CN108181817A - Fire control system modeling method - Google Patents
Fire control system modeling method Download PDFInfo
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- CN108181817A CN108181817A CN201810034722.7A CN201810034722A CN108181817A CN 108181817 A CN108181817 A CN 108181817A CN 201810034722 A CN201810034722 A CN 201810034722A CN 108181817 A CN108181817 A CN 108181817A
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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Abstract
The present invention relates to a kind of fire control system modeling methods, are related to Virtual Prototype Technique field.The present invention proposes a kind of fire control system modeling method, this method is based on overhead machine gun weapon station modular design concept, Modeling Research has been carried out to fire control system, the subsystem modeling of virtual prototype has been realized, facilitates the use the digital model that Virtual Prototype Technique establishes weapon station.
Description
Technical field
The present invention relates to Virtual Prototype Technique fields, and in particular to a kind of fire control system modeling method.
Background technology
Overhead machine gun weapon station is a kind of typical electromechanical integration complication system, design include weapon system design,
The design of stand system and Fire Control System Design etc., are complicated multidisciplinary, multi-user Cooperation an iterative repetition processes, are related to
Field span it is big, it is therefore desirable to the digital model of weapon station is established using Virtual Prototype Technique, for its development it is each
Stage be design, develop, testing and evaluation integrated virtual prototype is provided.Virtual Prototype Technique foundation during, how into
The each subsystem of row is modeled as the technical issues of urgently to be resolved hurrily.
Invention content
(1) technical problems to be solved
The technical problem to be solved by the present invention is to：How to realize the modeling to fire control system, facilitate the use virtual prototype
Technology establishes the digital model of weapon station.
(2) technical solution
In order to solve the above technical problem, the present invention provides a kind of 1, fire control system modeling methods, which is characterized in that packet
Include following steps：
It is modeled as follows into line control unit：
A kind of adaptive fuzzy controller, the if ... that rule is determined for condition are designed using Mamdani fuzzy models
Then ... rules, fuzzy reasoning method use Max-Min rationalistic methods, and the precision of fuzzy quantity then solves mould using weighted mean method
Paste；
The adaptive fuzzy controller is to pass through set-point yd(t) and system output value y (t) obtains system more afterwards
Error e and error change ec are as input, with the Proportional coefficient K of PID controllerp, time of integration COEFFICIENT Ki, derivative time coefficient
KdCorrected parameter Δ Kp、ΔKi、ΔKdAs output, according to the error e of different moments and error change ec to PID controller
Kp、Ki、KdSelf-tuning System is carried out, by the K after adjustingp、Ki、KdPID controller is sent into, forms adaptive fuzzy controller.
Preferably, the parameter K of the PID controller outputp、Ki、KdIt is shown below：
Wherein, Kp'、Ki'、Kd' it is pre-tuning value, a, b, c are predetermined coefficient.
Preferably, the modeling of the adaptive fuzzy controller is realized using Simulink/Fuzzy emulation tools, by mould
It pastes reasoning and fuzzy decision is sent into Fuzzy controllers, specific method is：Error e and error change ec respectively by quantization because
Subprocessing is mapped in respective fuzzy domain, and then being inquired in fuzzy control rule table according to the fuzzy class at place should
The pid parameter regulated quantity of output, finally acts on PID controller by pid parameter regulated quantity.
Preferably, the method further includes progress mechanical part modeling as follows：
The mechanical part refers to the executing agency of fire control system, including servo motor gear, elevating mechanism sector and seat ring
Gear ring, servo motor gear engages to transmit motor torque respectively by gear with elevating mechanism sector and race, in model
Defined in contact-impact come simulate gear engagement,
In ADAMS collides function, impact force FnIt is determined by equivalent stiffness k and power exponent q, uses Hertz elastic collisions
Model calculates k and q；
Wherein k depends on the material of impacting object and the planform of object：
In formula, R and E*It is calculated according to Hertz contact theories：
1/R=1/R1+1/R2
1/E*=(1- μ1 2)/E1+(1-μ2 2)/E2
In formula, R1、R2Equivalent radius for two Gear Contact points；
μ1、μ2Poisson's ratio for two gears；
E1、E2Young's modulus for two gears.
Preferably, the equivalent radius approximation of Gear Contact point is replaced with gear compound graduation radius of circle, can by each gear parameter
Obtain the equivalent radius of each gear, sector, gear material selects 45 steel, then its Poisson's ratio is 0.3, Young's modulus for 2 ×
1011Pa, so as to calculate the equivalent stiffness collided everywhere.
(3) advantageous effect
The present invention proposes a kind of fire control system modeling method, and this method is managed based on the modularized design of overhead machine gun weapon station
It reads, Modeling Research has been carried out to fire control system, realize the subsystem modeling of virtual prototype, facilitate the use Virtual Prototype Technique
Establish the digital model of weapon station.
Description of the drawings
Fig. 1 is the control principle drawing of PID controller；
Fig. 2 is the fuzzy control principle figure of the present invention；
Fig. 3 is the fuzzy adaptive controller structure chart of the present invention.
Specific embodiment
To make the purpose of the present invention, content and advantage clearer, with reference to the accompanying drawings and examples, to the present invention's
Specific embodiment is described in further detail.
The present invention proposes a kind of fire control system modeling method and includes the following steps：
Step 1: mechanical part models
Mechanical part is primarily referred to as the executing agency of fire control system, generally include servo motor gear, elevating mechanism sector and
Race.Servo motor gear engages to transmit motor torque by gear with elevating mechanism sector and race, in mould
Contact-impact engages to simulate gear defined in type.
In ADAMS collides function, impact force FnIt is determined by equivalent stiffness k and power exponent q, usually using Hertz elasticity
Collision model calculates k and q.
K depends on the material of impacting object and the planform of object：
In formula, R and E*It is calculated according to Hertz contact theories：
1/R=1/R1+1/R2
1/E*=(1- μ1 2)/E1+(1-μ2 2)/E2
In formula, R1、R2Equivalent radius for two Gear Contact points；
μ1、μ2Poisson's ratio for two gears；
E1、E2Young's modulus for two gears.
1 gear major parameter of table
The equivalent radius of Gear Contact point approximate can be replaced with gear compound graduation radius of circle, can obtain respectively by each gear parameter
The equivalent radius of a gear, sector.Gear material selects 45 steel, then its Poisson's ratio is 0.3, and Young's modulus is 2 × 1011pa.Root
The contact stiffness k collided everywhere can be calculated according to data above, as shown in table 2.
The setting collided in 2 model of table
Step 2: controller models
Control system performance simulation is the multi-field co-simulation based on each field modeling, simulation analysis CAx/DFx technologies
Technology.Wherein controller modeling is the basis of electromechanical collaborative simulation, is based primarily upon the Simulink control design cases emulation of MATLAB
Software.
The control technology used in overhead machine gun Remote Control Weapon Station is Fuzzy Self-adaptive PID.PID control is basis
The error of system, proportion of utilization, integration, difference gauge calculate controlled quentity controlled variable and are controlled, and principle is as shown in Figure 1.
PID controller is a kind of linear controller, it forms control partially according to set-point a (t) and real output value b (t)
Difference：
E (t)=a (t)-b (t) (2)
By deviation in proportion, integration and differential controlled quentity controlled variable is formed by linear combination, so as to controlling controlled device,
Therefore referred to as PID controller, in analog regulation control system, control law expression formula is expressed as below：
Wherein, u (t)：Output signal；e(t)：The difference of deviation signal, measured value and set-point；KP：Proportionality coefficient；Ki：Product
Divide time coefficient；Kd：Derivative time coefficient.
The nonlinear control algorithm of a kind of " unmounted model " is established using fuzzy logic, it is particularly fixed using tradition at those
The excessively complicated process of amount technology analysis or the information provided are non-stationary, non-precisely, in non-deterministic system, obscure
The effect of logic control is quite apparent.
The core of fuzzy control is fuzzy controller, and its principle is as shown in Figure 2.Fuzzy controller is by blurring, mould
Paste reasoning, four parts of ambiguity solution and knowledge base are formed.
The process of fuzzy control is immediately fuzzy by being accurate to, then from obscuring accurately process.Specific implementation mainly includes：
The selection of fuzzy control model, the blurring of precise volume, the foundation of fuzzy control rule, fuzzy reasoning and fuzzy quantity it is accurate
Change.If ... then ... the rules determined in the present invention using Mamdani fuzzy models, rule for condition, fuzzy reasoning method are adopted
With Max-Min rationalistic methods, Mamdani model structures are simple, convenience of calculation.The precision of fuzzy quantity then using weighted mean method come
Ambiguity solution.
Fuzzy controller is to pass through set-point yd(t) error e and mistake of system are obtained more afterwards with system output value y (t)
Difference variation ec is as input, with the K of PID controllerp、Ki、KdCorrected parameter Δ KpΔKiΔKdAs output, according to it is different when
The e and ec at quarter are to the K of Traditional PIDp、Ki、KdCarry out Self-tuning System.By the K after adjustingp、Ki、KdConventional PID controller is sent into, is formed
Adaptive fuzzy controller, structure are as shown in Figure 3.
The parameter K of PID controller outputp、Ki、KdIt is shown below, Kp'、Ki'、Kd' it is pre-tuning value.
A, b, c are predetermined coefficient.
Fuzzy controller modeling mainly employs Simulink/Fuzzy emulation tools, by fuzzy reasoning and fuzzy decision etc.
It is sent into Fuzzy controllers.Its specific method is：Error e and error change ec are mapped to each by quantizing factor processing respectively
From fuzzy domain in, the pid parameter that export then is inquired in fuzzy control rule table according to the fuzzy class at place
Regulated quantity is finally acted on conventional PID controller.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformation can also be made, these are improved and deformation
Also it should be regarded as protection scope of the present invention.
Claims (5)
1. a kind of fire control system modeling method, which is characterized in that include the following steps：
It is modeled as follows into line control unit：
A kind of adaptive fuzzy controller, if ... then ... the rule that rule is determined for condition are designed using Mamdani fuzzy models
Then, fuzzy reasoning method uses Max-Min rationalistic methods, and the precision of fuzzy quantity is then using weighted mean method come ambiguity solution；
The adaptive fuzzy controller is to pass through set-point yd(t) error e of system is obtained more afterwards with system output value y (t)
With error change ec as inputting, with the Proportional coefficient K of PID controllerp, time of integration COEFFICIENT Ki, derivative time COEFFICIENT KdRepair
Positive parameter, Δ Kp、ΔKi、ΔKdAs output, according to the error e of different moments and error change ec to the K of PID controllerp、Ki、
KdSelf-tuning System is carried out, by the K after adjustingp、Ki、KdPID controller is sent into, forms adaptive fuzzy controller.
2. the method as described in claim 1, which is characterized in that the parameter K of the PID controller outputp、Ki、KdSuch as following formula institute
Show：
Wherein, Kp'、Ki'、Kd' it is pre-tuning value, a, b, c are predetermined coefficient.
3. method as claimed in claim 2, which is characterized in that the modeling of the adaptive fuzzy controller uses
Simulink/Fuzzy emulation tools are realized, fuzzy reasoning and fuzzy decision are sent into Fuzzy controllers, specific method is：
Error e and error change ec are mapped to by quantizing factor processing in respective fuzzy domain respectively, then according to the mould at place
Paste grade inquires the pid parameter regulated quantity that export in fuzzy control rule table, finally acts on pid parameter regulated quantity
PID controller.
4. the method as described in claim 1, which is characterized in that the method further includes carries out mechanical part as follows
Modeling：
The mechanical part refers to the executing agency of fire control system, including servo motor gear, elevating mechanism sector and race,
Servo motor gear engages to transmit motor torque respectively by gear with elevating mechanism sector and race, defines in a model
Contact-impact engages to simulate gear,
In ADAMS collides function, impact force FnIt is determined by equivalent stiffness k and power exponent q, is come using Hertz elastic collision models
Calculate k and q；
Wherein k depends on the material of impacting object and the planform of object：
In formula, R and E*It is calculated according to Hertz contact theories：
1/R=1/R1+1/R2
1/E*=(1- μ1 2)/E1+(1-μ2 2)/E2
In formula, R1、R2Equivalent radius for two Gear Contact points；
μ1、μ2Poisson's ratio for two gears；
E1、E2Young's modulus for two gears.
5. method as claimed in claim 4, which is characterized in that the equivalent radius approximation of Gear Contact point justifies half with gear compound graduation
Diameter replaces, and the equivalent radius of each gear, sector is can obtain by each gear parameter, and gear material selects 45 steel, then its Poisson
Than being 0.3, Young's modulus is 2 × 1011Pa, so as to calculate the equivalent stiffness collided everywhere.
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| CN201810034722.7A CN108181817A (en) | 2018-01-15 | 2018-01-15 | Fire control system modeling method |
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| CN201810034722.7A CN108181817A (en) | 2018-01-15 | 2018-01-15 | Fire control system modeling method |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112129164A (en) * | 2020-09-23 | 2020-12-25 | 中国人民解放军陆军装甲兵学院 | Intelligent assistant decision-making system architecture of weapon station |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112129164A (en) * | 2020-09-23 | 2020-12-25 | 中国人民解放军陆军装甲兵学院 | Intelligent assistant decision-making system architecture of weapon station |
| CN112129164B (en) * | 2020-09-23 | 2022-09-27 | 中国人民解放军陆军装甲兵学院 | Intelligent assistant decision-making system architecture of weapon station |
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