Mathematical modelling of physical systems
Builds transfer-function models of electrical, mechanical, thermal and liquid-level systems, with analogies and linearisation about an operating point.
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Why it matters
You cannot design a controller for a plant you cannot describe. Before tuning a temperature loop or choosing a compensator, an instrumentation engineer writes the governing equations of the process — electrical, mechanical, thermal or fluid — and turns them into a transfer function. A good model tells you the gain, the time constants and the order of the system, which is most of what the rest of control theory needs.
Key ideas
From physical law to transfer function
- Write the balance law for each energy-storing element: Kirchhoff's laws (electrical), Newton's second law (mechanical), energy balance (thermal), mass balance (fluid).
- Assume the system is linear and time-invariant (LTI), or linearise it about an operating point.
- Take the Laplace transform with zero initial conditions.
- The transfer function is
G(s) = Output(s)/Input(s). It is a property of the system alone, not of the input.
Order of the model equals the number of independent energy-storing elements: an RC network is first order, a series RLC circuit or a mass-spring-damper is second order.
Element relations (time domain → s-domain impedance)
- Electrical: resistor
v = R·i, inductorv = L·di/dt(Ls), capacitori = C·dv/dt(1/(Cs)). - Mechanical translational: mass
f = M·d²x/dt², viscous damperf = B·dx/dt, springf = K·x. - Mechanical rotational: inertia
T = J·d²θ/dt², damperT = B·dθ/dt, torsional springT = K·θ. - Thermal: thermal resistance
R_th = Δθ/q(K/W), capacitanceC_th = m·c_p(J/K). - Liquid level: hydraulic resistance
R = dh/dq(s/m²), capacitanceC = A(tank cross-section, m²).
Electrical analogies let you build one circuit for any mechanical system.
- Force–voltage analogy (series): force ↔ voltage, mass ↔ inductance, damper B ↔ resistance, spring K ↔ 1/C, velocity ↔ current.
- Force–current analogy (parallel): force ↔ current, mass ↔ capacitance, damper B ↔ conductance 1/R, spring K ↔ 1/L, velocity ↔ voltage.
Linearisation
Real processes are nonlinear (valve characteristics, outflow ∝ √h, radiation ∝ T⁴). For small deviations about a steady operating point (x0, y0), replace y = f(x) by Δy ≈ (df/dx)|x0 · Δx. The resulting model is valid only near that point; time constants and gains change when the operating point moves.
Transfer function properties
- Defined only for LTI systems with zero initial conditions.
- It is the Laplace transform of the impulse response.
- Poles are roots of the denominator (they set the natural modes); zeros are roots of the numerator.
- Proper: degree of numerator ≤ degree of denominator — every physical system is proper.
Formulas
Vc(s)/Vi(s) = 1 / (LC·s² + RC·s + 1)— series RLC, output across C.ωn = 1/√(LC),ζ = (R/2)·√(C/L)— for the series RLC above.X(s)/F(s) = 1 / (M·s² + B·s + K)— mass-spring-damper.ωn = √(K/M),ζ = B / (2·√(K·M))— for the mass-spring-damper.θ(s)/T(s) = 1 / (J·s² + B·s + K)— rotational analogue.H(s)/Qi(s) = R / (A·R·s + 1)— liquid-level tank,τ = A·R.R = dh/dq = 2·h0/q0— linearised resistance for outflowq = k·√h.Θ(s)/Q(s) = R_th / (R_th·C_th·s + 1)— thermal first-order system.
Symbols and units: R Ω, L H, C F; M kg, B N·s/m, K N/m; J kg·m², rotational B N·m·s/rad, K N·m/rad; h m, q m³/s, A m²; R_th K/W, C_th J/K; ωn rad/s; ζ dimensionless.
Worked examples
Example 1 — series RLC network
Given: R = 10 Ω, L = 0.1 H, C = 100 µF; output is the capacitor voltage.
- KVL:
vi = R·i + L·di/dt + vc, withi = C·dvc/dt. - Laplace transform (zero initial conditions):
Vi = (LCs² + RCs + 1)·Vc. LC = 0.1 × 100×10⁻⁶ = 1×10⁻⁵ s²;RC = 10 × 100×10⁻⁶ = 1×10⁻³ s.G(s) = 1 / (1×10⁻⁵ s² + 1×10⁻³ s + 1).ωn = 1/√(1×10⁻⁵) = 316.2 rad/s;ζ = (10/2)·√(100×10⁻⁶/0.1) = 5 × 0.03162 = 0.158.
Result: G(s) = 1/(10⁻⁵ s² + 10⁻³ s + 1), with ωn = 316 rad/s and ζ = 0.158 (lightly damped).
Example 2 — linearised liquid-level process (GATE level)
Given: tank area A = 2 m²; outflow through a valve q = k·√h; steady state h0 = 4 m, q0 = 0.02 m³/s. Find the transfer function H(s)/Qi(s) for small deviations.
- Mass balance:
A·dh/dt = qi − k·√h. - Valve constant:
k = q0/√h0 = 0.02/2 = 0.01 m^2.5/s. - Linearise:
dq/dh = k/(2√h0) = q0/(2h0), soR = dh/dq = 2h0/q0 = 2 × 4 / 0.02 = 400 s/m². - Deviation equation:
A·dΔh/dt = Δqi − Δh/R, givingΔH(s)/ΔQi(s) = R/(ARs + 1). τ = A·R = 2 × 400 = 800 s.
Result: ΔH(s)/ΔQi(s) = 400/(800s + 1) m per (m³/s), a first-order lag with τ = 800 s. Note that R = 2h0/q0, not h0/q0: the square-root valve is twice as "stiff" in small-signal terms as its large-signal ratio suggests.
Common mistakes
- Forgetting that the transfer function assumes zero initial conditions, then trying to include them.
- Mixing up the two analogies — in force–current, mass maps to capacitance, not inductance.
- Linearising with
R = h0/q0instead of the slopedh/dq. - Dropping the sign of reaction forces when two masses are coupled through a spring: the spring force depends on
(x1 − x2). - Mixing grams and kilograms, or µF and F, in
ωnandζcalculations.
For GATE IN
Expect derivations of transfer functions for RLC networks, op-amp circuits, mechanical (translational and rotational) systems and thermal or liquid-level processes; identification of ωn and ζ from a model; force–voltage and force–current analogy matching; and linearisation of a nonlinear element about an operating point. Practise writing node or mass equations quickly and checking units at the end.
Quick check
- In the force–voltage analogy, what is the electrical analogue of a spring of stiffness K?
- A mass-spring-damper has
M = 2 kg,B = 3 N·s/m,K = 5 N/m. What is its static gainX/Fats = 0? - For the system in Q2, find
ωn. - Why must a linearised model be used only near its operating point?
Answers: 1. A capacitor with C = 1/K; 2. 1/K = 0.2 m/N; 3. √(5/2) = 1.58 rad/s; 4. Because the slope (gain) of the nonlinearity changes as the operating point changes.
Interview questions
All Control Systems interview questionsTry answering each one aloud before you open it.
1.What is mathematical modeling in the context of control systems?Concept
Mathematical modeling in control systems involves creating mathematical representations of physical systems to analyze and predict their behavior. This typically includes using differential equations, transfer functions, and state-space representations to describe the dynamics of the system. The goal is to understand how the system responds to various inputs and to design controllers that can achieve desired performance.
2.Explain the significance of transfer functions in control systems.Concept
Transfer functions are used to represent the relationship between the input and output of a linear time-invariant (LTI) system in the frequency domain. They simplify the analysis and design of control systems by allowing engineers to work with algebraic equations instead of differential equations. Transfer functions are particularly useful for understanding system stability, frequency response, and designing controllers.
3.How do state-space representations differ from transfer functions?Concept
A transfer function is an external, input-output description of an LTI system with zero initial conditions; it hides the internal variables and can cancel unobservable or uncontrollable modes. A state-space model uses a set of first-order differential equations in internal state variables, so it keeps initial conditions and every mode. It handles MIMO systems naturally (a transfer function needs a matrix for that), extends to time-varying and nonlinear systems, and is the basis for controllability, observability and state-feedback design.
4.Why is linearization used in the modeling of physical systems?Application
Linearization is used to approximate a non-linear system by a linear model around a specific operating point. This simplification allows engineers to apply linear control techniques, which are well-developed and easier to implement. Linearization is particularly useful when the system operates near a steady state, where the non-linear effects are minimal.
5.What happens if a system is not properly modeled in a control system design?Application
If a system is not properly modeled, the designed controller may not perform as expected, leading to poor system performance or instability. Inaccurate models can result in incorrect predictions of system behavior, causing issues such as overshoot, oscillations, or even system failure. Accurate modeling is crucial for ensuring that the control system meets performance specifications and operates safely.
6.Why are differential equations important in the mathematical modeling of physical systems?Application
Differential equations are fundamental in modeling the dynamic behavior of physical systems, as they describe how system states change over time. They capture the relationship between inputs, outputs, and internal states, allowing engineers to predict system responses to various inputs. Solving these equations provides insights into system stability, transient response, and steady-state behavior.
7.Explain how Laplace transforms are used in control systems.Concept
Laplace transforms are used to convert differential equations into algebraic equations in the s-domain, simplifying the analysis and design of control systems. This transformation allows engineers to work with transfer functions and perform operations like convolution and differentiation more easily. Laplace transforms are particularly useful for analyzing system stability and transient response.
8.A mass-spring-damper system has a mass of 2 kg, a damping coefficient of 3 Ns/m, and a spring constant of 5 N/m. Write the differential equation representing the system.Numerical
The differential equation for a mass-spring-damper system is given by: m·d²x/dt² + c·dx/dt + k·x = F(t). Substituting the given values, we have: 2·d²x/dt² + 3·dx/dt + 5·x = F(t), where x is the displacement and F(t) is the external force applied to the system.
9.Calculate the natural frequency of a system with a spring constant of 10 N/m and a mass of 1 kg.Numerical
The natural frequency (ω_n) of a system is given by the formula: ω_n = √(k/m). Substituting the given values, ω_n = √(10/1) = √10 rad/s.
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