CN112039520A - Digital phase-locked loop based on fuzzy RBF self-adaptive control - Google Patents

Digital phase-locked loop based on fuzzy RBF self-adaptive control Download PDF

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CN112039520A
CN112039520A CN202010935572.4A CN202010935572A CN112039520A CN 112039520 A CN112039520 A CN 112039520A CN 202010935572 A CN202010935572 A CN 202010935572A CN 112039520 A CN112039520 A CN 112039520A
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贠卫国
张梦豪
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Xian University of Architecture and Technology
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    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03L—AUTOMATIC CONTROL, STARTING, SYNCHRONISATION OR STABILISATION OF GENERATORS OF ELECTRONIC OSCILLATIONS OR PULSES
    • H03L7/00—Automatic control of frequency or phase; Synchronisation
    • H03L7/06—Automatic control of frequency or phase; Synchronisation using a reference signal applied to a frequency- or phase-locked loop
    • H03L7/08—Details of the phase-locked loop
    • H03L7/099—Details of the phase-locked loop concerning mainly the controlled oscillator of the loop
    • H03L7/0991—Details of the phase-locked loop concerning mainly the controlled oscillator of the loop the oscillator being a digital oscillator, e.g. composed of a fixed oscillator followed by a variable frequency divider

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Abstract

A digital phase-locked loop based on fuzzy RBF self-adaptive control comprises a phase discriminator, wherein a phase difference signal output by the phase discriminator passes through a low-pass filter to filter a high-frequency signal, a low-frequency phase error signal is transmitted to a loop filter, the low-frequency error signal and the change of the error signal are input into the fuzzy RBF self-adaptive controller, the fuzzy RBF self-adaptive controller generates proportional and integral adjusting parameters according to the two input signals, then the two adjusting parameters are transmitted to the loop filter, the loop filter carries out filtering on the error signal output by the filter again, in addition, the parameters are adjusted in an online self-adaptive mode according to the adjusting parameters output by the fuzzy RBF self-adaptive controller, finally, the loop filter outputs a control voltage, and the control voltage is input into a numerical control oscillator to adjust the numerical control to be the output frequency of. The invention improves the dynamic range of the phase-locked loop system, accelerates the locking speed of the phase-locked loop and improves the working stability of the phase-locked loop system.

Description

Digital phase-locked loop based on fuzzy RBF self-adaptive control
Technical Field
The invention relates to the technical field of digital phase-locked loops, in particular to a digital phase-locked loop based on fuzzy RBF (radial basis function) self-adaptive control.
Background
The function of the phase locked loop is to generate a control system that has a relationship with the frequency and phase of the input signal.
The analog phase-locked loop has the defects of aging of devices and high possibility of damage, so that the digital phase-locked loop is more stable compared with the analog phase-locked loop.
However, for the conventional phase-locked loop, system parameters are fixed, and the system greatly depends on the experience of a designer.
In order to increase the dynamic range of the system, increase the locking speed of the phase-locked loop, and improve the stability of the phase-locked loop system, it is necessary to design a phase-locked loop system capable of adjusting parameters along with the change of the system operating state.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a digital phase-locked loop based on fuzzy RBF adaptive control, which improves the dynamic range of a phase-locked loop system, accelerates the locking speed of the phase-locked loop and improves the working stability of the phase-locked loop system.
In order to achieve the purpose, the invention adopts the technical scheme that:
a digital phase-locked loop based on fuzzy RBF self-adaptive control comprises a phase discriminator, a low-pass filter, a loop filter and a digital control oscillator, wherein the phase discriminator is used for detecting the phase difference between an input signal of the phase-locked loop and an output signal of the digital control oscillator, a high-frequency signal is filtered out after a phase difference signal output by the phase discriminator passes through the low-pass filter, and a low-frequency phase error signal is transmitted to the loop filter, and the low frequency error signal and the variation of the error signal are input to a fuzzy RBF adaptive controller, the fuzzy RBF adaptive controller generates proportional and integral adjusting parameters according to the two input signals, then transmits the two adjusting parameters to the loop filter, the loop filter filters the error signal output by the filter again, and according to the adjusting parameters output by the fuzzy RBF adaptive controller, and (3) adjusting parameters in an online self-adaptive manner, finally outputting control voltage by the loop filter, and adjusting the numerical control to be the output frequency of the oscillator after the control voltage is input into the numerical control oscillator.
The specific working flow of the digital phase-locked loop is as follows:
the first step is as follows:
assume that the phase detector input signal is: u shapei＝sin(wit+θi(t))；
The output signal of the numerically controlled oscillator is: u shapeo＝sin(wot+θo(t))；
The signals obtained by multiplying the two signals by the phase discriminator are as follows:
Figure BDA0002671797010000021
wherein sin (w)it+wot+θi(t)+θo(t)) is the sum frequency signal sin (w)it-wot+θi(t)-θo(t)) is a difference frequency signal;
assuming that the difference frequency signal is:
e(k)＝sin(wik-wok+θi(k)-θo(k))
the change in the difference frequency signal is:
ec(k)＝e(k)-e(k-1)；
the output signal of the phase discriminator is filtered by a filter to remove a sum frequency signal, a difference frequency signal e (k) is output to a loop filter, and the difference frequency signal e (k) and the change ec (k) of the difference frequency signal are input to a fuzzy RBF self-adaptive controller;
the second step is that:
the first layer for the fuzzy RBF adaptive controller is the input layer:
each node of the layer is directly connected with each component of the input quantity, the input quantity is transmitted to the next layer, and the input and the output of each node are expressed as follows: f. of1(i)＝xi(ii) a Wherein:
f1(i)＝e(k),f2(i)＝ec(k)。
the second layer of the fuzzy RBF self-adaptive controller is a fuzzy layer which adopts a Gaussian function as a membership function, cijAnd bjThe mean value and the standard deviation of membership functions of jth fuzzy several of ith input variable are respectively obtained; namely:
Figure BDA0002671797010000031
Figure BDA0002671797010000032
the third layer of the fuzzy RBF self-adaptive controller is a fuzzy reasoning layer to realize regular reasoning; the output of j of each node is the product of all input signals of all input nodes of the node;
Figure BDA0002671797010000033
in the formula:
Figure BDA0002671797010000034
Nithe number of the ith input membership function in the input layer, namely the number of the fuzzification layer nodes;
the fourth layer of the fuzzy RBF self-adaptive controller is an output layer for realizing conclusion reasoning, and the output layer is f4(l) Namely:
Figure BDA0002671797010000041
in the formula, l is the number of output layer nodes, and W is a connection weight matrix of the output layer nodes and each node of the third layer;
in the output layer: f. of4(1)＝ΔKp，f4(2)＝ΔKi；
Obtaining a proportional coefficient adjusting parameter: Δ Kp, integral coefficient adjustment parameter: after the delta Ki is obtained, the temperature of the alloy is adjusted,
the proportional integral coefficients in the loop filter are according to the rule: kp + delta Kp, Ki + delta Ki modify proportion and integral coefficient on line;
and finally, the loop filter outputs a control signal to control the frequency of the output signal of the voltage-controlled oscillator so as to achieve the purpose of tracking the frequency of the input signal.
The structure of the numerically controlled oscillator comprises a phase control word, wherein the phase control word is used for setting the local oscillator signal frequency of the numerically controlled oscillator, and if Pw is the phase control word of the numerically controlled oscillator, then:
Figure BDA0002671797010000042
in the formula: f. ofclkIs sampling of the systemFrequency, foIs the local oscillator frequency of the numerically controlled oscillator, and Bnco is the phase word width of the numerically controlled oscillator.
The fuzzy RBF controller belongs to a nonlinear controller, and adjusts the proportion and the integral coefficient of a loop filter in a phase-locked loop through dynamic parameters such as tracking error, error conversion rate and the like, so as to achieve the purpose of improving the operation effect of a system;
the method comprises the following concrete steps:
the first step is as follows: determining the structure of the fuzzy controller as a two-dimensional fuzzy controller, and designing the fuzzy controller as a two-input two-output fuzzy controller;
the second step is that: determining an initial discourse domain: the input is error e (k), error change ec (k), and the output is proportional adjustment coefficient delta Kp and integral adjustment coefficient delta Ki;
the third step: establishing a fuzzy control table:
respectively establishing a fuzzy rule table of a proportional coefficient Kp and a fuzzy rule table of an integral coefficient Ki; fuzzy subsets of e, ec, Kp, Ki in the table are { NB, NM, NS, ZO, PS, PM, PB };
namely { negative is large, negative is medium, negative is small, zero is small, positive is medium, positive is large }, and the seven levels correspond to seven intervals in the input and output theory domain of the fuzzy controller.
The fuzzy RBF controller comprises the following working procedures:
e (k) is an error value at the moment k, r (k) is a reference amplitude, y (k) is an amplitude demodulation output value at the moment k, ec (k) is an error change rate at the moment k, e (k-1) is an error value at the last moment, and delta Kp and delta Ki are respectively modified values of a proportional coefficient and an integral coefficient after fuzzy setting;
after Δ Kp, Δ Ki are obtained, the following are taken: and Kp + delta Kp, Ki + delta Ki and the proportional and integral coefficients are modified on line.
The fuzzy RBF self-adaptive controller has four layers which are respectively an input layer, a fuzzy inference layer and an output layer, and the functions of each layer are as follows:
a first layer: an input layer, each node of the layer is directly connected with each component of the input quantity, the input quantity is transmitted to the next layer, and the input and the output of each node are expressed as: f. of1(i)＝xi；
A second layer: a blurring layer using a Gaussian function as a membership function, cijAnd bjThe mean value and the standard deviation of membership functions of jth fuzzy several of ith input variable are respectively obtained; namely:
Figure BDA0002671797010000051
Figure BDA0002671797010000052
and a third layer: and the fuzzy inference layer realizes the regular inference. The output of j for each node is the product of all the input signals for all the input nodes for that node. Namely:
Figure BDA0002671797010000061
in the formula:
Figure BDA0002671797010000062
Nithe number of the ith input membership function in the input layer, namely the number of the fuzzification layer nodes.
A fourth output layer: and implementing conclusion reasoning. Output layer is f4(l) Namely:
Figure BDA0002671797010000063
in the formula, l is the number of output layer nodes, and W is a connection weight matrix of the output layer nodes and each node of the third layer.
The input of the fuzzy RBF self-adaptive controller is an error signal output by a filter and the change rate, x, of the error signal1＝e,x2Ec, the output signal is a proportional regulation coefficient and an integral regulation coefficient, f4(1)＝ΔKp，f4(2)＝ΔKi。
The invention has the beneficial effects that:
the invention adds fuzzy RBF self-adaptive control in the traditional digital phase-locked loop system, and the fuzzy RBF self-adaptive controller dynamically adjusts the proportional-integral coefficient of the loop filter according to the error signal output by the phase discriminator and the change of the error signal. By adjusting the proportional-integral coefficient in real time, the time for the system to reach a steady state is reduced, and the tracking speed of the system is improved.
Drawings
Fig. 1 is a schematic diagram of a digital phase-locked loop system based on fuzzy RBF adaptive control.
Fig. 2 is a flow chart of the fuzzy RBF adaptive controller operation.
Fig. 3 is a schematic diagram of a digitally controlled oscillator.
Fig. 4 is a schematic diagram of a loop filter structure.
FIG. 5 is a block diagram of a fuzzy RBF controller.
Fig. 6 does not add the fuzzy RBF adaptive control error simulation result.
Figure 7 adds fuzzy RBF adaptive control error simulation results.
Table 1 Kp fuzzy rule table.
Table 2 Ki fuzzy rule table.
Detailed Description
The present invention will be described in further detail with reference to examples.
In fig. 1, a phase detector detects a phase difference between a phase-locked loop input signal and a digitally controlled oscillator output signal. The phase difference signal output by the phase discriminator passes through a filter to filter a high-frequency signal, a low-frequency phase error signal is transmitted to a loop filter, the low-frequency error signal and the change of the error signal are input into a fuzzy RBF self-adaptive controller, the fuzzy RBF self-adaptive controller generates a proportion and an integral adjusting parameter according to the two input signals, and then the two adjusting parameters are transmitted to the loop filter. And the loop filter carries out filtering again on the error signal output by the filter, and carries out online adaptive parameter adjustment according to the adjustment parameter output by the fuzzy RBF adaptive controller. And finally, the loop filter outputs a control voltage, and the control voltage is input into the numerical control oscillator to adjust the numerical control to be the output frequency of the oscillator.
Specifically, the method comprises the following steps:
assume that the phase detector input signal is: u shapei＝sin(wit+θi(t))
The input signals of the numerically controlled oscillator are: u shapeo＝sin(wot+θo(t))
The signals obtained by multiplying the two signals by the phase discriminator are as follows:
Figure BDA0002671797010000081
wherein sin (w)it+wot+θi(t)+θo(t)) is the sum frequency signal sin (w)it-wot+θi(t)-θo(t)) is a difference frequency signal.
Assuming that the difference frequency signal is:
Figure BDA0002671797010000082
the change in the difference frequency signal is:
ec(k)＝e(k)-e(k-1)
the output signal of the phase detector filters the sum frequency signal after passing through a filter, and outputs a difference frequency signal e (k) to a loop filter, and inputs the difference frequency signal e (k) and the change ec (k) of the difference frequency signal to a fuzzy RBF self-adaptive controller.
The inputs to the fuzzy RBF adaptive controller are: f. of1(i)＝e(k),f2(i)＝ec(k)。
The second layer calculates:
Figure BDA0002671797010000083
Figure BDA0002671797010000084
and a third layer: a fuzzy inference layer:
Figure BDA0002671797010000085
in the formula:
Figure BDA0002671797010000086
Nithe number of the ith input membership function in the input layer, namely the number of the fuzzification layer nodes.
A fourth output layer: and implementing conclusion reasoning. Output layer is f4(l) Namely:
Figure BDA0002671797010000091
in the formula, l is the number of output layer nodes, and W is a connection weight matrix of the output layer nodes and each node of the third layer.
In the output layer: f. of4(1)＝ΔKp，f4(2)＝ΔKi。
Obtaining a proportional coefficient adjusting parameter: Δ Kp, integral coefficient adjustment parameter: after the delta Ki is obtained, the temperature of the alloy is adjusted,
the proportional integral coefficients in the loop filter are according to the rule: and Kp + delta Kp, Ki + delta Ki and the proportional and integral coefficients are modified on line.
And finally, the loop filter outputs a control signal to control the frequency of the output signal of the voltage-controlled oscillator. So as to achieve the purpose of tracking the frequency of the input signal.
Fig. 3 is a schematic diagram of a structure of a numerically controlled oscillator, wherein a phase control word is used to set a local oscillation signal frequency of the numerically controlled oscillator. Assuming Pw is the phase control word of the digitally controlled oscillator, then:
Figure BDA0002671797010000092
in the formula: f. ofclkIs the sampling frequency of the system, foIs the local oscillator frequency of the numerically controlled oscillator, Bnco is the phase word of the numerically controlled oscillatorAnd (4) wide.
The fuzzy RBF controller belongs to a nonlinear controller. The fuzzy RBF controller adjusts the proportion and the integral coefficient of a loop filter in the phase-locked loop through dynamic parameters such as tracking error, error conversion rate and the like, thereby achieving the purpose of improving the operation effect of the system.
The method comprises the following concrete steps:
the first step is as follows: and determining the structure of the fuzzy controller as a two-dimensional fuzzy controller which is designed as a two-input two-output fuzzy controller.
The second step is that: determining an initial discourse domain: the input is error e (k), error change ec (k), and the output is proportional regulating coefficient delta Kp and integral regulating coefficient delta Ki.
The third step: establishing a fuzzy control table:
the fuzzy rule table of the proportional coefficient Kp is shown in table 1, and the fuzzy rule table of the integral coefficient Ki is shown in table 2.
The fuzzy subsets of e, ec, Kp, Ki in the table are { NB, NM, NS, ZO, PS, PM, PB }.
Namely { negative is large, negative is medium, negative is small, zero is small, positive is medium, positive is large }, and the seven levels correspond to seven intervals in the input and output theory domain of the fuzzy controller.
The fuzzy RBF controller workflow is shown in figure 2.
As shown in fig. 2 and fig. 4: e (k) is an error value at the time k, r (k) is a reference amplitude, y (k) is an amplitude demodulation output value at the time k, ec (k) is an error change rate at the time k, e (k-1) is an error value at the last time, and delta Kp and delta Ki are respectively modified values of the proportional coefficient and the integral coefficient after fuzzy setting.
After Δ Kp, Δ Ki are obtained, the following are taken: and Kp + delta Kp, Ki + delta Ki and the proportional and integral coefficients are modified on line.
The fuzzy RBF self-adaptive controller has four layers, namely an input layer, a fuzzy inference layer and an output layer.
The signal propagation and the functions of each layer of the fuzzy RBF self-adaptive controller are respectively as follows:
an input layer: the nodes of the layer are directly connected to the components of the input quantity, the input quantity is transmitted to the next layer, the input of each nodeThe output is represented as: f. of1(i)＝xi。
A second layer: the fuzzy layer uses Gaussian function as membership function, cijAnd bjThe mean and standard deviation of the membership functions of the ith input variable, the jth fuzzy several, respectively. Namely:
Figure BDA0002671797010000101
Figure BDA0002671797010000111
and a third layer: and the fuzzy inference layer realizes the regular inference. The output of j for each node is the product of all the input signals for all the input nodes for that node. Namely:
Figure BDA0002671797010000112
in the formula:
Figure BDA0002671797010000113
Nithe number of the ith input membership function in the input layer, namely the number of the fuzzification layer nodes.
A fourth output layer: and implementing conclusion reasoning. Output layer is f4(l) Namely:
Figure BDA0002671797010000114
in the formula, l is the number of output layer nodes, and W is a connection weight matrix of the output layer nodes and each node of the third layer.
The input of the fuzzy RBF adaptive controller is the error signal output by the filter and the rate of change of the error signal. As shown in fig. 5: x is the number of1＝e,x2Ec. The output signals are proportional regulating coefficient and integral regulating coefficient. As shown in fig. 5 at f4(1)＝ΔKp，f4(2)＝ΔKi。
The invention adds a fuzzy RBF self-adaptive controller on the basis of the traditional digital phase-locked loop, and the RBF self-adaptive controller self-adaptively adjusts the system parameters in real time according to the error signals in the system and the conversion rate of the error signals, thereby greatly improving the dynamic range and the system stability.
The simuluinink simulation model was constructed as shown in figure 1. Fig. 6 and 7 are graphs showing simulation results of system error results of adaptive control without adding fuzzy RBF and adaptive control with adding fuzzy RBF, respectively. The simulation time is 8s, the disturbance is added at the 4 th s, and the system output value is twice that of the previous system output value. According to the simulation result, the following results are obtained: the drive system without the addition of fuzzy RBF control reached steady state usage for about 3.19 seconds, and the system reached steady state usage for about 0.56 seconds after the addition of fuzzy control. After disturbance is added in the 4 th s, the system without adding the fuzzy control RBF system needs 2.42s to reach the steady state again, and the system with the fuzzy control system needs only 0.17s to reach the steady state again.
Figure BDA0002671797010000121

Claims (7)

1. A digital phase-locked loop based on fuzzy RBF adaptive control is characterized by comprising a phase discriminator, wherein the phase discriminator is used for detecting the phase difference between an input signal of the phase-locked loop and an output signal of a digital controlled oscillator, a high-frequency signal is filtered out after a phase difference signal output by the phase discriminator passes through a low-pass filter, a low-frequency phase error signal is transmitted to a loop filter, the low-frequency error signal and the change of the error signal are input into the fuzzy RBF adaptive controller, the fuzzy RBF adaptive controller generates a proportion and an integral adjusting parameter according to the two input signals, then the two adjusting parameters are transmitted to the loop filter, the loop filter carries out secondary filtering on the error signal output by the filter, in addition, the parameters are adaptively adjusted on line according to the adjusting parameter output by the fuzzy RBF adaptive controller, and finally the loop filter outputs a control voltage, the control voltage is input into the numerically controlled oscillator to adjust the numerical control to be the oscillator output frequency.
2. The digital phase-locked loop based on fuzzy RBF adaptive control as claimed in claim 1, wherein said digital phase-locked loop has a specific working flow as follows:
the first step is as follows:
assume that the phase detector input signal is: u shapei＝sin(wit+θi(t))；
The output signal of the numerically controlled oscillator is: u shapeo＝sin(wot+θo(t))；
The signals obtained by multiplying the two signals by the phase discriminator are as follows:
Figure FDA0002671796000000011
wherein sin (w)it+wot+θi(t)+θo(t)) is the sum frequency signal sin (w)it-wot+θi(t)-θo(t)) is a difference frequency signal;
assuming that the difference frequency signal is:
e(k)＝sin(wik-wok+θi(k)-θo(k))
the change in the difference frequency signal is:
ec(k)＝e(k)-e(k-1)；
the output signal of the phase discriminator is filtered by a filter to remove a sum frequency signal, a difference frequency signal e (k) is output to a loop filter, and the difference frequency signal e (k) and the change ec (k) of the difference frequency signal are input to a fuzzy RBF self-adaptive controller;
the second step is that:
the first layer for the fuzzy RBF adaptive controller is the input layer:
each node of the layer is directly connected with each component of the input quantity, the input quantity is transmitted to the next layer, and the input and the output of each node are expressed as follows: f. of1(i)＝xi(ii) a Wherein:
f1(i)＝e(k)，f2(i)＝ec(k)。
the second layer of the fuzzy RBF adaptive controller isThe fuzzy layer uses Gaussian function as membership function, cijAnd bjThe mean value and the standard deviation of membership functions of jth fuzzy several of ith input variable are respectively obtained; namely:
Figure FDA0002671796000000021
Figure FDA0002671796000000022
the third layer of the fuzzy RBF self-adaptive controller is a fuzzy reasoning layer to realize regular reasoning; the output of j of each node is the product of all input signals of all input nodes of the node;
Figure FDA0002671796000000023
in the formula:
Figure FDA0002671796000000024
Nithe number of the ith input membership function in the input layer, namely the number of the fuzzification layer nodes;
the fourth layer of the fuzzy RBF self-adaptive controller is an output layer for realizing conclusion reasoning, and the output layer is f4(l) Namely:
Figure FDA0002671796000000031
in the formula: l is the number of output layer nodes, and W is a connection weight matrix of the output layer nodes and each node of the third layer;
in the output layer: f. of4(1)＝ΔKp，f4(2)＝ΔKi；
Obtaining a proportional coefficient adjusting parameter: Δ Kp, integral coefficient adjustment parameter: delta Ki later;
the proportional integral coefficients in the loop filter are according to the rule: kp + delta Kp, Ki + delta Ki modify proportion and integral coefficient on line;
and finally, the loop filter outputs a control signal to control the frequency of the output signal of the voltage-controlled oscillator so as to achieve the purpose of tracking the frequency of the input signal.
3. The digital phase-locked loop based on fuzzy RBF adaptive control as claimed in claim 1, wherein said digitally controlled oscillator structure comprises a phase control word, said phase control word is used to set the local oscillator signal frequency of the digitally controlled oscillator, and assuming that Pw is the phase control word of the digitally controlled oscillator:
Figure FDA0002671796000000032
in the formula: f. ofclkIs the sampling frequency of the system, foIs the local oscillator frequency of the numerically controlled oscillator, and Bnco is the phase word width of the numerically controlled oscillator.
4. The digital phase-locked loop based on fuzzy RBF adaptive control as claimed in claim 1, wherein said fuzzy RBF controller belongs to a non-linear controller, the fuzzy RBF controller adjusts the proportion and integral coefficient of the loop filter in the phase-locked loop through dynamic parameters such as tracking error and error transformation rate, so as to achieve the purpose of improving the operation effect of the system;
the method comprises the following concrete steps:
the first step is as follows: determining the structure of the fuzzy controller as a two-dimensional fuzzy controller, and designing the fuzzy controller as a two-input two-output fuzzy controller;
the second step is that: determining an initial discourse domain: the input is error e (k), error change ec (k), and the output is proportional adjustment coefficient delta Kp and integral adjustment coefficient delta Ki;
the third step: establishing a fuzzy control table:
respectively establishing a fuzzy rule table of a proportional coefficient Kp and a fuzzy rule table of an integral coefficient Ki; fuzzy subsets of e, ec, Kp, Ki in the table are { NB, NM, NS, ZO, PS, PM, PB }; namely { negative is large, negative is medium, negative is small, zero is small, positive is medium, positive is large }, and the seven levels correspond to seven intervals in the input and output theory domain of the fuzzy controller.
5. The digital phase-locked loop based on fuzzy RBF adaptive control as claimed in claim 4, wherein said fuzzy RBF controller work flow is as follows:
e (k) is an error value at the moment k, r (k) is a reference amplitude, y (k) is an amplitude demodulation output value at the moment k, ec (k) is an error change rate at the moment k, e (k-1) is an error value at the last moment, and delta Kp and delta Ki are respectively modified values of a proportional coefficient and an integral coefficient after fuzzy setting;
after Δ Kp, Δ Ki are obtained, the following are taken: and Kp + delta Kp, Ki + delta Ki and the proportional and integral coefficients are modified on line.
6. The digital phase-locked loop based on fuzzy RBF adaptive control as claimed in claim 4, wherein there are four layers in said fuzzy RBF adaptive controller, which are input layer, fuzzy inference layer and output layer, and each layer has the following functions:
a first layer: an input layer, each node of the layer is directly connected with each component of the input quantity, the input quantity is transmitted to the next layer, and the input and the output of each node are expressed as: f. of1(i)＝xi；
A second layer: a blurring layer using a Gaussian function as a membership function, cijAnd bjThe mean value and the standard deviation of membership functions of jth fuzzy several of ith input variable are respectively obtained; namely:
Figure FDA0002671796000000051
Figure FDA0002671796000000052
and a third layer: and the fuzzy inference layer realizes the regular inference. The output of j for each node is the product of all the input signals for all the input nodes for that node. Namely:
Figure FDA0002671796000000053
in the formula:
Figure FDA0002671796000000054
Nithe number of the ith input membership function in the input layer, namely the number of the fuzzification layer nodes.
A fourth output layer: and implementing conclusion reasoning. Output layer is f4(l) Namely:
Figure FDA0002671796000000055
in the formula, l is the number of output layer nodes, and W is a connection weight matrix of the output layer nodes and each node of the third layer.
7. The digital phase locked loop based on fuzzy RBF adaptive control as claimed in claim 4, wherein said fuzzy RBF adaptive controller inputs said error signal and said error signal rate of change, x, of said filter output1＝e，x2Ec, the output signal is a proportional regulation coefficient and an integral regulation coefficient, f4(1)＝ΔKp，f4(2)＝ΔKi。
CN202010935572.4A 2020-09-08 2020-09-08 Digital phase-locked loop based on fuzzy RBF self-adaptive control Pending CN112039520A (en)

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CN112583139A (en) * 2020-12-17 2021-03-30 无锡职业技术学院 WPT (wavelet packet transform) system based on fuzzy RBF (radial basis function) neural network and frequency tracking method
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