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Step response
Step response
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A typical step response for a second order system, illustrating overshoot, followed by ringing, all subsiding within a settling time.

The step response of a system in a given initial state consists of the time evolution of its outputs when its control inputs are Heaviside step functions. In electronic engineering and control theory, step response is the time behaviour of the outputs of a general system when its inputs change from zero to one in a very short time. The concept can be extended to the abstract mathematical notion of a dynamical system using an evolution parameter.

From a practical standpoint, knowing how the system responds to a sudden input is important because large and possibly fast deviations from the long term steady state may have extreme effects on the component itself and on other portions of the overall system dependent on this component. In addition, the overall system cannot act until the component's output settles down to some vicinity of its final state, delaying the overall system response. Formally, knowing the step response of a dynamical system gives information on the stability of such a system, and on its ability to reach one stationary state when starting from another.

Formal mathematical description

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Figure 4: Black box representation of a dynamical system, its input and its step response.

This section provides a formal mathematical definition of step response in terms of the abstract mathematical concept of a dynamical system : all notations and assumptions required for the following description are listed here.

  • is the evolution parameter of the system, called "time" for the sake of simplicity,
  • is the state of the system at time , called "output" for the sake of simplicity,
  • is the dynamical system evolution function,
  • is the dynamical system initial state,
  • is the Heaviside step function

Nonlinear dynamical system

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For a general dynamical system, the step response is defined as follows:

It is the evolution function when the control inputs (or source term, or forcing inputs) are Heaviside functions: the notation emphasizes this concept showing H(t) as a subscript.

Linear dynamical system

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For a linear time-invariant (LTI) black box, let for notational convenience: the step response can be obtained by convolution of the Heaviside step function control and the impulse response h(t) of the system itself

which for an LTI system is equivalent to just integrating the latter. Conversely, for an LTI system, the derivative of the step response yields the impulse response:

However, these simple relations are not true for a non-linear or time-variant system.[1]

Time domain versus frequency domain

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Instead of frequency response, system performance may be specified in terms of parameters describing time-dependence of response. The step response can be described by the following quantities related to its time behavior,

In the case of linear dynamic systems, much can be inferred about the system from these characteristics. Below the step response of a simple two-pole amplifier is presented, and some of these terms are illustrated.

In LTI systems, the function that has the steepest slew rate that doesn't create overshoot or ringing is the Gaussian function. This is because it is the only function whose Fourier transform has the same shape.

Feedback amplifiers

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Figure 1: Ideal negative feedback model; open loop gain is AOL and feedback factor is β.

This section describes the step response of a simple negative feedback amplifier shown in Figure 1. The feedback amplifier consists of a main open-loop amplifier of gain AOL and a feedback loop governed by a feedback factor β. This feedback amplifier is analyzed to determine how its step response depends upon the time constants governing the response of the main amplifier, and upon the amount of feedback used.

A negative-feedback amplifier has gain given by (see negative feedback amplifier):

where AOL = open-loop gain, AFB = closed-loop gain (the gain with negative feedback present) and β = feedback factor.

With one dominant pole

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In many cases, the forward amplifier can be sufficiently well modeled in terms of a single dominant pole of time constant τ, that it, as an open-loop gain given by:

with zero-frequency gain A0 and angular frequency ω = 2πf. This forward amplifier has unit step response

,

an exponential approach from 0 toward the new equilibrium value of A0.

The one-pole amplifier's transfer function leads to the closed-loop gain:

This closed-loop gain is of the same form as the open-loop gain: a one-pole filter. Its step response is of the same form: an exponential decay toward the new equilibrium value. But the time constant of the closed-loop step function is τ / (1 + β A0), so it is faster than the forward amplifier's response by a factor of 1 + β A0:

As the feedback factor β is increased, the step response will get faster, until the original assumption of one dominant pole is no longer accurate. If there is a second pole, then as the closed-loop time constant approaches the time constant of the second pole, a two-pole analysis is needed.

Two-pole amplifiers

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In the case that the open-loop gain has two poles (two time constants, τ1, τ2), the step response is a bit more complicated. The open-loop gain is given by:

with zero-frequency gain A0 and angular frequency ω = 2πf.

Analysis

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The two-pole amplifier's transfer function leads to the closed-loop gain:

Figure 2: Conjugate pole locations for a two-pole feedback amplifier; Re(s) is the real axis and Im(s) is the imaginary axis.

The time dependence of the amplifier is easy to discover by switching variables to s = jω, whereupon the gain becomes:

The poles of this expression (that is, the zeros of the denominator) occur at:

which shows for large enough values of βA0 the square root becomes the square root of a negative number, that is the square root becomes imaginary, and the pole positions are complex conjugate numbers, either s+ or s; see Figure 2:

with

and

Using polar coordinates with the magnitude of the radius to the roots given by |s| (Figure 2):

and the angular coordinate φ is given by:

Tables of Laplace transforms show that the time response of such a system is composed of combinations of the two functions:

which is to say, the solutions are damped oscillations in time. In particular, the unit step response of the system is:[2]

which simplifies to

when A0 tends to infinity and the feedback factor β is one.

Notice that the damping of the response is set by ρ, that is, by the time constants of the open-loop amplifier. In contrast, the frequency of oscillation is set by μ, that is, by the feedback parameter through βA0. Because ρ is a sum of reciprocals of time constants, it is interesting to notice that ρ is dominated by the shorter of the two.

Results

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Figure 3: Step-response of a linear two-pole feedback amplifier; time is in units of 1/ρ, that is, in terms of the time constants of AOL; curves are plotted for three values of mu = μ, which is controlled by β.

Figure 3 shows the time response to a unit step input for three values of the parameter μ. It can be seen that the frequency of oscillation increases with μ, but the oscillations are contained between the two asymptotes set by the exponentials [ 1 − exp(−ρt) ] and [ 1 + exp(−ρt) ]. These asymptotes are determined by ρ and therefore by the time constants of the open-loop amplifier, independent of feedback.

The phenomenon of oscillation about the final value is called ringing. The overshoot is the maximum swing above final value, and clearly increases with μ. Likewise, the undershoot is the minimum swing below final value, again increasing with μ. The settling time is the time for departures from final value to sink below some specified level, say 10% of final value.

The dependence of settling time upon μ is not obvious, and the approximation of a two-pole system probably is not accurate enough to make any real-world conclusions about feedback dependence of settling time. However, the asymptotes [ 1 − exp(−ρt) ] and [ 1 + exp (−ρt) ] clearly impact settling time, and they are controlled by the time constants of the open-loop amplifier, particularly the shorter of the two time constants. That suggests that a specification on settling time must be met by appropriate design of the open-loop amplifier.

The two major conclusions from this analysis are:

  1. Feedback controls the amplitude of oscillation about final value for a given open-loop amplifier and given values of open-loop time constants, τ1 and τ2.
  2. The open-loop amplifier decides settling time. It sets the time scale of Figure 3, and the faster the open-loop amplifier, the faster this time scale.

As an aside, it may be noted that real-world departures from this linear two-pole model occur due to two major complications: first, real amplifiers have more than two poles, as well as zeros; and second, real amplifiers are nonlinear, so their step response changes with signal amplitude.

Figure 4: Step response for three values of α. Top: α  = 4; Center: α = 2; Bottom: α = 0.5. As α is reduced the pole separation reduces, and the overshoot increases.

Control of overshoot

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How overshoot may be controlled by appropriate parameter choices is discussed next.

Using the equations above, the amount of overshoot can be found by differentiating the step response and finding its maximum value. The result for maximum step response Smax is:[3]

The final value of the step response is 1, so the exponential is the actual overshoot itself. It is clear the overshoot is zero if μ = 0, which is the condition:

This quadratic is solved for the ratio of time constants by setting x = (τ1 / τ2)1/2 with the result

Because β A0 ≫ 1, the 1 in the square root can be dropped, and the result is

In words, the first time constant must be much larger than the second. To be more adventurous than a design allowing for no overshoot we can introduce a factor α in the above relation:

and let α be set by the amount of overshoot that is acceptable.

Figure 4 illustrates the procedure. Comparing the top panel (α = 4) with the lower panel (α = 0.5) shows lower values for α increase the rate of response, but increase overshoot. The case α = 2 (center panel) is the maximally flat design that shows no peaking in the Bode gain vs. frequency plot. That design has the rule of thumb built-in safety margin to deal with non-ideal realities like multiple poles (or zeros), nonlinearity (signal amplitude dependence) and manufacturing variations, any of which can lead to too much overshoot. The adjustment of the pole separation (that is, setting α) is the subject of frequency compensation, and one such method is pole splitting.

Control of settling time

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The amplitude of ringing in the step response in Figure 3 is governed by the damping factor exp(−ρt). That is, if we specify some acceptable step response deviation from final value, say Δ, that is:

this condition is satisfied regardless of the value of β AOL provided the time is longer than the settling time, say tS, given by:[4]

where the τ1 ≫ τ2 is applicable because of the overshoot control condition, which makes τ1 = αβAOL τ2. Often the settling time condition is referred to by saying the settling period is inversely proportional to the unity gain bandwidth, because 1/(2π τ2) is close to this bandwidth for an amplifier with typical dominant pole compensation. However, this result is more precise than this rule of thumb. As an example of this formula, if Δ = 1/e4 = 1.8 %, the settling time condition is tS = 8 τ2.

In general, control of overshoot sets the time constant ratio, and settling time tS sets τ2.[5][6][7]

System Identification using the Step Response: System with two real poles

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Step response of the system with . Measure the significant point , and .

This method uses significant points of the step response. There is no need to guess tangents to the measured Signal. The equations are derived using numerical simulations, determining some significant ratios and fitting parameters of nonlinear equations. See also.[8]

Here the steps:

  • Measure the system step-response of the system with an input step signal .
  • Determine the time-spans and where the step response reaches 25% and 75% of the steady state output value.
  • Determine the system steady-state gain with
  • Calculate
  • Determine the two time constants
  • Calculate the transfer function of the identified system within the Laplace-domain

Phase margin

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Figure 5: Bode gain plot to find phase margin; scales are logarithmic, so labeled separations are multiplicative factors. For example, f0 dB = βA0 × f1.

Next, the choice of pole ratio τ1/τ2 is related to the phase margin of the feedback amplifier.[9] The procedure outlined in the Bode plot article is followed. Figure 5 is the Bode gain plot for the two-pole amplifier in the range of frequencies up to the second pole position. The assumption behind Figure 5 is that the frequency f0 dB lies between the lowest pole at f1 = 1/(2πτ1) and the second pole at f2 = 1/(2πτ2). As indicated in Figure 5, this condition is satisfied for values of α ≥ 1.

Using Figure 5 the frequency (denoted by f0 dB) is found where the loop gain βA0 satisfies the unity gain or 0 dB condition, as defined by:

The slope of the downward leg of the gain plot is (20 dB/decade); for every factor of ten increase in frequency, the gain drops by the same factor:

The phase margin is the departure of the phase at f0 dB from −180°. Thus, the margin is:

Because f0 dB / f1βA0 ≫ 1, the term in f1 is 90°. That makes the phase margin:

In particular, for case α = 1, φm = 45°, and for α = 2, φm = 63.4°. Sansen[10] recommends α = 3, φm = 71.6° as a "good safety position to start with".

If α is increased by shortening τ2, the settling time tS also is shortened. If α is increased by lengthening τ1, the settling time tS is little altered. More commonly, both τ1 and τ2 change, for example if the technique of pole splitting is used.

As an aside, for an amplifier with more than two poles, the diagram of Figure 5 still may be made to fit the Bode plots by making f2 a fitting parameter, referred to as an "equivalent second pole" position.[11]

See also

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References and notes

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
In control systems engineering, the step response refers to the output behavior of a when subjected to a sudden input change from zero to a constant value, known as a step input or , assuming all initial conditions are zero prior to the input application. This response captures the system's transient dynamics and eventual steady-state value, providing a fundamental tool for analyzing how the system reacts to abrupt disturbances or set-point changes. Mathematically, for a system with H(s)H(s), the of the unit step response is Y(s)=H(s)/sY(s) = H(s) / s, where 1/s1/s represents the unit step input. The characteristics of the step response are quantified through several key performance metrics that evaluate the system's speed, accuracy, and stability. Rise time (trt_r) is defined as the duration required for the output to increase from 10% to 90% of its final steady-state value, indicating the system's responsiveness. Peak time (tpt_p) measures the time elapsed until the response reaches its first maximum value, particularly relevant for underdamped systems exhibiting oscillations. Settling time (tst_s) is the time needed for the output to enter and remain within a specified tolerance band, such as 2% of the steady-state value, reflecting how quickly the system stabilizes. Finally, percent overshoot (MpM_p) quantifies the maximum deviation above the steady-state value as a percentage, calculated as Mp=ymaxyssyss×100%M_p = \frac{y_{\max} - y_{ss}}{y_{ss}} \times 100\%, where excessive overshoot may indicate insufficient damping. These metrics are especially prominent in the analysis of first- and second-order systems, where the step response reveals inherent properties like the time constant for systems or the damping ratio and for second-order systems. For instance, underdamped second-order systems display oscillatory behavior with overshoot, while overdamped ones approach the more slowly without oscillation. In practice, step response analysis is integral to controller design, such as PID tuning via methods like Ziegler-Nichols, enabling engineers to optimize performance in applications ranging from to chemical processes. By studying these responses, systems can be tuned for desired trade-offs between speed, stability, and minimal error.

Fundamentals

Definition and Overview

The step response of a dynamic refers to the of its output when subjected to a input, assuming zero initial conditions prior to the step. This response captures the 's transient and steady-state behavior following an abrupt change, serving as a standard metric for evaluating how the transitions from one equilibrium to another. The concept of the step response was introduced by Karl Küpfmüller in 1928 as part of his analysis of feedback control systems in communications engineering. It emerged within early 20th-century and became a fundamental tool, later complemented by frequency-domain methods developed by and Hendrik Bode in the 1930s for assessing stability in feedback amplifiers and communication systems. The step response remains a fundamental test signal for assessing stability, transient dynamics, and steady-state accuracy across disciplines including , , and . A representative example is the step response of a RC low-pass filter, where the output voltage rises exponentially to approach its final value, reaching approximately 63% of that value after one τ=RC\tau = RC. This illustrates the system's inherent delay and smoothing characteristics in response to sudden inputs. While the definition assumes for precise predictability, nonlinear systems exhibit more complex step responses, such as or saturation effects.

Step Input Characteristics

The step input signal is fundamentally defined by the , commonly denoted as u(t)u(t), which provides an idealized representation of an instantaneous transition. Mathematically, it is expressed as u(t)={0t<01t0,u(t) = \begin{cases} 0 & t < 0 \\ 1 & t \geq 0 \end{cases}, with a unit amplitude that jumps discontinuously from zero to one at t=0t = 0. This definition captures the essence of a sudden onset without any preceding or transitional behavior, serving as the baseline for analyzing system responses to abrupt changes. In practical electronic circuits, the ideal is approximated by voltage jumps, such as applying a sudden change from 0 V to a target voltage level using a function generator or switch. However, physical constraints like parasitic capacitances, inductances, and driver slew rates prevent instantaneous transitions, resulting in a finite rise time—the duration for the signal to increase from 10% to 90% of its final value, often on the order of nanoseconds to microseconds depending on the circuit components. Similarly, in control systems, step inputs are realized through digital toggles, where a logic signal shifts from low (0) to high (1) states via microcontrollers or relays, though limited by switching delays and hardware propagation times. These approximations maintain the step's utility for testing while reflecting real-world limitations. The idealized abrupt change of the step input simplifies mathematical analysis by isolating the system's dynamic behavior from input transients, enabling clear identification of stability, settling times, and other performance metrics without confounding gradual ramps. This abstraction is particularly valuable in theoretical modeling, where exact discontinuities facilitate closed-form solutions via transforms like Laplace. Common variations of the step input extend its applicability: the unit step u(t)u(t) serves as the standard with amplitude 1; scaled versions Au(t)A u(t) adjust the magnitude to AA for testing different input levels; and delayed forms u(tτ)u(t - \tau) shift the transition to time τ>0\tau > 0, accommodating scenarios with onset delays. These modifications allow tailored excitation while preserving the core sudden-change characteristic. In the , the of the reveals its spectral properties as F{u(t)}(ω)=πδ(ω)+1jω,\mathcal{F}\{u(t)\}(\omega) = \pi \delta(\omega) + \frac{1}{j \omega}, where the Dirac delta at ω=0\omega = 0 represents the DC component, and the 1/(jω)1/(j \omega) term indicates a continuous spectrum across all frequencies, highlighting the step's role in system stimulation.

Mathematical Formulation

Linear Systems

In linear time-invariant (LTI) systems, the step response can be computed using the transfer function approach, where the output y(t)y(t) to a unit step input u(t)u(t) is given by the of H(s)/sH(s)/s, with H(s)H(s) denoting the system's and L1\mathcal{L}^{-1} the inverse Laplace operator. This method leverages the Laplace domain to simplify the analysis of , transforming the convolution integral into an algebraic multiplication. Alternatively, LTI systems are modeled by linear differential equations of the form dnydtn+an1dn1ydtn1++a0y=b0u(t)\frac{d^n y}{dt^n} + a_{n-1} \frac{d^{n-1} y}{dt^{n-1}} + \cdots + a_0 y = b_0 u(t) for an nth-order system, where the coefficients aia_i and b0b_0 characterize the system parameters. Solutions to this equation for a step input u(t)u(t) are obtained via Laplace transformation, yielding Y(s)=H(s)sY(s) = \frac{H(s)}{s} assuming zero initial conditions, or through state-space representations that evolve the system state vector x(t)\mathbf{x}(t) as x˙(t)=Ax(t)+Bu(t)\dot{\mathbf{x}}(t) = A \mathbf{x}(t) + B u(t) with output y(t)=Cx(t)+Du(t)y(t) = C \mathbf{x}(t) + D u(t), integrated over time or transformed to the s-domain. A canonical example is the first-order LTI system with transfer function H(s)=1τs+1H(s) = \frac{1}{\tau s + 1}, where the time constant τ\tau represents the reciprocal of the pole location, derived as τ=1/a\tau = 1/a from the differential equation τy˙(t)+y(t)=u(t)\tau \dot{y}(t) + y(t) = u(t). For a unit step input, the step response is y(t)=1et/τy(t) = 1 - e^{-t/\tau} for t0t \geq 0, obtained by applying the inverse Laplace transform to Y(s)=1s(τs+1)Y(s) = \frac{1}{s(\tau s + 1)} via partial fraction decomposition. This exponential form illustrates the system's approach to steady-state value 1, with τ\tau quantifying the response speed as the time to reach approximately 63% of the final value. The linearity of LTI systems enables the , allowing the total step response to be decomposed into the homogeneous solution (transient behavior satisfying the unforced ) plus a particular solution (steady-state response to the constant step input), as the is linear and thus preserves addition and scaling of solutions. This decomposition simplifies solving higher-order by first finding the general homogeneous solution via characteristic roots and then a constant particular solution matching the step's DC gain.

Nonlinear Systems

In nonlinear dynamical systems, the behavior is typically modeled by the state-space equations x˙=f(x,u)\dot{x} = f(x, u) and y=g(x)y = g(x), where xRnx \in \mathbb{R}^n is the state vector, uRmu \in \mathbb{R}^m is the input vector, ff and gg are nonlinear functions, and a step input u(t)=usH(t)u(t) = u_s H(t) (with H(t)H(t) as the Heaviside function) generally produces responses that violate the inherent to linear systems. This non-superposability arises because the system's dynamics depend nonlinearly on both state and input, leading to trajectories that cannot be decomposed into sums of individual responses. Analyzing step responses in such systems presents significant challenges, as no closed-form Laplace transform methods exist due to the state-dependent coefficients that prevent straightforward input-output relations. Moreover, these responses exhibit high sensitivity to conditions, where small variations in x(0)x(0) can lead to substantially different outcomes, and may involve bifurcations that alter the qualitative nature of the dynamics as parameters change. To approximate step responses, around an (typically an equilibrium xˉ\bar{x} where f(xˉ,us)=0f(\bar{x}, u_s) = 0) is commonly employed for small perturbations δx=xxˉ\delta x = x - \bar{x} and δu=uus\delta u = u - u_s. The matrix A=fxxˉ,usA = \frac{\partial f}{\partial x} \big|_{\bar{x}, u_s}
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