CN113671899A - Piezoelectric actuation vibration suppression method for element action unit - Google Patents
Piezoelectric actuation vibration suppression method for element action unit Download PDFInfo
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Abstract
The invention relates to the technical field of accurate control of numerical control machines, and discloses a method for suppressing piezoelectric actuation vibration of a meta-motion unit, which comprises the following steps: 1) decomposing the numerical control machine into mechanical element action units; 2) determining the specific composition of the mechanical element action unit; 3) the method comprises the steps of selecting a worm rotating element action unit in a numerical control machine tool as a controlled mechanical element action unit, measuring a vibration signal generated when the controlled unit operates by a measuring unit, transmitting the vibration signal to a control unit in real time, fuzzifying the measurement signal by the control unit, converting the fuzzification into a value of a universe of discourse, deducing by using fuzzy logic and fuzzy reasoning to obtain a corresponding fuzzy value, converting the fuzzy value into an analog voltage control signal by a fuzzy PID control program, amplifying the voltage by a power amplifier, driving a piezoelectric actuator to generate an inhibition signal, and transmitting the inhibition signal to the action unit, thereby realizing the inhibition of abnormal vibration of the controlled unit. The invention starts from the vibration suppression of the element action unit and improves the relative reliability of the element action unit and the whole machine of the numerical control machine.
Description
Technical Field
The invention relates to the technical field of accurate control of numerical control machines, in particular to a method for suppressing piezoelectric actuation vibration of a meta-motion unit.
Background
Various abnormal vibration and faults of the electromechanical products during operation can cause the increase of the maintenance time and delay time of the products, thereby reducing the usability and the use efficiency of the products. The worm rotating element action unit is one of the smallest motion units on the numerical control machine tool, and the reliability of the worm rotating element action unit is closely related to the reliability of the whole machine. However, the faults affecting the reliability of the worm rotary element action unit are generally classified as: the method is needed for inhibiting the vibration of the element action unit, improving the reliability of the element action unit and further improving the overall reliability of the numerical control machine.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, it is an object of the present invention to provide a method for suppressing piezoelectric actuation vibration of a unit cell.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for suppressing piezoelectric actuation vibration of a meta-motion unit includes the following steps:
1) directly decomposing the numerical control machine tool into mechanical element action units according to an FMA (frequency modulated analysis) structured decomposition method;
2) determining the specific composition of the mechanical element action unit according to the requirements of the numerical control machine tool and the definition of the mechanical element action unit;
3) the operation of the system is that firstly, a measuring unit measures a vibration signal generated when the controlled unit operates and transmits the vibration signal to a control unit in real time, the measuring signal is fuzzified and converted into a value of a universe of discourse by the control unit, then, inference is carried out by using fuzzy logic and fuzzy inference to obtain a corresponding fuzzy value, the obtained fuzzy value is converted into an analog voltage control signal by a fuzzy PID control program, and the analog voltage control signal is amplified by a power amplifier and then drives a piezoelectric actuator to generate an inhibition signal and transmit the inhibition signal to an actuating unit, so that the inhibition of abnormal vibration of the controlled unit is realized.
Furthermore, the vibration signal is processed by signal conditioning, filtering, A/D conversion and the like, and then transmitted to an upper computer (industrial personal computer), LabVIEW software in the upper computer performs rapid calculation processing on the obtained digital signal according to a fuzzy PID self-adaptive corresponding algorithm to obtain a control signal, the control signal is converted into an analog voltage signal by D/A, the analog voltage signal is used for driving a piezoelectric actuator after power amplification, and the suppression force is generated by utilizing the inverse piezoelectric effect of a PVDF film to suppress vibration generated in the operation process of the worm rotating element action unit so as to finish vibration suppression in the operation of the mechanical element action unit.
Further, the mechanical element action unit comprises a power source, a supporting piece, an executing piece, an intermediate transmission and a fastening piece.
Further, the measurement unit includes a vibration sensor.
Further, a fuzzy PID is used as a control method of the piezoelectric actuator, a fuzzy PID controller is set to be in a double-input three-output mode, the membership functions of the error E, the error change rate EC and the output variable of the operating system are designed to be triangular membership functions, and the triangular membership functions are described as follows:
taking N as 3, defining fuzzy subsets of errors E and error change rates EC as { positive large (PB), Positive Medium (PM), Positive Small (PS), Zero (ZE), Negative Small (NS), Negative Medium (NM), negative large (NB) }; the key of the controller is to find out three parameters K of PIDp、Ki、KdThe fuzzy relation between the deviation E and the error change rate EC utilizes a fuzzy control part in the controller to perform online real-time setting on three parameters of a PID control part; after output of the fuzzy controller is quantized, fuzzy subsets of control quantity of the fuzzy PID controller are obtained, wherein the fuzzy subsets are { positive large (PB), Positive Medium (PM), Positive Small (PS), Zero (ZE), Negative Small (NS), Negative Medium (NM) and negative large (NB) }. In the invention, the input values Kp, Ki and Kd of the PID control part are modified on line in real time according to the fuzzy control rule of the controller by continuously detecting E and EC in operation so as to meet different requirements of the deviation value E and the error change rate EC at different moments on the control system.
Preferably, the domain of the deviation E is classified as { -6, -5, -4, -3, -2,-1,0,1, 2, 3, 4, 5, 6}, the domain of error rate of change EC is given the following fuzzy classification, which is { -36, -30, -24, -18, -12, -6, 0, 6, 18, 24, 30, 36}, and the fuzzy subset is that the deviation amount E and the error rate of change EC are both { "PB" (positive large), "PM" (positive small), "PS" (positive small), "ZO" (zero), "NS" (negative small), "NM" (negative medium), "NB" (negative large). Three outputs Kp、Ki、KdThe variation range of (A) is defined as the domain of discourse on the fuzzy set and is respectively set as { -6, -5, -4, -3, -2, -1,0,1, 2, 3, 4, 5, 6}, { -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6}, { -6, -5, -4, -3, -2, -1,0,1, 2, 3, 4, 5, 6}, and the ranges are divided into 13 grades; dividing the three continuous variables of output into seven levels, Kp、Ki、KdThe three output quantities are { "PB" (positive large), "PM" (positive middle), "PS" (positive small), "ZO" (zero), "NS" (negative small), "NM" (negative middle), "NB" (negative large) }, and three output quantities Kp、Ki、KdAll the membership functions adopt triangular membership functions.
Further, three outputs of the fuzzy PID controller are delta Kp，ΔKi，ΔKdThe output value can be corrected for the fuzzy PID controller by delta Kp，ΔKi，ΔKdAdding the three correction amounts to the original formula to obtain the adjusted Kp，Ki，KdThe calculation formula is as follows:
Kp＝Kp0+ΔKp.................................................(1-2)
Ki＝Ki0+ΔKi....................................................(1-3)
Kd＝Kd0+ΔKd.................................................(1-4)
further, to convert the sharpness values into fuzzy inputs E, EC, a quantization factor is introduced. Let e be [ e ] as the domain of discourse of error and error change rateL,eH],ec＝[ecL,ecH]A pair of themThe ambiguity domain should be { -m, -m + 1., -1,0, 1., m-1, m }, { -n, -n + 1., -1,0, 1., n-1, n }, respectively, with the quantization factors of:
after the quantization factor is determined, the sharpness values of the error and the error rate of change EC are converted into fuzzy inputs E and EC, respectively, of the fuzzy controller.
The fuzzy control rule consists of a "IF... is.., the n.. is.." statement;
the fuzzy quantity is then converted into a precise control quantity, a linguistic variable is defined for the output quantity, the actual output control linguistic variable is defined as a "control quantity U", then the domains of the control quantity U are defined as { -l, -l +1, · 1,0,1, · 1, l }, and U is also divided into seven grades, namely "positive large (PB)", "Positive Medium (PM)", "Positive Small (PS)", "Zero (ZO)", "Negative Small (NS)", "Negative Medium (NM)", and "negative large (NB)". The fuzzy quantity sharpening selection gravity center method has the following formula:
the invention designs a vibration monitoring and intelligent control system aiming at the abnormal vibration amplitude of the whole worm rotating element action unit during operation, the system control core adopts a piezoelectric actuating device and an intelligent control algorithm fuzzy PID to design and program in Labview software, and the suppression force is generated by utilizing the inverse piezoelectric effect of a piezoelectric film PVDF (polyvinylidene fluoride) to suppress the vibration generated in the operation process of the worm rotating element action unit so as to finish the vibration suppression in the operation of the mechanical element action unit. And then the control system can carry out corresponding control measures aiming at different abnormal values under different abnormal conditions, thereby ensuring the normal operation of the worm rotating element action unit. The intelligent control system can improve the controllability of the worm rotating element action unit and realize the early warning and automatic regulation and control of abnormal conditions under the condition of no human intervention, thereby improving the reliability of the worm rotating element action unit and having important significance for improving the reliability of a numerical control machine tool.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a diagram of driver and controller connections;
FIG. 2 is a wiring diagram of the vibration sensor;
FIG. 3 is a schematic block diagram of fuzzy PID control;
FIG. 4 is a block diagram of a fuzzy control system;
FIG. 5 is a schematic diagram of the experimental composition of example 1.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
Example 1
As shown in fig. 1 to 5, a method for suppressing piezoelectric actuation vibration of a meta-motion unit includes the following steps:
1) directly decomposing the numerical control machine tool into mechanical element action units according to an FMA (frequency modulated analysis) structured decomposition method;
2) determining the specific composition of the mechanical element action unit according to the requirements of the numerical control machine tool and the definition of the mechanical element action unit; the mechanical element action unit comprises five parts, namely a power source, a supporting part, an executing part, an intermediate transmission part and a fastening part. The functions of the power source, the supporting piece, the executing piece, the intermediate transmission and the fastening piece are respectively as follows: the power source is used for providing power for the movement of the worm rotating element action unit and comprises a motor for directly providing power and power output by element action movement; the supporting piece is used for supporting parts of the executing piece; the executing piece is used for outputting the element action, and the result of the element action output comprises vibration, rotating speed, displacement and the like; the intermediate transmission is used for transmitting the power provided by the power source to the executing piece; the fastener is used for fixing the element action unit and is a foundation for mounting each part of the element action unit.
3) The method comprises the steps of selecting a worm rotating element action unit in a numerical control machine tool as a controlled mechanical element action unit, measuring a vibration signal generated when the controlled unit operates by a measuring unit, transmitting the vibration signal to a control unit in real time, fuzzifying the measurement signal by the control unit, converting the fuzzified measurement signal into a value of a universe of discourse, deducing by using fuzzy logic and fuzzy reasoning to obtain a corresponding fuzzy value, converting the obtained fuzzy value into an analog voltage control signal by a fuzzy PID control program, amplifying the voltage by a power amplifier, driving a piezoelectric actuator to generate an inhibition signal, and transmitting the inhibition signal to an action unit, so that the inhibition of abnormal vibration of the controlled unit is realized, wherein the measuring unit comprises a vibration sensor.
The vibration signal is processed by signal conditioning, filtering, A/D conversion and the like and then transmitted to an upper computer (industrial personal computer), LabVIEW software in the upper computer performs rapid calculation processing on the obtained digital signal according to a fuzzy PID self-adaptive corresponding algorithm to obtain a control signal, the control signal is converted into an analog voltage signal by D/A, the analog voltage signal is amplified by power and then used for driving a piezoelectric actuator, and the inverse piezoelectric effect of a PVDF film is utilized to generate inhibition force to inhibit vibration generated in the operation process of a worm rotating element action unit so as to complete vibration inhibition in the work of a mechanical element action unit.
The hardware system module comprises a controlled unit, a measuring unit, an actuating unit and a control unit, and the software system comprises a fuzzy PID control program designed by an upper computer in LabVIEW software;
and establishing a vibration suppression test bed model of the worm rotating element action unit, then carrying out a vibration suppression performance test of the worm rotating element action unit on the test bed model, and carrying out simulation in MATLAB to verify the effectiveness of the test method of the vibration suppression device.
The test bench model includes: the device comprises a voltage-stabilized power supply, a motor driver, a motor, an adapter, a worm rotating element action unit, a piezoelectric actuator, a power amplifier, a vibration suppression device, an elastic coupling, a bearing support, a base, a bearing, a vibration sensor, a displacement sensor, a signal output cable, an optical coupling module, a multifunctional data acquisition card, application program development software, a control program, driving program software and a signal output terminal.
The voltage-stabilized power supply adopts a PXN series linear direct-current voltage-stabilized power supply RXN-1503D; the motor and the motor driver adopt a Damak alternating current servo motor and a corresponding B2 series alternating current servo driver; the adapter adopts a CT5200 series constant current adapter; the element action unit adopts a worm rotating element action unit; the piezoelectric actuator adopts a PVDF film; the power amplifier adopts a YE5871 type power amplifier and is used for amplifying the vibration signal; the vibration suppression device is a roller screw, namely a corresponding B2 stepping motor; the elastic coupling adopts a quincunx elastic coupling; the bearing support adopts a T-shaped bearing support; the bearings adopt 6201 and 6202 bearings; the rotation speed sensor adopts a DK890 photoelectric rotation speed sensor and is used for measuring rotation speed and period, and the rotation speed sensor is of an NPN type; the vibration sensor adopts a CT1005LC piezoelectric acceleration sensor and a ZMT-YB40 magnetic attraction type integrated vibration transmitter and is used for measuring speed, acceleration and vibration; the signal output cable adopts a three-core shielding wire; the optical coupling module adopts an NPN (negative-positive-negative) ampere common anode connection method and has the function of converting an output signal of the rotating speed sensor into a signal which can be received by the multifunctional data acquisition card; the multifunctional data acquisition card adopts an American NI multifunctional data acquisition card USB-6002DAQLabview and uses a counter mode; the application development software adopts application development software Labview 2018; the control program adopts a fuzzy PID control program; the driver software adopts NI-DAQmx driver software; the signal output terminal adopts a computer.
The connection mode of the rotating speed sensor and the signal output cable is as follows: the sensor is respectively connected with one end of a line 1, a line 2 and one end of a line 3 of the three-core shielding line, the other end of the line 1 is connected with the anode of a power supply, the other end of the line 2 is connected with the cathode of the power supply and the cathode of the multifunctional data acquisition card, and the other end of the line 3 is connected with the anode of the multifunctional data acquisition card; the connection mode of the vibration sensor and the signal output cable is as follows: the vibration sensor is directly attached to the side face of the T-shaped support, a brown line of the sensor is connected to a DC12-28V external power supply, a blue line of the sensor is connected to a signal acquisition channel, and the negative end of the external power supply is in short circuit with the negative end of the signal acquisition channel. The anode of the power supply is connected with VCC, the cathode is connected with GND, and output signals are respectively 01+ and GND-.
The specific test process is as follows:
and selecting a worm rotating element action unit in the numerical control machine tool as an experimental object, and carrying out a vibration suppression experiment in the running process of the worm rotating element action unit. In this embodiment, vibration suppression experiments are respectively performed by simulating three rotation speed conditions in the operation of the worm rotating element action unit, so as to simulate three working conditions in the operation of the worm rotating element action unit during the actual operation of the machine tool. The simulated rotating speeds are respectively 500r/min, 1500r/min and 3000 r/min.
The invention is based on the inverse voltage characteristic of the piezoelectric material, selects a fuzzy PID as a control method of the piezoelectric actuator, completes the structural design of the controller, then selects a membership function of the fuzzy PID, establishes a fuzzy setting rule, and finally applies MATLAB software to carry out simulation analysis on the established element action vibration suppression model.
In order to complete the structural design of the system controller, the control system integrates the advantages of the traditional PID control and intelligent control system, and can carry out PID parameter online correction on the operating system according to the position feedback information in the system motion process. The fuzzy PID controller is composed of PID parameter fuzzy inference and PID regulator. The error E and the error change EC of the fuzzy PID are used as input quantity of fuzzy reasoning, and the input quantity can meet the requirements of different moments E and EC on PID self-tuning parameters of the control system after the fuzzy reasoning.
Applying fuzzy PID as a control method for the piezo actuator, the controller is set to a dual input, three output mode. And designing the membership functions of the error E, the error change rate EC and the output variable of the operating system to be triangular membership functions. The triangular membership function is described as follows:
n takes 3 and fuzzy subsets of error E and error rate of change EC are defined as { positive large (PB), Positive Medium (PM), Positive Small (PS), Zero (ZE), Negative Small (NS), Negative Medium (NM), negative large (NB) }. The key of the controller is to find out three parameters K of PIDp、Ki、KdAnd a fuzzy relation between the deviation E and the error change rate EC, and online real-time setting is carried out on three parameters of the PID control part by using a fuzzy control part in the controller. After output of the fuzzy controller is quantized, fuzzy subsets of control quantity of the fuzzy PID controller are obtained, wherein the fuzzy subsets are { positive large (PB), Positive Medium (PM), Positive Small (PS), Zero (ZE), Negative Small (NS), Negative Medium (NM) and negative large (NB) }. In the invention, the three parameters of the input values Kp, Ki and Kd of the PID control part are modified on line in real time by continuously detecting E and EC in operation according to the fuzzy control rule of the controller so as to meet different requirements of the deviation value E and the error change rate EC at different moments on the control system, so that the worm rotating element action unit has good dynamic adaptability and static robustness, and is easy to control and small in calculated amount, thereby obviously improving the response speed.
The structural design of the controller is as follows: the fuzzy PID self-adaptive control method is adopted to design the piezoelectric film actuating vibration suppression device, the control system integrates the advantages of the traditional PID control and intelligent control system, and PID parameter online correction can be carried out on the operating system according to position feedback information in the system motion process.
The domain of the deviation amount E is classified into { -6, -5, -4, -3, -2, -1,0,1, 2, 3, 4, 5, 6}, the domain of the error change rate EC is classified into { -36, -30, -24, -18, -12, -6, 0, 6, 18, 24, 30, 36}, and the fuzzy subset is that the deviation amount E and the error change rate EC are both { "PB" (positive large), "PM" (positive small), "PS" (positive small), "ZO" (zero), "NS" (negative small), "NM" (negative middle), "NB" (negative large). And setting the membership function of two continuous variables of E and EC to obey the triangular membership function. Three outputs Kp、Ki、KdThe variation range of (a) is defined as the domain of discourse on the fuzzy set, which is respectively set as { -6, -5, -4, -3, -2, -1,0,1, 2, 3, 4, 5, 6}, { -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6}, { -6, -5, -4, -3, -2, -1,0,1, 2, 3, 4, 5, 6}, and is divided into 13 levels.
Dividing the three continuous variables of output into seven levels, Kp、Ki、KdThe term "pa" refers to a "pa" which is a group of two or more (i.e., a "pa" and a "pa") that is a group of two or more (i.e., a "pa"). Three outputs Kp、Ki、KdAll the membership functions adopt triangular membership functions.
The fuzzy PID controller designed in the invention has two inputs and three outputs of delta Kp，ΔKi，ΔKdThe output value can be corrected for the fuzzy PID controller by delta Kp，ΔKi，ΔKdAdding the three correction amounts to the original formula to obtain the adjusted Kp，Ki，Kd. The calculation formula is as follows:
Kp＝Kp0+ΔKp..................................................(1-2)
Ki＝Ki0+ΔKi....................................................(1-3)
Kd＝Kd0+ΔKd..................................................(1-4)
in the process of setting PID parameters, three parameters K at different moments must be consideredp、Ki、KdAnd the relationship between each other. Considering the aspects of robustness, response speed, overshoot and the like of the system, the respective actions and characteristics of proportional, integral and differential links in PID control are integrated to summarize the deviation E and the error change rates EC and Kp、Ki、KdThe relationship between the three parameters.
(1) When the value of E is larger, K is used for obtaining system output response in time and increasing the operation speedpShould take a large value, in order to speed up the system response, KdThe value of (c) should be small; to avoid overshoot in the output response, where no integration is applied, K may be choseni＝0。
(2) When the values of E and EC are moderate, a smaller K is selected to reduce the overshoot value of the systempA value; in this case, KdHas great influence on the response speed of the system, and when EC is smaller, KdWhen EC is larger, KdGet smaller so as to maintain the response speed of the system, at this time KiThe value of (A) should not be too large, and should be moderately smaller.
(3) When the value of E is small, a larger K is required for good robustness of the systempAnd Ki(ii) a At the same time KdThe value of (A) is proper to avoid oscillation of the system when the system is stable, so that the anti-interference performance of the system can be effectively improved, and when EC is smaller, K is higherdSlightly larger, when EC is larger, KdThe smaller the size.
Based on the above relationship, an output variable Δ K is obtainedp、ΔKi、ΔKdThe fuzzy rule table of (1) is as follows:
TABLE 1 Δ Kp fuzzy rule Table
TABLE 2 Δ Ki fuzzy rule Table
TABLE 3 DeltaKd fuzzy rule Table
To convert the sharpness values into blurred input E, EC, a quantization factor is introduced. Let e be [ e ] as the domain of discourse of error and error change rateL,eH],ec＝[ecL,ecH]The corresponding fuzzy domains are { -m, -m + 1., -1,0, 1.. the m-1, m }, { -n, -n + 1., -1,0, 1.. the n-1, n }, and the quantization factors of the two are:
after the quantization factor is determined, the sharpness values of the error and the error rate of change EC are converted into fuzzy inputs E and EC, respectively, of the fuzzy controller.
The fuzzy control rule consists of a "IF... is.., the n.. is.." statement.
The fuzzy quantity is then converted into a precise control quantity, a linguistic variable is defined for the output quantity, the actual output control linguistic variable is defined as a "control quantity U", then the domains of the control quantity U are defined as { -l, -l +1, · 1,0,1, · 1, l }, and U is also divided into seven grades, namely "positive large (PB)", "Positive Medium (PM)", "Positive Small (PS)", "Zero (ZO)", "Negative Small (NS)", "Negative Medium (NM)", and "negative large (NB)". The fuzzy quantity sharpening selection gravity center method has the following formula:
and finally, designing a membership function and a control rule of a fuzzy PID controller in the piezoelectric actuation vibration suppression method of the element action unit by using a fuzzy control tool box of Matlab software, and performing simulation analysis by using a simulink. And (5) drawing a conclusion that: the fuzzy control real-time parameter setting enables the vibration amplitude to be obviously inhibited, and the intelligent control effect of the vibration inhibition of the worm rotating element action unit is better than that of the traditional manual control.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention.
Claims (8)
1. A method for suppressing vibration of a piezoelectric actuator of a unit motion element, comprising the steps of:
1) directly decomposing the numerical control machine tool into mechanical element action units according to an FMA (frequency modulated analysis) structured decomposition method;
2) determining the specific composition of the mechanical element action unit according to the requirements of the numerical control machine tool and the definition of the mechanical element action unit;
3) the operation of the system is that firstly, a measuring unit measures a vibration signal generated when the controlled unit operates and transmits the vibration signal to a control unit in real time, the measuring signal is fuzzified and converted into a value of a universe of discourse by the control unit, then, inference is carried out by using fuzzy logic and fuzzy inference to obtain a corresponding fuzzy value, the obtained fuzzy value is converted into an analog voltage control signal by a fuzzy PID control program, and the analog voltage control signal is amplified by a power amplifier and then drives a piezoelectric actuator to generate an inhibition signal and transmit the inhibition signal to an actuating unit, so that the inhibition of abnormal vibration of the controlled unit is realized.
2. The method for suppressing the piezoelectricity actuated vibration of the meta-motion unit as claimed in claim 1, wherein the vibration signal is processed by signal conditioning, filtering and a/D conversion, and then transmitted to the upper computer, the obtained digital signal is rapidly calculated by LabVIEW software in the upper computer according to the fuzzy PID adaptive corresponding algorithm, the obtained control signal is converted into an analog voltage signal by D/a, and then the analog voltage signal is amplified by power and used for driving the piezoelectric actuator, and the inverse piezoelectric effect of the PVDF film is used for generating the suppression force to suppress the vibration generated in the operation process of the worm rotary motion unit, thereby completing the vibration suppression in the operation of the mechanical meta-motion unit.
3. The method for suppressing the piezoelectric actuation vibration of the element action unit according to claim 1, wherein the concrete composition of the mechanical element action unit comprises five parts, namely a power source, a support member, an execution member, an intermediate transmission and a fastening member.
4. A method of suppressing piezoelectrically actuated vibrations of a meta-motion unit according to any of claims 1 to 3, characterized in that the measuring unit comprises a vibration sensor.
5. The method for suppressing vibration of a piezoelectric actuator of a meta-motion unit as claimed in claim 1, wherein a fuzzy PID is applied as a control method of the piezoelectric actuator, a fuzzy PID controller is set to a dual-input three-output mode, membership functions of an error E, an error change rate EC and an output variable of the operating system are designed to be triangular membership functions, and the triangular membership functions are described as follows:
taking N as 3, defining fuzzy subsets of errors E and error change rates EC as { positive large (PB), Positive Medium (PM), Positive Small (PS), Zero (ZE), Negative Small (NS), Negative Medium (NM), negative large (NB) }; the key of the controller is to find out three parameters K of PIDp、Ki、KdThe fuzzy relation between the deviation E and the error change rate EC utilizes a fuzzy control part in the controller to perform online real-time setting on three parameters of a PID control part; after output of the fuzzy controller is quantized, fuzzy subsets of control quantity of the fuzzy PID controller are obtained, wherein the fuzzy subsets are { positive large (PB), Positive Medium (PM), Positive Small (PS), Zero (ZE), Negative Small (NS), Negative Medium (NM) and negative large (NB) }.
6. The method of suppressing vibration by piezoelectric actuation of a meta-motion unit according to claim 5, wherein the domain of the deviation amount E is classified as { -6, -5, -4, -3, -2, -1,0,1, 2, 3, 4, 5, 6}, and the domain of the error change rate EC is classified as { -36, -30, -24, -18, -12, -6, 0, 6, 18, 24, 30, 36}, and the fuzzy subsets are such that the deviation amount E and the error change rate EC are both { "PB" (positive large), "PM" (positive), PS "(positive small)," ZO "(zero)," NS "(negative small)," NM "(negative middle), and" NB "(negative large); three outputs Kp、Ki、KdThe variation range of (A) is defined as the domain of discourse on the fuzzy set and is respectively set as { -6, -5, -4, -3, -2, -1,0,1, 2, 3, 4, 5, 6}, { -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6}, { -6, -5, -4, -3, -2, -1,0,1, 2, 3, 4, 5, 6}, and the ranges are divided into 13 grades; dividing the three continuous variables of output into seven levels, Kp、Ki、KdThe three output quantities are { "PB" (positive large), "PM" (positive middle), "PS" (positive small), "ZO" (zero), "NS" (negative small), "NM" (negative middle), "NB" (negative large) }, and three output quantities Kp、Ki、KdAll the membership functions adopt triangular membership functions.
7. The method of suppressing piezoelectrically actuated vibrations of a meta-action unit of claim 6, wherein the fuzzy PID controller has three outputs of Δ Kp，ΔKi，ΔKdThe output value can be corrected for the fuzzy PID controller by delta Kp，ΔKi，ΔKdAdding the three correction amounts to the original formula to obtain the adjusted Kp，Ki，KdThe calculation formula is as follows:
Kp＝Kp0+ΔKp.....................................................(1-2)
Ki＝Ki0+ΔKi.......................................................(1-3)
Kd＝Kd0+ΔKd....................................................(1-4)。
8. a method of suppressing piezoelectrically actuated vibrations of a meta-action unit as claimed in claim 5, characterized in that, in order to convert the sharpness values into fuzzy inputs E, EC, quantization factors are introduced; let e be [ e ] as the domain of discourse of error and error change rateL,eH],ec＝[ecL,ecH]The corresponding fuzzy domains are { -m, -m + 1., -1,0, 1.. the m-1, m }, { -n, -n + 1., -1,0, 1.. the n-1, n }, and the quantization factors of the two are:
after the quantization factor is determined, the sharpness values of the error and the error rate of change EC are converted into fuzzy inputs E and EC, respectively, of a fuzzy controller:
the fuzzy control rule consists of a "IF... is.., the n.. is.." statement;
then, the fuzzy quantity is converted into a precise control quantity, a linguistic variable is defined for the output quantity, an actual output control linguistic variable is defined as a 'control quantity U', then the domains of the 'control quantity U' are defined as { -l, -l +1, · 1,0,1,. once, l-1, l }, U is also divided into seven grades, namely 'Positive Big (PB),' Positive Middle (PM), 'Positive Small (PS),' Zero (ZO), 'Negative Small (NS),' Negative Middle (NM) 'Negative Big (NB),' the fuzzy quantity is clearly selected as a gravity center method, and the formula is as follows:
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000158282A (en) * | 1998-11-25 | 2000-06-13 | Mitsubishi Heavy Ind Ltd | Main spindle head for machine tool and its vibration damping method |
| CN1952819A (en) * | 2005-10-17 | 2007-04-25 | 中国科学院沈阳计算技术研究所有限公司 | Fuzzy PID control method and execution apparatus of numerical control machine |
| CN101187704A (en) * | 2007-12-17 | 2008-05-28 | 奇瑞汽车有限公司 | Reversing radar fuzzy controller |
| JP2009250260A (en) * | 2008-04-01 | 2009-10-29 | Nsk Ltd | Worm reduction gear and electric power steering device provided with the same |
| CN102354229A (en) * | 2011-07-26 | 2012-02-15 | 上海理工大学 | Liquid level fuzzy controller and working method thereof |
| CN102530057A (en) * | 2010-11-09 | 2012-07-04 | 株式会社捷太格特 | Electric power steering system |
| CN103869702A (en) * | 2014-03-13 | 2014-06-18 | 大连理工大学 | Active chattering control method for flexible cantilever beam structure |
| CN105965320A (en) * | 2016-04-25 | 2016-09-28 | 西安交通大学 | Intelligent detection and active inhibition device for fluttering of high-speed milling electric spindle |
| EP3312690A1 (en) * | 2016-10-19 | 2018-04-25 | HSD S.p.A. | Vibration damping system for an machining head of a machine tool and corresponding operating head |
| CN207683593U (en) * | 2018-01-05 | 2018-08-03 | 浙江吉利汽车研究院有限公司 | A kind of worm gear mechanism of electric boosting steering system |
| CN111059269A (en) * | 2019-11-20 | 2020-04-24 | 中国第一汽车股份有限公司 | Self-adaptive control method for starting of wet-type double-clutch automatic transmission and vehicle |
-
2021
- 2021-08-12 CN CN202110926573.7A patent/CN113671899A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000158282A (en) * | 1998-11-25 | 2000-06-13 | Mitsubishi Heavy Ind Ltd | Main spindle head for machine tool and its vibration damping method |
| CN1952819A (en) * | 2005-10-17 | 2007-04-25 | 中国科学院沈阳计算技术研究所有限公司 | Fuzzy PID control method and execution apparatus of numerical control machine |
| CN101187704A (en) * | 2007-12-17 | 2008-05-28 | 奇瑞汽车有限公司 | Reversing radar fuzzy controller |
| JP2009250260A (en) * | 2008-04-01 | 2009-10-29 | Nsk Ltd | Worm reduction gear and electric power steering device provided with the same |
| CN102530057A (en) * | 2010-11-09 | 2012-07-04 | 株式会社捷太格特 | Electric power steering system |
| CN102354229A (en) * | 2011-07-26 | 2012-02-15 | 上海理工大学 | Liquid level fuzzy controller and working method thereof |
| CN103869702A (en) * | 2014-03-13 | 2014-06-18 | 大连理工大学 | Active chattering control method for flexible cantilever beam structure |
| CN105965320A (en) * | 2016-04-25 | 2016-09-28 | 西安交通大学 | Intelligent detection and active inhibition device for fluttering of high-speed milling electric spindle |
| EP3312690A1 (en) * | 2016-10-19 | 2018-04-25 | HSD S.p.A. | Vibration damping system for an machining head of a machine tool and corresponding operating head |
| CN207683593U (en) * | 2018-01-05 | 2018-08-03 | 浙江吉利汽车研究院有限公司 | A kind of worm gear mechanism of electric boosting steering system |
| CN111059269A (en) * | 2019-11-20 | 2020-04-24 | 中国第一汽车股份有限公司 | Self-adaptive control method for starting of wet-type double-clutch automatic transmission and vehicle |
Non-Patent Citations (5)
| Title |
|---|
| 徐付伟: "基于元动作单元的数控机床可靠性分配与预计研究", 《中国优秀硕士学位论文全文数据库（电子期刊）工程科技Ⅰ辑》, 30 June 2018 (2018-06-30), pages 1 - 22 * |
| 徐付伟: "基于元动作单元的数控机床可靠性分配与预计研究", 《中国优秀硕士学位论文全文数据库（电子期刊）工程科技Ⅰ辑》, pages 1 - 22 * |
| 李永明: "《模糊数学及其应用》", 东北大学出版社, pages: 182 - 184 * |
| 陈无畏: "《汽车车身电子与控制技术》", 29 February 2008, 机械工业出版社, pages: 61 - 62 * |
| 马驰骋: "基于模糊PID控制器的变质量-柔性梁结构振动主动控制", 《振动与冲击》, pages 197 - 203 * |
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