CN105759607B - The design method of PAC controllers based on intelligent control algorithm - Google Patents
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Abstract
The invention discloses the design methods of the PAC controllers based on intelligent control algorithm, the present invention is on the basis of traditional PAC controllers, use the intelligent control algorithms such as advanced PID control algorithm, variable universe Fuzzy PID, pid control algorithm based on neural network, control accuracy can be improved, control response is improved, higher control requirement is met.In terms of network communication, the communication modes such as Ethernet, CAN bus are increased on the basis of traditional serial ports 232,485, use Modbus RTU, Modbus TCP, CANopen, custom protocol etc., to realize interconnecting for network.Being increased on the basis of Traditional PID prevents integral saturation algorithm, carries out differential, differential forward scheduling algorithm to control variable, can significantly improve the effect of PID, improve the response time, reduce control overshoot.
Description
Technical field
The invention belongs to field of industrial automatic control, it is related to the design of embedded PAC controllers, high accuracy temperature control,
Intelligent control algorithm etc..
Background technology
In industrial manufacturing industry production process, the demand to control is higher and higher.PAC controllers are on control performance, letter
Breath processing above and on Internet communication capacity has the advantages that some are more notable.PAC controllers combine intrinsic reliable of PLC
Property, robustness and distribution character, while compared with PC is controlled, PAC uses real time operating system, has in process performance real-time
The incomparable advantages of PC machine such as property, certainty.However traditional PAC controllers are relatively simple in control algolithm, are being related to
When some complicated algorithms, through frequently with method be to be combined control with modes such as PC machine, also expose some defects, example
Such as：The effect of control is poor, the retardance of communication, the increase of cost.
Invention content
According to above-mentioned the problem of proposing in the prior art, the purpose of the present invention is：The control performance for improving control system, is adopted
With advanced PID control algorithm, variable universe Fuzzy PID, based on the pid control algorithm of neural network, can improve
Control accuracy improves control response.In addition the scalability of acquisition signal is improved, analog input signal can be that voltage is believed
Number either the signals such as current signal and resistance.In terms of network communication, increase on the basis of traditional serial ports 232,485
The communication modes such as Ethernet, CAN bus, using Modbus-RTU, Modbus-TCP, CANopen, custom protocol etc. comes
Realize interconnecting for network.
The present invention is realized based on a kind of intelligent controlling device, and the intelligent controlling device includes mainly following
Part：
1, senser sampling circuit, the circuit include the sampled signal modulate circuit of voltage, electric current, resistance signal, to
The signal high precision acquisition for realizing sensor, such as pressure transmitter, PT100 temperature sensors, angular transducer, 4~20mA
Current sensor etc..
2, control output circuit controls big voltage high-current containing isolated location using solid-state relay.It is defeated using PWM
Go out signal, control output accuracy is high.
3, the microcontroller and Ethernet communication circuit for participating in operation, 232,485, CAN bus communicating circuit,
Reset circuit, watchdog circuit.
4, ensure the power module circuitry of power good.
Sensor is interacted by input signal conditioning circuit with microcontroller.
The present invention is to propose some intelligent control algorithms and realization, includes mainly following components：
Step 1：PID Self-tuning System algorithms are devised, common Industry Control object is with non-linear, time variation and not
The factors such as certainty are caused pid parameter to be compared using the method manually adjusted and expend the time, and the effect adjusted is also poor,
The algorithm of PID Self-tuning Systems is increased in the present invention, can be automatically determined the pid parameter of equipment operation, be saved the time, improve
Control effect.
Step 2：In addition on the basis of traditional PID control, increase prevent integral saturation algorithm, to control variable into
Row differential, differential forward scheduling algorithm, can significantly improve the effect of PID, improve the response time, reduce control overshoot.
Step 3：Due to factors such as the non-linear of control object, time variation and uncertainties, PID control is only used only,
Control effect is poor.This method increases FUZZY ALGORITHMS FOR CONTROL on the basis of improving regulatory PID control method, passes through
The fuzzy reasoning table for imitating human knowledge language and membership function are established to carry out fuzzy control operation, uses Fuzzy Control
The mode being combined with regulatory PID control is made, it is with non-linear, time variation and not true in control object that controller can be improved
The control effect of qualitative factor.
Step 4：In fuzzy control, the acquisition of fuzzy rule and membership function is greatly mostly from the experience of people, control effect
Selection of the quality of fruit also with fuzzy rule and membership function is closely bound up.However neural network has very strong Nonlinear Quasi
Conjunction ability, can learn with the advantages such as adaptive ability, neural network is combined with PID control, neural network can be utilized
Self-learning capability calculates the optimized parameter of PID control, is optimal control, significant effect.
The realization for the PID Self-tuning Systems that the step 1 is mentioned is specifically divided into following steps：
1.1, it is realized using Z-N (Ziegler-Nichols rule) relay feedback formula setting methods, compares Z-N methods
There is good advantage, i.e., generates oscillation element, the wherein descriptive equation of relay characteristics using relay method：In formula, M is relay characteristics amplitude, and X is to measure output peak difference calculating to acquire.When meeting argG (j
ω)=- π,A is acquired by measuring the maximum value exported and minimum value in formula, KuFor threshold oscillation ratio
Gain, d are the amplitude of the symmetrical relay characteristics divided.
1.2, by the oscillating curve of generation, obtain the above KuAnd critical period of the oscillation Tu, certainly by Ziegler-Nichols
The calculation formula of setting method, as shown in table 1, performance requirement as needed calculate the pid parameter adjusted, and complete relay
The PID Self-tuning System processes of feedback.
1 Z-N selftuning PID parameters (quick performance) of table
The realization for the improvement Traditional PID that the step 2 is mentioned is specifically divided into following steps：
2.1, normal PID lgorithm can expose easy overshoot, system time stable period if use without modification
Length has the shortcomings that oscillatory occurences when stablizing.
2.2, PID common type is：
Over control is led to limit integral using anti-windup saturation algorithm, and can quickly be improved the rise time.Wherein,
Umin≤u (t)≤Umax, whenWhen, enable u1(t)=Umax, wherein enablingLimit integral saturated phenomenon.In addition the variable quantity for improving differential, due toWherein e (t)=r (t)-y (t), wherein r (t) are setting value, and y (t) is sensor sample value, however works as r
(t) when adjustment, necessarily lead to the instantaneous variation of differential term, the unstability of system can be increased, therefore enable
The realization for the Varied scope fuzzy control PID that the step 3 is mentioned is specifically divided into following steps：
3.1, theory-region changed adaptive-fuzzy control is using error e and error rate ec as the input of system, in difference
Moment is automatically adjusted pid parameter according to the difference of e and ec, and fuzzy rule online carries out Kp, Ki, Kd of pid parameter
Modification.Fuzzy selftuning PID is on the basis of pid algorithm, by calculating current system error e and error rate ec, profit
Fuzzy reasoning is carried out with fuzzy rule, inquiry fuzzy reasoning table carries out parameter adjustment.
3.2, the foundation of fuzzy control table is not unique, for different control systems difference, but is directed to common system
System can meet control using following fuzzy control table and require：
Δ KP fuzzy reasoning tables:
Δ Ki fuzzy reasoning tables:
Δ Kd fuzzy reasoning tables:
The selection of 3.3 domains and the determination of membership function are determined by artificial experience, it is ensured that accurate as possible.In addition lead to
The variation for crossing monitoring e and ec, the problem of continuing to optimize and reduce the range of domain, reach variable universe Self-tuning System.The step 4
The realization for the Neural network PID mentioned is specifically divided into following steps：
4.1 this method use single neuron Neural Network PID, can avoid as using BP neural network, losing
The computationally intensive problem of propagation algorithm, particle cluster algorithm, can also ensure real-time control, online constantly to correct.
4.2 input the structure singly exported as shown in figure 4, Neuron PID structural model is 3, wherein 3 inputs are e
(k),The output of neuron is u (k), wherein the weights of neuron be the ratio of PID, it is integral, micro-
Divide three coefficient, that is, Kp, Ki, Kd.
It can similarly obtain,
Wherein μ be learning rate, 0<μ<1；
Kp (k+1)=Kp (k)+Δ Kp (k+1),
Ki (k+1)=Ki (k)+Δ Ki (k+1),
Kd (k+1)=Kd (k)+Δ Kd (k+1).
The initial value of 4.5 neural network weights is using the calculated value of PID automatic setting methods that step 1 is related to as just
Initial value can accelerate the speed of Neural network PID Self-tuning System.
The present invention improves acquisition precision in conventional PAC controller designs on hardware, improve network interconnection intercommunication,
Using CANopen and Modbus-TCP, Modbus-RTU as common communications protocol, it is conventional that improvement is used on software
PID control method, variable universe fuzzy self-adaption method, Neural network PID algorithm, more accurate good realization industry control scene
The control of equipment can be preferably useful in such as petroleum pipeline electric tracer heating system, cotton textile industry Temperature and Humidity Control, boiler temperature
The occasions such as control.
Description of the drawings
Fig. 1 system hardware structure block diagrams；
Fig. 2 PID relay autotuner block diagrams；
Fig. 3 Varied scope fuzzy control PID structure diagrams；
Fig. 4 Neural network PID structure diagrams；
Specific implementation mode
The detailed description that the present invention is embodied according to the accompanying drawings, will become more apparent to one of ordinary skill in the art
The above-mentioned advantages and features of the present invention.
It is hardware configuration first, as shown in Figure 1.
Microcontroller selects the STM32F207VET6 of ST Microelectronics, the memory space with large capacity and high property
The arithmetic speed of energy, it is ensured that run some control algolithms.
House dog and reset circuit select SP706, are special watchdog reset chip, can improve the stabilization of system
Property and anti-interference.
Power module, equipment turn 24v using Switching Power Supply 220v outside, LM2576 and SPX1117- are used in system equipment
3.3 carry out voltage conversion, and to meet system module voltage requirements, in addition ADC sampling modules need accurately reference voltage, select
The sources as the reference voltage TL431.
Sensor and input signal conditioning circuit, to ensure 4~20mA current modes, voltage-type, resistance change type sensing
In the access systems such as device, selecting switch can be emitted in same port according to corresponding jump of selection sensor difference setting by devising.
Such as temperature sensor uses PT100, signal conditioning circuit to select electric bridge mode, and the PT100 resistance values changed are converted into pair
The voltage form answered send the ADC of controller to be sampled.Actual temperature value is obtained by conversion, the temperature of acquisition is sent to micro- place
Reason device is handled.Other sensor circuits also similar process.
Output signal conditioning circuit and actuator, the digital control amount that controller operation obtains can be by way of output root
It is converted into switching value, PWM outputs etc. according to demand.Electrical isolation module is contained in inside, prevents from being interfered by the external world, in addition to
Powerful output control, in-built solid state relay.
Data memory module selects FM24CL04, can store a small amount of data and power down preserves the function of not losing.
Some critical parameter informations can be used to store.
Communication module, using MAX3485, MAX3232, PCA82C250, DP83848 etc. respectively as RS485, RS232,
CAN communication, the data processing chip of ethernet communication and physical layer transceiver circuit.The selection of multiple communication modes can be with difference
Equipment between carry out with more compatibility information exchange.
PID relay autotuner methods are as shown in Figure 2.
The oscillating curve generated by relay link obtains KuAnd critical period of the oscillation Tu(seeing above described), by
The calculation formula of Ziegler-Nichols automatic setting methods, as shown in table 2, performance requirement that can be as needed calculate whole
Fixed pid parameter completes the PID Self-tuning System processes of relay feedback.
2 relay feedback formula Z-N selftuning PID parameters (quick performance) of table
Pid parameter can be obtained by calculating, it can be as fuzzy control and the initial value of ANN Control.
Varied scope fuzzy control method is as shown in Figure 3.Mainly it is made of fuzzy control and PID controller two parts.
The input of Digital PID Controller is the collection value r (t) obtained by sensor sample and the desired value manually set
Y (t) obtains numeral output controlled quentity controlled variable u (t) controlled quentity controlled variable u (t) by advanced PID control algorithm and changes or lead to by D/A
It crosses numeral output PWM wave and carries out output control.And then the operation of control device.Reach the function of making output control in desired value.
Varied scope fuzzy control pid algorithm is as shown in Figure 3.
1. determining domain and membership function.It is determined according to the deviation e of input signal and deviation variation rate ec input ranges
Domain, the determination of domain generally according to artificial experience, the fuzzy set of domain be divided into it is negative big, in bearing, bear it is small, zero, it is just small, just
In, it is honest }.Specific domain range needs that scene is used in combination to determine.Membership function is the signal value institute for determining input
In the proportion of interval range, generally select the curve of rectilinear(-al) as membership function model in embedded device, in order to
Facilitate embeded processor to calculate, prepares for following fuzzy rule and ambiguity solution.
2. determining fuzzy reasoning table.The establishment of fuzzy reasoning table should ensure accurately as possible because the quality of control algolithm with
The accuracy of fuzzy rule is closely bound up.The range of e and ec is generally divided into seven sections, i.e.,：It is negative big, and it is negative small in bearing, zero, just
Small, center is honest }.Therefore 49 complete fuzzy rule set can be established.By taking temperature rise curve as an example, when the temperature rise phase
Between when, it should allow Kp to take bigger value as possible to guarantee quick response, reduce the rise time, Ki takes relatively small
Value prevents integral to be saturated, integrates the generation of over control, Kd takes relatively large value, and overshoot in order to prevent reduces the defeated of system
Go out, controls climbing speed.
Therefore rule 1：When e is " PB " and ec is " Z0 ", Δ Kp is " PM ", and Δ Ki is " PS ", and Δ Kd is " PM ".
Rule 2：When e is " PB " and ec is " NS ", Δ Kp is " PM ", and Δ Ki is " PS ", and Δ Kd is " PM ".
Rule 3：When e is " PB " and ec is " NM ", Δ Kp is " PS ", and Δ Ki is " Z0 ", and Δ Kd is " PM ".
It is pushed away with this, specific rules list is：
Δ KP fuzzy reasoning tables:
Δ Ki fuzzy reasoning tables:
Δ Kd fuzzy reasoning tables:
3. true PID controller parameter Kp and Ki and Kd in order to obtain, is changed as follows：
Kp=Kp0+ Δs Kp；Ki=Ki0+ Δs Ki；(wherein Kp0, Ki0, Kd0 indicate PID coefficient to Kd=Kd0+ Δs Kd respectively
Initial value)；
Neuron PID structural model as shown in figure 4,
1. structure is the structure of triple input single output, wherein three inputs are e (k),Neuron
Output be u (k), wherein the weights of neuron are three ratio of PID, integral, differential coefficients (Kp, Ki, Kd).
(wherein μ be learning rate, 0<μ<1)
It can similarly obtain,
4, the PID automatic setting methods that the initial value of neural network weight can use this method step 1 to mention are calculated
Value is used as initial value, can accelerate the speed of Neural network PID Self-tuning System.
Claims (1)
1. the design method of the PAC controllers based on intelligent control algorithm, this method are realized based on a kind of intelligent controlling device,
The intelligent controlling device includes mainly following components：
1) senser sampling circuit, which includes the sampled signal modulate circuit of voltage, electric current, resistance signal, to realize
The signal high precision of sensor acquires；
2) control output circuit controls big voltage high-current containing isolated location using solid-state relay；It is exported and is believed using PWM
Number, control output accuracy is high；
3) be used to participate in the microcontroller and Ethernet communication circuit of operation, 232,485, CAN bus communicating circuit, reset
Circuit, watchdog circuit；
4) ensure the power module circuitry of power good；
Sensor is interacted by input signal conditioning circuit with microcontroller；
It is characterized in that：This approach includes the following steps：
Step 1：PID Self-tuning System algorithms are designed, common Industry Control object has non-linear, time variation and uncertainty
Factor is caused pid parameter to be compared using the method manually adjusted and expends the time, and the effect adjusted is also poor, in the method
The algorithm of PID Self-tuning Systems is increased, the pid parameter of equipment operation can be automatically determined, save the time, improves control effect
Fruit；
Step 2：In addition on the basis of traditional PID control, increase prevent integral saturation algorithm, to control variable carry out it is micro-
Divide, differential forward algorithm, the effect of PID can be significantly improved, improve the response time, reduces control overshoot；
Step 3：Due to the non-linear of control object, time variation and uncertain factor, PID control, control effect is only used only
Fruit is poor；This method increases FUZZY ALGORITHMS FOR CONTROL on the basis of improving regulatory PID control method, by establishing mould
Fuzzy reasoning table and the membership function of human knowledge language are imitated to carry out fuzzy control operation, using fuzzy control and often
The mode that is combined of rule PID control, can improve controller control object have non-linear, time variation and it is uncertain because
The control effect of element；
Step 4：In fuzzy control, the acquisition of fuzzy rule and membership function greatly mostly from the experience of people, control effect
Selection of the quality also with fuzzy rule and membership function is closely bound up；However neural network has very strong nonlinear fitting energy
Power, can learn with adaptive ability advantage, neural network is combined with PID control, can utilize neural network self study
Ability calculates the optimized parameter of PID control, is optimal control, significant effect；
The realization for the PID Self-tuning Systems that the step 1 is mentioned is specifically divided into following steps：
1.1, realize have very compared to Z-N methods using Z-N, that is, Ziegler-Nichols rule relay feedback formula setting method
Good advantage, i.e., generate oscillation element, the wherein descriptive equation of relay characteristics using relay method：
In formula, M is relay characteristics amplitude, and X is to measure output peak difference calculating to acquire；When meeting argG (j ω)=- π,A is acquired by measuring the maximum value exported and minimum value in formula, KuFor threshold oscillation proportional gain, d is
The amplitude of the symmetrical relay characteristics divided；
1.2, by the oscillating curve of generation, obtain the above KuAnd critical period of the oscillation Tu, by Ziegler-Nichols Self-tuning Systems
The calculation formula of method, as shown in table 1, performance requirement as needed calculate the pid parameter adjusted, and complete relay feedback
PID Self-tuning System processes；
1 Z-N selftuning PID parameters of table
The realization for the improvement Traditional PID that the step 2 is mentioned is specifically divided into following steps：
2.1, normal PID lgorithm if use without modification, can expose easy overshoot, system time stable period it is long,
The shortcomings that oscillatory occurences is had when stablizing；
2.2, PID common type is：
Over control is led to limit integral using anti-windup saturation algorithm, and can quickly be improved the rise time；Wherein, Umin≤
U (t)≤Umax, whenWhen, enable u1(t)=Umax, wherein enablingLimit integral saturated phenomenon；In addition the variable quantity for improving differential, due toWherein e (t)=r (t)-y (t), wherein r (t) are setting value, and y (t) is sensor sample value, however works as r
(t) when adjustment, necessarily lead to the instantaneous variation of differential term, the unstability of system can be increased, therefore enable
The realization for the Varied scope fuzzy control PID that the step 3 is mentioned is specifically divided into following steps：
3.1, theory-region changed adaptive-fuzzy control is using error e and error rate ec as the input of system, in different moments
Pid parameter is automatically adjusted according to the difference of e and ec, fuzzy rule online modifies to Kp, Ki, Kd of pid parameter；
Fuzzy selftuning PID is on the basis of pid algorithm, by calculating current system error e and error rate ec, using fuzzy
Rule carries out fuzzy reasoning, and inquiry fuzzy reasoning table carries out parameter adjustment；
3.2, the foundation of fuzzy control table is not unique, for different control systems difference, but is directed to system, use with
Lower fuzzy control table can meet control and require：
Δ KP fuzzy reasoning tables:
Δ Ki fuzzy reasoning tables:
Δ Kd fuzzy reasoning tables:
The selection of 3.3 domains and the determination of membership function are determined by artificial experience, it is ensured that accurate as possible；Additionally by prison
The variation for surveying e and ec, the problem of continuing to optimize and reduce the range of domain, reach variable universe Self-tuning System；
The realization for the Neural network PID that the step 4 is mentioned is specifically divided into following steps：
4.1 this method use single neuron Neural Network PID, can avoid as being calculated using BP neural network, heredity
The computationally intensive problem of method, particle cluster algorithm, can also ensure real-time control, online constantly to correct；
4.2 Neuron PID structural models are 3 and input the structure singly exported, wherein 3 inputs are e (k),e(k)-e
(k-1), the output of neuron is u (k), wherein the weights of neuron be the ratio of PID, integral, three coefficient, that is, Kp of differential,
Ki、Kd；
The output of 4.3 neurons isWherein Ts is sampling time, error
Function is e (k)=rin(k)-yout(k), performance indicator is takenUsing steepest descent method, the weights tune of neuron
It is whole as follows：
It can similarly obtain,
Wherein μ be learning rate, 0<μ<1；
4.4 enablingIt can then be acquired successively by step 3：
Kp (k+1)=Kp (k)+Δ Kp (k+1),
Ki (k+1)=Ki (k)+Δ Ki (k+1),
Kd (k+1)=Kd (k)+Δ Kd (k+1)；
The initial value of 4.5 neural network weights using the calculated value of PID automatic setting methods that step 1 is related to as initial value,
It can accelerate the speed of Neural network PID Self-tuning System.
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| CN106774037A (en) * | 2017-03-19 | 2017-05-31 | 北京工业大学 | A kind of intelligent electric tracing control system based on Internet of Things cloud platform |
| CN107422640B (en) * | 2017-08-01 | 2020-08-11 | 东华大学 | Combined integral system identification method based on relay feedback |
| CN107885075B (en) * | 2017-11-15 | 2020-08-28 | 机械工业仪器仪表综合技术经济研究所 | Method and system for detecting intelligent setting of process control |
| CN107894716A (en) * | 2017-11-28 | 2018-04-10 | 昆山艾派科技有限公司 | Temprature control method |
| CN108181802A (en) * | 2017-12-05 | 2018-06-19 | 东南大学 | A kind of controllable PID controller parameter optimization setting method of performance |
| CN109995580B (en) * | 2019-03-13 | 2022-08-16 | 北京工业大学 | VN mapping method based on GA _ PSO hybrid algorithm in 5G network slice |
| CN110109346A (en) * | 2019-05-29 | 2019-08-09 | 中国计量大学 | The pid control algorithm of aluminium-air cell power converter |
| CN110374789B (en) * | 2019-07-04 | 2020-12-04 | 南方电网科学研究院有限责任公司 | PID parameter switching method and device for speed regulator of hydraulic turbine set |
| CN111399369A (en) * | 2020-02-20 | 2020-07-10 | 西北工业大学 | Digital closed-loop control method for photoelectric accelerometer sensor |
| CN113741174A (en) * | 2021-09-03 | 2021-12-03 | 中石化石油机械股份有限公司三机分公司 | Self-adaptive pressure control algorithm of reciprocating natural gas compressor |
| CN114510092B (en) * | 2022-02-17 | 2023-02-10 | 太原理工大学 | Transition packet internal temperature control system and method based on fuzzy PID (proportion integration differentiation) of prediction variable universe |
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| KR100573189B1 (en) * | 2003-12-30 | 2006-04-24 | 김동화 | Method and device for pH control of water supply facilities using tuning method of 2-DOF PID controller by neural network |
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