describing function


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describing function

[di′skrīb·iŋ ‚fəŋk·shən]
(control systems)
A function used to represent a nonlinear transfer function by an approximately equivalent linear transfer function; it is the ratio of the phasor representing the fundamental component of the output of the nonlinearity, determined by Fourier analysis, to the phasor representing a sinusoidal input signal.
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A detailed describing function analysis for theoretical support of the controller gain change method.
A theoretical justification through describing function analysis is also presented.
In this section, describing function analysis is used to provide a theoretical justification for the observed closed-loop behaviour due to changes in controller gain.
Further insights into the behaviour of such systems may be achieved through a describing function analysis (Cook, 1986).
m]) is the describing function that depends on the magnitude of the controller output, [X.
Thus, the describing function analysis predicts that an increase in controller gain will increase the frequency of limit cycle oscillation and conversely a reduction in the gain will reduce the frequency of limit cycle oscillation.
Comparison of Describing Function Analysis (DFA) Results with Simulation Results Figure 6 compares the frequencies and amplitudes of limit cycles predicted by DFA with those obtained from simulation.
includes vital nonlinear topics such as limit cycle prediction and forced oscillations analysis on the basis of the describing function method and absolute stability analysis by means of the primary classical frequency-domain criteria (e.
Working hierarchically from most important information to the least, Anthony describes Bi-design, overall processing flows and network browsers, network site servers, server processing reorganization, and progresses to his system of derived simplicity, describing function set network representation, database representation, data relationship management and a dream language.
It systematically analyzes several fuzzy proportional-integral-derivative (PID) control systems and state space control and also takes on self-learning control mechanisms while covering fuzzy reasoning, fuzzy control, linear and non-linear fuzzy PID control, stability analysis by describing functions, and supervisory control.