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190              Chapter 3  State Variable Models
                            simply the inverse transform  of  <&(s); that is,

                                                       ¢(0  =  X-'i&is)}.                      (3.83)

                               The relationship between a state variable Xj(s) and the initial conditions x(0) is
                            obtained by using Mason's signal-flow gain formula. Thus, for a second-order system,
                            we would have
                                                 Ai(5)  =  011(5)^(0)  +  * 12(*)*2(0),

                                                 *2(*)  =  <fcl(*)*l(0)  +  ^22(^)^2(0),       (3.84)
                            and the relation  between  X 2{s) as an output  and Xi(0) as an input can be evaluated
                            by Mason's signal-flow  gain formula. All the elements  of the state transition  matrix,
                            <f>ij(s), can be obtained  by evaluating the individual relationships between  X^s)  and
                            xj(0) from the state model flow graph. An example will illustrate this approach to de-
                            termining the transition matrix.

                            EXAMPLE 3.6    Evaluation of the state transition matrix
                            We will consider  the RLC  network  of Figure  3.4. We seek  to  evaluate  ¢(5)  by (1)
                                                                        -1
                            determining  the matrix  inversion  ¢($)  =  [si -  A]  and  (2)  using the  signal-flow
                            diagram and Mason's signal-flow  gain  formula.
                                                                                -1
                               First, we determine ¢(5) by evaluating ®(s)  = [si -  A] . We note from Equa-
                            tion (3.18) that
                                                             ^0    -2
                                                             J.   -3

                            Then

                                                                 s    2
                                                    [.I  -  A]  =                             (3.85)
                                                                -1   s  +  3
                            The inverse matrix is

                                                                      s  +  3  -2
                                                            -1
                                             <&(*) =  [si  ~  A]  =                           (3.86)
                                                                 A(s)   1     s
                            where A(s)  = s(s  +  3)  +  2  =  s 2  + 3s  + 2  =  (s  + \)(s  + 2).
                               The signal-flow  graph state model  of the RLC  network of Figure 3.4 is shown in
                           Figure  3.8. This RLC  network, which  was discussed  in  Sections 3.3  and  3.4, can be
                            represented  by the state  variables X\ =  v c  and  x 2  = if  The initial  conditions, *i(0)
                            and x 2(0), represent  the initial capacitor voltage and inductor current, respectively.
                           The  flow  graph, including  the  initial  conditions  of  each  state  variable, is  shown  in
                            Figure 3.23. The initial conditions appear  as the initial value  of the state variable  at
                           the output  of each integrator.
                               To obtain $(s), we set U(s)  =  0. When  R  =  3, L  =  1, and C  =  1/2,  we obtain
                            the  signal-flow  graph  shown  in  Figure  3.24, where  the  output  and  input  nodes  are
                           deleted because they are not involved in the evaluation  of ¢(5). Then, using Mason's
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