CN110095981A - A kind of setting method, device and the electronic equipment of automatic disturbance rejection controller parameter - Google Patents
A kind of setting method, device and the electronic equipment of automatic disturbance rejection controller parameter Download PDFInfo
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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
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- 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
Abstract
The invention discloses setting method, device and the electronic equipments of a kind of automatic disturbance rejection controller parameter, the setting method of the automatic disturbance rejection controller parameter, include the steps of determining that automatic disturbance rejection controller to setting parameter, be one or more parameters in the Controlling model of automatic disturbance rejection controller to setting parameter；Multiple parameters group is formed according to setting parameter；Optimizing is carried out to multiple parameters group using whale optimization algorithm；It is adjusted according to parameter of the optimizing result to automatic disturbance rejection controller.It is determined as by one or more parameters (parameter being generally affected to automatic disturbance rejection controller) in the Controlling model by automatic disturbance rejection controller to setting parameter, further reduce the quantity to setting parameter, and optimizing is carried out to by the parameter group of multiple and different values by whale optimization algorithm, realize the determination of the value of each control parameter when reaching ADRC compared with high control precision.
Description
Technical field
The present invention relates to automatic control technology field more particularly to a kind of setting method of automatic disturbance rejection controller parameter,
Device and electronic equipment.
Background technique
Auto Disturbances Rejection Control Technique (ADRC) based on extended state observer is a kind of nonlinear robust control technology, certainly anti-
Disturbing controller is the improvement to PID, eliminates integral element, increases extended state observer to realize to inner model of system
The real-time estimation of perturbation and external disturbance, and nonlinearity erron state feedback strategy is used, remain not depending on for PID controller
The mathematical model of controlled device relies solely on the error between control target and agenda to determine and eliminates the excellent of this error
Point overcomes it and extracts unreasonable, the low defect of control precision to error.
But since ADRC includes differential tracker (Tracking differentiator, TD), nonlinear feedback
(Nonlinear State Error Feedback Control Law, NLSEF) and extended state observer (Extended
State observer, ESO), and contain to ADRC control precision in the Controlling model of TD, NLSEF and ESO with one
Be fixed sound multiple control parameters, therefore, though user rule of thumb by the value of parameters be restricted to one it is lesser
Range determines the value of each control parameter, so that ADRC can reach still larger compared with the realization difficulty of high control precision.
Summary of the invention
In view of this, the embodiment of the invention provides setting method, device and the electronics of a kind of automatic disturbance rejection controller parameter
Equipment, to solve to determine the value of each control parameter, so that ADRC can reach biggish compared with the realization difficulty of high control precision
Problem.
According in a first aspect, the embodiment of the invention provides a kind of setting methods of automatic disturbance rejection controller parameter, including such as
Lower step: determine automatic disturbance rejection controller to setting parameter, be one in the Controlling model of automatic disturbance rejection controller to setting parameter
A or multiple parameters；Multiple parameters group is formed according to setting parameter；Multiple parameters group is sought using whale optimization algorithm
Excellent calculating；It is adjusted according to parameter of the optimizing result to automatic disturbance rejection controller.
By one or more parameters in the Controlling model by automatic disturbance rejection controller (generally to automatic disturbance rejection controller shadow
Ring biggish parameter, other are determined the value that automatic disturbance rejection controller influences lesser parameter by experience) be determined as to
Setting parameter further reduces the quantity to setting parameter, and forms multiple ginsengs according to multiple parameters group is formed to setting parameter
(parameters group includes needed setting parameter to array, but all at least one waits adjusting between any two parameter group
The value of parameter is different), so that optimizing is carried out to by the parameter group of multiple and different values by whale optimization algorithm, according to
The parameter of optimizing result (the optimized parameter group in multiple parameters group) the adjusting automatic disturbance rejection controller of whale optimization algorithm, thus real
The determination of the value of each control parameter when now achieving the purpose that ADRC compared with high control precision.
With reference to first aspect, in first aspect first embodiment, the Controlling model of automatic disturbance rejection controller are as follows:
Wherein, TD, ESO and NLSEF refer respectively to derivative controller, nonlinear feedback device and expansion in automatic disturbance rejection controller
State observer is opened, k refers to variate-value, v0Refer to tracking control signal, v1And v2The mistake for the input signal v that respectively TD is provided
Spend journey and its differential, e1And e2Refer respectively to v1And v2The state variable z of control object relative to AD automatic disturbance rejection controller1With
z2Error, z3Refer to total disturbance real-time control actuating quantity of AD control object, b0Refer to the control input of AD automatic disturbance rejection controller
Amplification coefficient, u refer to nonlinear Feedback Control rate, and r refers to the speed factor, and h refers to step-length, β01, β02, β03, βiRefer to amendment system
Number, u0Refer to that control signal, fal () refer to the nonlinear function of output error corrected rate；AD parameter group includes β01, β02, β03
And βi。
With reference to first aspect, in first aspect second embodiment, using whale optimization algorithm to the multiple parameter
The step of group progress optimizing includes: using multiple parameters group as the population in whale optimization algorithm；In multiple parameters group
Any one is the individual in population；Population is initialized by chaos sequence, and the population after initialization is drawn
It is divided into the first sub- population and the second sub- population；It is lower to the fitness function value in G the first sub- population of generation using mutation algorithm
The individual of first preset quantity is replaced, and forms G+1 the first sub- population of generation；Mutation algorithm are as follows: Wherein,Refer to individualJth tie up element,Refer to G generation kind
3 inequality individuals of group, F is zoom factor；It is lower to the fitness function value in G the second sub- population of generation using WOA algorithm
The individual of second preset quantity is replaced, and forms G+1 the second sub- population of generation；Judge optimal in G+1 the first sub- population of generation
Whether the fitness function value of body is less than the fitness function value of optimum individual in G+1 the second sub- population of generation；The first son kind of G+1 generation
Optimum individual in group refers to the highest individual of fitness function value in the first sub- population；When optimal in G+1 the first sub- population of generation
When the fitness function value of individual is less than the fitness function value of optimum individual in G+1 the second sub- population of generation, by the first son of G+1 generation
Third preset quantity in the lower individual of the fitness function value of third preset quantity in population, with G+1 the second sub- population of generation
The higher individual of fitness function value is replaced；Judge whether G+1 is equal to default the number of iterations；When G+1 is equal to default iteration
When number, then using the optimum individual in G+1 the first sub- population of generation as the optimum individual of population；When G+1 is not equal to default iteration
When number, then using the first sub- population of G+1 generation as G the first sub- population of generation, using the second sub- population of G+1 generation as the second son kind of G generation
Group, and continue optimizing, until G+1 is equal to default number of iterations.
Since chaos phenomenon is the random process of the certainty showed in nonlinear dynamic system, class, and chaos is transported
It is dynamic that initial value extreme sensitivity can in a certain range constantly traverse all states according to certain self-law,
Therefore carrying out initialization to population by chaos sequence can be improved whale optimization algorithm in the search capability of initial stage, accelerate
The convergence rate of whale optimization algorithm；And pass through the population dividing after initializing as the first sub- population and the second sub- population, and
Mutation algorithm is introduced in updating the second of the first sub- population, it can be individual multifarious same in increasing by the first sub- population
When, prevent the first sub- population from falling into local optimum by individual variation, and be better than first by the individual in the second sub- population
(fitness function value of optimum individual is less than in G+1 the second sub- population of generation most individual in sub- population in G+1 the first sub- population of generation
The fitness function value of excellent individual) when, by fitness function value lower of the second preset quantity in G+1 the first sub- population of generation
The higher individual of fitness function value of two preset quantities is replaced in body, with G+1 the second sub- population of generation, is further speeded up and is sought
Look for the speed of population optimal solution.
Second embodiment with reference to first aspect, the individual in first aspect third embodiment, in the first sub- population
Number of individuals in several and the second sub- population is the half of the number of individuals in population.
Second embodiment with reference to first aspect, in the 4th embodiment of first aspect, chaos sequence is to use
Logistic mapping algorithm generates.
Second embodiment with reference to first aspect, in the 5th embodiment of first aspect, fitness function refers to for ITAE
Scalar functions.
According to second aspect, the embodiment of the invention provides a kind of setting devices of automatic disturbance rejection controller parameter, comprising: ginseng
Number determining modules, for determine automatic disturbance rejection controller to setting parameter, be the control mould of automatic disturbance rejection controller to setting parameter
One or more parameters in type；Parameter group forms module, for forming multiple parameters group according to setting parameter；Algorithm optimizing
Module, for carrying out optimizing to multiple parameters group using whale optimization algorithm；Parameter tuning module, for according to optimizing knot
Fruit adjusts the parameter of automatic disturbance rejection controller.
In conjunction with second aspect, algorithm optimizing module includes: that population forms unit, for multiple parameters group is excellent as whale
Change the population in algorithm；Any of multiple parameters group is the individual in population；Population dividing unit, for passing through
Chaos sequence initializes population, and is the first sub- population and the second sub- population by the population dividing after initialization；First
Updating unit, for using mutation algorithm, to lower first preset quantity of fitness function value in G the first sub- population of generation
Individual is replaced, and forms G+1 the first sub- population of generation；Mutation algorithm are as follows:
Wherein,Refer to individualJth tie up element,Refer to G for 3 inequalities of population
Body, F are zoom factors；Second updating unit, for using WOA algorithm to G generation the second sub- population in fitness function value compared with
The individual of the second low preset quantity is replaced, and forms G+1 the second sub- population of generation；First judging unit, for judging G+1 generation
Whether the fitness function value of the optimum individual in the first sub- population is less than the fitness of optimum individual in G+1 the second sub- population of generation
Functional value；Optimum individual in first sub- population refers to the highest individual of fitness function value in the first sub- population；Third updates
Unit, for being less than in G+1 the second sub- population of generation optimal when the fitness function value of optimum individual in G+1 the first sub- population of generation
When the fitness function value of body, by G+1 generation the first sub- population in third preset quantity the lower individual of fitness function value, with
The higher individual of fitness function value of third preset quantity is replaced in G+1 the second sub- population of generation；Second judgment unit is used
In judging whether G+1 is equal to default the number of iterations；As a result determination unit is used for when G+1 is equal to default the number of iterations, then by G+
Optimum individual of the optimum individual as population in 1 the first sub- population of generation；Assignment cycling element, for when G+1 is not equal to default
When the number of iterations, then using the first sub- population of G+1 generation as G the first sub- population of generation, using the second sub- population of G+1 generation as G generation second
Sub- population, and continue optimizing, until G+1 is equal to default number of iterations.
According to the third aspect, the embodiment of the invention provides a kind of electronic equipment, comprising: memory and processor, it is described
Connection is communicated with each other between memory and the processor, computer instruction is stored in the memory, and the processor is logical
Cross and execute the computer instruction, thereby executing described in any one of first aspect or first aspect embodiment from
The setting method of disturbance rejection control device parameter.
It is described computer-readable the embodiment of the invention provides a kind of computer readable storage medium according to fourth aspect
Storage medium stores computer instruction, and the computer instruction is for making the computer execute first aspect or first aspect
Any one embodiment described in automatic disturbance rejection controller parameter setting method.
Detailed description of the invention
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution in the prior art
Embodiment or attached drawing needed to be used in the description of the prior art be briefly described, it should be apparent that, it is described below
Attached drawing is some embodiments of the present invention, for those of ordinary skill in the art, before not making the creative labor
It puts, is also possible to obtain other drawings based on these drawings.
Fig. 1 is the application scenarios schematic diagram of the embodiment of the present invention；
A kind of Fig. 2 method flow diagram of the setting method of automatic disturbance rejection controller parameter provided in an embodiment of the present invention；
Fig. 3 is the flow chart of step S103 in a kind of optional embodiment of the embodiment of the present invention；
Fig. 4 is that the setting method of automatic disturbance rejection controller parameter provided in an embodiment of the present invention is applied to two-freedom cascade machine
The flow diagram of device people；
Fig. 5 is the mechanical arm q of two-freedom serial manipulator provided in an embodiment of the present invention1Control error result figure；
Fig. 6 is the mechanical arm q of two-freedom serial manipulator provided in an embodiment of the present invention2Control error result figure；
Fig. 7 is the schematic diagram of the setting device of automatic disturbance rejection controller parameter provided in an embodiment of the present invention；
Fig. 8 is the hardware structural diagram of a kind of electronic equipment provided in an embodiment of the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those skilled in the art are not having
Every other embodiment obtained under the premise of creative work is made, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that term " first ", " second ", " third " are used for description purposes only,
It is not understood to indicate or imply relative importance.
Fig. 1 shows the application scenarios schematic diagram of the embodiment of the present invention, wherein being shown with the Active Disturbance Rejection Control of a second-order system
Device (ADRC), including differential tracker (Tracking differentiator, TD), nonlinear feedback (Nonlinear
State Error Feedback Control Law, NLSEF) and extended state observer (Extended state
Observer, ESO).
Wherein, TD is used for extraction system input signal transition process arranging, and as described in Figure 1, v is system input signal, v1
And v2The transitional processes and its differential (v that respectively TD is provided2=v1, thus can be v2It is regarded as v1" approximate differential "), TD root
Limitation according to input signal and control object carrys out transition process arranging, obtains smooth input signal, and proposes this transient process
All-order derivative, specific Controlling model are as follows:
Wherein, k refers to variable
Value, v1And v2The transitional processes and its differential for the input signal v that respectively TD is provided, r refer to the speed factor, and h refers to step-length, and r
With the equal adjustable parameter of h, r is bigger, and tracking velocity is faster, and h is bigger, and filter effect is better, v0Refer to tracking control signal, h0Refer to filter
The wave factor, fst () refer to such as undefined nonlinear function:
D=rh, d0=dh, y=v1-v+hv2
a0=d2+8r|y|
ESO is to each state variable (z1And z2) estimated, and to estimate ambiguous model and the reality disturbed outside
When actuating quantity (z3), and be compensated in feedback, specifically, the job control model of ESO is as follows:
Wherein, z1And z2Refer to the state variable of the control object of automatic disturbance rejection controller, z3Refer to total disturbance of control object
Real-time control actuating quantity, h refer to step-length, ε1Refer to the value of extended state observer feedback and the difference of output signal, ε2Refer to controlled
The difference of object velocity output and command signal differential, β01, β02And β03Each mean correction factor, b0Refer to the Active Disturbance Rejection Control
The control of device inputs amplification coefficient, and fal () refers to the nonlinear function of output error corrected rate, expression formula are as follows:
NLSEF is the nonlinear combination of the error between the state variable estimation that TD and ESO are generated, it and ESO are to always disturbing
Dynamic compensation rate composition control amount together, Controlling model are as follows:
Wherein, v1And v2The transitional processes and its differential for the input signal v that respectively TD is provided, ε1And ε2Refer respectively to v1With
v2The state variable z of control object relative to automatic disturbance rejection controller1And z2Error, βiRefer to that correction factor, fal () refer to
The nonlinear function of output error corrected rate, expression formula is identical as fal () function in ESO, and details are not described herein.
Setting method, device and the electronic equipment of a kind of automatic disturbance rejection controller parameter provided in an embodiment of the present invention are mainly used
It is determined with the best value of the parameter group formed to several parameters in above-mentioned TD, ESO and NLSEF model, so that from
Disturbance rejection control device reaches higher control precision.
Embodiment 1
Fig. 2 shows the flow charts of the setting method of the automatic disturbance rejection controller parameter of the embodiment of the present invention, as shown in Fig. 2,
This method may include steps of:
S101: determine automatic disturbance rejection controller to setting parameter, be the Controlling model of automatic disturbance rejection controller to setting parameter
In one or more parameters.Herein, one or more is determined as from anti-the parameter that automatic disturbance rejection controller is affected
Disturb controller to setting parameter, specifically, according to the derivative controller (TD) in above-mentioned automatic disturbance rejection controller, nonlinear feedback
The Controlling model of device (ESO) and extended state observer (NLSEF) is it is found that the adjustable parameter in the model includes: TD control mould
Correction factor β in type, in ESO Controlling model01、β02And β03, nonlinear factor α1、δ1、、α2And δ2, NLSEF Controlling model
In correction factor βi, and rule of thumb it is found that speed factor r, step-length h and nonlinear factor α therein1、δ1、α2And δ2It is right
The influence of the control precision of automatic disturbance rejection controller is smaller, thus can rule of thumb preset numerical value, and will be to active disturbance rejection control
The β that the control precision of device processed is affected01, β02, β03And βiAs parameter to be adjusted.
S102: multiple parameters group is formed according to setting parameter.Herein, each parameter group includes needing to be adjusted ginseng
Number, but all at least one waits for that the value of setting parameter is different between any two parameter group.
S103: optimizing is carried out to multiple parameters group using whale optimization algorithm.Herein, each parameter group conduct
An individual, then multiple parameters group forms the population of whale optimizing algorithm, and whale optimization algorithm carries out optimizing to population,
The optimum individual in a population is obtained, which refers in population that the highest individual of fitness function value is (namely multiple
Optimized parameter group in parameter group).Herein, whale optimization algorithm can WOA algorithm based on, or existing right
The adaptive whale optimization algorithm (AWOA) of the backward learning that WOA algorithm is formed after improving, Chaos particle swarm optimization algorithm
(CPSO) etc..
S104: it is adjusted according to parameter of the optimizing result to automatic disturbance rejection controller.
In embodiments of the present invention, (general by one or more parameters in the Controlling model by automatic disturbance rejection controller
For the parameter being affected to automatic disturbance rejection controller, other pass through experience to the value that automatic disturbance rejection controller influences lesser parameter
It is determined) it is determined as further reducing the quantity to setting parameter to setting parameter, and it is multiple according to being formed to setting parameter
Parameter group formation multiple parameters group (parameters group includes needed setting parameter, but between any two parameter group all
At least one waits for that the value of setting parameter is different), thus by whale optimization algorithm to the parameter group by multiple and different values
Optimizing is carried out, active disturbance rejection control is adjusted according to the optimizing result (the optimized parameter group in multiple parameters group) of whale optimization algorithm
The parameter of device processed, to realize the determination of the value of each control parameter when achieving the purpose that ADRC compared with high control precision.
As a kind of optional embodiment of the present embodiment, to use changing based on chaos sequence and double population optimizing strategies
Into whale optimization algorithm (hereinafter referred to as double population chaos whale optimization algorithms, Double population Chaos
Whale Optimization Algorithm, DCWOA).Then as shown in figure 3, step S103 may include steps of:
S201: using multiple parameters group as the population in whale optimization algorithm.Herein, any of multiple parameters group
It is the individual in population.
S202: initializing population by chaos sequence, and is the first sub- population by the population dividing after initialization
With the second sub- population.
Herein, encirclement prey that the whale optimization algorithm (WOA algorithm) being modified includes, Bubble-net attack and with
Machine searches for 3 kinds of behaviors and the corresponding execution algorithm of above-mentioned three behaviors belongs to the prior art, and details are not described herein.
Herein, since the quality of the initial population in whale optimization algorithm affects the solving precision and convergence speed of algorithm
Degree, the preferable initial population of diversity is helpful to boosting algorithm performance, but WOA algorithm is usual in solving optimization problem
Initial population is generated using random device, may initial population be unevenly distributed, cause initial population diversity poor, because
This, initializes population using chaos sequence, and being formed has preferable multifarious chaos initial population, to enhance population
Diversity and improve solution efficiency, for algorithm carry out global search establish diversity basis.Herein, it can be used
The chaos sequence that Logistic mapping algorithm generates initializes population, it is of course also possible to use Tent mapping algorithm etc.
Other are died in function generation chaos sequence and initialize to population.
Herein, the individual amount in the first sub- population and the second sub- population is the number of individuals of the population clock after initialization
The half of amount.
S203: using mutation algorithm, to lower first preset quantity of fitness function value in G the first sub- population of generation
Individual is replaced, and forms G+1 the first sub- population of generation.Herein, mutation algorithm are as follows:Wherein,Refer to individualJth tie up element,Refer to G for 3 inequality individuals of population, F is zoom factor.
Herein, ITAE target function can be chosen as fitness letter.
Herein, it should be noted that use mutation algorithm to the first sub- population (directly to the kind after initialization for the first time
Group obtains after being divided) in the individual of fitness function value lower first preset quantity be replaced, formation is 1
The first sub- population of generation, second default to the fitness function value lower first in 1 the first sub- population of generation using mutation algorithm
The individual of quantity is replaced, and formation is 2 the first sub- populations of generation, and so on, G the first sub- population of generation refers to the G times use
Mutation algorithm is replaced rear shape to the individual of lower first preset quantity of fitness function value in G-1 the first sub- population of generation
At.
Herein, the first preset quantity can be configured according to the experience of concrete application scene and user, herein not
Do any restrictions.
S204: using WOA algorithm to of lower second preset quantity of fitness function value in G the second sub- population of generation
Body is replaced, and forms G+1 the second sub- population of generation.Herein, similar to G the first sub- population of generation, G the second sub- population of generation refers to the
G use WOA algorithm replaces the individual of lower second preset quantity of fitness function value in G-1 the second sub- population of generation
It is formed after changing.
Herein, the second preset quantity can be configured according to the experience of concrete application scene and user, herein not
Any restrictions are done, in addition, the second preset quantity can be identical as the first preset quantity, it can also be different from the first preset quantity.
S205: judge whether the fitness function value of the optimum individual in G+1 the first sub- population of generation is less than the second son of G+1 generation
The fitness function value of optimum individual in population.Herein, the optimum individual in G+1 the first sub- population of generation refers to G+1 generation first
The highest individual of fitness function value in sub- population.Herein, when the fitness function of optimum individual in G+1 the first sub- population of generation
When value is less than the fitness function value of optimum individual in G+1 the second sub- population of generation, step S206 is executed；When the first son kind of G+1 generation
When the fitness function value of optimum individual is not less than the fitness function value of optimum individual in G+1 the second sub- population of generation in group, then
Execute step S207.
S206: by the lower individual of fitness function value of third preset quantity in G+1 the first sub- population of generation, with G+1 generation
The higher individual of fitness function value of third preset quantity is replaced in second sub- population.
Herein, identical as the first preset quantity and the second preset quantity, third preset quantity is also that basis is specifically answered
It is configured with the experience of scene and user.
S207: judge whether G+1 is equal to default the number of iterations.Herein, it when G+1 is equal to default the number of iterations, executes
Step S208；When G+1 is not equal to default the number of iterations, then above-mentioned steps S201-S207 is repeated, until G+1 is equal in advance
If number of iterations.
S208: using the optimum individual in G+1 the first sub- population of generation as the optimum individual of population.
Since chaos phenomenon is the random process of the certainty showed in nonlinear dynamic system, class, and chaos is transported
It is dynamic that initial value extreme sensitivity can in a certain range constantly traverse all states according to certain self-law,
Therefore in embodiments of the present invention, carrying out initialization to population by chaos sequence can be improved whale optimization algorithm in initial rank
The search capability of section accelerates the convergence rate of whale optimization algorithm；It and is the first son kind by the population dividing after initializing
Group and the second sub- population, and mutation algorithm is introduced in the second update of the first sub- population, it can be in increasing by the first sub- population
Individual it is multifarious simultaneously, prevent the first sub- population from falling into local optimum by individual variation, and by the second sub- population
In individual better than the individual in the first sub- population, (fitness function value of optimum individual is less than G+1 in G+1 the first sub- population of generation
The fitness function value of optimum individual in the second sub- population of generation) when, by the adaptation of the second preset quantity in G+1 the first sub- population of generation
The lower individual of functional value is spent, is carried out with the higher individual of fitness function value of two preset quantities in G+1 the second sub- population of generation
Replacement further speeds up the speed for finding population optimal solution.
In the present embodiment, the parameter of automatic disturbance rejection controller can be effectively adjusted in order to verify DCWOA algorithm, certainly with two
By the artificial controlled device of degree serial machine, as shown in figure 4, being carried out by parameter of the DCWOA algorithm to automatic disturbance rejection controller online
Adjusting, and compared with other three kinds of control algolithms, verify the validity of DCWOA algorithm.Herein, the dynamics of mechanical arm
Math equation are as follows:
Wherein D refers to that inertial matrix, C refer to that centrifugal force matrix, G refer to that gravity matrix, τ refer to control rate, mathematical table
It is as follows up to formula:
Firstly, as described in above-described embodiment, by the parameter of DCWOA algorithm on-line tuning automatic disturbance rejection controller (ADRC),
It obtains adjusting result and (rule of thumb, r=100, h=100, α is set1=0.5, α2=0.25, δ1=δ2=0.01):
β01=98.375,
β02=298.462,
β03=95.42,
β1=29.42,
β2=30.25,
Then, the automatic disturbance rejection controller that above-mentioned numerical value is set as using parameter carries out track control to two degrees of freedom mechanical arm
System verifies the parameter of the automatic disturbance rejection controller in above-described embodiment by comparing the obtained control result of different control modes
Adjust validity.Specifically, control result is as shown in Figure 5 and Figure 6, wherein Fig. 5 and Fig. 6 is respectively after DCWOA is adjusted
After ADRC after ADRC after ADRC and AWOA adjusting, CPSO adjusting, the ADRC and Experience Tuning Method adjusting after WOA adjusting
Angle q of the ADRC for two-freedom serial manipulator1With angle q2Control error comparison diagram, it can be seen from the figure that use
Each whale algorithm army can effectively adjust the parameter of automatic disturbance rejection controller, colleague, using certainly anti-after DCWOA algorithm setting parameter
The control precision for disturbing control is higher than the control precision of the Active Disturbance Rejection Control after using other 4 kinds of algorithm setting parameters, and can be more
Fast reaches stable state, and the response time is shorter.
Embodiment 2
Fig. 7 shows a kind of functional block diagram of the setting device of automatic disturbance rejection controller parameter of the embodiment of the present invention, the dress
Set the setting method that can be used to implement automatic disturbance rejection controller parameter described in embodiment 1 or its any optional embodiment.
As shown in fig. 7, the device includes: parameter determination module 10, parameter group forms module 20, algorithm optimizing module 30 and parameter tuning
Module 40.Wherein,
Parameter determination module 10 is used for the Controlling model based on automatic disturbance rejection controller, determines in Controlling model to active disturbance rejection control
The parameter that device processed is affected forms parameter group.
Parameter group forms module 20 and is used to form multiple parameters group according to setting parameter；
Algorithm optimizing module 30 is used for using whale optimization algorithm to the population formed by the multiple parameters group of different numerical value
Optimizing is carried out, the optimum individual in population is obtained.Herein, optimum individual refers to that fitness function value is highest in population
Individual.
Parameter tuning module 40 is used to for optimum individual being determined as the optimal value of parameter group, completes to join automatic disturbance rejection controller
Several adjustings.
As the optional embodiment of the embodiment of the present invention, algorithm optimizing module 30 includes: that population forms unit, and population is drawn
Sub-unit, the first updating unit, the second updating unit, the first judging unit, third updating unit, second judgment unit, as a result
Determination unit and assignment cycling element.Wherein,
Population forms unit and is used for using multiple parameters group as the population in whale optimization algorithm.Herein, multiple parameters
Any of group is the individual in population.
Population dividing unit is used to initialize population by chaos sequence, and is by the population dividing after initialization
First sub- population and the second sub- population.
First updating unit is used to use mutation algorithm, to the fitness function value lower the in G the first sub- population of generation
The individual of one preset quantity is replaced, and forms G+1 the first sub- population of generation.Herein, mutation algorithm are as follows:Wherein,Refer to individualJth tie up element,Refer to G for 3 inequality individuals of population, F is zoom factor.
Second updating unit is used for using WOA algorithm to the fitness function value lower second in G the second sub- population of generation
The individual of preset quantity is replaced, and forms G+1 the second sub- population of generation.
First judging unit is used to judge whether the fitness function value of optimum individual in G+1 the first sub- population of generation to be less than G+
The fitness function value of optimum individual in 1 the second sub- population of generation.Herein, the optimum individual in G+1 the first sub- population of generation refers to G
The highest individual of fitness function value in+1 the first sub- population of generation.
Third updating unit is used to be less than G+1 generation the when the fitness function value of optimum individual in G+1 the first sub- population of generation
In two sub- populations when the fitness function value of optimum individual, by the fitness letter of third preset quantity in G+1 the first sub- population of generation
The higher individual of fitness function value of third preset quantity is replaced in the lower individual of numerical value, with G+1 the second sub- population of generation
It changes.
Second judgment unit is for judging whether G+1 is equal to default the number of iterations.
As a result determination unit is used for when G+1 is equal to default the number of iterations, then by optimal in G+1 the first sub- population of generation
Optimum individual of the body as population.
Assignment cycling element is used for when G+1 is not equal to default the number of iterations, then using G+1 the first sub- population of generation as G generation
First sub- population using the second sub- population of G+1 generation as G the second sub- population of generation, and continues optimizing, until G+1 is equal to
Default number of iterations.
The embodiment of the invention also provides a kind of electronic equipment, as shown in figure 8, the electronic equipment may include processor 81
With memory 82, wherein processor 81 can be connected with memory 82 by bus or other modes, to pass through bus in Fig. 8
For connection.
Processor 81 can be central processing unit (Central Processing Unit, CPU).Processor 81 can be with
For other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
The combination of the chips such as discrete hardware components or above-mentioned all kinds of chips.
Memory 82 is used as a kind of non-transient computer readable storage medium, can be used for storing non-transient software program, non-
Transient computer executable program and module, the setting method such as the automatic disturbance rejection controller parameter in the embodiment of the present invention are corresponding
Program instruction/module (parameter determination module 10 in such as Fig. 7, parameter group form module 20, algorithm optimizing module 30 and parameter
Adjust module 40).Non-transient software program, instruction and the module that processor 81 is stored in memory 82 by operation, from
And executing the various function application and data processing of processor, i.e., the automatic disturbance rejection controller in realization above method embodiment is joined
Several adjustings.
Memory 82 may include storing program area and storage data area, wherein storing program area can storage program area,
Application program required at least one function；It storage data area can the data etc. that are created of storage processor 81.In addition, storage
Device 82 may include high-speed random access memory, can also include non-transient memory, for example, at least a magnetic disk storage
Part, flush memory device or other non-transient solid-state memories.In some embodiments, it includes relative to place that memory 82 is optional
The remotely located memory of device 81 is managed, these remote memories can pass through network connection to processor 81.The reality of above-mentioned network
Example includes but is not limited to internet, intranet, local area network, mobile radio communication and combinations thereof.
One or more of modules are stored in the memory 82, when being executed by the processor 81, are executed
The setting method of automatic disturbance rejection controller parameter in embodiment as shown in Figs 1-4.
Above-mentioned electronic equipment detail can correspond to corresponding associated description in embodiment referring to FIG. 1 to 4
Understood with effect, details are not described herein again.
It is that can lead to it will be understood by those skilled in the art that realizing all or part of the process in above-described embodiment method
Computer program is crossed to instruct relevant hardware and complete, the program can be stored in a computer-readable storage medium
In, the program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, the storage medium can for magnetic disk,
CD, read-only memory (Read-Only Memory, ROM), random access memory (Random Access
Memory, RAM), flash memory (Flash Memory), hard disk (Hard Disk Drive, abbreviation: HDD) or solid state hard disk
(Solid-State Drive, SSD) etc.；The storage medium can also include the combination of the memory of mentioned kind.
Obviously, the above embodiments are merely examples for clarifying the description, and does not limit the embodiments.It is right
For those of ordinary skill in the art, can also make on the basis of the above description it is other it is various forms of variation or
It changes.There is no necessity and possibility to exhaust all the enbodiments.And it is extended from this it is obvious variation or
It changes still within the protection scope of the invention.
Claims (10)
1. a kind of setting method of automatic disturbance rejection controller parameter, which comprises the steps of:
Determine the automatic disturbance rejection controller to setting parameter, described is the control mould of the automatic disturbance rejection controller to setting parameter
One or more parameters in type；
Multiple parameters group is formed to setting parameter according to described；
Optimizing is carried out to the multiple parameter group using whale optimization algorithm；
It is adjusted according to parameter of the optimizing result to the automatic disturbance rejection controller.
2. the setting method of automatic disturbance rejection controller parameter according to claim 1, which is characterized in that the Active Disturbance Rejection Control
The Controlling model of device are as follows:
Wherein, TD, ESO and NLSEF refer respectively to derivative controller, nonlinear feedback device and expansion shape in automatic disturbance rejection controller
State observer, k refer to variate-value, v0Refer to tracking control signal, v1And v2The mistake for the input signal v that respectively TD is provided is spent
Journey and its differential, e1And e2Refer respectively to v1And v2The state variable z of control object relative to the automatic disturbance rejection controller1And z2
Error, z3Refer to total disturbance real-time control actuating quantity of the control object, b0Refer to that the control of the automatic disturbance rejection controller is defeated
Enter amplification coefficient, u refers to nonlinear Feedback Control rate, and r refers to the speed factor, and h refers to step-length, β01, β02, β03, βiRefer to amendment
Coefficient, u0Refer to that control signal, fal () refer to the nonlinear function of output error corrected rate；
The parameter group includes β01, β02, β03And βi。
3. the setting method of automatic disturbance rejection controller parameter according to claim 1, which is characterized in that described excellent using whale
Changing the step of algorithm carries out optimizing to the multiple parameter group includes:
Using the multiple parameter group as the population in the whale optimization algorithm；Any of the multiple parameter group is
An individual in the population；
The population is initialized by chaos sequence, and is the first sub- population and second by the population dividing after initialization
Sub- population；
Using mutation algorithm, the individual of lower first preset quantity of fitness function value in G the first sub- population of generation is carried out
Replacement forms G+1 the first sub- population of generation；The mutation algorithm are as follows:Wherein,Refer to individualJth tie up element, Refer to G for 3 inequality individuals of population, F is contracting
Put the factor；
It is replaced using individual of the WOA algorithm to lower second preset quantity of fitness function value in G the second sub- population of generation
It changes, forms G+1 the second sub- population of generation；
Judge whether the fitness function value of the optimum individual in the first sub- population of the G+1 generation is less than second son of G+1 generation
The fitness function value of optimum individual in population；Optimum individual in the first sub- population, which refers in the described first sub- population, fits
The highest individual of response functional value；
When the fitness function value of optimum individual in the first sub- population of the G+1 generation is less than in the second sub- population of the G+1 generation most
It is when the fitness function value of excellent individual, the fitness function value of third preset quantity in the first sub- population of the G+1 generation is lower
Individual, replaced with the higher individual of fitness function value of third preset quantity described in the G+1 the second sub- population of generation
It changes；
Judge whether the G+1 is equal to default the number of iterations；
When the G+1 is equal to default the number of iterations, then using the optimum individual in the first sub- population of the G+1 generation as described kind
The optimum individual of group；
It, then, will using the first sub- population of the G+1 generation as G the first sub- population of generation when the G+1 is not equal to default the number of iterations
The second sub- population of the G+1 generation continues optimizing as G the second sub- population of generation, presets repeatedly until the G+1 is equal to
Algebra.
4. the setting method of automatic disturbance rejection controller parameter according to claim 3, which is characterized in that the first sub- population
In number of individuals and the second sub- population in number of individuals be the number of individuals in the population half.
5. the setting method of automatic disturbance rejection controller parameter according to claim 3, which is characterized in that the chaos sequence is
It is generated using Logistic mapping algorithm.
6. according to the setting method of the described in any item automatic disturbance rejection controller parameters of claim 3, which is characterized in that the adaptation
Degree function is ITAE target function.
7. a kind of setting device of automatic disturbance rejection controller parameter characterized by comprising
Parameter determination module, for determine the automatic disturbance rejection controller to setting parameter, it is described to setting parameter be it is described from
One or more parameters in the Controlling model of disturbance rejection control device；
Parameter group forms module, for forming multiple parameters group to setting parameter according to described；
Algorithm optimizing module, for carrying out optimizing to the multiple parameter group using whale optimization algorithm；
Parameter tuning module, for being adjusted according to parameter of the optimizing result to the automatic disturbance rejection controller.
8. the setting device of automatic disturbance rejection controller parameter according to claim 7, which is characterized in that the algorithm optimizing mould
Block includes:
Population forms unit, for using the multiple parameter group as the population in the whale optimization algorithm；The multiple ginseng
Any of array is the individual in the population；
Population dividing unit is divided into for initializing by chaos sequence to the population, and by initialization population
One sub- population and the second sub- population；
First updating unit, it is pre- to the fitness function value lower first in G the first sub- population of generation for using mutation algorithm
If the individual of quantity is replaced, G+1 the first sub- population of generation is formed；The mutation algorithm are as follows:Wherein,Refer to individualJth tie up element,Refer to G for 3 inequality individuals of population, F is zoom factor；
Second updating unit, for default to the fitness function value lower second in G the second sub- population of generation using WOA algorithm
The individual of quantity is replaced, and forms G+1 the second sub- population of generation；
Whether the first judging unit, the fitness function value for judging the optimum individual in the first sub- population of the G+1 generation are small
The fitness function value of optimum individual in the second sub- population of the G+1 generation；Optimum individual in the first sub- population of the G+1 generation
Refer to the highest individual of fitness function value in the first sub- population of the G+1 generation；
Third updating unit, for being less than the G+1 when the fitness function value of optimum individual in the first sub- population of the G+1 generation
In the second sub- population of generation when the fitness function value of optimum individual, by third preset quantity in the first sub- population of the G+1 generation
The lower individual of fitness function value, the fitness function value with third preset quantity described in the second sub- population of the G+1 generation
Higher individual is replaced；
Second judgment unit, for judging whether the G+1 is equal to default the number of iterations；
As a result determination unit then will be in the first sub- population of the G+1 generation for when the G+1 is equal to default the number of iterations
Optimum individual of the optimum individual as the population；
Assignment cycling element, for when the G+1 is not equal to default the number of iterations, then making the sub- population of G+1 generation first
For G the first sub- population of generation, using the second sub- population of the G+1 generation as G the second sub- population of generation, and continue optimizing, directly
It is equal to default number of iterations to the G+1.
9. a kind of electronic equipment characterized by comprising memory and processor, between the memory and the processor
Connection is communicated with each other, computer instruction is stored in the memory, the processor, which passes through, executes the computer instruction, from
And perform claim requires the setting method of the described in any item automatic disturbance rejection controller parameters of 1-6.
10. a kind of computer readable storage medium, which is characterized in that the computer-readable recording medium storage has computer to refer to
It enables, the computer instruction is for making the computer perform claim require the described in any item automatic disturbance rejection controller parameters of 1-6
Setting method.
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| CN111812983A (en) * | 2020-07-19 | 2020-10-23 | 国网山西省电力公司电力科学研究院 | Wind turbine generator primary frequency modulation load shedding control method based on differential flat active disturbance rejection control |
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