoretically and practically.