CN104391444A - Improved single-neuron PID tuning method based on discrete system - Google Patents
Improved single-neuron PID tuning method based on discrete system Download PDFInfo
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- CN104391444A CN104391444A CN201410750241.8A CN201410750241A CN104391444A CN 104391444 A CN104391444 A CN 104391444A CN 201410750241 A CN201410750241 A CN 201410750241A CN 104391444 A CN104391444 A CN 104391444A
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Abstract
The invention relates to an improved single-neuron PID tuning method based on a discrete system. When a control system is in the dynamic state, a single-neuron PID controller with the self-adaptive proportionality coefficient lambda is adopted, and the single-neuron proportionality coefficient is completely controlled by the input-output error e(k), so that parameters of the single-neuron PID are completely and automatically tuned by the system, and manual evaluation is not required; the single-neuron PID is poor in steady state effect, so that after the system is in the steady state, the conventional PID with the self-adaptive integral grain Ki is adopted to improve the steady-state performance, and the integral grain parameter Ki is also completely controlled by the input-output error e(k) of a controlled object. According to the method, all the parameters are automatically tuned, the method is high in robustness, and the control effect cannot be degraded along with aging and damage to the controlled object; the parameters can be adjusted automatically during interference, so that the effect caused by the interference on the control system is reduced.
Description
Technical field
The present invention relates to a kind of discrete system pid parameter setting method, especially one improves mononeuric PID setting method based on discrete system.
Background technology
Known, PID controller is widely used in various industrial control system, and its control effects is good, and structure is simple.In discrete system, the parameter tuning of PID is mainly for proportional gain
kp(Proportional Gain), storage gain
ki(Integral Gain) and the differential gain
kd(Derivative Gain).At present, for these three parameters adjust by rule of thumb people for adjusting, although also good effect can be obtained, but for layman and introduction personage, parameter tuning is not a nothing the matter.Along with the development of intelligent control technology, the method for some pid parameter self-adaptive sites starts to be paid close attention to by people, and wherein single neuron PID controller structure is relatively simple, and have the function of Parameter Self-learning, the advantage that robustness is high, its control principle can be expressed as:
Wherein,
represent iterations;
for the output of Single Neuron Controller;
represent the output of last controller;
for mononeuron scale-up factor;
represent the input variable of system, specifically can be expressed as:
Wherein
for error,
for input,
for the output of control object.
respectively relative to the storage gain of PID controller, proportional gain, the differential gain, be equivalent to the weights of input variable, the computing formula of weights is:
The adjustment formula of weight coefficient is:
Wherein,
be respectively the learning rate of integration, ratio, differential,
represent mononeuric output,
for error,
represent the input variable of system.Adaptive single neuron controller is by realizing self-adaptation self organizing function to the adjustment of weighting coefficient, and learning rules are exactly the Hebb algorithm of adjustment weight.
Although single neuron PID controller has had many advantages, also there is very large defect: the output of single neuron PID controller
with mononeuron scale-up factor
selection have great relation, finally
selection be also experience select, be not the adaptive controller in complete meaning; During due to stable state, controlled device inputs
with output
error
always exist, cause mononeuric output
not stablize constant, but fluctuate up and down, fluctuation size directly affects control effects, and that is during stable state, the effect of single neuron PID controller is unsatisfactory.
Summary of the invention
The object of the present invention is to provide a kind of overcome the deficiency of above-mentioned single neuron PID controller improve mononeuric PID setting method based on discrete system.
For achieving the above object, technical scheme of the present invention is: one improves mononeuric PID setting method based on discrete system, in conjunction with scale-up factor adaptive single neuron PID algorithm and the adaptive normal PID lgorithm of storage gain, deducted the systematic error of system output by the input of judgement system
with the size of set-point, when control system is in dynamic, the adaptive single neuron PID algorithm of adoption rate coefficient; After system enters stable state, adopt the adaptive normal PID lgorithm of storage gain.
In embodiments of the present invention, the method specific implementation step is as follows,
Step S01: initialization proportional gain
kp, storage gain
ki, the differential gain
kdwith mononeuron scale-up factor
value, wherein,
kpwith
kdall get a minimal value,
kiwith
all get the arbitrary value that is not 0;
Step S02: initialization system input deducts the systematic error that system exports
switching value, i.e. set-point, so that compartment system running status;
Step S03: start control system, and real-time detection system input deducts the systematic error that system exports
;
Step S04: judge systematic error
whether be greater than set-point, if
be greater than set-point, then the adaptive single neuron PID algorithm of selection percentage coefficient; Otherwise, select the adaptive normal PID lgorithm of storage gain;
Step S05: the output action of the single neuron PID improved by step S04 in passive object, and by the output feedack of passive object to step S03, continues detection system error
, thus system is among closed-loop control.
In embodiments of the present invention, in described step S04, being implemented as follows of the adaptive single neuron PID algorithm of described scale-up factor:
Step S11: read in scale-up factor
initial value;
Step S12: read in systematic error
, and by systematic error now
a sampling period of time delay obtains
;
Step S13: according to the scale-up factor that following formulae discovery first time is revised
, and should
value feeds back to step S12, as
value;
Wherein,
represent iterations,
conventional letter function, scale-up factor
be controlled by the error between input and output completely
if, front secondary system error
than rear secondary system error
time large, make
value increase, and increase number, be controlled by the ratio of front and back systematic error;
Step S14: the output calculating the adaptive single neuron PID of scale-up factor;
Step S15: by the output action of adaptive for scale-up factor single neuron PID in controlled device, and repeated execution of steps S11 ~ S15.
In embodiments of the present invention, in described step S04, the specific implementation process of the adaptive normal PID lgorithm of described storage gain is as follows:
Step S21: the storage gain reading in setting
kiinitial value;
Step S22: read in setting
kpwith
kdvalue, and read in systematic error
, by systematic error now
a sampling period of time delay obtains
;
Step S23: according to the storage gain that following formulae discovery first time is revised
ki, and should
kivalue feeds back to step S22, as
value;
Wherein,
represent iterations,
conventional letter function, storage gain parameter
kibe controlled by the input-output system error of controlled device completely
;
Step S24: the output calculating the Traditional PID of integration gain-adaptive;
Step S25: by the output action of adaptive for storage gain single neuron PID in controlled device, and repeated execution of steps S21 ~ S25.
Compared to prior art, the present invention has following beneficial effect:
The all parameters of improvement single neuron PID controller that the present invention proposes are all automatic adjustings, and the method robustness is high, can not be aging along with controlled device, impaired, make control effects poor, with regard to antijamming capability, the improvement single neuron PID controller proposed due to the present invention belongs to intelligent controller, disturbs interim, and parameter can adjust to reduce to disturb the impact on control system automatically.
Accompanying drawing explanation
Fig. 1 is the calcspar improving mononeuric PID control principle.
Fig. 2 is the process flow diagram that the mononeuric pid parameter of improvement is adjusted.
Fig. 3 is single neuron PID scale-up factor in the inventive method
the process flow diagram of Self-tuning System.
Fig. 4 is the process flow diagram of Traditional PID storage gain Ki Self-tuning System in the inventive method.
Embodiment
Below in conjunction with accompanying drawing, technical scheme of the present invention is specifically described.
One of the present invention improves mononeuric PID setting method based on discrete system, and its implementation procedure is: when control system is in dynamic, adoption rate coefficient
adaptive single neuron PID controller, realizes dynamically adaptive control completely,
tuning formulae specifically can be expressed as:
Wherein,
represent iterations,
for system input and system export between error,
conventional letter function, mononeuron scale-up factor is controlled by the error between input and output completely
if, previous error
than rear error
time large, make
value increases, and increase number, be controlled by the ratio of front and back error, the parameter of such single neuron PID is just complete in system automatic adjusting, need not artificial value again; Single neuron PID due to steady state effect poor, thus after system enters stable state, adopt storage gain
kiadaptive Traditional PID, improves steady-state behaviour, and after in general control system enters stable state, PID proportional parts is just very little on the impact of system, can get one minimum, differential part generally only gets a minimum value, but storage gain
kivalue just extremely important on the impact of the steady-state behaviour of system, obtain too large or too little all bad, therefore the present invention proposes storage gain
kiadaptive method, specifically can be expressed as:
Wherein,
represent iterations,
for system input and system export between error,
conventional letter function, such storage gain parameter
kialso the input and output error of controlled device will be controlled by completely
.
Step of adjusting specifically describes as follows: initialization proportional gain
kp, storage gain
ki, the differential gain
kdwith mononeuron scale-up factor
, in these 4 parameters
kiwith
the number of arbitrary non-zero can be got,
kpwith
kda minimal value can be got, as 1; The switching law of setting single neuron PID and Traditional PID, namely sets an error
value, when the input error originated from input of controlled device
when equaling this value, automatically complete the switching of single neuron PID controller and storage gain self-adaptive PID controller; System starts startup optimization moment, is controlled to export, according to the error between system input and output by single neuron PID
, single neuron PID controller is adjusted its scale-up factor parameter rapidly
, and very fast sliding near a fixed value, this value can be good at adaptive system dynamic process, is an adaptive process completely; Work as error
when equaling set-point, illustrative system completes dynamic process, and controller is switched to rapidly storage gain self-adaptive PID controller, the value of storage gain also by the value of careless setting, will begin in a minute automatically adjustment, makes steady-state error in a very little scope.
For better telling about the present invention, be below specific embodiments of the invention.
As shown in Figure 1, improve the parameter tuning of mononeuric PID, containing the adaptive single neuron PID of scale-up factor and the adaptive Traditional PID of storage gain two kinds of algorithms, deducted the systematic error of system output by the input of judgement system
size select to use which kind of algorithm, work as systematic error
when being greater than set-point, the adaptive single neuron PID of selection percentage coefficient; Otherwise, select the adaptive Traditional PID of storage gain.Improve the output action of single neuron PID in passive object, thus it is Guaranteed in real time that controlled device is in, simultaneously owing to improving the complete Self-tuning System of single neuron PID parameter, so have good robustness.
The process flow diagram that the mononeuric pid parameter of improvement is as shown in Figure 2 adjusted, the first step, given proportional gain Kp, storage gain Ki, differential gain Kd and mononeuron scale-up factor
initial value, Kp and Kd can give a minimal value, Ki with
then need one be not 0 Arbitrary Digit; Second step, needs certainty annuity error
switching value, namely when
equal how many times, switch, because systematic error time dynamic is general comparatively large, and during stable state, systematic error is less, so to systematic error
the setting of switching value, be exactly in order to compartment system operates in dynamically or stable state in fact; 3rd step, after first two steps work completes, starts control system; 4th step, real-time detection system error; 5th step, by systematic error
compare with between the switching value of setting, determine the adaptive single neuron PID of adoption rate coefficient or the adaptive Traditional PID of storage gain; 6th step, improves the output action of single neuron PID in passive object; 7th step, output feedack to the 4th step, is continued detection system error, thus system is among closed-loop control by passive object.
Single neuron PID scale-up factor as shown in Figure 3
the process flow diagram of Self-tuning System, systematic error
when being greater than the switching value of setting, the first step, first single neuron PID reads in scale-up factor
initial value; Second step, reads in systematic error
, and a sampling period of systematic error time delay is now obtained
; 3rd step, the scale-up factor revised by formulae discovery first time
, and this value is fed back to second step, as
value; 4th step, calculates the output of single neuron PID; 5th step, the output action of single neuron PID is in controlled device.When system is normally run, the flow process of this five step is by repeated action, and mononeuric output also can adjustment repeatedly, until obtain a relatively stable output, whole process, is all parameter automatic adjusting, realizes adaptation function.
Traditional PID storage gain as shown in Figure 4
kithe process flow diagram of Self-tuning System, systematic error
when being less than or equal to the switching value of setting, the first step, first reads in the storage gain of setting
kiinitial value; Second step, reads in setting
kpwith
kdvalue, and read in the systematic error calculated
, by systematic error now
a sampling period of time delay obtains
; 3rd step, the storage gain revised by formulae discovery first time
ki, and this value is fed back to second step, as
value; 4th step, calculates the output of Traditional PID; 5th step, the output action of single neuron PID is in controlled device.When system is normally run, the flow process of this five step, by repeated action, realizes storage gain
kithe function of Self-tuning System.
Be more than preferred embodiment of the present invention, all changes done according to technical solution of the present invention, when the function produced does not exceed the scope of technical solution of the present invention, all belong to protection scope of the present invention.
Claims (4)
1. improve a mononeuric PID setting method based on discrete system, it is characterized in that: in conjunction with scale-up factor adaptive single neuron PID algorithm and the adaptive normal PID lgorithm of storage gain, deducted the systematic error of system output by the input of judgement system
with the size of set-point, when control system is in dynamic, the adaptive single neuron PID algorithm of adoption rate coefficient; After system enters stable state, adopt the adaptive normal PID lgorithm of storage gain.
2. one according to claim 1 improves mononeuric PID setting method based on discrete system, it is characterized in that: the method specific implementation step is as follows,
Step S01: initialization proportional gain
kp, storage gain
ki, the differential gain
kdwith mononeuron scale-up factor
value, wherein,
kpwith
kdall get a minimal value,
kiwith
all get the arbitrary value that is not 0;
Step S02: initialization system input deducts the systematic error that system exports
switching value, i.e. set-point, so that compartment system running status;
Step S03: start control system, and real-time detection system input deducts the systematic error that system exports
;
Step S04: judge systematic error
whether be greater than set-point, if
be greater than set-point, then the adaptive single neuron PID algorithm of selection percentage coefficient; Otherwise, select the adaptive normal PID lgorithm of storage gain;
Step S05: the output action of the single neuron PID improved by step S04 in passive object, and by the output feedack of passive object to step S03, continues detection system error
, thus system is among closed-loop control.
3. one according to claim 2 improves mononeuric PID setting method based on discrete system, it is characterized in that: in described step S04, being implemented as follows of the adaptive single neuron PID algorithm of described scale-up factor:
Step S11: read in scale-up factor
initial value;
Step S12: read in systematic error
, and by systematic error now
a sampling period of time delay obtains
;
Step S13: according to the scale-up factor that following formulae discovery first time is revised
, and should
value feeds back to step S12, as
value;
Wherein,
represent iterations,
conventional letter function, scale-up factor
be controlled by the error between input and output completely
if, front secondary system error
than rear secondary system error
time large, make
value increase, and increase number, be controlled by the ratio of front and back systematic error;
Step S14: the output calculating the adaptive single neuron PID of scale-up factor;
Step S15: by the output action of adaptive for scale-up factor single neuron PID in controlled device, and repeated execution of steps S11 ~ S15.
4. one according to claim 2 improves mononeuric PID setting method based on discrete system, it is characterized in that: in described step S04, and the specific implementation process of the adaptive normal PID lgorithm of described storage gain is as follows:
Step S21: the storage gain reading in setting
kiinitial value;
Step S22: read in setting
kpwith
kdvalue, and read in systematic error
, by systematic error now
a sampling period of time delay obtains
;
Step S23: according to the storage gain that following formulae discovery first time is revised
ki, and should
kivalue feeds back to step S22, as
value;
Wherein,
represent iterations,
conventional letter function, storage gain parameter
kibe controlled by the input-output system error of controlled device completely
;
Step S24: the output calculating the Traditional PID of integration gain-adaptive;
Step S25: by the output action of adaptive for storage gain single neuron PID in controlled device, and repeated execution of steps S21 ~ S25.
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| CN110018634A (en) * | 2019-04-28 | 2019-07-16 | 北京控制工程研究所 | A kind of adaptive frame control system and method promoting control-moment gyro bandwidth |
| CN110995066A (en) * | 2019-12-21 | 2020-04-10 | 中国特种设备检测研究院 | Double-servo motor control method for amusement facility track detection device |
| CN111158282A (en) * | 2019-12-27 | 2020-05-15 | 吉林大学 | Single-neuron FPGA control method and system for crosslinked cable production line |
| CN111796510A (en) * | 2020-06-29 | 2020-10-20 | 邯郸钢铁集团有限责任公司 | Application method of PID controller in primary control system PLC |
| CN112666827A (en) * | 2021-01-19 | 2021-04-16 | 四川阿泰因机器人智能装备有限公司 | Method for controlling liquid distribution in grading manner based on PID |
| CN113359410A (en) * | 2021-04-29 | 2021-09-07 | 武汉华海通用电气有限公司 | Digital PI controller |
| CN113515037A (en) * | 2021-08-03 | 2021-10-19 | 成都航空职业技术学院 | Improved PID controller parameter setting method of model-free system |
| CN114063436A (en) * | 2021-10-09 | 2022-02-18 | 广州大学 | Anti-interference control method, system, equipment and medium for water-surface robot |
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| CN115963730A (en) * | 2023-03-16 | 2023-04-14 | 广州市景泰科技有限公司 | Selective control method for injection dispensing valve cavity liquid temperature |
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| CN111158282A (en) * | 2019-12-27 | 2020-05-15 | 吉林大学 | Single-neuron FPGA control method and system for crosslinked cable production line |
| CN111796510A (en) * | 2020-06-29 | 2020-10-20 | 邯郸钢铁集团有限责任公司 | Application method of PID controller in primary control system PLC |
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| CN113359410A (en) * | 2021-04-29 | 2021-09-07 | 武汉华海通用电气有限公司 | Digital PI controller |
| CN113515037A (en) * | 2021-08-03 | 2021-10-19 | 成都航空职业技术学院 | Improved PID controller parameter setting method of model-free system |
| CN113515037B (en) * | 2021-08-03 | 2023-06-30 | 成都航空职业技术学院 | Improved PID controller parameter setting method for model-free system |
| CN114063436A (en) * | 2021-10-09 | 2022-02-18 | 广州大学 | Anti-interference control method, system, equipment and medium for water-surface robot |
| CN114063436B (en) * | 2021-10-09 | 2023-09-26 | 广州大学 | Anti-interference control method, system, equipment and medium for water surface robot |
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| CN115963730A (en) * | 2023-03-16 | 2023-04-14 | 广州市景泰科技有限公司 | Selective control method for injection dispensing valve cavity liquid temperature |
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