CN108762062A - A kind of lift truck attachment clamping force self-adaptation control method and system - Google Patents
A kind of lift truck attachment clamping force self-adaptation control method and system Download PDFInfo
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- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
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Abstract
The invention discloses a kind of lift truck attachment clamping force self-adaptation control method and systems, it is related to lift truck attachment clamping force control field, the fitness of individual is calculated using genetic algorithm, when fitness function threshold value is less than given threshold ε or reaches iterations, genetic algorithm is stopped, and using the result of genetic algorithm as the primary group of particle cluster algorithm；Optimal solution is obtained by particle cluster algorithm, using obtained optimal solution as the parameter of pid algorithm；The clamping force value of setting is compared with using the output valve after pid algorithm adjusting, is terminated if difference is in the error range of setting；Otherwise, it returns and resets fitness function threshold value and iterations, obtain optimal solution；So cycle, until obtaining the pid parameter met the requirements.The advantage of the invention is that：It realizes the self adaptive control to lift truck attachment clamping force, improves lift truck attachment clamping force control precision.
Description
Technical field
The present invention relates to lift truck attachment clamping force control field more particularly to a kind of lift truck attachment clamping force self adaptive controls
Method and system.
Background technology
Fork truck, can also be according to actual conditions other than using basic accessory pallet fork in order to realize different functions
Using Special accessory, such as hold device, environmental sanitation accessory.The use of Special accessory, which has the working efficiency of fork truck, significantly to be carried
It rises, and production cost can be reduced.Special accessory can complete the impossible compound action of basic accessory, such as hold, revolve
Turn etc..
The clamping force of existing lift truck attachment output is by being arranged on the oil circuit of fork truck oil pump to accessory hydraulic system
Mechanical valve and control, under the control of mechanical valve, the clamping force of lift truck attachment output only has several fixed gears, and every
A gear is also difficult to steadily power output, significantly limits the application of lift truck attachment.Replace mechanical valve can be with using electrically-controlled valve
Lift truck attachment clamping force is set to realize continuously adjustable, but electrically-controlled valve has energy loss, while lift truck attachment in actual use
Also mechanical loss is had, the clamping force value of the clamping force value and actual set that can cause output is deviated.Therefore it needs to folder
Clamp force value is modified.It there is no a kind of self-adaptation control method for lift truck attachment clamping force at present.
Invention content
Technical problem to be solved by the invention is to provide a kind of forks that can improve lift truck attachment clamping force control precision
Vehicle accessory clamping force self-adaptation control method and system.
The present invention is to solve above-mentioned technical problem by the following technical programs：
A kind of lift truck attachment clamping force self-adaptation control method, includes the following steps：
Step 1 generates several body using the real coding rule of genetic algorithm, the fitness of individual is calculated, according to suitable
The size of response selected, made a variation, crossover operation, when fitness function threshold value is less than given threshold ε or reaches iteration time
When number, genetic algorithm is stopped, using the result of genetic algorithm as the primary group of particle cluster algorithm；
Step 2 is arranged fitness function threshold value and iterations, the initial value of PSO algorithm search and search range is made to lean on
Nearly optimal solution, using the optimal solution obtained as the parameter of pid algorithm；
Step 3: adjusting clamping force output valve using pid algorithm, comparing the clamping force value of setting and using pid algorithm tune
Clamping force output valve after section terminates if difference is in the error range of setting；Otherwise, return to step two is reset suitable
Response function threshold and iterations obtain optimal solution；So cycle, until obtaining the pid parameter met the requirements.
Technical solution as an optimization, the genetic algorithm include the following steps：
Step 1, the initial population P of offering question solution1；
Step 2 calculates the individual adaptation degree of population P1 according to fitness function, if acquisition optimal solution or iterations reach
To maximum, then step 6 is gone to, step 3 is gone to if being unsatisfactory for condition；
Step 3 carries out selection operation, is selected so that the result of fitness evaluation is foundation, thus generates the next generation
New individual；
The individual obtained in step 3 is obtained new individual by step 4 by intersection, mutation operation；
Step 5 carries out fitness calculating, if acquisition optimal solution or iterations reach to the individual obtained in step 4
Maximum then goes to step 6, and step 3 is gone to if being unsatisfactory for condition；
Step 6, algorithm terminate, and obtain population P2。
Technical solution as an optimization, the particle cluster algorithm include the following steps：
Step 1, for the population P obtained by genetic algorithm in search space2Position and speed initialization, calculate
Fitness fid, initializationIn formula, fidIt is i-th
A particle fitness function value, d are search space dimension,For non-iteration when i-th of particle fitness function value,For not
Optimum position of the entire population in d ties up search space when iteration；
Step 2, during algorithm iteration, particle position follows following calculation criterion with velocity variations：
In the above formulas,For i-th particle iteration k+1 times when flying speed,For i-th particle iteration k times
When flying speed,For i-th particle iteration k times when fitness,For i-th particle iteration k times when position, d
For search space dimension, ω is inertia weight, c1To recognize parameter, c2For social parameter, r1、r2For random function；
Step 3 updates fitness when i-th particle iteration k timesEntire population ties up in d and searches for when with iteration k times
Optimum position in space
Step 4, if particle cluster algorithm reaches the abort criterion of design in step 3, algorithm terminates, and obtains PID
The optimal solution of parameter；Otherwise, step 2 is gone to.
Technical solution as an optimization, inertia weight ω is by formulaIt acquires, in formula, t is repeatedly
Generation number, maxDT are maximum iteration, and the value range of ω is [0.8,1.2].
Technical solution as an optimization, the transmission function such as following formula of the pid algorithm：
In formula, Kp、Ki、KdRespectively ratio control parameter, integration control parameter, differential control parameter.
A kind of lift truck attachment clamping force adaptive control system, which is characterized in that including
Genetic algorithm module：For using the real coding rule of genetic algorithm to generate several body, the suitable of individual is calculated
Response is selected according to the size of fitness, is made a variation, crossover operation, when fitness function threshold value be less than given threshold ε or
When reaching iterations, genetic algorithm is stopped, using the result of genetic algorithm as the primary group of particle cluster algorithm；
Particle cluster algorithm module：For fitness function threshold value and iterations are arranged, make the initial value of PSO algorithm search
With search range close to optimal solution, using the optimal solution obtained as the parameter of pid algorithm；
Pid algorithm module：For using pid algorithm to adjust clamping force output valve, clamping force value and the use of setting are compared
Clamping force output valve after pid algorithm adjusting, terminates if difference is in the error range of setting；Otherwise, population is returned
Algoritic module resets fitness function threshold value and iterations, obtains optimal solution；So cycle, until being met the requirements
Pid parameter.
Technical solution as an optimization, the genetic algorithm module include with lower unit：
Setting unit：For the initial population P of offering question solution1；
Initial individuals fitness computing unit：For calculating the individual adaptation degree of population P1 according to fitness function, if obtaining
It obtains optimal solution or iterations reaches maximum, then go to end unit, selecting unit is gone to if being unsatisfactory for condition；
Selecting unit：For carrying out selection operation, is selected, thus generated so that the result of fitness evaluation is foundation
Next-generation new individual；
Cross and variation unit：For the individual obtained in selecting unit is obtained new individual by intersection, mutation operation；
New individual fitness computing unit：For carrying out fitness calculating to the individual obtained in cross and variation unit, if
It obtains optimal solution or iterations reaches maximum, then go to end unit, selecting unit is gone to if being unsatisfactory for condition；
End unit：For making algorithm terminate, population P is obtained2。
Technical solution as an optimization, the particle cluster algorithm include with lower unit：
Initialization unit：For being directed to the population P obtained by genetic algorithm in search space2Position and speed it is initial
Change,
Calculate fitness fid, initialization
In formula, fidFor i-th of particle fitness function value, d is search space dimension,For non-iteration when i-th of particle
Fitness function value,For non-iteration when optimum position in d ties up search space of entire population；
Particle position and speed computing unit：During algorithm iteration, particle position follows following with velocity variations
Calculation criterion：
In the above formulas,For i-th particle iteration k+1 times when flying speed,For i-th particle iteration k times
When flying speed,For i-th particle iteration k times when fitness,For i-th particle iteration k times when position, d
For search space dimension, ω is inertia weight, c1To recognize parameter, c2For social parameter, r1、r2For random function；
Updating unit：For updating fitness when i-th particle iteration k timesEntire population is in d when with iteration k times
Tie up the optimum position in search space
Judging unit：For judging that algorithm terminates if particle cluster algorithm reaches the abort criterion of design in updating unit,
And obtain the optimal solution of pid parameter；Otherwise, particle position and speed computing unit are gone to.
Technical solution as an optimization, the inertia weight ω is by formulaIt acquires, in formula, t
For iterations, maxDT is maximum iteration, and the value range of ω is [0.8,1.2].
Technical solution as an optimization, the transmission function such as following formula of the pid algorithm：
In formula, Kp、Ki、KdRespectively ratio control parameter, integration control parameter, differential control parameter.
The advantage of the invention is that：It realizes the self adaptive control to lift truck attachment clamping force, is calculated compared to single heredity
Method, particle cluster algorithm algorithm have higher precision, convergence using genetic algorithm/particle cluster algorithm combination parameter optimizing algorithm
Speed is fast, is not easy to be absorbed in Local Extremum, improves lift truck attachment clamping force control precision, promotes safety in production.
Description of the drawings
Fig. 1 is the algorithm flow chart of lift truck attachment clamping force self-adaptation control method of the present invention.
Fig. 2 is the algorithm flow chart of genetic algorithm in the present invention.
Fig. 3 is the algorithm flow chart of particle cluster algorithm in the present invention.
Specific implementation mode
As shown in Figs. 1-3, a kind of lift truck attachment clamping force self-adaptation control method, includes the following steps：
Step 1 generates several body using the real coding rule of genetic algorithm, the fitness of individual is calculated, according to suitable
The size of response selected, made a variation, crossover operation so that is solved domain and is gathered in the region where globally optimal solution, works as adaptation
When degree function threshold is less than given threshold ε or reaches iterations, genetic algorithm is stopped, and the result of genetic algorithm is made
For the primary group of particle cluster algorithm；
Fitness function threshold value and iterations are arranged in step 2, select smaller threshold value, make rising for PSO algorithm search
Initial value and search range are close to optimal solution, using the optimal solution obtained as the parameter of pid algorithm；
Step 3: adjusting clamping force output valve using pid algorithm, comparing the clamping force value of setting and using pid algorithm tune
Clamping force output valve after section terminates if difference is in the error range of setting；Otherwise, return to step two is reset suitable
Response function threshold and iterations obtain optimal solution；So cycle, until obtaining the pid parameter met the requirements.
The genetic algorithm includes the following steps：
Step 1, the initial population P of offering question solution1；
Step 2 calculates the individual adaptation degree of population P1 according to fitness function, if acquisition optimal solution or iterations reach
To maximum, then step 6 is gone to, step 3 is gone to if being unsatisfactory for condition；
Step 3 carries out selection operation, is selected so that the result of fitness evaluation is foundation, thus generates the next generation
New individual；
The individual obtained in step 3 is obtained new individual by step 4 by intersection, mutation operation；
Step 5 carries out fitness calculating, if acquisition optimal solution or iterations reach to the individual obtained in step 4
Maximum then goes to step 6, and step 3 is gone to if being unsatisfactory for condition；
Step 6, algorithm terminate, and obtain population P2。
The particle cluster algorithm includes the following steps：
Step 1, for the population P obtained by genetic algorithm in search space2Position and speed initialization, calculate
Fitness fid, initializationIn formula, fidIt is i-th
A particle fitness function value, d are search space dimension,For non-iteration when i-th of particle fitness function value,For not
Optimum position of the entire population in d ties up search space when iteration；
Step 2, during algorithm iteration, particle position follows following calculation criterion with velocity variations：
In the above formulas,For i-th particle iteration k+1 times when flying speed,For i-th particle iteration k times
When flying speed,For i-th particle iteration k times when fitness,For i-th particle iteration k times when position, d
For search space dimension；
ω is inertia weight, the search capability for adjusting particle cluster algorithm, by formulaIt asks
, in formula, t is iterations, and maxDT is maximum iteration, and the value range of ω is [0.8,1.2]；
c1For cognition parameter (cognitive), c2For social parameter (social), c1、c2All it is in particle cluster algorithm
The factor is practised, is used for determining the maximum step-length of individual preferably particle and global preferably particle direction flight, in order to quickly determine
Optimal Studying factors are obtained using gaussian random；
r1、r2For random function, quickly determined again by gaussian random, r1、r2Value range be [0,1]；
Formula (1) indicates that population velocity variations rule, formula (2) limit the maximum flying speed of particle, and formula (3) indicates particle
Change in location rule, formula (4) control the flight range of particle；
Step 3 updates fitness when i-th particle iteration k timesEntire population ties up in d and searches for when with iteration k times
Optimum position in space
Step 4, if particle cluster algorithm reaches the abort criterion of design in step 3, algorithm terminates, and obtains PID
The optimal solution of parameter；Otherwise, step 2 is gone to.
The transmission function of the pid algorithm such as following formula：
In formula, Kp、Ki、KdRespectively ratio control parameter, integration control parameter, differential control parameter.
A kind of lift truck attachment clamping force adaptive control system, which is characterized in that including
Genetic algorithm module：For using the real coding rule of genetic algorithm to generate several body, the suitable of individual is calculated
Response is selected according to the size of fitness, is made a variation, crossover operation so that is solved domain and is gathered in where globally optimal solution
Region, when fitness function threshold value is less than given threshold ε or reaches iterations, genetic algorithm is stopped, will be hereditary
Primary group of the result of algorithm as particle cluster algorithm；
Particle cluster algorithm module：For fitness function threshold value and iterations are arranged, smaller threshold value is selected, PSO is made
The initial value of algorithm search and search range are close to optimal solution, using the optimal solution obtained as the parameter of pid algorithm；
Pid algorithm module：For using pid algorithm to adjust clamping force output valve, clamping force value and the use of setting are compared
Clamping force output valve after pid algorithm adjusting, terminates if difference is in the error range of setting；Otherwise, population is returned
Algoritic module resets fitness function threshold value and iterations, obtains optimal solution；So cycle, until being met the requirements
Pid parameter.
Technical solution as an optimization, the genetic algorithm module include with lower unit：
Setting unit：For the initial population P of offering question solution1；
Initial individuals fitness computing unit：For calculating the individual adaptation degree of population P1 according to fitness function, if obtaining
It obtains optimal solution or iterations reaches maximum, then go to end unit, selecting unit is gone to if being unsatisfactory for condition；
Selecting unit：For carrying out selection operation, is selected, thus generated so that the result of fitness evaluation is foundation
Next-generation new individual；
Cross and variation unit：For the individual obtained in selecting unit is obtained new individual by intersection, mutation operation；
New individual fitness computing unit：For carrying out fitness calculating to the individual obtained in cross and variation unit, if
It obtains optimal solution or iterations reaches maximum, then go to end unit, selecting unit is gone to if being unsatisfactory for condition；
End unit：For making algorithm terminate, population P is obtained2。
Technical solution as an optimization, the particle cluster algorithm include with lower unit：
Initialization unit：For being directed to the population P obtained by genetic algorithm in search space2Position and speed it is initial
Change,
Calculate fitness fid, initialization
In formula, fidFor i-th of particle fitness function value, d is search space dimension,For non-iteration when i-th of particle
Fitness function value,For non-iteration when optimum position in d ties up search space of entire population；
Particle position and speed computing unit：During algorithm iteration, particle position follows following with velocity variations
Calculation criterion：
In the above formulas,For i-th particle iteration k+1 times when flying speed,For i-th particle iteration k times
When flying speed,For i-th particle iteration k times when fitness,For i-th particle iteration k times when position, d
For search space dimension；
ω is inertia weight, the search capability for adjusting particle cluster algorithm, by formulaIt asks
, in formula, t is iterations, and maxDT is maximum iteration, and the value range of ω is [0.8,1.2]；
c1For cognition parameter (cognitive), c2For social parameter (social), c1、c2All it is in particle cluster algorithm
The factor is practised, is used for determining the maximum step-length of individual preferably particle and global preferably particle direction flight, in order to quickly determine
Optimal Studying factors are obtained using gaussian random；
r1、r2For random function, quickly determined again by gaussian random, r1、r2Value range be [0,1]；
Formula (1) indicates that population velocity variations rule, formula (2) limit the maximum flying speed of particle, and formula (3) indicates particle
Change in location rule, formula (4) control the flight range of particle；
Updating unit：For updating fitness when i-th particle iteration k timesEntire population is in d when with iteration k times
Tie up the optimum position in search space
Judging unit：For judging that algorithm terminates if particle cluster algorithm reaches the abort criterion of design in updating unit,
And obtain the optimal solution of pid parameter；Otherwise, particle position and speed computing unit are gone to.
The transmission function of the pid algorithm such as following formula：
In formula, Kp、Ki、KdRespectively ratio control parameter, integration control parameter, differential control parameter.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all the present invention spirit and
All any modification, equivalent and improvement made by within principle etc., should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of lift truck attachment clamping force self-adaptation control method, which is characterized in that include the following steps：
Step 1 generates several body using the real coding rule of genetic algorithm, the fitness of individual is calculated, according to fitness
Size selected, made a variation, crossover operation, when fitness function threshold value is less than given threshold ε or when reaching iterations,
Genetic algorithm is stopped, using the result of genetic algorithm as the primary group of particle cluster algorithm；
Fitness function threshold value and iterations are arranged in step 2, keep the initial value of PSO algorithm search and search range close most
Excellent solution, using the optimal solution obtained as the parameter of pid algorithm；
Step 3: clamping force output valve is adjusted using pid algorithm, after comparing the clamping force value of setting and being adjusted using pid algorithm
Clamping force output valve, terminate if difference is in the error range of setting；Otherwise, return to step two resets fitness
Function threshold and iterations obtain optimal solution；So cycle, until obtaining the pid parameter met the requirements.
2. lift truck attachment clamping force self-adaptation control method as described in claim 1, which is characterized in that the genetic algorithm packet
Include following steps：
Step 1, the initial population P of offering question solution1；
Step 2 calculates the individual adaptation degree of population P1 according to fitness function, if acquisition optimal solution or iterations reach most
Greatly, then step 6 is gone to, step 3 is gone to if being unsatisfactory for condition；
Step 3 carries out selection operation, is to thus generate next-generation new according to being selected with the result of fitness evaluation
Individual；
The individual obtained in step 3 is obtained new individual by step 4 by intersection, mutation operation；
Step 5 carries out fitness calculating to the individual obtained in step 4, if acquisition optimal solution or iterations reach maximum,
Step 6 is then gone to, step 3 is gone to if being unsatisfactory for condition；
Step 6, algorithm terminate, and obtain population P2。
3. lift truck attachment clamping force self-adaptation control method as described in claim 1, which is characterized in that the particle cluster algorithm
Include the following steps：
Step 1, for the population P obtained by genetic algorithm in search space2Position and speed initialization, calculate fitness
fid, initializationIn formula, fidFor i-th of particle
Fitness function value, d are search space dimension,For non-iteration when i-th of particle fitness function value,For non-iteration
When optimum position in d ties up search space of entire population；
Step 2, during algorithm iteration, particle position follows following calculation criterion with velocity variations：
In the above formulas,For i-th particle iteration k+1 times when flying speed,For i-th particle iteration k times when
Flying speed,For i-th particle iteration k times when fitness,For i-th particle iteration k times when position, d is to search
Rope Spatial Dimension, ω are inertia weight, c1To recognize parameter, c2For social parameter, r1、r2For random function；
Step 3 updates fitness when i-th particle iteration k timesEntire population ties up search space in d when with iteration k times
In optimum position
Step 4, if particle cluster algorithm reaches the abort criterion of design in step 3, algorithm terminates, and obtains pid parameter
Optimal solution；Otherwise, step 2 is gone to.
4. lift truck attachment clamping force self-adaptation control method as claimed in claim 3, which is characterized in that inertia weight ω is by formulaIt acquires, in formula, t is iterations, and max DT are maximum iteration, the value range of ω
For [0.8,1.2].
5. lift truck attachment clamping force self-adaptation control method as described in claim 1, which is characterized in that the pid algorithm
Transmission function such as following formula：
In formula, Kp、Ki、KdRespectively ratio control parameter, integration control parameter, differential control parameter.
6. a kind of lift truck attachment clamping force adaptive control system, which is characterized in that including
Genetic algorithm module：For use genetic algorithm real coding rule generate several body, calculate individual fitness,
It selected, made a variation according to the size of fitness, crossover operation, when fitness function threshold value is less than given threshold ε or reaches
When iterations, genetic algorithm is stopped, using the result of genetic algorithm as the primary group of particle cluster algorithm；
Particle cluster algorithm module：For fitness function threshold value and iterations are arranged, make the initial value of PSO algorithm search and search
Rope range is close to optimal solution, using the optimal solution obtained as the parameter of pid algorithm；
Pid algorithm module：For using pid algorithm to adjust clamping force output valve, the clamping force value for comparing setting is calculated with using PID
Clamping force output valve after method adjusting, terminates if difference is in the error range of setting；Otherwise, particle cluster algorithm mould is returned
Block resets fitness function threshold value and iterations, obtains optimal solution；So cycle, until obtaining the PID met the requirements
Parameter.
7. lift truck attachment clamping force adaptive control system as claimed in claim 6, which is characterized in that the genetic algorithm mould
Block includes with lower unit：
Setting unit：For the initial population P of offering question solution1；
Initial individuals fitness computing unit：For calculating the individual adaptation degree of population P1 according to fitness function, if obtaining most
Excellent solution or iterations reach maximum, then go to end unit, selecting unit is gone to if being unsatisfactory for condition；
Selecting unit：For carrying out selection operation, is selected, thus generated next with the result of fitness evaluation is foundation
The individual of Dai Xin；
Cross and variation unit：For the individual obtained in selecting unit is obtained new individual by intersection, mutation operation；
New individual fitness computing unit：For carrying out fitness calculating to the individual obtained in cross and variation unit, if obtaining
Optimal solution or iterations reach maximum, then go to end unit, selecting unit is gone to if being unsatisfactory for condition；
End unit：For making algorithm terminate, population P is obtained2。
8. lift truck attachment clamping force adaptive control system as claimed in claim 6, which is characterized in that the particle cluster algorithm
Including with lower unit：
Initialization unit：For being directed to the population P obtained by genetic algorithm in search space2Position and speed initialization,
Calculate fitness fid, initializationIn formula,
fidFor i-th of particle fitness function value, d is search space dimension,For non-iteration when i-th of particle fitness function value,For non-iteration when optimum position in d ties up search space of entire population；
Particle position and speed computing unit：During algorithm iteration, particle position follows following calculating with velocity variations
Criterion：
In the above formulas,For i-th particle iteration k+1 times when flying speed,For i-th particle iteration k times when
Flying speed,For i-th particle iteration k times when fitness,For i-th particle iteration k times when position, d is to search
Rope Spatial Dimension, ω are inertia weight, c1To recognize parameter, c2For social parameter, r1、r2For random function；
Updating unit：For updating fitness when i-th particle iteration k timesEntire population is searched in d dimensions when with iteration k times
Optimum position in rope space
Judging unit：For judging that algorithm terminates if particle cluster algorithm reaches the abort criterion of design in updating unit, and
Obtain the optimal solution of pid parameter；Otherwise, particle position and speed computing unit are gone to.
9. lift truck attachment clamping force adaptive control system as claimed in claim 8, which is characterized in that the inertia weight ω
By formulaIt acquires, in formula, t is iterations, and maxDT is maximum iteration, the value model of ω
It encloses for [0.8,1.2].
10. lift truck attachment clamping force adaptive control system as claimed in claim 6, which is characterized in that the pid algorithm
Transmission function such as following formula：
In formula, Kp、Ki、KdRespectively ratio control parameter, integration control parameter, differential control parameter.
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| CN109856976A (en) * | 2019-03-12 | 2019-06-07 | 哈尔滨工程大学 | It is a kind of that Auto-disturbance-rejection Control is tracked based on the adaptive track laying air cushion vehicle for intersecting particle group optimizing |
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