CN106406100A - Rotor dynamic balancing control system based on fuzzy self-tuning single neure PID control and method thereof - Google Patents
Rotor dynamic balancing control system based on fuzzy self-tuning single neure PID control and method thereof Download PDFInfo
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- CN106406100A CN106406100A CN201611038073.5A CN201611038073A CN106406100A CN 106406100 A CN106406100 A CN 106406100A CN 201611038073 A CN201611038073 A CN 201611038073A CN 106406100 A CN106406100 A CN 106406100A
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- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
Abstract
The invention discloses a system based on fuzzy self-tuning single neure PID control and a method thereof. A fuzzy controller is used to calculate the gain K needed by a single neure PID controller, after the single neure PID controller obtains the gain K, a control step length is solved and obtained through the single neure PID controller, and finally the effective control of rotor unbalance is realized. The defects of low balance efficiency and low balance precision of a traditional PID control method can be overcome, thus the control efficiency is improved, and the effective control of a balance head is realized.
Description
Technical field
The present invention relates to slewing dynamic balancing control method field, specially one kind are based on Fuzzy self- turning mononeuron
The rotor dynamic balancing control system of PID control and its method.
Background technology
At present, rotating machinery is widely used in the various aspects of industry, because rotor unbalance reason makes rotating machinery produce
Raw vibration, serious possible damage is mechanical, causes unnecessary loss in engineering.In order to reduce these unnecessary losses, fall
The vibration of low rotor is it is necessary to enter action balance to rotor.
At present, traditional PID control method is extensively applied in rotating machinery balance controls.But, traditional PID control method
Balance efficiency is not high with balance quality, and the design of conventional PID controllers needs prior test of many times just to can determine that control ginseng
Number.Artificial neural network can arbitrarily approach linearly or nonlinearly system, be capable of almost all of conventional non-linear with not
Determine the control of system, be therefore widely used in intelligence control system.But neural network structure is complicated, Weight Training when
Between longer, be unfavorable for real-time control.
Content of the invention
The present invention is the weak point overcoming prior art to exist, and provides one kind to be based on Fuzzy self- turning single neuron PID
The rotor dynamic balancing control system controlling and its method, to traditional PID control method balance efficiency can be overcome low, balance quality
Not high defect, thus improving control efficiency, realizes the efficient control to balancing head.
In order to reach foregoing invention purpose, the present invention adopts the following technical scheme that：
A kind of feature bag of the rotor dynamic balancing control system based on Fuzzy self- turning Single neuron PID control of the present invention
Include：Rotor-support-foundation system, balancing head, vibrating sensor, fuzzy controller, single neuron PID controller, processor；
Described vibrating sensor gathers vibration values y (k) under kth time circulation for the described rotor-support-foundation system and passes to described place
Reason device, received vibration signal y (k) is compared by described processor with set desired value, obtains kth time circulation
Under difference e (k) as described fuzzy controller an input value；Described processor carries out difference gauge to described difference e (k)
Calculate, obtain the rate of change of described difference e (k) as another input value of described fuzzy controller after, by described fuzzy control
Device carries out Fuzzy Processing and obtains gain K (k) under kth time circulation；k≥1；
Described processor is calculated described single neuron PID controller under kth time circulation according to described difference e (k)
Input signal xi(k)；I=1,2,3；
Described single neuron PID controller is according to described input signal xiK (), using the improved Hebb algorithm having supervision
Respectively obtain described input signal x under kth time circulationiThe weights W of (k)i(k) export；
Described processor is according to described input signal xi(k), gain K (k) and weights WiK () is calculated under kth time circulation
Control step-length u (k) and pass to described balancing head, thus using described balancing head control described rotor-support-foundation system to realize rotor
Dynamic balancing.
A kind of feature of the rotor dynamic balancing control method based on Fuzzy self- turning Single neuron PID control of the present invention is should
For the control system being made up of rotor-support-foundation system, balancing head, vibrating sensor, fuzzy controller, single neuron PID controller
In, and carry out as follows：
Step 1, gather vibration values y (k) of kth time circulation lower rotor part system using vibrating sensor, and with set
Desired value is compared, if vibration values y (k) are more than described desired value, calculate the difference e (k) under kth time circulation, and executes
Step 2；Otherwise, represent that described rotor-support-foundation system meets rotor dynamic balancing；
Step 2, differential calculation is carried out to described difference e (k), obtain the rate of change of described difference e (k)；
Step 3, using fuzzy controller, the rate of change of described difference e (k) and difference e (k) is carried out with obfuscation, fuzzy pushes away
Reason and de-fuzzy are processed, and obtain gain K (k) under kth time circulation；
Step 4, obtain three input signals x of described single neuron PID controller under kth time circulation using formula (1)1
(k)、x2(k) and x3(k)：
In formula (1), as k=1, e (k-1) and e (k-2) are 0；As k=2, e (k-2) is 0；
Step 5, obtain the weights W of i-th input signal using the improved Hebb algorithm having supervision shown in formula (2)i
(k) i=1,2,3；
Wi(k)=Wi(k-1)+ηie(k-1)u(k-1)xi(k-1) (2)
In formula (2), ηiRepresent the learning efficiency of i-th input signal；U (k-1) represents the control step under -1 circulation of kth
Long, as k=1, Wi(k-1) it is initially to give weights, u (k-1)=0, e (k-1)=0；
Step 6, control step-length u (k) being obtained using formula (3) under kth time circulation：
In formula (3),Represent the meansigma methodss of i-th input signal weights；And have：
Step 7, described balancing head enter activity control according to control step-length u (k) to described rotor-support-foundation system；
Step 8, k+1 is assigned to k, and return to step 1.
Compared with prior art, the invention has the beneficial effects as follows：
1st, the present invention adopts Fuzzy self- turning Single neuron PID control method, compared with traditional PID control method, base
In Fuzzy self- turning Single neuron PID control method fast response time, overshoot and the number of oscillation significantly reduce, and have well
Robustness and stability, so that the equilibration time of rotor-support-foundation system shortens, control efficiency improves, and improves balance quality.
2nd, the present invention is on the basis of Single neuron PID control, introduces fuzzy control technology, by ambiguity in definition variable,
Design membership function obtains gain K (k) of Single neuron PID control, by the variable control of gain K (k), improves control
Efficiency, thus solve accurately exported step-length it is achieved that efficient control to balancing head.
3rd, mononeuron technology is combined with traditional PID control and the control parameter of PID can be adjusted with self adaptation by the present invention
Whole, compared with traditional PID control, the parameter improving conventional PID controllers fixes defect, by the self-adaptative adjustment of parameter,
Improve the control efficiency of rotor-support-foundation system.
4th, after interference signal in rotor-support-foundation system, the present invention adopts Fuzzy self- turning Single neuron PID control method,
Compared with traditional PID control method, what rotor-support-foundation system can be the fastest reaches steady statue, so this inventive method has well
Stability and robustness.
Brief description
Fig. 1 is the present invention based on Fuzzy self- turning Single neuron PID control method schematic；
Fig. 2 is the control algolithm flow chart of the inventive method；
Fig. 3 is the experiment frame composition of the present invention；
Fig. 4 a is that the inventive method is right with the MATLAB emulation amplitude change of traditional PI D and Single neuron PID control method
Than figure；
Fig. 4 b is to add interference signal back panel value changes comparison diagram；
Fig. 5 a is that the inventive method repeats to test damping time comparing result figure to dynamic balancing control with traditional PID approach；
Fig. 5 b is to repeat to test effectiveness in vibration suppression comparing result figure.
Specific embodiment
In the present embodiment, a kind of rotor dynamic balancing control system based on Fuzzy self- turning Single neuron PID control, bag
Include：Rotor-support-foundation system, balancing head, vibrating sensor, fuzzy controller, single neuron PID controller, processor；
As shown in figure 1, the control principle of this method is as follows：
Vibrating sensor gathers vibration values y (k) under kth time circulation for the rotor-support-foundation system and passes to processor, processor
Received vibration signal y (k) is compared with set desired value, the difference e (k) obtaining under kth time circulation is made
An input value for fuzzy controller；Processor carries out differential calculation to difference e (k), and the rate of change obtaining difference e (k) is made
After another input value of fuzzy controller, Fuzzy Processing is carried out by fuzzy controller and obtains the gain K under kth time circulation
(k)；k≥1；Wherein, the comprising the following steps that of Fuzzy Processing：
1., by input quantity difference e (k) of fuzzy controller, rower is entered in the rate of change of difference e (k) and output flow gain K (k)
Fixed, it is assigned in a certain interval range, using symmetric mode, input quantity and output are classified.
2. the membership function of setting input quantity difference e (k), difference e (k) rate of change and output K.
3. the fuzzy rule according to fuzzy controller, fuzzy reasoning obtains the fuzzy value of gain K (k).
4. de-fuzzy operation is carried out using centroid method, obtain the accurate output of gain K (k).
Processor is calculated input signal x of the lower single neuron PID controller of kth time circulation according to difference e (k)i
(k)；I=1,2,3；
Single neuron PID controller is according to input signal xiK (), is respectively obtained using the improved Hebb algorithm having supervision
Lower input signal x of kth time circulationiThe weights W of (k)i(k) export；
Processor is according to input signal xi(k), gain K (k) and weights WiK () is calculated the control step under kth time circulation
Long u (k) simultaneously passes to balancing head, thus controlling rotor-support-foundation system to realize rotor dynamic balancing using balancing head, specifically, is profit
With mass rotational angle in balancing head, realize rotor dynamic balancing by adjusting the rotational angle of mass.
In the present embodiment, a kind of rotor dynamic balancing control method based on Fuzzy self- turning Single neuron PID control, application
In the control system being made up of rotor-support-foundation system, balancing head, vibrating sensor, fuzzy controller, single neuron PID controller
In, as shown in Fig. 2 specific algorithm flow process is carried out as follows：
Step 1, gather vibration values y (k) of kth time circulation lower rotor part system using vibrating sensor, in the present embodiment, adopt
With eddy current displacement sensor, vibrating sensor is placed in and is used for obtaining rotor radial displacement above bearing seating face rotor center
Situation of change, be compared by vibration values y (k) and set desired value collecting, if vibration values y (k) be more than mesh
Scale value, then calculate the difference e (k) under kth time circulation, and execution step 2；Otherwise, represent that rotor-support-foundation system meets rotor dynamic balancing；
Step 2, differential calculation is carried out to difference e (k), obtain the rate of change of difference e (k)；
Step 3, using fuzzy controller, the rate of change of difference e (k) and difference e (k) is carried out obfuscation, fuzzy reasoning and
De-fuzzy is processed, and obtains gain K (k) under kth time circulation；
Step 4, obtain three input signals x of the lower single neuron PID controller of kth time circulation using formula (1)1(k)、x2
(k) and x3(k)：
In formula (1), as k=1, e (k-1) and e (k-2) are 0；As k=2, e (k-2) is 0；
Step 5, obtain the weights W of i-th input signal using the improved Hebb algorithm having supervision shown in formula (2)i
(k) i=1,2,3；
Wi(k)=Wi(k-1)+ηie(k-1)u(k-1)xi(k-1) (2)
In formula (2), ηiRepresent the learning efficiency of i-th input signal；U (k-1) represents the control step under -1 circulation of kth
Long, as k=1, Wi(k-1) it is initially to give weights, u (k-1)=0, e (k-1)=0；
Step 6, control step-length u (k) being obtained using formula (3) under kth time circulation：
In formula (3),Represent the meansigma methodss of i-th input signal weights；And have：
Step 7, balancing head enter activity control according to control step-length u (k) to rotor-support-foundation system；
Step 8, k+1 is assigned to k, and return to step 1.
Below based on experiment, the implementation result of the inventive method is described in detail：
For verifying effectiveness and the superiority based on Fuzzy self- turning Single neuron PID control method, built using motor
Rotor-support-foundation system laboratory table and MATLAB emulation carry out experiment of dynamic balancing.Experiment frame composition is as shown in Figure 3.Experiment porch includes frequency conversion
Motor (power 750W, moment of torsion 2.4NM), converter (Siemens MM4206SE6420-2UC17-5AA1, power 750W, single-phase electricity
Pressure 220V), experiment bearing NSK#1302, rotating shaft, bearing block and vibrating sensor (eddy current displacement sensor YXS-DWA).Shake
Dynamic sensor is placed in above bearing seating face rotor center and is used for obtaining the situation of change of rotor radial displacement, and balancing head is placed in
Rotor tip is used for providing imbalance compensation quality to rotor.Controller is connected with vibrating sensor and balancing head respectively,
Obtain the vibration values of vibrating sensor collection, and output control signal drives balancing head to carry out imbalance compensation.
As can be seen that Single neuron PID control significantly reduces with respect to the overshoot of traditional PID control from Fig. 4 a, this
Inventive method is completely eliminated with respect to the overshoot of Single neuron PID control, and control efficiency also increases.Accordingly, with respect to
Traditional PID control method and Single neuron PID control method, based on Fuzzy self- turning Single neuron PID control method, it is to avoid
Overshoot, also improves control efficiency simultaneously.From Fig. 4 b as can be seen that after being interfered the inventive method amplitude minimum and
Recover stable with speed the fastest, so the robustness based on Fuzzy self- turning Single neuron PID control method is good.
In experiment, mononeuron Self-tuning Fuzzy PID Control and traditional PID control method are applied simultaneously to rotor
In dynamic balance system, and experimental result is contrasted.Set rotor initial speed as 1200 revs/min, record and initially shake
Be worth for 30.0um, to based on Fuzzy self- turning Single neuron PID control method with traditional PID control method respectively with the beginning of identical
Beginning condition has carried out 20 experiments, and time comparing result figure is as shown in Figure 5 a.Fig. 5 a reflects based on Fuzzy self- turning list nerve
First PID control method averagely only needs to just to eliminate within 3.9 seconds about 50% initial vibration amount, and traditional PID approach reaches
But 9.8 seconds about are needed to same effect；Fig. 5 b is to repeat to test effectiveness in vibration suppression comparing result figure.Fig. 5 b reflects identical
Initial condition under Fuzzy self- turning single neuron PID method averagely can reduce 70% about vibratory output, and traditional PI D control
The vibratory output that system reduces only has 55% about.Experiment demonstrates effectiveness and the superiority of the inventive method.
Claims (2)
1. a kind of rotor dynamic balancing control system based on Fuzzy self- turning Single neuron PID control, its feature includes：Rotor system
System, balancing head, vibrating sensor, fuzzy controller, single neuron PID controller, processor；
Described vibrating sensor gathers vibration values y (k) under kth time circulation for the described rotor-support-foundation system and passes to described process
Device, received vibration signal y (k) is compared by described processor with set desired value, obtains under kth time circulation
Difference e (k) as described fuzzy controller an input value；Described processor carries out difference gauge to described difference e (k)
Calculate, obtain the rate of change of described difference e (k) as another input value of described fuzzy controller after, by described fuzzy control
Device carries out Fuzzy Processing and obtains gain K (k) under kth time circulation；k≥1；
Described processor is calculated the input of described single neuron PID controller under kth time circulation according to described difference e (k)
Signal xi(k)；I=1,2,3；
Described single neuron PID controller is according to described input signal xi(k), using the improved Hebb algorithm having supervision respectively
Obtain described input signal x under kth time circulationiThe weights W of (k)i(k) export；
Described processor is according to described input signal xi(k), gain K (k) and weights WiK () is calculated the control under kth time circulation
Step-length u (k) processed simultaneously passes to described balancing head, thus controlling described rotor-support-foundation system dynamic flat to realize rotor using described balancing head
Weighing apparatus.
2. a kind of rotor dynamic balancing control method based on Fuzzy self- turning Single neuron PID control, it is characterized in that being applied to by
In the control system that rotor-support-foundation system, balancing head, vibrating sensor, fuzzy controller, single neuron PID controller are formed, and
Carry out as follows：
Step 1, gather vibration values y (k) of kth time circulation lower rotor part system using vibrating sensor, and with set target
Value is compared, if vibration values y (k) are more than described desired value, calculates the difference e (k) under kth time circulation, and execution step
2；Otherwise, represent that described rotor-support-foundation system meets rotor dynamic balancing；
Step 2, differential calculation is carried out to described difference e (k), obtain the rate of change of described difference e (k)；
Step 3, using fuzzy controller, the rate of change of described difference e (k) and difference e (k) is carried out obfuscation, fuzzy reasoning and
De-fuzzy is processed, and obtains gain K (k) under kth time circulation；
Step 4, obtain three input signals x of described single neuron PID controller under kth time circulation using formula (1)1(k)、x2
(k) and x3(k)：
In formula (1), as k=1, e (k-1) and e (k-2) are 0；As k=2, e (k-2) is 0；
Step 5, obtain the weights W of i-th input signal using the improved Hebb algorithm having supervision shown in formula (2)i(k) i=
1,2,3；
Wi(k)=Wi(k-1)+ηie(k-1)u(k-1)xi(k-1) (2)
In formula (2), ηiRepresent the learning efficiency of i-th input signal；U (k-1) represents the control step-length under -1 circulation of kth, when
During k=1, Wi(k-1) it is initially to give weights, u (k-1)=0, e (k-1)=0；
Step 6, control step-length u (k) being obtained using formula (3) under kth time circulation：
In formula (3),Represent the meansigma methodss of i-th input signal weights；And have：
Step 7, described balancing head enter activity control according to control step-length u (k) to described rotor-support-foundation system；
Step 8, k+1 is assigned to k, and return to step 1.
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| CN108267970A (en) * | 2018-01-25 | 2018-07-10 | 合肥工业大学 | Time lag rotor active balance control system and its method based on Smith models and single neuron PID |
| CN108983596A (en) * | 2018-08-08 | 2018-12-11 | 太原理工大学 | ORC system Single Neuron Controller and its control method based on broad sense cross-entropy algorithm |
| CN109343351A (en) * | 2018-12-07 | 2019-02-15 | 桂林电子科技大学 | A kind of switched reluctance machines moment controlling system of advanced PID control |
| CN116965371A (en) * | 2023-07-17 | 2023-10-31 | 南京农业大学 | Automatic sewage disposal system and method for aquaculture pond |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108267970A (en) * | 2018-01-25 | 2018-07-10 | 合肥工业大学 | Time lag rotor active balance control system and its method based on Smith models and single neuron PID |
| CN108267970B (en) * | 2018-01-25 | 2021-03-09 | 合肥工业大学 | Time-lag rotor active balance control system and method based on Smith model and single neuron PID |
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| CN116965371A (en) * | 2023-07-17 | 2023-10-31 | 南京农业大学 | Automatic sewage disposal system and method for aquaculture pond |
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