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Transfer function matrix
Transfer function matrix
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In control system theory, and various branches of engineering, a transfer function matrix, or just transfer matrix is a generalisation of the transfer functions of single-input single-output (SISO) systems to multiple-input and multiple-output (MIMO) systems.[1] The matrix relates the outputs of the system to its inputs. It is a particularly useful construction for linear time-invariant (LTI) systems because it can be expressed in terms of the s-plane.

In some systems, especially ones consisting entirely of passive components, it can be ambiguous which variables are inputs and which are outputs. In electrical engineering, a common scheme is to gather all the voltage variables on one side and all the current variables on the other regardless of which are inputs or outputs. This results in all the elements of the transfer matrix being in units of impedance. The concept of impedance (and hence impedance matrices) has been borrowed into other energy domains by analogy, especially mechanics and acoustics.

Many control systems span several different energy domains. This requires transfer matrices with elements in mixed units. This is needed both to describe transducers that make connections between domains and to describe the system as a whole. If the matrix is to properly model energy flows in the system, compatible variables must be chosen to allow this.

General

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A MIMO system with m outputs and n inputs is represented by a m × n matrix. Each entry in the matrix is in the form of a transfer function relating an output to an input. For example, for a three-input, two-output system, one might write,

where the un are the inputs, the ym are the outputs, and the gmn are the transfer functions. This may be written more succinctly in matrix operator notation as,

where Y is a column vector of the outputs, G is a matrix of the transfer functions, and U is a column vector of the inputs.

In many cases, the system under consideration is a linear time-invariant (LTI) system. In such cases, it is convenient to express the transfer matrix in terms of the Laplace transform (in the case of continuous time variables) or the z-transform (in the case of discrete time variables) of the variables. This may be indicated by writing, for instance,

which indicates that the variables and matrix are in terms of s, the complex frequency variable of the s-plane arising from Laplace transforms, rather than time. The examples in this article are all assumed to be in this form, although that is not explicitly indicated for brevity. For discrete time systems s is replaced by z from the z-transform, but this makes no difference to subsequent analysis. The matrix is particularly useful when it is a proper rational matrix, that is, all its elements are proper rational functions. In this case the state-space representation can be applied.[2]

In systems engineering, the overall system transfer matrix G (s) is decomposed into two parts: H (s) representing the system being controlled, and C(s) representing the control system. C (s) takes as its inputs the inputs of G (s) and the outputs of H (s). The outputs of C (s) form the inputs for H (s).[3]

Electrical systems

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In electrical systems it is often the case that the distinction between input and output variables is ambiguous. They can be either, depending on circumstance and point of view. In such cases the concept of port (a place where energy is transferred from one system to another) can be more useful than input and output. It is customary to define two variables for each port (p): the voltage across it (Vp) and the current entering it (Ip). For instance, the transfer matrix of a two-port network can be defined as follows,

where the zmn are called the impedance parameters, or z-parameters. They are so called because they are in units of impedance and relate port currents to a port voltage. The z-parameters are not the only way that transfer matrices are defined for two-port networks. There are six basic matrices that relate voltages and currents each with advantages for particular system network topologies.[4] However, only two of these can be extended beyond two ports to an arbitrary number of ports. These two are the z-parameters and their inverse, the admittance parameters or y-parameters.[5]

Voltage divider circuit

To understand the relationship between port voltages and currents and inputs and outputs, consider the simple voltage divider circuit. If we only wish to consider the output voltage (V2) resulting from applying the input voltage (V1) then the transfer function can be expressed as,

which can be considered the trivial case of a 1×1 transfer matrix. The expression correctly predicts the output voltage if there is no current leaving port 2, but is increasingly inaccurate as the load increases. If, however, we attempt to use the circuit in reverse, driving it with a voltage at port 2 and calculate the resulting voltage at port 1 the expression gives completely the wrong result even with no load on port 1. It predicts a greater voltage at port 1 than was applied at port 2, an impossibility with a purely resistive circuit like this one. To correctly predict the behaviour of the circuit, the currents entering or leaving the ports must also be taken into account, which is what the transfer matrix does.[6] The impedance matrix for the voltage divider circuit is,

which fully describes its behaviour under all input and output conditions.[7]

At microwave frequencies, none of the transfer matrices based on port voltages and currents are convenient to use in practice. Voltage is difficult to measure directly, current next to impossible, and the open circuits and short circuits required by the measurement technique cannot be achieved with any accuracy. For waveguide implementations, circuit voltage and current are entirely meaningless. Transfer matrices using different sorts of variables are used instead. These are the powers transmitted into, and reflected from a port which are readily measured in the transmission line technology used in distributed-element circuits in the microwave band. The most well known and widely used of these sorts of parameters is the scattering parameters, or s-parameters.[8]

Mechanical and other systems

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A gear train in the control cabin of the former Gianella Bridge which operated this swing bridge. Gear trains are two-ports.

The concept of impedance can be extended into the mechanical, and other domains through a mechanical-electrical analogy, hence the impedance parameters, and other forms of 2-port network parameters, can be extended to the mechanical domain also. To do this an effort variable and a flow variable are made analogues of voltage and current respectively. For mechanical systems under translation these variables are force and velocity respectively.[9]

Expressing the behaviour of a mechanical component as a two-port or multi-port with a transfer matrix is a useful thing to do because, like electrical circuits, the component can often be operated in reverse and its behaviour is dependent on the loads at the inputs and outputs. For instance, a gear train is often characterised simply by its gear ratio, a SISO transfer function. However, the gearbox output shaft can be driven round to turn the input shaft requiring a MIMO analysis. In this example the effort and flow variables are torque T and angular velocity ω respectively. The transfer matrix in terms of z-parameters will look like,

However, the z-parameters are not necessarily the most convenient for characterising gear trains. A gear train is the analogue of an electrical transformer and the h-parameters (hybrid parameters) better describe transformers because they directly include the turns ratios (the analogue of gear ratios).[10] The gearbox transfer matrix in h-parameter format is,

where
h21 is the velocity ratio of the gear train with no load on the output,
h12 is the reverse direction torque ratio of the gear train with input shaft clamped, equal to the forward velocity ratio for an ideal gearbox,
h11 is the input rotational mechanical impedance with no load on the output shaft, zero for an ideal gearbox, and,
h22 is the output rotational mechanical admittance with the input shaft clamped.

For an ideal gear train with no losses (friction, distortion etc), this simplifies to,

where N is the gear ratio.[11]

Transducers and actuators

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A mechanical filter opened to show the mechanical-electrical transducers at either end

In a system that consists of multiple energy domains, transfer matrices are required that can handle components with ports in different domains. In robotics and mechatronics, actuators are required. These usually consist of a transducer converting, for instance, signals from the control system in the electrical domain into motion in the mechanical domain. The control system also requires sensors that detect the motion and convert it back into the electrical domain through another transducer so that the motion can be properly controlled through a feedback loop. Other sensors in the system may be transducers converting yet other energy domains into electrical signals, such as optical, audio, thermal, fluid flow and chemical. Another application is the field of mechanical filters which require transducers between the electrical and mechanical domains in both directions.

A simple example is an electromagnetic electromechanical actuator driven by an electronic controller. This requires a transducer with an input port in the electrical domain and an output port in the mechanical domain. This might be represented simplistically by a SISO transfer function, but for similar reasons to those already stated, a more accurate representation is achieved with a two-input, two-output MIMO transfer matrix. In the z-parameters, this takes the form,

where F is the force applied to the actuator and v is the resulting velocity of the actuator. The impedance parameters here are a mixture of units; z11 is an electrical impedance, z22 is a mechanical impedance and the other two are transimpedances in a hybrid mix of units.[12]

Acoustic systems

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Acoustic systems are a subset of fluid dynamics, and in both fields the primary input and output variables are pressure, P, and volumetric flow rate, Q, except in the case of sound travelling through solid components. In the latter case, the primary variables of mechanics, force and velocity, are more appropriate. An example of a two-port acoustic component is a filter such as a muffler on an exhaust system. A transfer matrix representation of it may look like,

Here, the Tmn are the transmission parameters, also known as ABCD-parameters. The component can be just as easily described by the z-parameters, but transmission parameters have a mathematical advantage when dealing with a system of two-ports that are connected in a cascade of the output of one into the input port of another. In such cases the overall transmission parameters are found simply by the matrix multiplication of the transmission parameter matrices of the constituent components.[13]

Compatible variables

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A pneumatic rack and pinion actuator controlling a valve in a water pipe. The actuator is a two-port device that converts from the pneumatic domain to the mechanical domain. Together with the valve itself it comprises a three-port system; the pneumatic control port and the fluid flow input and output water pipe ports of the valve.

When working with mixed variables from different energy domains consideration needs to be given on which variables to consider analogous. The choice depends on what the analysis is intended to achieve. If it is desired to correctly model energy flows throughout the entire system then a pair of variables whose product is power (power conjugate variables) in one energy domain must map to power conjugate variables in other domains. Power conjugate variables are not unique so care needs to be taken to use the same mapping of variables throughout the system.[14]

A common mapping (used in some of the examples in this article) maps the effort variables (ones that initiate an action) from each domain together and maps the flow variables (ones that are a property of an action) from each domain together. Each pair of effort and flow variables is power conjugate. This system is known as the impedance analogy because a ratio of the effort to the flow variable in each domain is analogous to electrical impedance.[15]

There are two other power conjugate systems on the same variables that are in use. The mobility analogy maps mechanical force to electric current instead of voltage. This analogy is widely used by mechanical filter designers and frequently in audio electronics also. The mapping has the advantage of preserving network topologies across domains but does not maintain the mapping of impedances. The Trent analogy classes the power conjugate variables as either across variables, or through variables depending on whether they act across an element of a system or through it. This largely ends up the same as the mobility analogy except in the case of the fluid flow domain (including the acoustics domain). Here pressure is made analogous to voltage (as in the impedance analogy) instead of current (as in the mobility analogy). However, force in the mechanical domain is analogous to current because force acts through an object.[16]

There are some commonly used analogies that do not use power conjugate pairs. For sensors, correctly modelling energy flows may not be so important. Sensors often extract only tiny amounts of energy into the system. Choosing variables that are convenient to measure, particularly ones that the sensor is sensing, may be more useful. For instance, in the thermal resistance analogy, thermal resistance is considered analogous to electrical resistance, resulting in temperature difference and thermal power mapping to voltage and current respectively. The power conjugate of temperature difference is not thermal power, but rather entropy flow rate, something that cannot be directly measured. Another analogy of the same sort occurs in the magnetic domain. This maps magnetic reluctance to electrical resistance, resulting in magnetic flux mapping to current instead of magnetic flux rate of change as required for compatible variables.[17]

History

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The matrix representation of linear algebraic equations has been known for some time. Poincaré in 1907 was the first to describe a transducer as a pair of such equations relating electrical variables (voltage and current) to mechanical variables (force and velocity). Wegel, in 1921, was the first to express these equations in terms of mechanical impedance as well as electrical impedance.[18]

The first use of transfer matrices to represent a MIMO control system was by Boksenbom and Hood in 1950, but only for the particular case of the gas turbine engines they were studying for the National Advisory Committee for Aeronautics.[19] Cruickshank provided a firmer basis in 1955 but without complete generality. Kavanagh in 1956 gave the first completely general treatment, establishing the matrix relationship between system and control and providing criteria for realisability of a control system that could deliver a prescribed behaviour of the system under control.[20]

See also

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References

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Bibliography

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
In , the transfer function matrix G(s)G(s) is a matrix-valued function of the complex variable ss that encapsulates the input-output dynamics of a linear time-invariant (LTI) multivariable , relating the of the output vector y(s)Cly(s) \in \mathbb{C}^l to the input vector u(s)Cmu(s) \in \mathbb{C}^m via y(s)=G(s)u(s)y(s) = G(s) u(s), where each entry gij(s)g_{ij}(s) is an individual describing the influence of the jj-th input on the ii-th output. This representation extends the scalar to multiple-input multiple-output (MIMO) systems, enabling analysis in the by evaluating G(jω)G(j\omega), a complex matrix that captures and phase responses to sinusoidal inputs at frequency ω\omega. For systems described by state-space models x˙=Ax+Bu\dot{x} = A x + B u and y=Cx+Duy = C x + D u, with state vector xRnx \in \mathbb{R}^n, the transfer function matrix is derived as G(s)=C(sIA)1B+DG(s) = C (sI - A)^{-1} B + D, where II is the , providing a compact algebraic form independent of the choice of state coordinates. This formulation assumes the system is controllable and , and it reveals essential structural properties such as poles, which are the eigenvalues of AA (roots of det(sIA)=0\det(sI - A) = 0), determining stability if all lie in the open left-half . In multivariable control design, the transfer function matrix facilitates frequency-domain techniques for performance specification and robustness assessment, including the computation of singular values σˉ(G(jω))\bar{\sigma}(G(j\omega)) and σ(G(jω))\underline{\sigma}(G(j\omega)), which quantify the maximum and minimum gains over all input directions, respectively, aiding in bandwidth limitations and disturbance rejection analysis. Zeros of the matrix, defined as values of ss where G(s)G(s) loses rank (via the Rosenbrock system matrix ), influence controller synthesis by affecting invertibility and decoupling possibilities in feedback loops. Applications span , chemical processes, and , where MIMO interactions necessitate tools beyond single-variable SISO methods for achieving desired closed-loop behaviors like tracking and stability margins.

Fundamentals

Definition

In , the transfer function matrix provides a frequency-domain representation for multi-input multi-output (MIMO) linear time-invariant (LTI) systems, extending the scalar concept from single-input single-output (SISO) systems. It captures the dynamic relationships between multiple inputs and outputs through rational functions of the complex variable ss, enabling analysis of system behavior, stability, and design of controllers. The transfer function matrix G(s)G(s) is defined as an m×nm \times n matrix, where mm is the number of outputs and nn is the number of inputs, and each entry gij(s)g_{ij}(s) represents the from the jj-th input to the ii-th output when all other inputs are zero. This structure arises naturally from the linearity of LTI systems, allowing superposition of input effects on outputs. The overall input-output relation in the Laplace domain is given by the vector equation Y(s)=G(s)U(s),Y(s) = G(s) U(s), where Y(s)CmY(s) \in \mathbb{C}^m is the of the output vector, U(s)CnU(s) \in \mathbb{C}^n is the of the input vector, and ss is the complex frequency variable. Each entry gij(s)g_{ij}(s) is a proper , expressed as the ratio of two polynomials in ss where the degree of the denominator is greater than or equal to the degree of the numerator. Proper rational functions ensure finite high-frequency gain, allowing direct feedthrough in realizations while maintaining . Improper functions, though causal in some cases like differentiators, often lead to practical unrealizability due to noise sensitivity. As a simple example of a 2×2 transfer function matrix for a coupled system, consider two unit masses, each connected to a fixed wall and to each other by springs (k=1) and dampers (c=1), with positions x1(t)x_1(t) and x2(t)x_2(t) as outputs and external forces u1(t)u_1(t) and u2(t)u_2(t) as inputs. The governing second-order differential equations from Newton's laws are x¨1+2x˙1+2x1x2x˙2=u1,\ddot{x}_1 + 2\dot{x}_1 + 2x_1 - x_2 - \dot{x}_2 = u_1, x¨2+2x˙2+2x2x1x˙1=u2.\ddot{x}_2 + 2\dot{x}_2 + 2x_2 - x_1 - \dot{x}_1 = u_2. Assuming zero initial conditions, applying the Laplace transform yields (s2+2s+2)X1(s)(s+1)X2(s)=U1(s),(s^2 + 2s + 2) X_1(s) - (s + 1) X_2(s) = U_1(s), (s+1)X1(s)+(s2+2s+2)X2(s)=U2(s).-(s + 1) X_1(s) + (s^2 + 2s + 2) X_2(s) = U_2(s). In matrix form, [s2+2s+2(s+1)(s+1)s2+2s+2][X1(s)X2(s)]=[U1(s)U2(s)].\begin{bmatrix} s^2 + 2s + 2 & -(s + 1) \\ -(s + 1) & s^2 + 2s + 2 \end{bmatrix} \begin{bmatrix} X_1(s) \\ X_2(s) \end{bmatrix} = \begin{bmatrix} U_1(s) \\ U_2(s) \end{bmatrix}. The transfer function matrix G(s)G(s) is the inverse of the coefficient matrix, G(s)=1(s2+2s+2)2(s+1)2[s2+2s+2s+1s+1s2+2s+2],G(s) = \frac{1}{(s^2 + 2s + 2)^2 - (s + 1)^2} \begin{bmatrix} s^2 + 2s + 2 & s + 1 \\ s + 1 & s^2 + 2s + 2 \end{bmatrix}, where the denominator simplifies to s4+4s3+7s2+6s+3s^4 + 4s^3 + 7s^2 + 6s + 3, confirming proper rational entries with denominator degree 4 exceeding numerator degrees. The single-input single-output transfer function is a special case when m=n=1m = n = 1.

SISO to MIMO Extension

In single-input single-output (SISO) systems, the transfer function is a scalar H(s)=Y(s)U(s)H(s) = \frac{Y(s)}{U(s)}, which describes the input-output relationship in the Laplace domain assuming zero initial conditions. This simplifies analysis for decoupled dynamics where a single input directly influences a single output without interactions. In contrast, multi-input multi-output (MIMO) systems extend this to a transfer function matrix G(s)=[gij(s)]G(s) = [g_{ij}(s)], where each element gij(s)=Yi(s)Uj(s)g_{ij}(s) = \frac{Y_i(s)}{U_j(s)} represents the transfer function from the jj-th input to the ii-th output, with off-diagonal terms gij(s)g_{ij}(s) for iji \neq j capturing cross-coupling effects between multiple variables. This extension addresses the limitations of SISO models in representing coupled dynamics, where inputs and outputs interact, leading to phenomena like non-minimum phase behavior or directionality that scalar functions cannot capture. For instance, in multi-loop control systems such as chemical reactors or flight control, ignoring cross-coupling can result in unstable or suboptimal performance, as disturbances in one loop propagate to others; the framework is thus essential to model these interactions accurately and design decentralized or centralized controllers. Key matrix operations on G(s)G(s) provide insights into system behavior: the determinant detG(s)\det G(s) provides a measure of the multivariable determinant gain, related to the product of the system's singular values, though singular values are more commonly used for directional gain analysis, while the inverse G(s)1G(s)^{-1} (when it exists) enables decoupling designs that eliminate cross-interactions by pre-compensating inputs. These operations highlight the conceptual shift from independent SISO channels to holistic MIMO analysis, where full-rank conditions ensure invertibility for square systems.

Mathematical Formulation

Laplace Transform Representation

The transfer function matrix provides a frequency-domain representation of linear time-invariant (LTI) multi-input multi-output (MIMO) systems through the application of the Laplace transform to their time-domain differential equations, assuming zero initial conditions. For a general MIMO LTI system described by coupled linear differential equations k=0nAkdky(t)dtk=k=0mBkdku(t)dtk,\sum_{k=0}^{n} A_k \frac{d^k \mathbf{y}(t)}{dt^k} = \sum_{k=0}^{m} B_k \frac{d^k \mathbf{u}(t)}{dt^k}, where y(t)Rp\mathbf{y}(t) \in \mathbb{R}^p denotes the output vector, u(t)Rq\mathbf{u}(t) \in \mathbb{R}^q the input vector, An=IpA_n = I_p, and the system is proper if m<nm < n, the Laplace transform converts this to the algebraic form P(s)Y(s)=N(s)U(s)P(s) \mathbf{Y}(s) = N(s) \mathbf{U}(s), with P(s)=k=0nAkskP(s) = \sum_{k=0}^n A_k s^k and N(s)=k=0mBkskN(s) = \sum_{k=0}^m B_k s^k. Solving for the output yields the transfer function matrix G(s)=P(s)1N(s)\mathbf{G}(s) = P(s)^{-1} N(s), where each entry Gij(s)G_{ij}(s) relates the jj-th input to the ii-th output. This matrix form extends the scalar transfer function concept to capture inter-channel couplings in MIMO systems. The poles of G(s)\mathbf{G}(s) are the roots of detP(s)=0\det P(s) = 0, representing the system's dynamic modes, while transmission zeros are the values of ss where the normal rank of G(s)\mathbf{G}(s) drops below its generic rank, defined via the zeros of the invariant polynomials in the Smith-McMillan ; these indicate frequencies at which certain input-output transmission is blocked. The relative degree of G(s)\mathbf{G}(s) is characterized by the differences in degrees between the denominator and numerator polynomials across its entries or in , quantifying the highest order of differentiation required to express outputs in terms of inputs without direct feedthrough. The McMillan degree, defined as the sum of the degrees of the pole polynomials in the Smith-McMillan form, measures the intrinsic of G(s)\mathbf{G}(s) and equals the order of any minimal realization of the system. Improper transfer function matrices arise when mnm \geq n, leading to polynomial terms in G(s)\mathbf{G}(s), or in systems with direct input-output coupling; such cases are handled by polynomial division to separate the strictly proper and polynomial parts. For generalized or descriptor systems, described in implicit form without assuming a standard state-space structure, the transfer function matrix is given by G(s)=C(sEA)1B+D\mathbf{G}(s) = C (sE - A)^{-1} B + D, where the matrix pencil sEAsE - A must be regular (i.e., det(sEA)≢0\det(sE - A) \not\equiv 0) to ensure a well-defined rational form; this representation accommodates algebraic constraints and potential impropriety while preserving minimality when the realization achieves the McMillan degree. Minimal realizations correspond to controllable and descriptor forms where the system order matches the McMillan degree, avoiding redundant dynamics. As a numerical example, consider a 2-input 2-output mass-spring-damper system with two equal masses m1=m2=1m_1 = m_2 = 1 kg connected to ground and each other by springs of stiffness k1=k2=1k_1 = k_2 = 1 N/m, and dampers with coefficients c1=c2=0.5c_1 = c_2 = 0.5 Ns/m, where inputs u1,u2u_1, u_2 are forces applied to each mass and outputs y1,y2y_1, y_2 are their displacements. The time-domain equations are y1¨+1.5y1˙+2y10.5y2˙y2=u1,\ddot{y_1} + 1.5 \dot{y_1} + 2 y_1 - 0.5 \dot{y_2} - y_2 = u_1, y2¨+0.5y2˙+y20.5y1˙y1=u2.\ddot{y_2} + 0.5 \dot{y_2} + y_2 - 0.5 \dot{y_1} - y_1 = u_2. Applying the with zero initial conditions gives P(s)[Y1(s)Y2(s)]=[U1(s)U2(s)],P(s) \begin{bmatrix} Y_1(s) \\ Y_2(s) \end{bmatrix} = \begin{bmatrix} U_1(s) \\ U_2(s) \end{bmatrix}, where P(s)=[s2+1.5s+20.5s10.5s1s2+0.5s+1].P(s) = \begin{bmatrix} s^2 + 1.5 s + 2 & -0.5 s - 1 \\ -0.5 s - 1 & s^2 + 0.5 s + 1 \end{bmatrix}. The transfer function matrix is then G(s)=P(s)1\mathbf{G}(s) = P(s)^{-1}, with entries such as G11(s)=s2+0.5s+1detP(s),G12(s)=0.5s+1detP(s),G_{11}(s) = \frac{s^2 + 0.5 s + 1}{\det P(s)}, \quad G_{12}(s) = \frac{0.5 s + 1}{\det P(s)}, where detP(s)=(s2+1.5s+2)(s2+0.5s+1)(0.5s+1)2=s4+2s3+3.5s2+1.5s+1\det P(s) = (s^2 + 1.5 s + 2)(s^2 + 0.5 s + 1) - (0.5 s + 1)^2 = s^4 + 2 s^3 + 3.5 s^2 + 1.5 s + 1; the poles are the roots of this quartic, and the McMillan degree is 4 for this minimal system.

State-Space Equivalence

The state-space representation of a linear time-invariant (LTI) multi-input multi-output (MIMO) system is given by the equations x˙(t)=Ax(t)+Bu(t)\dot{x}(t) = A x(t) + B u(t) and y(t)=Cx(t)+Du(t)y(t) = C x(t) + D u(t), where x(t)Rnx(t) \in \mathbb{R}^n is the state vector, u(t)Rmu(t) \in \mathbb{R}^m is the input vector, y(t)Rpy(t) \in \mathbb{R}^p is the output vector, and AA, BB, CC, DD are constant matrices of appropriate dimensions. The corresponding transfer function matrix G(s)G(s) in the Laplace domain is derived as G(s)=C(sIA)1B+DG(s) = C (sI - A)^{-1} B + D, establishing a direct equivalence between the time-domain state-space model and the frequency-domain transfer function representation for strictly proper systems when D=0D = 0. This relationship holds for proper rational transfer functions, allowing the transfer matrix to fully capture the system's input-output dynamics from the state-space parameters. To convert a transfer function matrix G(s)G(s) to a state-space realization, algorithms construct an initial representation and then minimize it by ensuring controllability and observability. A common method involves forming a realization using the Markov parameters (impulse response coefficients) or partial fraction expansions of G(s)G(s)'s entries, followed by checking the rank of the controllability matrix C=[B,AB,,An1B]\mathcal{C} = [B, AB, \dots, A^{n-1}B] and observability matrix O=[CCACAn1]\mathcal{O} = \begin{bmatrix} C \\ CA \\ \vdots \\ CA^{n-1} \end{bmatrix}
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