Linear State Space Models

As introduced in Book II [452, Appendix G], in the linear, time-invariant case, a discrete-time state-space model looks like a vector first-order finite-difference model:

$$\underline{y}(n) = C \underline{x}(n) + D\underline{u}(n)$$

$$\underline{x}(n+1) = A \underline{x}(n) + B \underline{u}(n) \protect$$ (2.8)

where $\underline{x}(n)$ is the length $N$ state vector at discrete time $n$ , $\underline{u}(n)$ is in general a $q\times 1$ vector of inputs, and $\underline{y}(n)$ the $p\times 1$ output vector. $A$ is the $N\times N$ state transition matrix, and it determines the dynamics of the system (its poles, or modal resonant frequencies and damping).

The state-space representation is especially powerful for multi-input, multi-output (MIMO) linear systems, and also for time-varying linear systems (in which case any or all of the matrices in Eq.(1.8) may have time subscripts $n$ ) [221].

To cast the previous force-driven mass example in state-space form, we may first observe that the state of the mass is specified by its velocity $v(t)$ and position $x(t)=\int v(t)\, dt$ , or $x[(n+1)T] = x(nT) + T\,v(nT)$ .^2.9Thus, to Eq.(1.5) we may add the explicit difference equation

$$x[(n+1)T] \eqsp x(nT) + T\,v(nT) \eqsp x(nT) + T\,v[(n-1)T] + \frac{T^2}{m} f[(n-1)T]$$

which, in canonical state-space form, becomes (letting $x_n\isdef x(nT)$ , etc., for notational simplicity)

$$\left[\begin{array}{c} x_{n+1} \\ [2pt] v_{n+1} \end{array}\right] \eqsp \left[\begin{array}{cc} 1 & T \\ [2pt] 0 & 1 \end{array}\right]\left[\begin{array}{c} x_n \\ [2pt] v_n \end{array}\right] + \left[\begin{array}{c} 0 \\ [2pt] T/m \end{array}\right] f_n, \quad n=0,1,2,\ldots\,, \protect$$ (2.9)

with $x_0=v_0=0$ being a typical initial state.

General features of this example are that the entire physical state of the system is collected together into a single vector, and the elements of the $(A,B,C,D)$ matrices include physical parameters (and the sampling interval, in the discrete-time case). The parameters may also vary with time (time-varying systems), or be functions of the state (nonlinear systems).

The general procedure for building a state-space model is to label all the state variables and collect them into a vector $\underline {x}$ , and then work out the state-transition matrix $A$ , input gains $B$ , output gains $C$ , and any direct coefficient $D$ . A state variable $\underline{x}_i(n)$ is needed for each lumped energy-storage element (mass, spring, capacitor, inductor), and one for each sample of delay in sampled distributed systems. After that, various equivalent (but numerically preferable) forms can be generated by means of similarity transformations [452, pp. 360-374]. We will make sparing use of state-space models in this book, because they can be linear-algebra intensive, and therefore rarely used in practical real-time signal processing systems for music and audio effects. However, the state-space framework is an important general-purpose tool that should be kept in mind [221], and there is extensive support for state-space models in the matlab (``matrix laboratory'') language and its libraries. We will use it mainly as an analytical tool from time to time.

As noted earlier, a point mass only requires a first-order model:

$$v_{n+1} \eqsp v_n + \frac{T}{m} f_n$$

Position $x_n$ can always be computed from initial position $x_0$ and velocity $v_n$ by numerical integration:

$$x_{n+1} \eqsp x_n + T\,v_n$$