CN108549240A - A kind of motor speed Optimization about control parameter method and system based on simplex search - Google Patents

A kind of motor speed Optimization about control parameter method and system based on simplex search Download PDF

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CN108549240A
CN108549240A CN201810482102.XA CN201810482102A CN108549240A CN 108549240 A CN108549240 A CN 108549240A CN 201810482102 A CN201810482102 A CN 201810482102A CN 108549240 A CN108549240 A CN 108549240A
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control parameter
iteration
optimization
sequence
parameter combination
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孔祥松
汤泽宇
肖逸宁
申庆航
梁倩敏
张辑
徐敏
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Xiamen University of Technology
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    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • 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
    • G05B13/042—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 in which a parameter or coefficient is automatically adjusted to optimise the performance

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Abstract

The motor speed Optimization about control parameter method and system based on simplex search that the present invention relates to a kind of, method include the following steps：S1, initialization；S2, optimization process provide iteration control parameter combination new, after testing, upscaled according to simplex search method；S3, preprocessing process, will be upscaled after iteration control parameter combination pre-process at pratical and feasible iteration control parameter combination；S4, experimental test procedures；S5, last handling process, by formulaIts ITAE index is calculated to assess the control performance of motor, and pratical and feasible iteration control parameter combination is carried out upscaled；S6, evaluation process, according to pratical and feasible iteration control parameter combination and its corresponding ITAE values, it is assessed in real time whether pratical and feasible iteration control parameter combination meets optimality, if meeting optimality, optimal control parameter combination is exported, S2 is otherwise gone to and is iterated.Present invention is implemented as originally small, saving optimization time and experiment consumings.

Description

A kind of motor speed Optimization about control parameter method and system based on simplex search
Technical field
The present invention relates to motor speed control fields, are controlled more particularly to a kind of motor speed based on simplex search Parameter optimization method and system.
Background technology
The performance of control system is the key that Control System Design.And control system performance is influenced by factors, one It is the structure of control system, second is that process noise or disturbance, third, the parameter of control system.Once and the structure of control system is true Fixed, then control system performance can only be improved by the adjustment to control system parameter.Therefore, the parameter of control system is whole Surely it is an important job.
Current most important control parameter is adjusted can be divided into four classes with optimization method.The first kind is trial and error procedure, and this method is Control parameter is constantly adjusted by engineer according to operating experience to be arranged and test, until finding one group of acceptable control Parameter combination.This method depends critically upon engineer personal experience, and the optimality of tuning process time and effort consuming and setting valve cannot It is guaranteed.Second class method is empirical formula method, and this method is mainly used for pid control parameter and adjusts.For example, Ziegler- Nichols setting methods andRelay feedback method etc..In such methods, engineer usually requires elder generation Object model is obtained by transient response experiment, parameter Estimation or frequency response test etc., then further according to experience tuning formulae Provide parameter tuning value.The advantages of this method is to implement simple.But, it there is also problems with：First, this method needs Dependent on process model, but accurate model is difficult or impossible to obtain；Secondly, whole by the control parameter acquired in this method Definite value is not usually optimal value；Again, the selection of empirical equation needs to rely on engineer experience and the understanding to process characteristic. These problems cause this method that can not in most cases provide ideal result.Third class method is based on the excellent of model Change method, it be built upon control performance model it is known under the premise of a kind of method.It is assumed that control performance and control parameter Between model it is known that so can pass through the optimization method based on model realize control parameter optimization.However, practical feelings Under condition, the correlativity between control performance and control parameter is extremely complex, is difficult to obtain under normal circumstances.Therefore, the party Method is actually difficult to apply to the research of control parameter setting valve.4th class is to pass through something lost on the basis of Control System Imitation model The optimization algorithms such as propagation algorithm, swarm intelligence algorithm carry out simulation optimization, but emulate obtained control parameter and be applied to real process It is often difficult to be optimal effect when control.
Invention content
The present invention for Motor Rotating Speed Control System control parameter adjust existing optimizing is of high cost, rely on expertise, Be difficult to ensure the main problems such as optimality, it is proposed that a kind of motor speed Optimization about control parameter method based on simplex search and System, by a small amount of online experiment, is quickly found out its object is to realize under conditions of reducing quality optimization cost as far as possible The optimal control parameter of control system combines, to improve the control performance of Motor Rotating Speed Control System.For this purpose, what the present invention used Specific technical solution is as follows：
A kind of motor speed Optimization about control parameter method based on simplex search, wherein the method includes following steps Suddenly：
S1, initialization, artificially given initial controlling parameter group, which merges to close initial controlling parameter group, carries out upscaled, setting The initial value of relevant parameter needed for optimization process and evaluation process；
S2, optimization process provide iteration control new, after testing, upscaled according to simplex search method and join Array is closed；
S3, preprocessing process, will be upscaled after iteration control parameter combination pretreatment at pratical and feasible iteration control join Array is closed；
S4, experimental test procedures send pratical and feasible iteration control parameter combination the control system of motor to, and make electricity Machine is run under control parameter combination, and control system is acquired from initial time t0Start to EOT end of test moment tfBetween in Actual speed constitutes rotating speed sequence, wherein in moment t0, rotating speed zero, in moment tf, rotating speed is target set point vsp；
S5, last handling process press formula according to actual speedIts ITAE index is calculated to assess electricity The control performance of machine, wherein tiFor sampling instant, e (i) is the deviation of actual speed and setting speed under the sampling instant, and Pratical and feasible iteration control parameter combination is carried out upscaled；
S6, evaluation process according to pratical and feasible iteration control parameter combination and its corresponding ITAE values, and are believed according to history Breath calculates to obtain opposite optimality sequence, and according to the track characteristic of the sequence, to pratical and feasible iteration control parameter combination whether Meet optimality to be assessed in real time, if meeting optimality, exports optimal control parameter combination, otherwise go to S2 and change Generation.
Further, upscaled in S1 passes through formula
It carries out, whereinIt is closed for initial controlling parameter group, (Xt)L=inf (Xt) is lower bound, (Xt)H=sup (Xt) it is the upper bound, n is the control parameter number of optimization,Indicate the initial value of i-th of control parameter, t=1, 2,…,n。
Further, assignment is carried out to the parameter of simplex search method { α, beta, gamma, δ } in S1, and sets simplex Search iteration count value s=0, simplex build operator v=1；And the relevant parameter of evaluation process is set, setting terminates Coefficient of regime initial value κ=0, lower threshold κF, termination factor lower threshold ξΓ, slipping smoothness coefficient lambda, sliding termination coefficient η.
Further, parameter { α, beta, gamma, δ } takes { α=1, β=0.5, γ=2, δ=0.5 }, lower threshold κF=3, Termination factor lower threshold ξΓ=0.05, slipping smoothness coefficient lambda=1, sliding terminates coefficient η=1.
Further, the simplex search method of S2 the specific steps are：
S21, initialization condition judgement：If current v >=n+1, goes to step S23；Otherwise, S22 is gone to step, is executed simple Shape is built；
S22, initial simplex structure：Based on initial pointUsing sequential perturbation method build initial simplex, if perturbation because Son is τ (τ ∈ (5,50]), then：IfK=k+1, v=v+1,Otherwise,K=k+1, v=v+1,Wherein, ek=[0 ..., 1 ... 0]T, wherein i-th Element is 1, other are 0；
S23, the sequence of simplex vertex：Enable Vs+1=Vs, Fs+1=Fs, s=s+1, by simplex's Vertex corresponds to ITAE indexs according to itSize be ranked up, wherein V after sequence1 sIndicate ITAE Index smallest point,Indicate ITAE index maximum points,Indicate that ITAE indexs time are a little bigger；ITAE index values are smaller, indicate control Performance processed is better；
S24, reflection：According toGenerate reflective operation pointWherein K=k+1 is enabled,If Yref＜ F1 s, go to step S25 and execute expansive working；IfTurn Step S26 executes shrinkage operation；In the case of other, V is usedrefInstead of Vn+1, YrefInstead ofGo to step S23；
S25, expansion：According toGenerate expansion pointK=k+1 is enabled, If Yexp≤Yref, useInstead ofGo to step S23；Otherwise it usesInstead of S23 is gone to step again；
S26 is shunk：According to formulaGenerate constriction pointWhenWhen,Otherwise,K=k+1 is enabled, After contraction, compares constriction point and shrink reference pointIfWithInstead ofTurn step Rapid S23；Otherwise, it goes to step S27 and executes operation of collapsing, if v=2；
S27 collapses：Execution is collapsed operationK=k+1, v=v+1 are enabled,As v >=n + 1, go to step S23；Otherwise continue step S27.
Further, S3 the specific steps are：
S31, according toIt will be corresponding Iteration control parameter combination be reduced to practical iteration control parameter, whereinFor the iteration control parameter combination after reduction；Each dimensional representation with it is formerCorresponding actual physics parameter；
S32, ifThen practicable iteration control parameterOtherwise, it chooses one and meets feasible zone Interior distanceThe nearest point of Euclidean distanceTo replaceAnd enable practicable iteration control parameterIt chooses The rule of approximate feasible point is such as
Wherein,For space In certain point arriveEuclidean distance, Φ is the disaggregation for meeting minimum euclidean distance.
Further, S6 is as follows：
S61 generates or updates opposite optimality sequence：If preceding a batch of iteration control parameter combination sequence is Mk-1= {(X1,Y1),(X2,Y2),…(Xk-1,Yk-1), wherein XiFor pratical and feasible iteration control parameter combination, YiFor the control parameter group ITAE calculated values under closing, (Xi,Yi) constitute an iteration control parameter combination information collection.New iteration control parameter combination information Collection is (Xk,Yk), after being updated iteration point sequence, form current iteration composite sequence Mk；Each control parameter is combined again and is believed Size of the breath collection based on iteration control parameter combination ITAE is resequenced, and forms one group by the incremental sequence of ITAE valuesWhereinFor ITAE in current iteration point control parameter composite sequence Value minimum, the iteration control parameter combination of control performance optimal (by taking minimum problem as an example)；And by the iteration control parameter group Close the opposite optimality sequence of information collection write-inWherein current optimal sequence it is new Increase pointAs
S62 generates or updates smooth track：Using n+1 as the calculating basis of sliding trace, λ is that slipping smoothness coefficient (takes Integer 1,2 ...), sliding window size is then λ (n+1), and the computation rule that sliding trace is formed is as follows：
Smooth, generation sliding trace is carried out to opposite optimality sequence using the computation rule
S63, generates or update terminates track：In sliding traceOn the basis of, it is further sliding Dynamic average computation must terminate trackIts computation rule is as follows：
Wherein, η is that sliding terminates coefficient；
S64 generates or updates sequence of differences and termination factor：According to termination trackIt can obtain Obtain its sequence of differences Δ YT, desired value growth trend of the sequence characterization at different iteration control parameter combinations, sequence of differences ΔYTGeneration rule it is as follows：
The termination factor of Optimization Progress can be calculated based on sequence of differences and termination trackThe factor Mathematical sense be current iteration control parameter combined spot improvement relative to current iteration point ITAE values ratio, reflect excellent The relative progress of change process, ξ is bigger, indicates that the improvement degree at current iteration control parameter combined spot is bigger；Conversely, then table Show that improvement degree at this point is smaller, the lower threshold ξ of the factorΓ, designation system optimization is close to stagnate；
S65, Optimization Progress, which terminates, to be judged：As ξ ＜ ξΓWhen condition meets, κ sets 1 by 0, then, in successive iterations batch, When iteration control parameter combination meets ξ ＜ ξ againΓ, κ incremental 1；And as κ ≠ 0, in case of ξ ＞ ξΓ, indicate Optimization Progress Dead state is jumped out, again sets to 0 κ, only when κ is equal to its lower threshold κFWhen, it is believed that Optimization Progress meets end condition, iteration Stop criterion condition is (ξ ＜ ξΓ) ∩ (κ=κF)；
S66 exports Optimization Progress state of a control mark ψ=1, system output optimum control ginseng when Optimization Progress terminates Array closes (X*,Y*)；If not yet met end condition, then jumps to S2 and continue iteration execution.
Further, control parameter includes proportionality coefficient P, integral coefficient I and differential coefficient D.
A kind of motor speed Optimization about control parameter system based on simplex search, wherein the motor speed control ginseng Number optimization system includes initialization module, optimization module, preprocessing module, experiment test module, post-processing module and assessment mould Block, wherein initialization module, which is used to receive, to be come from the initial controlling parameter group that engineer or operating personnel are provided and close and respectively The bound of control parameter, and initial controlling parameter group is closed and carries out upscaled, ginseng related to evaluation module to optimization module Number is set；Optimization module for receives the control parameter after upscaled combine, according to simplex search method search for Go out iteration control parameter combination new, after testing, upscaled；Preprocessing module is for the iteration control after will be upscaled Parameter combination is pre-processed into pratical and feasible iteration control parameter combination；Experiment test module is for joining pratical and feasible iteration control Array closes the control system for sending motor to, and motor is made to be run under control parameter combination, and control system is acquired from starting Moment t0Start to EOT end of test moment tfBetween in actual speed, constitute rotating speed sequence and simultaneously send it to post-processing module, Wherein in moment t0, rotating speed zero, in moment tf, rotating speed is target set point vsp；Post-processing module is used for according to actual speed By formulaIts ITAE index is calculated to assess the control performance of motor, wherein tiFor sampling instant, e (i) For the deviation of actual speed and setting speed under the sampling instant, and scale is carried out to pratical and feasible iteration control parameter combination Change；Evaluation module is used for according to pratical and feasible iteration control parameter combination and its corresponding ITAE values, and according to historical information meter Opposite optimality sequence is calculated to obtain, and according to the track characteristic of the sequence, whether pratical and feasible iteration control parameter combination is met Optimality is assessed in real time, if meeting optimality, is exported optimal control parameter combination, is otherwise gone to optimization module and change Generation.
The present invention uses above-mentioned technical proposal, has an advantageous effect in that：
1, implementation cost is small, saves the optimization time and experiment expends；
2, expertise is not depended on, it is easy to implement；
3, optimal control parameter combination can efficiently be provided under minimum optimization cost.
Description of the drawings
Fig. 1 is the system structure diagram of the present invention；
Fig. 2 is the overall procedure step schematic diagram of the method for the present invention；
Fig. 3 is the step schematic diagram of the simplex search method in the method for the present invention；
Fig. 4 is the schematic diagram of initial simplex construction；
Fig. 5 is the step schematic diagram of evaluation process.
Specific implementation mode
To further illustrate that each embodiment, the present invention are provided with attached drawing.These attached drawings are that the invention discloses one of content Point, mainly to illustrate embodiment, and the associated description of specification can be coordinated to explain the operation principles of embodiment.Cooperation ginseng These contents are examined, those of ordinary skill in the art will be understood that other possible embodiments and advantages of the present invention.In figure Component be not necessarily to scale, and similar component symbol is conventionally used to indicate similar component.
In conjunction with the drawings and specific embodiments, the present invention is further described.
Fig. 1 is the structure chart of the motor speed Optimization about control parameter system proposed by the invention based on simplex search,
Fig. 2 is the general steps of the motor speed Optimization about control parameter method proposed by the invention based on simplex search Figure.The motor speed Optimization about control parameter system includes initialization module 1, optimization module 2, preprocessing module 3, experiment Test module 4, post-processing module 5 and evaluation module 6, wherein initialization module 1 comes from engineer or operator for receiving The initial controlling parameter group that member is provided is closed and the bound of each control parameter, and is closed to initial controlling parameter group and carry out scale Change, the relevant parameter of optimization module and evaluation module is set.Optimization module 2 is for receiving the ginseng of the control after upscaled Array is closed, and iteration control parameter combination new, after testing, upscaled is provided according to the search of simplex search method.In advance Processing module 3 is pre-processed for the iteration control parameter combination after will be upscaled into pratical and feasible iteration control parameter combination.It is real Test module 4 is tested for sending pratical and feasible iteration control parameter combination the control system of motor to, and makes motor in the control It is run under parameter combination processed, control system is acquired from initial time t0Start to EOT end of test moment tfBetween in actual speed, It constitutes rotating speed sequence and sends it to post-processing module, wherein in moment t0, rotating speed zero, in moment tf, rotating speed sets for target Definite value vsp.Post-processing module 5 is used to press formula according to actual speedIts ITAE index is calculated to assess electricity The control performance of machine, wherein ti is sampling instant, and e (i) is the deviation of actual speed and setting speed under the sampling instant, and Pratical and feasible iteration control parameter combination is carried out upscaled.Evaluation module 6 is used for according to pratical and feasible iteration control parameter group It closes and its corresponding ITAE values, and calculates to obtain opposite optimality sequence according to historical information, and according to the track characteristic of the sequence, It is assessed in real time whether pratical and feasible iteration control parameter combination meets optimality, if meeting optimality, is exported optimal Control parameter combines, and otherwise goes to optimization module 2 and is iterated.
Ginseng is controlled referring to Fig. 1 to 5 and in conjunction with a specific example to describe the motor speed based on simplex search Number optimization method, the optimization method include the following steps：
S1：Operating personnel determine control system parameter according to Control System Design scheme, choose proportionality coefficient kp, integration system Count the parameters such as Ti, differential coefficient Td control parameter as an optimization.If X1Indicate proportionality coefficient kp, X2Indicate integral coefficient Ti, X3Table Show differential coefficient Td.Initial controlling parameter group, which is given, by operating personnel closes setting valueX0=[1,0.1, 0.51]T；The lower limiting value and upper limit value that each control parameter is rule of thumb set by operating personnel obtain the control ginseng of considered critical Number optimization sections, proportionality coefficient kp, integral coefficient Ti, differential coefficient Td upper limit value be denoted asThis reality It applies in example and takes：Xmax=[30,10,5]T, lower limiting value is denoted as:It is taken in the present embodiment：Xmin=[0, 0,0]T；It is 100 times that largest optimization iterations, which are arranged, by operating personnel.Engineer or operating personnel are according to each control parameter Bound determines optimization problem feasible zone, be expressed as D=X | (Xt)L≤Xt≤(Xt)H, t=1 ..., n }, wherein (Xt)L=inf (Xt) it is lower bound, (Xt)H=sup (Xt) it is the upper bound.1 typing above- mentioned information of initialization module is called, and presses formula (1) by X0= [1,0.1,0.51]TScale turns to
After upscaled, the control parameter variable of each dimension has unified scale, each control parameter upscaled In [0,100] section.Assignment is carried out to the parameter { α, beta, gamma, δ } of simplex search method, takes { α=1, β=0.5, γ=2, δ =0.5 }, and simplex search iteration count value s=0 is set, simplex builds operator v=1.Mould is assessed to Optimization Progress simultaneously Block parameter is set, setting final state coefficient initial value κ=0, lower threshold κF=3, termination factor lower threshold ξΓ= 0.05, slipping smoothness coefficient lambda=1, sliding terminates coefficient η=1.
Step 2：Optimization module 2 receives the combination of the control parameter after upscaledIt is searched for according to simplex search method Provide iteration control parameter combination new, after testing, upscaledEnable i=i+1.As shown in figure 3, its given side Method with steps are as follows：
S21：Execute initialization judgement.If current v >=n+1, goes to step S23；Otherwise, S22 is gone to step, is executed simple Shape is built.
S22：Carry out initial simplex structure.With initial pointBased on, using sequential perturbation method successively to each control Parameter perturbs to build initial simplex V1.If the perturbation factor is τ=10, then the control parameter that perturbs builds initial simplex Criterion it is as follows, as shown in Figure 4：IfThenV=v+1, k=k+1；Otherwise,V=v+1, k=k+1.ek=[0 ..., 1 ... 0]T, wherein i-th of element is 1, other It is 0.
S23：Simplex vertex is ranked up.Enable Vs+1=Vs, Fs+1=Fs, s=s+1.By simplexVertex according to its correspond to ITAE indexsSize be ranked up.Its In, V after sequence1 sIndicate ITAE index smallest points,Indicate ITAE index maximum points,Indicate that ITAE indexs time are a little bigger； ITAE index values are smaller, indicate that control performance is better；
S24：Carry out reflective operation.According toGenerate reflective operation pointWhereinK=k+1 is enabled,If Yref＜ F1 s, go to step S25 and execute expansion behaviour Make；IfIt goes to step S26 and executes shrinkage operation；In the case of other, V is usedrefInstead of Vn+1, YrefInstead ofTurn step Rapid S23.
S25：Carry out expansive working.According toGenerate expansion pointK=k+1 is enabled,If Yexp≤Yref, useInstead ofGo to step S23；Otherwise it usesGeneration It replacesS23 is gone to step again.
S26：Carry out shrinkage operation.According to formulaGenerate constriction pointWhenWhen,Otherwise,K=k+1 is enabled, After contraction, compares constriction point and shrink reference pointIfWithInstead ofTurn step Rapid S23；Otherwise, it goes to step S27 and executes operation of collapsing, if v=2.
S27：It collapses：Execution is collapsed operationK=k+1, v=v+1 are enabled,As v >=n + 1, go to step S23；Otherwise continue step S27.
S3：By by simplex search optimization module provide it is upscaled after iteration control parameter combination iIt is sent to pre- Processing module.Iteration control parameter combination after upscaled iBy preprocessing module practical iteration control is reduced to by formula (2) Parameter processed.
Wherein,For the iteration control parameter combination after reduction；Each dimensional representation with it is formerCorresponding reality Control parameter.
IfIteration control parameter combination feasible pointOtherwise, it chooses one and meets distance in feasible zoneThe nearest point of Euclidean distanceTo replaceAnd enable new iteration control parameter combination feasible pointIt chooses close Like the rule such as following formula (3) of feasible point.
Wherein,It is arrived for certain point in spaceEuclidean distance, Φ is to meet minimum euclidean distance Disaggregation.
Pratical and feasible iteration control parameter combination XiIt is transferred to control system module.
S4：Experiment test module 4 is by pratical and feasible iteration control parameter combination XiIt sends the control system of motor to, and makes Motor is run under control parameter combination.The module acquires motor actual speed, and by the realization pair of incremental timestamp algorithm The control of motor speed.Shown in the calculation formula of its controlled quentity controlled variable such as following formula (4).
Wherein, u (n) is the controlled quentity controlled variable of n timing control systems output, and e (n) characterizes n moment rotating speed deviations.Wherein, kp, Ti,TdX is combined by control parameteriIt determines.
Originate t0It is 0 that moment, which sets motor actual speed, and control system is from t0Moment starts by above-mentioned control mode regulation motor Rotating speed is to target set point vsp, length of testing speech tf, t is taken in the present embodimentfIt is 10 seconds.Work as tfMoment to resetting motor speed be 0.Module is recorded simultaneously from initial time t0Start to EOT end of test moment tfBetween in actual speed, constitute rotating speed sequence simultaneously It is sent to post-processing module 5.
S5：Motor actual speed sequence is obtained from experiment test module 4 by post-processing module 5, according to actual speed by public affairs Formula (5) calculates its ITAE index to assess the control performance of motor, and ITAE numerical value is smaller, and control performance is better.
Wherein, tiFor sampling instant, e (i) is the deviation of actual speed and setting speed under the sampling instant.
Upscaled to the progress of practical iteration control parameter combination, each control parameter arrives [0,100] section by upscaled. Upscaled rule is as the following formula (6).
Wherein, optimization section be D=X | (Xt)L≤Xt≤(Xt)H, t=1 ..., n }, (Xt)L=inf (Xt), (Xt)H= sup(Xt)。
S6：Control parameter combination during Optimization Progress evaluation module optimization of collection and its corresponding ITAE values, and root Opposite optimality sequence is calculated to obtain according to historical information, and according to the track characteristic of the sequence, Optimization Progress is assessed in real time, Identify that section state of stagnating, control Optimization Progress terminate in due course in time according to assessment result, output optimal control parameter combination.Such as figure Shown in 5, key step is as follows：
S61：Generate or update opposite optimality sequence.If preceding a batch of iteration control parameter combination sequence is Mk-1= {(X1,Y1),(X2,Y2),…(Xk-1,Yk-1), wherein XiFor pratical and feasible iteration control parameter combination, YiFor the control parameter group ITAE values under closing, (Xi,Yi) constitute an iteration control parameter combination information collection.Newly iteration control parameter combination information collection is (Xk,Yk), after being updated iteration point sequence, form current iteration composite sequence Mk.Again by each control parameter combined information collection Size based on iteration control parameter combination ITAE values is resequenced, and forms one group by the incremental sequence of ITAE valuesWhereinTo be controlled in current iteration point control parameter composite sequence The iteration control parameter combination of best performance (by taking minimum problem as an example).And the iteration control parameter combination information collection is written Opposite optimality sequenceThe newly-increased point of wherein current optimal sequence As
S62：Generate or update smooth track.Using n+1 as the calculating basis of sliding trace, λ is that slipping smoothness coefficient (takes Integer 1,2 ...), sliding window size is then λ (n+1).The computation rule such as following formula (7) that sliding trace is formed.
Smooth, generation sliding trace is carried out to opposite optimality sequence using the computation rule
S63：It generates or update terminates track.In sliding traceOn the basis of, it is further sliding Dynamic average computation must terminate trackIts computation rule such as following formula (8).
Wherein, η is that sliding terminates coefficient.
S64：Generate or update sequence of differences and termination factor.According to termination trackIt can obtain Obtain its sequence of differences Δ YT, desired value growth trend of the sequence characterization at different iteration control parameter combinations.Sequence of differences ΔYTGeneration rule such as following formula (9).
The termination factor of Optimization Progress can be calculated based on sequence of differences and termination trackThe factor Mathematical sense be current iteration control parameter combined spot improvement relative to current iteration point ITAE values ratio, reflect excellent The relative progress of change process.ξ is bigger, indicates that the improvement degree at current iteration control parameter combined spot is bigger；Conversely, then table Show that improvement degree at this point is smaller.The lower threshold ξ of the factorΓ, designation system optimization is close to stagnate.
S65：Optimization Progress, which terminates, to be judged.As ξ ＜ ξΓWhen condition meets, κ sets 1 by 0.Then, in successive iterations batch, When iteration control parameter combination meets ξ ＜ ξ againΓ, κ incremental 1；And as κ ≠ 0, in case of ξ ＞ ξΓ, indicate Optimization Progress Dead state is jumped out, again sets to 0 κ.Only when κ is equal to its lower threshold κFWhen, it is believed that Optimization Progress meets end condition. Stopping criteria condition such as following formula (10).
(ξ ＜ ξΓ) ∩ (κ=κF) (10)
S66：When Optimization Progress evaluation module judgement Optimization Progress terminate when, i.e., (κ=3) ∩ (ξ ＜ 0.1) when, output it is excellent Change Process flowchart Status Flag ψ=1, system output optimal control parameter combines (X*,Y*), optimization system is out of service；As not yet Meet end condition, then optimization system go to step S2 continue iteration execute.
In the present embodiment, after 44 iteration are tested, optimization system proposed by the invention finds optimal control parameter Combination is as follows：X=[0.4314,0.0241,0.3144]T.I.e. proportionality coefficient is 0.4314, integral coefficient 0.0241, differential Coefficient is 0.3144.
Although specifically showing and describing the present invention in conjunction with preferred embodiment, those skilled in the art should be bright In vain, it is not departing from the spirit and scope of the present invention defined by the appended claims, it in the form and details can be right The present invention makes a variety of changes, and is protection scope of the present invention.

Claims (9)

1. a kind of motor speed Optimization about control parameter method based on simplex search, it is characterised in that：The method includes with Lower step：
S1, initialization, artificially given initial controlling parameter group, which merges to close initial controlling parameter group, carries out upscaled, setting optimization The initial value of relevant parameter needed for process and evaluation process；
S2, optimization process provide iteration control parameter group new, after testing, upscaled according to simplex search method It closes；
S3, preprocessing process, will be upscaled after iteration control parameter combination pre-process at pratical and feasible iteration control parameter group It closes；
S4, experimental test procedures send pratical and feasible iteration control parameter combination the control system of motor to, and motor are made to exist The lower operation of control parameter combination, control system are acquired from initial time t0Start to EOT end of test moment tfBetween in reality Rotating speed constitutes rotating speed sequence, wherein in moment t0, rotating speed zero, in moment tf, rotating speed is target set point vsp；
S5, last handling process press formula according to actual speedIts ITAE index is calculated to assess motor Control performance, wherein tiFor sampling instant, e (i) is the deviation of actual speed and setting speed under the sampling instant, and to reality The feasible iteration control parameter combination in border carries out upscaled；
S6, evaluation process, according to pratical and feasible iteration control parameter combination and its corresponding ITAE values, and according to historical information meter Opposite optimality sequence is calculated to obtain, and according to the track characteristic of the sequence, whether pratical and feasible iteration control parameter combination is met Optimality is assessed in real time, if meeting optimality, is exported optimal control parameter combination, is otherwise gone to S2 and be iterated.
2. the motor speed Optimization about control parameter method based on simplex search as described in claim 1, it is characterised in that：S1 In upscaled pass through formula
It carries out, whereinIt is closed for initial controlling parameter group, (Xt)L=inf (Xt) it is lower bound, (Xt)H= sup(Xt) it is the upper bound, n is the control parameter number of optimization,Indicate the initial value of i-th of control parameter, t=1,2 ..., n.
3. the motor speed Optimization about control parameter method based on simplex search as claimed in claim 2, it is characterised in that： Assignment is carried out to the parameter of simplex search method { α, beta, gamma, δ } in S1, and sets simplex search iteration count value s=0, Simplex builds operator v=1；And the relevant parameter of evaluation process is set, setting final state coefficient initial value κ=0, under Limit threshold value κF, termination factor lower threshold ξΓ, slipping smoothness coefficient lambda, sliding termination coefficient η.
4. the motor speed Optimization about control parameter method based on simplex search as claimed in claim 3, it is characterised in that：Ginseng Number { α, beta, gamma, δ } takes { α=1, β=0.5, γ=2, δ=0.5 }, lower threshold κF=3, termination factor lower threshold ξΓ= 0.05, slipping smoothness coefficient lambda=1, sliding terminates coefficient η=1.
5. the motor speed Optimization about control parameter method based on simplex search as claimed in claim 3, it is characterised in that：S2 Simplex search method the specific steps are：
S21, initialization condition judgement：If current v >=n+1, goes to step S23；Otherwise, S22 is gone to step, simplex structure is executed It builds；
S22, initial simplex structure：Based on initial pointInitial simplex is built using sequential perturbation method, if the perturbation factor is τ (τ ∈ (5,50]), then：IfK=k+1, v=v+1,Otherwise,K=k+1, v=v+1,Wherein, ek=[0 ..., 1 ... 0]T, wherein i-th Element is 1, other are 0；
S23, the sequence of simplex vertex：Enable Vs+1=Vs, Fs+1=Fs, s=s+1, by simplexVertex ITAE indexs are corresponded to according to itSize be ranked up, wherein V after sequence1 sIndicate ITAE indexs Smallest point,Indicate ITAE index maximum points,Indicate that ITAE indexs time are a little bigger；ITAE index values are smaller, indicate control performance Better；
S24, reflection：According toGenerate reflective operation pointWhereinEnable k=k + 1,If Yref＜ F1 s, go to step S25 and execute expansive working；IfGo to step S26 Execute shrinkage operation；In the case of other, V is usedrefInstead of Vn+1, YrefInstead ofGo to step S23；
S25, expansion：According toGenerate expansion pointK=k+1 is enabled, Such as Fruit Yexp≤Yref, useInstead of Go to step S23；Otherwise it usesInstead of Turn to walk again Rapid S23；
S26 is shunk：According to formulaGenerate constriction pointWhenWhen,Otherwise,K=k+1 is enabled, After contraction, compares constriction point and shrink reference pointIfWithInstead of It goes to step S23；Otherwise, it goes to step S27 and executes operation of collapsing, if v=2；
S27 collapses：Execution is collapsed operationK=k+1, v=v+1 are enabled,As v >=n+1, turn Step S23；Otherwise continue step S27.
6. the motor speed Optimization about control parameter method based on simplex search as claimed in claim 5, it is characterised in that：S3 The specific steps are：
S31, according toBy corresponding iteration Control parameter combination is reduced to practical iteration control parameter, whereinFor the iteration control parameter combination after reduction；It is each A dimensional representation and originalCorresponding actual physics parameter；
S32, ifThen practicable iteration control parameterOtherwise, choose one meet in feasible zone away from FromThe nearest point of Euclidean distanceTo replaceAnd enable practicable iteration control parameterChoosing approximation can The rule of row point is such asWherein,For Certain point arrives in spaceEuclidean distance, Φ is the disaggregation for meeting minimum euclidean distance.
7. the motor speed Optimization about control parameter method based on simplex search as claimed in claim 6, it is characterised in that：S6 It is as follows：
S61 generates or updates opposite optimality sequence：If preceding a batch of iteration control parameter combination sequence is Mk-1={ (X1, Y1),(X2,Y2),…(Xk-1,Yk-1), wherein XiFor pratical and feasible iteration control parameter combination, YiUnder being combined for the control parameter ITAE calculated values, (Xi,Yi) constitute an iteration control parameter combination information collection.Newly iteration control parameter combination information collection is (Xk,Yk), after being updated iteration point sequence, form current iteration composite sequence Mk；Again by each control parameter combined information collection Size based on iteration control parameter combination ITAE is resequenced, and forms one group by the incremental sequence of ITAE valuesWhereinFor ITAE in current iteration point control parameter composite sequence It is worth the iteration control parameter combination of optimal (by taking minimum problem as an example)；And phase is written into the iteration control parameter combination information collection To optimality sequenceThe newly-increased point of wherein current optimal sequenceI.e. For
S62 generates or updates smooth track：Using n+1 as the calculating basis of sliding trace, λ is slipping smoothness coefficient (round numbers 1,2 ...), sliding window size is then λ (n+1), and the computation rule that sliding trace is formed is as follows：
Smooth, generation sliding trace is carried out to opposite optimality sequence using the computation rule
S63, generates or update terminates track：In sliding traceOn the basis of, further sliding is flat It calculates to terminate trackIts computation rule is as follows：
Wherein, η is that sliding terminates coefficient；
S64 generates or updates sequence of differences and termination factor：According to termination trackIt can be obtained Sequence of differences Δ YT, desired value growth trend of the sequence characterization at different iteration control parameter combinations, sequence of differences Δ YT's Generation rule is as follows：
The termination factor of Optimization Progress can be calculated based on sequence of differences and termination trackThe mathematics of the factor Be meant that the ratio of the improvement of current iteration control parameter combined spot relative to the ITAE values of current iteration point, reflect optimize into The relative progress of journey, ξ is bigger, indicates that the improvement degree at current iteration control parameter combined spot is bigger；Otherwise, it means that Improvement degree at the point is smaller, the lower threshold ξ of the factorΓ, designation system optimization is close to stagnate；
S65, Optimization Progress, which terminates, to be judged：As ξ ＜ ξΓWhen condition meets, κ sets 1 by 0, then, in successive iterations batch, when repeatedly Meet ξ ＜ ξ again for control parameter combinationΓ, κ incremental 1；And as κ ≠ 0, in case of ξ ＞ ξΓ, indicate that Optimization Progress is jumped out Dead state again sets to 0 κ, only when κ is equal to its lower threshold κFWhen, it is believed that Optimization Progress meets end condition, iteration ends Criterion condition is (ξ ＜ ξΓ) ∩ (κ=κF)；
S66 exports Optimization Progress state of a control mark ψ=1, system output optimal control parameter group when Optimization Progress terminates Close (X*,Y*)；If not yet met end condition, then jumps to S2 and continue iteration execution.
8. the motor speed Optimization about control parameter method based on simplex search as described in claim 1, it is characterised in that：Control Parameter processed includes proportionality coefficient P, integral coefficient I and differential coefficient D.
9. a kind of motor speed Optimization about control parameter system based on simplex search, it is characterised in that：The motor speed control Parameter Optimization System processed includes initialization module, optimization module, preprocessing module, experiment test module, post-processing module and comments Estimate module, wherein initialization module, which is used to receive, comes from the initial controlling parameter group conjunction that engineer or operating personnel are provided And the bound of each control parameter, and it is upscaled to initial controlling parameter group conjunction progress, to the phase of optimization module and evaluation module Related parameter is set；Optimization module is searched for receiving the combination of the control parameter after upscaled according to simplex search method Rope provides iteration control parameter combination new, after testing, upscaled；Preprocessing module is for the iteration after will be upscaled Control parameter combination pretreatment is at pratical and feasible iteration control parameter combination；Experiment test module is used for pratical and feasible iteration control Parameter combination processed sends the control system of motor to, and make motor the control parameter combination under run, control system acquisition from Initial time t0Start to EOT end of test moment tfBetween in actual speed, constitute rotating speed sequence simultaneously send it to post-processing Module, wherein in moment t0, rotating speed zero, in moment tf, rotating speed is target set point vsp；Post-processing module is used for according to reality Rotating speed presses formulaIts ITAE index is calculated to assess the control performance of motor, wherein tiFor sampling instant, E (i) is the deviation of actual speed and setting speed under the sampling instant, and to pratical and feasible iteration control parameter combination into rower Degreeization；Evaluation module is used for according to pratical and feasible iteration control parameter combination and its corresponding ITAE values, and according to historical information Opposite optimality sequence is calculated to obtain, and according to the track characteristic of the sequence, whether pratical and feasible iteration control parameter combination is accorded with It closes optimality to be assessed in real time, if meeting optimality, exports optimal control parameter combination, otherwise go to optimization module progress Iteration.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117057169A (en) * 2023-10-12 2023-11-14 中国水利水电科学研究院 Hydrological model parameter calibration method based on heuristic migration complex competition evolution algorithm

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102101352A (en) * 2010-12-22 2011-06-22 浙江大学 Model-free weight controlling method for injection molding product
CN106950849A (en) * 2015-11-30 2017-07-14 欧姆龙株式会社 Control parameter adjusting apparatus, control parameter method of adjustment
CN107577144A (en) * 2017-08-22 2018-01-12 浙江工业大学 A kind of aircraft finite time Adaptive Attitude control method based on enhanced index Reaching Law

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102101352A (en) * 2010-12-22 2011-06-22 浙江大学 Model-free weight controlling method for injection molding product
CN106950849A (en) * 2015-11-30 2017-07-14 欧姆龙株式会社 Control parameter adjusting apparatus, control parameter method of adjustment
CN107577144A (en) * 2017-08-22 2018-01-12 浙江工业大学 A kind of aircraft finite time Adaptive Attitude control method based on enhanced index Reaching Law

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
关健生等: "《基于单纯形搜索无模型优化方法的蒸汽发生器液位控制》", 《福建省电机工程学会第十五届学术年会》 *
姜重然等: "《永磁同步电机控制系统控制器参数的优化设计》", 《控制与应用技术》 *
孔祥松: "《快速、低成本间歇过程无模型优化方法研究》", 《万方学位论文》 *
陈娜等: "《基于FOC的永磁同步电机速度控制器参数优化设计》", 《伺服控制》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117057169A (en) * 2023-10-12 2023-11-14 中国水利水电科学研究院 Hydrological model parameter calibration method based on heuristic migration complex competition evolution algorithm
CN117057169B (en) * 2023-10-12 2024-01-09 中国水利水电科学研究院 Hydrological model parameter calibration method based on heuristic migration complex competition evolution algorithm

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