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Mason's gain formula

Mason's gain formula is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Mason's gain formula rather than just read about it. In short: Mason's gain formula (MGF) is a method for finding the transfer function of a linear signal-flow graph (SFG). The formula was derived by Samuel Jefferson Mason, for whom it is named.

Mason's gain formula — main illustration
Mason's gain formula — illustration

Key takeaways

  • Mason's gain formula belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Mason's gain formula to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Mason's gain formula from memory before moving on to harder problems.

Reference excerpt

Mason's gain formula (MGF) is a method for finding the transfer function of a linear signal-flow graph (SFG). The formula was derived by Samuel Jefferson Mason, for whom it is named. MGF is an alternate method to finding the transfer function algebraically by labeling each signal, writing down the equation for how that signal depends on other signals, and then solving the multiple equations for the output signal in terms of the input signal. MGF provides a step by step method to obtain the transfer function from a SFG. Often, MGF can be determined by inspection of the SFG. The method can easily handle SFGs with many variables and loops including loops with inner loops. MGF comes up often in the context of control systems, microwave circuits and digital filters because these are often represented by SFGs.

Formula The gain formula is as follows:

G = y out y in = ∑ k = 1 N G k Δ k Δ {\displaystyle G={\frac {y_{\text{out}}}{y_{\text{in}}}}={\frac {\sum _{k=1}^{N}{G_{k}\Delta _{k}}}{\Delta \ }}}

Δ = 1 − ∑ L i + ∑ L i L j − ∑ L i L j L k + ⋯ + ( − 1 ) m ∑ ⋯ + ⋯ {\displaystyle \Delta =1-\sum L_{i}+\sum L_{i}L_{j}-\sum L_{i}L_{j}L_{k}+\cdots +(-1)^{m}\sum \cdots +\cdots }

where:

Δ = the determinant of the graph. yin = input-node variable yout = output-node variable G = complete gain between yin and yout N = total number of forward paths between yin and yout Gk = path gain of the kth forward path between yin and yout Li = loop gain of each closed loop in the system LiLj = product of the loop gains of any two non-touching loops (no common nodes) LiLjLk = product of the loop gains of any three pairwise nontouching loops Δk = the cofactor value of Δ for the kth forward path, with the loops touching the kth forward path removed.

Definitions Source:

Path: a continuous set of branches traversed in the direction that they indicate. Forward path: A path from an input node to an output node in which no node is touched more than once. Loop: A path that originates and ends on the same node in which no node is touched more than once. Path gain: the product of the gains of all the branches in the path. Loop gain: the product of the gains of all the branches in the loop.

Procedure to find the solution Make a list of all forward paths, and their gains, and label these Gk. Make a list of all the loops and their gains, and label these Li (for i loops). Make a list of all pairs of non-touching loops, and the products of their gains (LiLj). Make a list of all pairwise non-touching loops taken three at a time (LiLjLk), then four at a time, and so forth, until there are no more. Compute the determinant Δ and cofactors Δk. Apply the formula.

Examples

Circuit containing two-port

The transfer function from Vin to V2 is desired. There is only one forward path:

Vin to V1 to I2 to V2 with gain G 1 = − y 21 R L {\displaystyle G_{1}=-y_{21}R_{L}\,}

There are three loops:

V1 to I1 to V1 with gain L 1 = − R in y 11 {\displaystyle L_{1}=-R_{\text{in}}y_{11}\,}

V2 to I2 to V2 with gain L 2 = − R L y 22 {\displaystyle L_{2}=-R_{L}y_{22}\,}

V1 to I2 to V2 to I1 to V1 with gain L 3 = y 21 R L y 12 R in {\displaystyle L_{3}=y_{21}R_{L}y_{12}R_{\text{in}}\,}

Δ = 1 − ( L 1 + L 2 + L 3 ) + ( L 1 L 2 ) {\displaystyle \Delta =1-(L_{1}+L_{2}+L_{3})+(L_{1}L_{2})\,} note: L1 and L2 do not touch each other whereas L3 touches both of the other loops.

… excerpt ends here. Continue reading the full article.

Illustrations

Mason's gain formula: The signal flow graph (SFG) for a digital infinite impulse response bi-quad filter. This SFG has three forward paths and two loops.
The signal flow graph (SFG) for a digital infinite impulse response bi-quad filter. This SFG has three forward paths and two loops.
Mason's gain formula: Angular position servo and signal flow graph. θC = desired angle command, θL = actual load angle, KP = position loop gain, VωC = velocity command, VωM = motor velocity sense voltage, KV = velocity loop gain, VIC = current command, VIM = current sense voltage, KC = current loop gain, VA = power amplifier output voltage, VM = effective voltage across the inductance, LM = motor inductance, IM = motor current, RM = motor resistance, RS = current sense resistance, KM = motor torque constant (Nm/amp), T = torque, M = moment of inertia of all rotating components α = angular acceleration, ω = angular velocity, β = mechanical damping, GM = motor back EMF constant, GT = tachometer conversion gain constant. There is one forward path (shown in a different color) and six feedback loops. The drive shaft assumed to be stiff enough to not treat as a spring. Constants are shown in black and variables in purple.
Angular position servo and signal flow graph. θC = desired angle command, θL = actual load angle, KP = position loop gain, VωC = velocity command, VωM = motor velocity sense voltage, KV = velocity loop gain, VIC = current command, VIM = current sense voltage, KC = current loop gain, VA = power amplifier output voltage, VM = effective voltage across the inductance, LM = motor inductance, IM = motor current, RM = motor resistance, RS = current sense resistance, KM = motor torque constant (Nm/amp), T = torque, M = moment of inertia of all rotating components α = angular acceleration, ω = angular velocity, β = mechanical damping, GM = motor back EMF constant, GT = tachometer conversion gain constant. There is one forward path (shown in a different color) and six feedback loops. The drive shaft assumed to be stiff enough to not treat as a spring. Constants are shown in black and variables in purple.

Worked examples

Example 1 — a first encounter with Mason's gain formula

Start with the simplest possible case. Write down what Mason's gain formula claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Mason's gain formula before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Mason's gain formula ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Mason's gain formula

In research
Mason's gain formula appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Mason's gain formula in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Mason's gain formula is common in secondary-school and first-year university syllabi. It links to neighbouring topics Control theory, so understanding it makes those chapters shorter.
In everyday life
Look for Mason's gain formula outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Mason's gain formula in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Mason's gain formula means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Mason's gain formula out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Mason's gain formula in simple terms?

Mason's gain formula (MGF) is a method for finding the transfer function of a linear signal-flow graph (SFG). The formula was derived by Samuel Jefferson Mason, for whom it is named.

Why does Mason's gain formula matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Mason's gain formula?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Mason's gain formula.

Tags

  • Control theory

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