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Step response
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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
[edit]
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
[edit]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
[edit]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
[edit]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
[edit]
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
[edit]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
[edit]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
[edit]The two-pole amplifier's transfer function leads to the closed-loop gain:

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
[edit]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:
- 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.
- 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.
Control of overshoot
[edit]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
[edit]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
[edit]
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
[edit]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 / f1 = βA0 ≫ 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
[edit]References and notes
[edit]- ^ Yuriy Shmaliy (2007). Continuous-Time Systems. Springer Science & Business Media. p. 46. ISBN 978-1-4020-6272-8.
- ^ Benjamin C Kuo & Golnaraghi F (2003). Automatic control systems (Eighth ed.). New York: Wiley. p. 253. ISBN 0-471-13476-7.
- ^ Benjamin C Kuo & Golnaraghi F (2003). p. 259. Wiley. ISBN 0-471-13476-7.
- ^ This estimate is a bit conservative (long) because the factor 1 /sin(φ) in the overshoot contribution to S (t) has been replaced by 1 /sin(φ) ≈ 1.
- ^ David A. Johns & Martin K W (1997). Analog integrated circuit design. New York: Wiley. pp. 234–235. ISBN 0-471-14448-7.
- ^ Willy M C Sansen (2006). Analog design essentials. Dordrecht, The Netherlands: Springer. p. §0528 p. 163. ISBN 0-387-25746-2.
- ^ According to Johns and Martin, op. cit., settling time is significant in switched-capacitor circuits, for example, where an op amp settling time must be less than half a clock period for sufficiently rapid charge transfer.
- ^ "Identification of a damped PT2 system | Hackaday.io". hackaday.io. Retrieved 2018-08-06.
- ^ The gain margin of the amplifier cannot be found using a two-pole model, because gain margin requires determination of the frequency f180 where the gain flips sign, and this never happens in a two-pole system. If we know f180 for the amplifier at hand, the gain margin can be found approximately, but f180 then depends on the third and higher pole positions, as does the gain margin, unlike the estimate of phase margin, which is a two-pole estimate.
- ^ Willy M C Sansen (2006-11-30). §0526 p. 162. Springer. ISBN 0-387-25746-2.
- ^ Gaetano Palumbo & Pennisi S (2002). Feedback amplifiers: theory and design. Boston/Dordrecht/London: Kluwer Academic Press. pp. § 4.4 pp. 97–98. ISBN 0-7923-7643-9.
Further reading
[edit]- Robert I. Demrow Settling time of operational amplifiers [1]
- Cezmi Kayabasi Settling time measurement techniques achieving high precision at high speeds [2]
- Vladimir Igorevic Arnol'd "Ordinary differential equations", various editions from MIT Press and from Springer Verlag, chapter 1 "Fundamental concepts"
External links
[edit]Step response
View on GrokipediaFundamentals
Definition and Overview
The step response of a dynamic system refers to the time evolution of its output when subjected to a Heaviside step function input, assuming zero initial conditions prior to the step.[1] This response captures the system's transient and steady-state behavior following an abrupt change, serving as a standard metric for evaluating how the system transitions from one equilibrium to another.[6] 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.[11] It emerged within early 20th-century control theory and became a fundamental tool, later complemented by frequency-domain methods developed by Harry Nyquist 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 control engineering, signal processing, and electronics.[6] A representative example is the step response of a first-order RC low-pass filter, where the output voltage rises exponentially to approach its final value, reaching approximately 63% of that value after one time constant .[6] This illustrates the system's inherent delay and smoothing characteristics in response to sudden inputs. While the definition assumes linearity for precise predictability, nonlinear systems exhibit more complex step responses, such as asymmetry or saturation effects.Step Input Characteristics
The step input signal is fundamentally defined by the Heaviside step function, commonly denoted as , which provides an idealized representation of an instantaneous transition. Mathematically, it is expressed as with a unit amplitude that jumps discontinuously from zero to one at . 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.[12][13] In practical electronic circuits, the ideal Heaviside step 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.[14][15][16] 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.[17] Common variations of the step input extend its applicability: the unit step serves as the standard with amplitude 1; scaled versions adjust the magnitude to for testing different input levels; and delayed forms shift the transition to time , accommodating scenarios with onset delays. These modifications allow tailored excitation while preserving the core sudden-change characteristic.[18] In the frequency domain, the Fourier transform of the unit step function reveals its spectral properties as where the Dirac delta at represents the DC component, and the term indicates a continuous spectrum across all frequencies, highlighting the step's role in broadband system stimulation.[19]Mathematical Formulation
Linear Systems
In linear time-invariant (LTI) systems, the step response can be computed using the transfer function approach, where the output to a unit step input is given by the inverse Laplace transform of , with denoting the system's transfer function and the inverse Laplace operator.[1] This method leverages the Laplace domain to simplify the analysis of system dynamics, transforming the convolution integral into an algebraic multiplication.[20] Alternatively, LTI systems are modeled by linear differential equations of the form for an nth-order system, where the coefficients and characterize the system parameters.[21] Solutions to this equation for a step input are obtained via Laplace transformation, yielding assuming zero initial conditions, or through state-space representations that evolve the system state vector as with output , integrated over time or transformed to the s-domain.[22] A canonical example is the first-order LTI system with transfer function , where the time constant represents the reciprocal of the pole location, derived as from the differential equation .[6] For a unit step input, the step response is for , obtained by applying the inverse Laplace transform to via partial fraction decomposition.[1] This exponential form illustrates the system's approach to steady-state value 1, with quantifying the response speed as the time to reach approximately 63% of the final value.[7] The linearity of LTI systems enables the superposition principle, allowing the total step response to be decomposed into the homogeneous solution (transient behavior satisfying the unforced equation) plus a particular solution (steady-state response to the constant step input), as the differential operator is linear and thus preserves addition and scaling of solutions.[23] This decomposition simplifies solving higher-order equations by first finding the general homogeneous solution via characteristic roots and then a constant particular solution matching the step's DC gain.[24]Nonlinear Systems
In nonlinear dynamical systems, the behavior is typically modeled by the state-space equations and , where is the state vector, is the input vector, and are nonlinear functions, and a step input (with as the Heaviside function) generally produces responses that violate the superposition principle 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.[25] Moreover, these responses exhibit high sensitivity to initial conditions, where small variations in 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, linearization around an operating point (typically an equilibrium where ) is commonly employed for small perturbations and . The Jacobian matrix and input matrix yield the linearized model , allowing application of linear tools like eigenvalue analysis for local behavior near the step-induced equilibrium.[26] For systems with potential periodic components, describing function analysis provides another approximation by representing the nonlinearity's response to a sinusoidal input via its first harmonic, enabling prediction of limit cycles through intersection conditions in the Nyquist plane, such as , where is the describing function amplitude and is the linear part's transfer function.[27] A representative example is the Van der Pol oscillator, governed by with , where a step input drives the system from rest toward a stable limit cycle characterized by sustained, nearly sinusoidal oscillations of amplitude approximately 2, independent of the step magnitude for moderate .[28] Qualitatively, the response transitions from initial exponential growth (due to negative damping for small ) to relaxation oscillations that settle onto the limit cycle, illustrating how nonlinearity sustains periodic motion without decay, in contrast to the damped settling of linear oscillators.[28]Response Analysis
Time Domain Features
The time domain features of a step response characterize the transient and steady-state behaviors of a linear time-invariant system, providing quantitative measures of speed, stability, and accuracy in reaching the desired output. Key metrics include rise time, defined as the duration for the response to increase from 10% to 90% of the steady-state value, which indicates the system's initial speed of response.[29] Peak time is the interval from the step input application to the first occurrence of the maximum overshoot value, relevant primarily for underdamped systems exhibiting oscillations.[4] Overshoot percentage quantifies the extent of deviation beyond the steady-state value, calculated aswhere is the peak response and is the steady-state value; this metric highlights potential instability or ringing in the waveform.[30] Settling time measures the period required for the response to enter and remain within a specified error band, typically 2% or 5% of the steady-state value, serving as an indicator of overall convergence speed.[6] Steady-state analysis of the step response focuses on the long-term output value and associated errors, determined using the final value theorem, which states that for a stable system,
where is the Laplace transform of the output.[31] The steady-state error for a unit step input depends on the system type, defined by the number of integrators (poles at the origin) in the open-loop transfer function. Type 0 systems exhibit a finite non-zero steady-state error of , where is the position error constant; type 1 systems achieve zero error for step inputs due to one integrator; and type 2 systems also yield zero error for steps, with the additional capability for zero error on ramp inputs.[32] Waveform interpretation in the time domain distinguishes between monotonic and oscillatory responses, influenced by pole locations in the s-plane. Systems with all real poles produce monotonic responses that approach the steady-state value without overshoot or ringing, reflecting overdamped or critically damped behavior. In contrast, systems with complex conjugate poles generate oscillatory responses, where the imaginary part determines the oscillation frequency and the real part governs the decay envelope, often leading to damped sinusoids in the transient phase.[33] Measurement standards for these features ensure precision in experimental and simulation data, as outlined in IEEE Std 1057-2017, which defines rise time as the interval between 10% and 90% of the step amplitude, settling time as the duration to remain within a specified percentage band (commonly 0.1% to 5%), and overshoot as the maximum deviation beyond the final value relative to the step height. These definitions facilitate consistent evaluation across applications in control and signal processing.[34]
Frequency Domain Perspectives
In the frequency domain, the step response of a linear time-invariant (LTI) system is fundamentally linked to its impulse response. Specifically, the step response is obtained as the time integral of the impulse response , given byfor . This relationship arises from the convolution integral for LTI systems, where the unit step input is the integral of the Dirac delta function, making the step response the cumulative effect of the impulse response up to time .[35] This connection allows frequency-domain tools, such as the Fourier or Laplace transform, to analyze how spectral components shape the transient buildup observed in .[36] Bode plots offer practical correlations between frequency response metrics and key step response traits. The closed-loop bandwidth , defined as the frequency where the magnitude drops to dB, approximates the inverse of the 10%-90% rise time via for typical second-order systems, indicating that wider bandwidths enable faster rise times but may introduce higher-frequency noise sensitivity.[37] Additionally, the phase margin , measured at the gain crossover frequency, relates to overshoot through the damping ratio , with for ; larger phase margins thus reduce percent overshoot by increasing damping and mitigating oscillations in the step response.[38] These approximations guide controller design by predicting time-domain performance from open-loop frequency data.[39] The Nyquist stability criterion provides another frequency-domain perspective, plotting the open-loop transfer function to assess closed-loop stability. Instability occurs if the plot encircles the critical point in the complex plane (with encirclements equal to the number of right-half-plane poles for stability assessment), leading to unbounded or highly oscillatory step responses that fail to settle.[40] For stable systems, the absence of encirclement ensures the step response remains bounded, with the plot's proximity to hinting at damping levels that influence ringing.[41] In simulation and analysis, the Fast Fourier Transform (FFT) extracts frequency content directly from step response time data, identifying dominant modes and natural frequencies that underpin transient behaviors like ringing or settling.[42] This technique transforms the non-periodic step data into a spectrum, revealing how low- or high-frequency components contribute to rise time or overshoot, aiding system identification without explicit modeling.[43]