CN109940618A - A kind of Serial manipulator drive system motion control method based on orthogonal fuzzy - Google Patents
A kind of Serial manipulator drive system motion control method based on orthogonal fuzzy Download PDFInfo
- Publication number
- CN109940618A CN109940618A CN201910270959.XA CN201910270959A CN109940618A CN 109940618 A CN109940618 A CN 109940618A CN 201910270959 A CN201910270959 A CN 201910270959A CN 109940618 A CN109940618 A CN 109940618A
- Authority
- CN
- China
- Prior art keywords
- fuzzy
- orthogonal
- drive system
- angular deviation
- parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Feedback Control In General (AREA)
Abstract
The invention discloses a kind of Serial manipulator drive system motion control method based on orthogonal fuzzy, it include: the mathematical model for establishing electric steering engine, obtain the closed loop transfer function, of the electric steering engine drive system, based on the closed loop transfer function, the output angle of the electric steering engine is controlled, it include: that parameter k is determined by orthogonal test according to the overshoot and adjustment time in the drive systemp、ki、kd；Respectively by angular deviation e (t), angular deviation change rate de (t)/dt and the Δ k of adjustment PID controller parameterp、Δki、ΔkdBe converted to the quantification gradation in fuzzy domain；The angular deviation e (t), angular deviation change rate de (t)/dt are inputted into fuzzy controller, fuzzy controller output is the Δ k for adjusting PID controller parameterp、Δki、Δkd；Three parameter k are formed by fuzzy controllerp′、ki′、kd', it obtains stablizing output angle, and then control the drive system；Wherein, kp'=kp+Δkp, ki'=ki+Δki, kd'=kd+Δkd。
Description
Technical field
The present invention relates to industrialization robot automation fields, and in particular to a kind of series connection based on orthogonal fuzzy
Machine-hand driving system motion control method.
Background technique
Since Developing Track for Modern Service Industry is rapid, service robot needs to meet fast response time, precision height, kinetic stability
The features such as good, to meet these conditions must just optimize control to it.Since PID control is the control to grow up earliest
One of strategy, because of the features such as its algorithm is simple, robustness is good, so being widely used in Industry Control.But it also has ginseng
Number is difficult to determine, is unable to satisfy complicated high-precision control occasion, many defects such as adaptability decline in time-varying system.
In industrial servo drive system, the setting of pid parameter is typically not capable of global optimal solution, but utilizes orthogonal
The method of test can obtain the preferable pid parameter value of control effect in limited test number (TN).Wang Youmin etc. was in 2007
PID Parameter for Hydraulic Servo System is adjusted using orthogonal experiment, improves the control precision of electrohydraulic servo system, is reduced
Response time and reduce test number (TN).Peng Anhua etc. 2011 using orthogonal experiment to lathe closed loop servo system into
The optimization of row pid parameter, reduces overshoot and rise time.
On the other hand, traditional PID control can be because the minor change of external environment will be detached from best stable state, can not
Meet the stability control of complicated high-precision control and industrial servo drive system.Traditional PID control method is introduced
Fuzzy controller can make system obtain good static and dynamic c haracteristics using fuzzy-adaptation PID control.Harbin Engineering University river
Rosy clouds etc. have good dynamic quality with fuzzy-adaptation PID control manipulator in the force feedback using manipulator in 2009, rise
Time is fast, and overshoot is smaller, and in simulation time, control precision with higher, stronger robustness.
Summary of the invention
The present invention has designed and developed a kind of Serial manipulator drive system motion control method based on orthogonal fuzzy,
The object of the invention first is that the optimal pid parameter of control effect can be obtained in limited test number (TN) by orthogonal test
Value.
The second object of the present invention is to introducing fuzzy controller in traditional PID controller, and then meet the high-precision of complexity
The stability of degree control and industrial servo drive system.
Technical solution provided by the invention are as follows:
A kind of Serial manipulator drive system motion control method based on orthogonal fuzzy, establishes the number of electric steering engine
Model is learned, the closed loop transfer function, of the electric steering engine drive system is obtained, the closed loop transfer function, is based on, to described electronic
The output angle of steering engine is controlled, comprising:
According to the overshoot and adjustment time in the drive system, parameter k is determined by orthogonal testp、ki、kd；
Respectively by angular deviation e (t), angular deviation change rate de (t)/dt and the Δ k of adjustment PID controller parameterp、
Δki、ΔkdBe converted to the quantification gradation in fuzzy domain；By the angular deviation e (t), angular deviation change rate de (t)/dt
Fuzzy controller is inputted, fuzzy controller output is the Δ k for adjusting PID controller parameterp、Δki、Δkd；
Three parameter k ' are formed by fuzzy controllerp、k′i、k′d, obtain stablizing output angle, and then to the drive
Dynamic system is controlled；
Wherein, k 'p=kp+Δkp, k 'i=ki+Δki, k 'd=kd+Δkd。
Preferably, the closed loop transfer function, is
In formula, K1=KcKPWM, K2=180/KEKiπ,KEFor back EMF coefficient, KiFor transmission mechanism
Transmission ratio, TeReferred to as electromagnetic time constant, TmReferred to as electromechanical time constant, KFFor rudder face feedback factor.
Preferably, the domain of the angular deviation e (t) is [- 1,1], and quantization is because be 2, the angular deviation variation
Rate de (t)/dt domain is [- 0.014,0.014], quantizing factor 0.007, the parameter, Δ kp、Δki、ΔkdDomain
Respectively [- 0.1,0.1], [- 20,20], [- 0.02,0.02], quantizing factor are respectively 0.05,10,0.01.
Preferably, the angular deviation e (t) is divided into 5 grades, and angular deviation change rate de (t)/dt points are 5 etc.
Grade, the angular deviation e (t) and the angular deviation change rate de (t)/dt fuzzy set are { NL, NS, ZO, PS, PL }；
The Δ k of PID controller parameterp、Δki、Δkd5 grades are divided into, fuzzy set is { NL, NS, ZO, PS, PL }.
Preferably, the fuzzy control rule are as follows:
If angular deviation e (t) input is larger, parameter, Δ kpOutput is larger, Δ kiOutput is smaller, Δ kdOutput
It is smaller；
If angular deviation e (t) input is medium, parameter, Δ kpOutput is smaller, Δ kiOutput is medium, Δ kdOutput
It is medium；
If angular deviation e (t) input is smaller, parameter, Δ kpOutput is larger, Δ kiOutput is larger；And if
Angular deviation change rate de (t)/dt input is smaller, Δ kdIt is larger for exporting, if angular deviation change rate de (t)/dt is defeated
Enter to be larger, Δ kdOutput is smaller.
Preferably, the overshoot in the drive system is within 15%.
Preferably, 0.1≤kp≤1、10≤ki≤20、0.01≤kd≤0.03。
Preferably, kp=0.1, ki=20, kd=0.01.
Preferably, the electric steering engine is ASMC-03B.
The present invention compared with prior art possessed by the utility model has the advantages that
1, since experience chooses the randomness of pid parameter, the control effect of fuzzy is caused to be unable to reach most preferably, this hair
It is bright that its pid parameter is adjusted using the method for orthogonal optimization on its basis.Compared to fuzzy PID control method, benefit
Pid parameter is adjusted with orthogonal experiment, test number (TN) is less, can quickly determine suitable pid parameter, make the mould of system
Paste controlling extent reaches best.Comparative analysis is orthogonal-fuzzy-adaptation PID control and fuzzy-adaptation PID control, find orthogonal-fuzzy optimization
The system of best factor level combination belongs to overdamp system out, although when the rise time of orthogonal-fuzzy-adaptation PID control and adjustment
Between increase about 0.65s, eliminate concussion process of the manipulator near stable state, improve system motion control stabilization
Property；
2, relative to opened loop control, fuzzy-adaptation PID control increases 1~2 second in adjustment time, average angle relative error
Reduce 4% or so, average position error reduces 60% or so；Relative to fuzzy-adaptation PID control, orthogonal-fuzzy-adaptation PID control is rising
Time has a small amount of increase in adjustment time, and average angle relative error reduces 1.5% or so, and average position error reduces 50%
Left and right.This is research shows that orthogonal-fuzzy-adaptation PID control can greatly improve the accuracy of system.
Detailed description of the invention
Fig. 1 is the linear transfer function block diagram of electric steering engine servo drive system of the present invention.
Fig. 2 is the Bode diagram of steering engine closed loop transfer function, of the present invention.
Fig. 3 is opened loop control Simulink simulation result of the present invention.
Fig. 4 is PID control Simulink simulation result of the present invention.
Fig. 5 is the PID control Simulink simulation result of orthogonal optimization of the present invention.
Fig. 6 is fuzzy controller schematic illustration of the present invention.
Fig. 7 is fuzzy-adaptation PID control Simulink simulation result of the present invention.
Fig. 8 builds experiment porch schematic diagram to be of the present invention.
Specific embodiment
Present invention will be described in further detail below with reference to the accompanying drawings, to enable those skilled in the art referring to specification text
Word can be implemented accordingly.
The present invention is directed to the control of robot for picking up joint drive system electric steering engine, proposes a kind of based on orthogonal-mould
PID servo-drive intelligent control method is pasted, the mathematical model of electric steering engine is established, analyzes the stability of its closed-loop system；
The method that orthogonal optimization and fuzzy control are added in traditional PID control, orthogonal optimization can reduce the adjusting time of pid parameter
The adaptability of PID control can be improved in number, fuzzy control；The system category of the best factor level combination of orthogonal-fuzzy optimization
In overdamp system, concussion process of the underdamping near stable state is avoided；Emulation and the experimental verification present invention propose control
The validity of method processed.
The one kind proposed in the present invention is based on orthogonal-fuzzy servo-drive intelligent control method, specifically includes following step
It is rapid:
Step 1: establishing the mathematical model of electric steering engine；
The drive system of electric steering engine is generally by controller, driver, direct current generator, reduction gearing mechanism, feedback level
The modules such as device composition；Direct current generator is that electric steering engine core component can under conditions of not considering damping torque, moment of friction
With the mathematical model established in its dynamic process；The voltage equation in direct current generator circuit may be expressed as:
In formula, uaFor electric motor loop voltage；iaFor electric motor loop electric current；RaFor electric motor loop all-in resistance；EbIt is anti-electric for motor
Kinetic potential；L is motor total inductance.
The counter electromotive force of direct current generator may be expressed as:
In formula, KEFor back EMF coefficient；θ is the rotation angle of direct current generator.
The torque equation and torque balance equation of direct current generator may be expressed as:
In formula, KTFor torque coefficient；M is the torque of motor, MLFor the load torque on motor output shaft；J is that motor shaft is total
Rotary inertia.
The M under idle conditionL=0, the transmission function of direct current generator are as follows:
If δ0For the output corner of electric steering engine, KiFor the transmission ratio of transmission mechanism, can obtain:
δ0(s)=Ki×θ(s) (5)
Simultaneous formula (4), (5) available electric steering engine output corner and input voltage between transmission function:
It enablesTeReferred to as electromagnetic time constant, TmReferred to as electromechanical time constant.It can obtain:
In linear region, the transmission function of pwm driver be may be expressed as:
According to the principle of feedback potentiometer, the transmission function of feedback potentiometer is usually constant, be may be expressed as:
In formula, KFFor rudder face feedback factor, δ is the output corner of electric steering engine, ufFor current potential ultramagnifier feedback voltage.
As shown in Figure 1, analyzing each building block composition of electric steering engine and principle, available electric steering engine drive system
Linear transfer function block diagram.
The closed loop transfer function, of electric steering engine drive system is obtained by Fig. 1 are as follows:
Enable K1=KcKPWM, K2=180/KEKiπ, KcFor error amplification coefficient, then formula (10) can be with abbreviation are as follows:
In another embodiment, the present invention joint use ASMC-03B type electric steering engine, power stability and
Included retarder function, ASMC-03B type electric steering engine items technical parameter are as shown in table 1:
1 ASMC-03B type electric steering engine technical parameter of table
K can be obtained by data in table 12≈ 7.96 substitutes into all parameters in formula (11), available electric steering engine closed loop
Transmission function are as follows:
As shown in Fig. 2, the closed loop transfer function, of steering engine is carried out in the Linear Analysis Tool of Simulink
The analysis of amplitude-frequency characteristic, the Bode diagram of available steering engine closed loop transfer function,.
In engineering practice, in order to make system have satisfied stability deposit, it is generally desirable to phase margins at 30 °~60 °,
Magnitude margin requires to be greater than 6db；It can be obtained by the Bode diagram of steering engine closed loop transfer function, phase margin γ=28.7 °, amplitude is abundant
Spend Kg=11.2dB, so steering engine closed-loop system is a systems stabilisation.
Step 2: Matlab simulation analysis
1, opened loop control and emulation
As shown in figure 3, the closed loop transfer function, of the electric steering engine of foundation is encapsulated into submodule, unit step letter is carried out
Response test number as input signal, Simulink opened loop control Simulation result data are as shown in table 2；
2 opened loop control of table emulates data
It is available by Fig. 3 and table 2, when carrying out opened loop control, because being equipped with current potential ultramagnifier inside electric steering engine,
Its one semi-closed loop system of internal formation can tend towards stability so system passes through of short duration adjustment, guarantee the accuracy adjusted,
Rise time and to stablize the time short, but there is very big overshoot, it vibrating also than stronger, system has unstability,
So needing to combine with other control methods, reduce the overshoot and oscillation of system.
2, PID control and emulation
PID control is a kind of typical negative feedback control mode, is to use system given value r (t), subtracts the output of system
Value y (t) obtains system output bias e (t)=r (t)-y (t), carries out ratio, integral, differential to output bias e (t) later
Operation is controlled, the output result u (t) of PID controller is finally obtained.
In continuous time-domain, the control algolithm expression formula of PID are as follows:
In formula, kpFor proportionality coefficient；TiFor integration time constant；TdFor derivative time constant；ki=kp/TiFor integration system
Number；kd=kpTdFor differential coefficient.
In pid control algorithm, proportionality coefficient kpEffect be to speed up the response speed of system, improve the adjusting essence of system
Degree；Integration time constant TiEffect be elimination system steady-state error；Derivative time constant TdEffect be the dynamic of improvement system
State property energy.
Because open-loop control system has very big overshoot, the present invention is to reduce the overshoot of system as mesh
Mark, according to pid parameter setting principle, by constantly adjusting and attempting；K is worked as in final determinationp、ki、kdRespectively section [0.1,
1], [10,20] can guarantee system overshoot when within 15% when in [0.01,0.03] range.
As shown in figure 4, working as kp=0.1, ki=20, kdThe PID control that system is carried out when=0.03, uses unit step signal
As the input signal of model, PID control Simulation result data is as shown in table 3；
3 PID control Simulation result data of table
By the preliminary adjusting of PID control, by Fig. 4 and table 3 statistics indicate that, opened loop control and PID control, system all may be used
To tend towards stability, predetermined position is accurately reached, guarantees the accuracy adjusted；When PID control is added, when the rise time is with adjusting
Between increase on a small quantity, adjustment time is differed in 0.12s or so, but its overshoot has differed 33% or so；Simulation result explanation, is answered
With traditional PID control technology, increases and pick up the System for Joint Motion of Manipulator rise time and stablize the time, but substantially reduce it
Overshoot enhances kinetic stability.
3, the PID control and emulation of orthogonal optimization
(1) traditional orthogonal test process
The purpose of test is exactly to select one group of suitable pid parameter, makes the Stability and veracity of manipulator control system
Reach best；Since index most important in this control system is overshoot and adjustment time, but carrying out traditional PID
When control, to the preliminary overshoot for adjusting the system that considerably reduced of pid parameter, so special determine adjustment time conduct
The inspection target of this test.
This obvious test is influence of tri- parameter of PID to be studied to Con trolling index, it is thus determined that parameter is Kp, Ki, Kd；Water
The selection of flat range and the theoretical level of operator and practical experience are closely related.The selection of horizontal extent of the present invention is to establish
On the basis of a large amount of practical experiences.
, can be according to number of levels and because of prime number when selecting orthogonal arrage, selection can satisfy factor and horizontal minimal orthogonality
Table.For the reliability of guarantee test, the present invention chooses orthogonal arrage L9(34)；Although being shown between 3 parameters by experiment of single factor
There are reciprocations, but the conspicuousness influenced is smaller, so putting aside reciprocation.
This test analyzes data using intiutive analysis method.
Optimal data after analysis is substituted into system, sees overshoot that whether its effect really makes system and adjustment time all
Reach best.
(2) PID control and emulation based on orthogonal test
By the preliminary adjusting of PID, system overshoot has been able to meet work requirements when within 15%.Next
To reduce stabilization time of system as target, to realize better control effect, the parameter for carrying out PID using orthogonal test is whole
It is fixed；Devise L9(34) orthogonal arrage, factor level table is as shown in table 4:
4 factor level table of table
It is successively emulated, Simulation result data is as shown in table 5 according to factor level table using stablizing the time as target:
5 PID orthogonal experiments tables of data of table
As shown in Table 5, kp、ki、kdThree the very poor of parameter are respectively Rkp=0.324, Rki=0.278, Rkd=0.144,
Rkp>Rki>RkdIllustrate within the system, kpIt is to stablizing the maximum factor of time effects, followed by ki, it is finally kd；In conjunction with
The horizontal experiment mean analysis of each factor, the results showed that, in the range of above-mentioned parameter, kp=0.1, ki=20, kd=0.01,
PID control effect is preferable, is emulated using it as pid control parameter, simulation result as shown in figure 5, orthogonal optimization PID control
Simulation result data is as shown in table 6:
6 orthogonal optimization PID control Simulation result data of table
Simulation result shows that the PID control after orthogonal optimization is compared with the preliminary PID control that carries out, and the rise time has on a small quantity
Increase, adjustment time further decreases, and increases the rapidity of system adjusting, and the system of optimization belongs to overdamp system, surpasses
Tune amount is 0, and the stability of adjusting further increases, and the adjusting of parameter is carried out by orthogonal test, and PID control ability is mentioned
It rises.
4, fuzzy-adaptation PID control and emulation
For with large time delay, big inertia, the control object with complicated signal tracing, PID control also has office very much
Sex-limited, Fuzzy PID Control Technique can improve the control defect of PID.
As shown in fig. 6, fuzzy controller is realized on the basis of conventional PID controller, generally by e (t), de
(t)/dt is as fuzzy control input quantity, to Δ kp、Δki、ΔkdIt is two input of one kind, three output as fuzzy control output quantity
Fuzzy controller.
It is added with the parameter in Traditional PID, the new pid control parameter of formation:
Parameter k after selecting above orthogonal optimizationp=0.1, ki=20, kd=0.01, the Δ with fuzzy control output
kp、Δki、ΔkdIt carries out new pid parameter to calculate, carries out the control of drive system.
The basic domain for determining e (t) is [- 1,1], and the basic domain of de (t)/dt is [- 0.014,0.014], Δ kpBase
This domain is [- 0.1,0.1], Δ kiBasic domain is [- 20,20], Δ kdBasic domain is [- 0.02,0.02]；For the ease of
It calculating, the universe of fuzzy sets of input and output value is all set to [- 2,2] by us, it is hereby achieved that, the quantizing factor of e (t)
Ke=2/1=2, de (t)/dt quantizing factor Kec=0.007, Δ kpQuantizing factor Gp=0.1/2=0.05, Δ kiAmount
Change factor Gi=20/2=10, Δ kdQuantizing factor Gd=0.02/2=0.01.Using e (t), de (t)/dt as fuzzy control
Input quantity, to Δ kp、Δki、ΔkdAs fuzzy control output quantity, the fuzzy control of two inputs, three output is carried out.
Based on practical experience, parameter kp、kiAnd kdIn different e and ecLower adjustment need to meet following Adjustment principle:
(1) when error e is larger, to make system that there is preferable quick tracking performance, no matter the variation tendency of error such as
What should all take biggish kpWith lesser kd, while to avoid system response from larger overshoot occur, reply integral action is limited
System, takes lesser kiValue.
(2) when error e is in median size, to make system response that there is lesser overshoot, kpIt should take smaller, be simultaneously
The response speed of guarantee system, kiAnd kdSize wants moderate.Wherein kdValue be affected to system response.
(3) when error e is smaller, to guarantee that system has preferable steady-state performance, kpAnd kiWhat should be taken is bigger, is simultaneously
It avoids system from vibrating near setting value, and considers the interference free performance of system, when ec is smaller, kdIt is desirable big；When
When ec is larger, kdIt should take smaller.
It on the basis of expertise, is adjusted by emulation experiment, fuzzy control rule table such as table 7 can be summarized
Shown in~9:
7 Δ k of tablepFuzzy reasoning table
8 Δ k of tableiFuzzy reasoning table
9 Δ k of tabledFuzzy reasoning table
Similink emulation, fuzzy-adaptation PID control Simulink simulation result such as Fig. 7 are carried out according to the fuzzy rule of table 7~9
It is shown.
5, simulation result compares
Opened loop control, PID control, the PID control of orthogonal optimization, Fuzzy PID Control Simulation result data are arranged
Compare, as shown in table 8.
Table 8 controls the Simulation result data table of comparisons
It is available by simulation result, opened loop control, PID control, orthogonal PID control, orthogonal-fuzzy-adaptation PID control, system
It can tend towards stability；Compared with orthogonal PID control, orthogonal-fuzzy-adaptation PID control rise time reduces on a small quantity, and adjustment time has
It is a small amount of to increase；The system of the best factor level combination of orthogonal-fuzzy optimization belongs to overdamp system, steady in enhancing system
While qualitative, accelerate adjustment speed, reforming phenomena is also not present in stable state in simultaneity factor；By above-mentioned analysis of simulation result
Know, orthogonal-fuzzy-adaptation PID control well combines fuzzy control with the advantage of the two of orthogonal optimization, and PID may be implemented
The real-time adjusting of parameter has good performance of control to manipulator joint driving is picked up, while also preventing manipulator steady
Determine the dither process near state.
Step 3: test result analysis
Experiment has been substantially carried out the type selecting of hardware device and the design of mounting means, for the feedback for realizing data, chooses exhausted
Joint rotation angle measurement is carried out to formula encoder, carry out the operation of fuzzy with STM32 embedded system and picks up manipulator
The communication and feedback of data are realized in control with serial ports.The design of main control program program has been carried out simultaneously, including angle is adopted
Collect program, increment type PID program, fuzzy parameter calculation procedure etc..
1, simple joint is tested
As shown in figure 8, being equipped with HK50-D8G type encoder at manipulator joint 120,130, simple joint tests measuring machine
When tool hand angular error, locked processing is carried out to other joints 110,140 and 150, joint 120 inputs different given turns respectively
Angle θ2, controller carries out opened loop control, PID control and fuzzy-adaptation PID control to it respectively, by encoder to manipulator joint
120 practical rotation angle, θs '2Measurement.
It being measured respectively relative to 0 ° of direction of robot coordinate system, angle is -30 °, -60 °, -85 ° of control target angle,
Experiment test by three kinds of control modes, the angular error and average adjustment time of available manipulator joint angle are surveyed
The results are shown in Table 9 for amount:
The angular error and average adjustment time of 9 manipulator joint of table
Different given angles is inputted, carries out the opened loop control of single-DOF-joint respectively, orthogonal PID control is orthogonal-fuzzy
PID control is led among 0 °~-85 ° control ranges, can be obtained from measurement result analysis:
Relative to opened loop control, orthogonal PID control and orthogonal-fuzzy-adaptation PID control adjustment time increase 1s~2s.For just
PID control is handed over, for orthogonal-fuzzy-adaptation PID control by increasing on a small quantity in adjustment time, reason may be algorithm in fuzzy-adaptation PID control
Sequential operation increases adjustment time or position of manipulator constantly adjusts, and the process of feedback regulation increases adjustment time.
Relative to opened loop control, the average temperature angular error of orthogonal PID control and orthogonal-fuzzy-adaptation PID control is averaged surely
Determine angle relative error, reduction that can be different degrees of, average temperature angle relative error reduces 4% or so, illustrates two kinds of controls
Method processed has played the effect of feedback regulation.
Relative to orthogonal PID control, average temperature angular error, the average temperature angle of orthogonal-fuzzy-adaptation PID control are opposite
Error can also reduce on a small quantity, and average temperature angle relative error subtracts 2% or so.Illustrate that orthogonal-fuzzy-adaptation PID control can be into one
Step reduces angle and controls error.
In opened loop control, PID control and fuzzy-adaptation PID control, manipulator still has certain error, and reason may be manipulator
The influence of measurement error in the influence of torque, the influence of rigging error or the measurement process of generation of being self-possessed.
2, doublejointed is tested
When doublejointed experiment measurement locational error of manipulator, a coordinate position is given, according to Inverse Kinematics Solution operation, control
Device processed carries out opened loop control, PID control and fuzzy-adaptation PID control to it respectively, reaches target position, carry out theoretical position and
The error of coordinate of physical location calculates.
It is measured respectively relative to robot coordinate system Y direction (Y=0), provides the coordinate of X, Z, by three kinds of controlling parties
The experiment of formula is tested, and can be measured arm end location error, be measured multi-group data, and the mean place for obtaining each coordinate is missed
Difference, the results are shown in Table 10:
The position error of 10 two-freedom manipulator of table
Different coordinate positions is inputted, respectively the opened loop control of 2 degree-of-freedom joints of progress, the PID control of orthogonal optimization,
Orthogonal-fuzzy-adaptation PID control can be obtained from measurement result analysis:
Relative to opened loop control, the PID control of orthogonal optimization reduces 50% or so on average position error；Relative to just
The PID control of optimization is handed over, the average position error of fuzzy reduces 30% or so, keeps its control more accurate.System is by owing resistance
Damping system is changed into overdamp system, prevents jitter phenomenon of the manipulator near stable state, keeps control more steady.
It can be obtained according to 10 data of table, when the corner corresponding to the coordinate of test is smaller, machine error is smaller, so open loop
Control error is smaller, and PID control and fuzzy-adaptation PID control strength of adjustment are small but more accurate；Turn corresponding to the coordinate of test
When angle is larger, at this moment because stress problem causes machine error to increase suddenly, opened loop control error is caused to become larger, at this moment PID is controlled
System and fuzzy-adaptation PID control strength of adjustment increase, it is made to accelerate to deviate to theoretical coordinate.The corner corresponding to the coordinate of test compared with
Error is also larger when big, the influence for the torque that reason may generate for manipulator self weight.
Although the embodiments of the present invention have been disclosed as above, but its is not only in the description and the implementation listed
With it can be fully applied to various fields suitable for the present invention, for those skilled in the art, can be easily
Realize other modification, therefore without departing from the general concept defined in the claims and the equivalent scope, the present invention is simultaneously unlimited
In specific details and legend shown and described herein.
Claims (9)
1. a kind of Serial manipulator drive system motion control method based on orthogonal fuzzy, which is characterized in that establish electronic
The mathematical model of steering engine obtains the closed loop transfer function, of the electric steering engine drive system, is based on the closed loop transfer function, right
The output angle of the electric steering engine is controlled, comprising:
According to the overshoot and adjustment time in the drive system, parameter k is determined by orthogonal testp、ki、kd；
Respectively by angular deviation e (t), angular deviation change rate de (t)/dt and the Δ k of adjustment PID controller parameterp、Δki、
ΔkdBe converted to the quantification gradation in fuzzy domain；By the angular deviation e (t), angular deviation change rate de (t)/dt input
Fuzzy controller, fuzzy controller output are the Δ k for adjusting PID controller parameterp、Δki、Δkd；
Three parameter k are formed by fuzzy controllerp′、ki′、kd', it obtains stablizing output angle, and then be to the driving
System is controlled；
Wherein, kp'=kp+Δkp, ki'=ki+Δki, kd'=kd+Δkd。
2. the Serial manipulator drive system motion control method based on orthogonal fuzzy as described in claim 1, feature
It is, the closed loop transfer function, is
In formula, K1=KcKPWM, K2=180/KEKiπ,KEFor back EMF coefficient, KiFor the transmission of transmission mechanism
Than TeReferred to as electromagnetic time constant, TmReferred to as electromechanical time constant, KFFor rudder face feedback factor.
3. the Serial manipulator drive system motion control method based on orthogonal fuzzy as claimed in claim 2, feature
It is, the domain of the angular deviation e (t) is [- 1,1], and quantization is because be 2, the angular deviation change rate de (t)/dt
Domain is [- 0.014,0.014], quantizing factor 0.007, the parameter, Δ kp、Δki、ΔkdDomain be respectively [- 0.1,
0.1], [- 20,20], [- 0.02,0.02], quantizing factor are respectively 0.05,10,0.01.
4. the Serial manipulator drive system motion control method based on orthogonal fuzzy as claimed in claim 3, feature
It is, the angular deviation e (t) is divided into 5 grades, and angular deviation change rate de (t)/dt points are 5 grades, and the angle is inclined
Poor e (t) and the angular deviation change rate de (t)/dt fuzzy set are { NL, NS, ZO, PS, PL }；
The Δ k of PID controller parameterp、Δki、Δkd5 grades are divided into, fuzzy set is { NL, NS, ZO, PS, PL }.
5. the Serial manipulator drive system motion control method based on orthogonal fuzzy as claimed in claim 4, feature
It is, the fuzzy control rule are as follows:
If angular deviation e (t) input is larger, parameter, Δ kpOutput is larger, Δ kiOutput is smaller, Δ kdOutput for compared with
It is small；
If angular deviation e (t) input is medium, parameter, Δ kpOutput is smaller, Δ kiOutput is medium, Δ kdDuring output is
Deng；
If angular deviation e (t) input is smaller, parameter, Δ kpOutput is larger, Δ kiOutput is larger；And if angle
Deviation variation rate de (t)/dt input is smaller, Δ kdOutput is larger, if angular deviation change rate de (t)/dt input is
It is larger, Δ kdOutput is smaller.
6. the Serial manipulator drive system motion control method based on orthogonal fuzzy as claimed in claim 5, feature
It is, the overshoot in the drive system is within 15%.
7. the Serial manipulator drive system motion control method based on orthogonal fuzzy as claimed in claim 6, feature
It is, 0.1≤kp≤1、10≤ki≤20、0.01≤kd≤0.03。
8. the Serial manipulator drive system motion control method based on orthogonal fuzzy as claimed in claim 7, feature
It is, kp=0.1, ki=20, kd=0.01.
9. the Serial manipulator drive system motion control method based on orthogonal fuzzy as claimed in claim 8, feature
It is, the electric steering engine is ASMC-03B.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910270959.XA CN109940618A (en) | 2019-04-04 | 2019-04-04 | A kind of Serial manipulator drive system motion control method based on orthogonal fuzzy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910270959.XA CN109940618A (en) | 2019-04-04 | 2019-04-04 | A kind of Serial manipulator drive system motion control method based on orthogonal fuzzy |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109940618A true CN109940618A (en) | 2019-06-28 |
Family
ID=67012542
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910270959.XA Pending CN109940618A (en) | 2019-04-04 | 2019-04-04 | A kind of Serial manipulator drive system motion control method based on orthogonal fuzzy |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109940618A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111113411A (en) * | 2019-12-05 | 2020-05-08 | 珠海格力电器股份有限公司 | Robot control method and system based on two-dimensional code recognition and robot |
| CN111308887A (en) * | 2020-04-08 | 2020-06-19 | 江苏盛海智能科技有限公司 | Optimization method and optimization terminal for brake control of unmanned vehicle |
| CN112847372A (en) * | 2021-01-12 | 2021-05-28 | 河南科技大学 | Delta robot control system based on PMAC |
| CN114248911A (en) * | 2021-12-24 | 2022-03-29 | 兰州飞行控制有限责任公司 | Helicopter electric series steering engine control signal adjusting method |
| CN117610380A (en) * | 2024-01-19 | 2024-02-27 | 中国科学院苏州生物医学工程技术研究所 | Robot lightweight design method based on orthogonal experiment combined topology optimization |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050216105A1 (en) * | 2004-03-26 | 2005-09-29 | Tabor Keith A | Hydraulic system with coordinated multiple axis control of a machine member |
| CN101859096A (en) * | 2009-04-10 | 2010-10-13 | 鸿富锦精密工业（深圳）有限公司 | Control method by adopting PID (Proportion Integration Differentiation) controller, control device and robot |
| CN103984234A (en) * | 2014-05-15 | 2014-08-13 | 张万军 | Electro hydraulic servo system self-correction fuzzy PID control method |
| CN104808708A (en) * | 2015-04-22 | 2015-07-29 | 重庆工商职业学院 | Method and system for self-adjusting fuzzy PID (Proportion Integration Differentiation) parameters in furnace temperature control system |
| CN107127751A (en) * | 2017-03-21 | 2017-09-05 | 宁波韦尔德斯凯勒智能科技有限公司 | Articulated manipulator controls integral control system and control method |
| CN107618018A (en) * | 2017-10-26 | 2018-01-23 | 杭州电子科技大学 | A kind of manipulator behavior speed proportional control method based on myoelectricity |
| US9891599B2 (en) * | 2016-02-01 | 2018-02-13 | Varian Semiconductor Equipment Associates, Inc. | Proportional integral derivative control incorporating multiple actuators |
| CN107825449A (en) * | 2017-11-30 | 2018-03-23 | 南京邮电大学 | Tendon is driving singly to refer to Dextrous Hand and its control system and control method |
-
2019
- 2019-04-04 CN CN201910270959.XA patent/CN109940618A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050216105A1 (en) * | 2004-03-26 | 2005-09-29 | Tabor Keith A | Hydraulic system with coordinated multiple axis control of a machine member |
| CN101859096A (en) * | 2009-04-10 | 2010-10-13 | 鸿富锦精密工业（深圳）有限公司 | Control method by adopting PID (Proportion Integration Differentiation) controller, control device and robot |
| CN103984234A (en) * | 2014-05-15 | 2014-08-13 | 张万军 | Electro hydraulic servo system self-correction fuzzy PID control method |
| CN104808708A (en) * | 2015-04-22 | 2015-07-29 | 重庆工商职业学院 | Method and system for self-adjusting fuzzy PID (Proportion Integration Differentiation) parameters in furnace temperature control system |
| US9891599B2 (en) * | 2016-02-01 | 2018-02-13 | Varian Semiconductor Equipment Associates, Inc. | Proportional integral derivative control incorporating multiple actuators |
| CN107127751A (en) * | 2017-03-21 | 2017-09-05 | 宁波韦尔德斯凯勒智能科技有限公司 | Articulated manipulator controls integral control system and control method |
| CN107618018A (en) * | 2017-10-26 | 2018-01-23 | 杭州电子科技大学 | A kind of manipulator behavior speed proportional control method based on myoelectricity |
| CN107825449A (en) * | 2017-11-30 | 2018-03-23 | 南京邮电大学 | Tendon is driving singly to refer to Dextrous Hand and its control system and control method |
Non-Patent Citations (1)
| Title |
|---|
| 于恒彬: "基于模糊PID的舵机驱动串联捡拾机械手运动控制技术研究", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111113411A (en) * | 2019-12-05 | 2020-05-08 | 珠海格力电器股份有限公司 | Robot control method and system based on two-dimensional code recognition and robot |
| CN111308887A (en) * | 2020-04-08 | 2020-06-19 | 江苏盛海智能科技有限公司 | Optimization method and optimization terminal for brake control of unmanned vehicle |
| CN111308887B (en) * | 2020-04-08 | 2023-10-24 | 江苏盛海智能科技有限公司 | Optimization method and optimization end for braking control of unmanned vehicle |
| CN112847372A (en) * | 2021-01-12 | 2021-05-28 | 河南科技大学 | Delta robot control system based on PMAC |
| CN114248911A (en) * | 2021-12-24 | 2022-03-29 | 兰州飞行控制有限责任公司 | Helicopter electric series steering engine control signal adjusting method |
| CN114248911B (en) * | 2021-12-24 | 2024-03-01 | 兰州飞行控制有限责任公司 | Helicopter electric series steering engine control signal adjustment method |
| CN117610380A (en) * | 2024-01-19 | 2024-02-27 | 中国科学院苏州生物医学工程技术研究所 | Robot lightweight design method based on orthogonal experiment combined topology optimization |
| CN117610380B (en) * | 2024-01-19 | 2024-04-09 | 中国科学院苏州生物医学工程技术研究所 | Robot lightweight design method based on orthogonal experiment combined topology optimization |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109940618A (en) | A kind of Serial manipulator drive system motion control method based on orthogonal fuzzy | |
| CN107179682B (en) | Passive load simulator and redundant moment restraining method | |
| CN111173678B (en) | Wind power cabin suspension control method for RBF neural network pitch interference compensation | |
| Iyasere et al. | Nonlinear robust control to maximize energy capture in a variable speed wind turbine | |
| CN113107768B (en) | Wind turbine cabin two-end suspension control method based on RBF neural network self-adaptive decoupling | |
| CN102509152A (en) | Switched reluctance motor on-line modeling method based RBF neural network | |
| CN112910322B (en) | Self-adaptive double-motor anti-backlash control method based on anti-interference control | |
| CN111371357A (en) | Permanent magnet synchronous motor speed regulation control method based on self-adaptive supercoiling algorithm | |
| CN109507869A (en) | A kind of optimization method of the motor control PI parameter suitable for permanent magnet synchronous motor | |
| CN113904598A (en) | Predictive control method for alternating current permanent magnet synchronous motor | |
| CN114939869A (en) | Mechanical arm trajectory tracking method based on nonsingular rapid terminal sliding mode | |
| CN105892294B (en) | A kind of servo system control Parameters design of parallel architecture main tapping | |
| CN109176480B (en) | Sliding mode control method and system for parallel robot | |
| Baroudi et al. | State-feedback and linear quadratic regulator applied to a single-link flexible manipulator | |
| CN108429501B (en) | Method for observing load disturbance of permanent magnet synchronous motor | |
| CN102269971A (en) | Self-adapting servo controller based on model tracking | |
| CN112350352A (en) | Method for increasing energy storage reactive power regulation rate | |
| CN112152539A (en) | Neural network compensation motor load moment observer implementation method | |
| CN108267970B (en) | Time-lag rotor active balance control system and method based on Smith model and single neuron PID | |
| Ahmad et al. | Investigations of NCTF with input shaping for sway control of a double-pendulum-type overhead crane | |
| CN114977928A (en) | Parameter self-tuning method for speed ring and position ring of permanent magnet synchronous servo system | |
| CN112523945B (en) | Active disturbance rejection nonlinear control method for maximum wind energy capture of double-fed wind turbine | |
| Huang et al. | Torque balance of double motors drive system based on exponential reaching law VSSMC | |
| Ran et al. | Robust Adaptive MPPT Control of Wind Turbine Based on Prescribed Performance | |
| CN117886226B (en) | Crane system nonlinear control method and system based on flat output |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190628 |
|
| RJ01 | Rejection of invention patent application after publication |