CN105182753B - A kind of workpiece platform micro-motion MIMO robust fuzzies neural networks sliding mode control method - Google Patents

A kind of workpiece platform micro-motion MIMO robust fuzzies neural networks sliding mode control method Download PDF

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CN105182753B
CN105182753B CN201510628029.9A CN201510628029A CN105182753B CN 105182753 B CN105182753 B CN 105182753B CN 201510628029 A CN201510628029 A CN 201510628029A CN 105182753 B CN105182753 B CN 105182753B
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motion
degree
workpiece platform
mover
mrow
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CN105182753A (en
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王光
王一光
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Heilongjiang University
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Abstract

A kind of workpiece platform micro-motion MIMO robust fuzzies neural networks sliding mode control method, belong to ultraprecise motion control field, in order to solve the problems, such as photo-etching machine work-piece platform fine motion part for realize approximation that completely diagonal decoupling form carries out and ignore operational issue and level, vertical direction does not model coupled characteristic.The present invention includes：One：Establish the workpiece platform micro-motion six degree of freedom coupling model with disturbance term；Two：Model parameter uncertainty is determined for a workpiece platform micro-motion six degree of freedom coupling model with disturbance term established；Three：According to one and two, nonlinear function is estimated using neutral net, obtains estimated result；Four：Workpiece platform micro-motion MIMO robust fuzzy neural networks sliding mode control laws are determined according to one and three；Five：Workpiece platform micro-motion six degree of freedom system is controlled according to the control law that four determine.The present invention is used for workpiece platform micro-motion six degree of freedom control system.

Description

A kind of workpiece platform micro-motion MIMO robust fuzzies neural networks sliding mode control method
Technical field
The invention belongs to ultraprecise motion control field, is primarily related to a kind of photo-etching machine work-piece platform fine motion MIMO robusts Fuzzy neural network sliding-mode control.
Background technology
Litho machine is the equipment for manufacturing large scale integrated circuit.Workpiece table system is the key components of litho machine, main It is that carrying silicon chip to be exposed realizes that ultraprecise moves to want function.The kinematic accuracy and speed of work stage to the resolution ratio of litho machine and Yield has directly influence.Work stage ultraprecise dynamic tracks and positioning is the key technology of litho machine research and development.Due to long row Journey linear electric motors can not ensure nano level kinematic accuracy, it usually needs using voice coil motor as actuator, but voice coil motor Stroke is very limited.Then litho machine control in, traditional single kind method of actuator control can not solve high accuracy with Contradiction between big stroke.In view of considerations above, the grand micro-structural of generally use in Optical Coatings for Photolithography.Grand dynamic main completion is at a high speed Big stroke motion, fine motion part main task are to realize that ultraprecise dynamic is tracked and positioned.
Photo-etching machine work-piece platform fine motion part is multivariable, multivariant precise motion device, and its motor function is by six The individual voice coil motor according to the arrangement of certain geometric position is realized jointly.Traditional workpiece platform micro-motion part SISO uneoupled control modes It is to use decoupling compensation technology, the controlled device after decoupling is changed into diagonal system, so that substantially has the work of coupled characteristic The decoupling of part platform fine motion mimo system is multiple independent SISO system and is individually controlled respectively.The main of the control mode is asked Topic is due to that the coupled characteristic of workpiece platform micro-motion system is more complicated, wherein various uncertain effects, do not model characteristic and The factors such as mechanical resonant cause decoupling compensation process to be difficult completely right to realize by Accurate Model, and during uneoupled control Approximation that angle decouples form and carried out and ignore operation largely to have impact on conventional decoupling control mode micro- in work stage Performance in dynamic componental movement control system.In addition, conventional decoupling control mode typically examines the motion of workpiece platform micro-motion part Consider for horizontally and vertically two parts, realize step-scan and leveling and focusing respectively.But because vertical portion is in course of adjustment meeting Scanning and step motion on horizontal direction produce certain influence, and this coupled characteristic that do not model is in traditional uneoupled control side Typically it is taken as external disturbance to be handled in formula, this can also influence workpiece platform micro-motion part closed-loop control system to a certain extent The performance of system.To sum up, traditional photo-etching machine work-piece platform fine motion partial decoupling control mode can not meet current photo-etching machine work-piece Platform fine motion high speed, the needs of ultraprecise motion control.
The content of the invention
The invention aims to solve photo-etching machine work-piece platform fine motion part coupling effect, model uncertainty and outside The factors such as disturbance reduce the problem of conventional decoupling control system performance, there is provided a kind of workpiece platform micro-motion MIMO robust fuzzies nerve Sliding-Mode Control Based on Network method.
A kind of workpiece platform micro-motion MIMO robust fuzzies neural networks sliding mode control method of the present invention, the control method bag Include following steps：
Step 1: according to workpiece platform micro-motion six degree of freedom system, the workpiece platform micro-motion six degree of freedom with disturbance term is established Coupling model；
Step 2: the workpiece platform micro-motion six degree of freedom coupling model with disturbance term established for step 1 determines model Parameter uncertainty；
Step 3: the workpiece platform micro-motion six degree of freedom coupling model and step 2 with disturbance term established for step 1 Model parameter uncertainty, nonlinear function is estimated using neutral net and obtains estimated result；
Step 4: the workpiece platform micro-motion six degree of freedom coupling model and step 3 with disturbance term established according to step 1 Neutral net estimated result determine workpiece platform micro-motion MIMO robust fuzzy neural networks sliding mode control laws；
Step 5: the control law determined according to step 4 is controlled to workpiece platform micro-motion six degree of freedom system.
In the step 1, establishing the workpiece platform micro-motion six degree of freedom coupling model with disturbance term is：
Wherein, M is inertial matrix, and C is kirschner matrix, and f is voice coil motor thrust, fexFor disturbance term.
In step 2, model parameter uncertainty is：
Wherein,WithThe respectively nominal value of inertial matrix and kirschner matrix, △ M and △ C be respectively inertial matrix and The Additive Generator parameter of kirschner matrix.
In step 3, using neutral net to nonlinear functionEstimated.
Estimated result is：
Wherein,WithThe respectively output speed and acceleration of workpiece platform micro-motion six degree of freedom system,For function T Neutral net estimation,Estimate weight matrix for neutral net, a (θ) is activation vector, and θ inputs for neutral net.
In the step 4, workpiece platform micro-motion MIMO robust fuzzy neural networks sliding mode control laws are：
Wherein, u is fuzzy control item, c=diag { c1 c2 c3 c4 c5 c6And σ=diag { σ1 σ2 σ3 σ4 σ5 σ6} It is positive definite diagonal matrix, c1、c2、c3、c4、c5And c6Represent c six degree of freedom component, σ1、σ2、σ3、σ4、σ5And σ6Represent σ's Six degree of freedom component, sliding formwork item s=diag { s1 s2 s3 s4 s5 s6, s1、s2、s3、s4、s5And s6The six of expression s is free respectively Component is spent, diag represents diagonal matrix,Represent reference acceleration,Tracking velocity error is represented, α represents proportionality coefficient square Battle array.
In step 4, fuzzy rule, fuzzy membership function, value and σ that fuzzy control item u is related to adaptive law tool Body is：
Fuzzy rule：
IF si is NB,Then uif is NB
IF si is N,Then uif is N
IF si is Z,Then uif is Z
IF si is P,Then uif is P
IF si is PB,Then uif is PB
Wherein, uifFor the output of the free degree of fuzzy system i-th, siFor the component of the free degree of sliding formwork item i-th, NB, N, Z, P and PB represent respectively it is negative it is big, negative, zero, just with honest, i ∈ 1,2,3,4,5,6；
The Indistinct Input membership function that fuzzy control item u is related to is triangle membership function；
The fuzzy output membership function that fuzzy control item u is related to is monodrome membership function；
Fuzzy control item u value is u=η uf；
Wherein, u=[u1 u2 u3 u4 u5 u6]T, u1、u2、u3、u4、u5And u6U six degree of freedom component, mould are represented respectively The output u of paste systemf=[u1f u2f u3f u4f u5f u6f]T, u1f、u2f、u3f、u4f、u5fAnd u6fU is represented respectivelyfSix freely Spend component, gain coefficient η=diag { η1 η2 η3 η4 η5 η6, η1、η2、η3、η4、η5And η6The six of gain coefficient η is represented respectively Free degree component, η adaptive law are Represent ηiDifferential, γiFor postiive gain；Fuzzy control item u is related to σ adaptive law Represent σiDifferential, KiFor postiive gain, subscript i=1,2 ... 6.
Invention effect：
Due to photo-etching machine work-piece platform fine motion parts of traditional uneoupled control mode, to realize, diagonally decoupling form is carried out completely Approximation and ignore operation and ignore level and the coupled characteristic problem that do not model of vertical direction largely have impact on Performance and performance in workpiece platform micro-motion componental movement control system.Workpiece platform micro-motion MIMO robust fuzzies proposed by the invention Neural networks sliding mode control method can be very good to solve the above problems.Using method provided by the invention work stage can be made micro- Dynamic part control system by do not model characteristic, uncertainty and external disturbance influenceed it is smaller, so as to increase substantially Precision, robustness and the antijamming capability of closed-loop control system.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of control method in embodiment one；
Fig. 2 is the principle schematic of the input membership function in embodiment three；
Fig. 3 is the principle schematic of the output membership function in embodiment three.
Embodiment
Embodiment one：Illustrate present embodiment, a kind of workpiece platform micro-motion described in present embodiment with reference to Fig. 1 MIMO robust fuzzy neural networks sliding mode control methods, the control method comprise the following steps：
Step 1: according to workpiece platform micro-motion six degree of freedom system, the workpiece platform micro-motion six degree of freedom with disturbance term is established Coupling model
Wherein, M is inertial matrix, and C is kirschner matrix, and f is voice coil motor thrust, fexFor disturbance term；
Step 2: the workpiece platform micro-motion six degree of freedom coupling model with disturbance term established for step 1 determines model Parameter uncertainty is
Wherein, WithThe respectively nominal value of inertial matrix and kirschner matrix, △ M and △ C be respectively inertial matrix and The Additive Generator parameter of kirschner matrix；
Step 3: the workpiece platform micro-motion six degree of freedom coupling model and step 2 with disturbance term established for step 1 Model parameter uncertainty, using neutral net to nonlinear functionEstimated, estimated Counting result is
Wherein,WithThe respectively output speed and acceleration of workpiece platform micro-motion six degree of freedom system,For function T Neutral net estimation,Estimate weight matrix for neutral net, a (θ) is activation vector, and θ inputs for neutral net；
Step 4: the workpiece platform micro-motion six degree of freedom coupling model and step 3 with disturbance term established according to step 1 Neutral net estimated result determine that workpiece platform micro-motion MIMO robust fuzzy neural networks sliding mode control laws are：
Wherein, u is fuzzy control item, c=diag { c1 c2 c3 c4 c5 c6And σ=diag { σ1 σ2 σ3 σ4 σ5 σ6} It is positive definite diagonal matrix, c1、c2、c3、c4、c5And c6Represent c six degree of freedom component, σ1、σ2、σ3、σ4、σ5And σ6Represent σ's Six degree of freedom component, sliding formwork item s=diag { s1 s2 s3 s4 s5s6, s1、s2、s3、s4、s5And s6The six of expression s is free respectively Spend component,；Diag represents diagonal matrix；Represent reference acceleration,Tracking velocity error is represented, α represents proportionality coefficient square Battle array；
Step 5: the control law determined according to step 4 is controlled to workpiece platform micro-motion six degree of freedom system.
Embodiment two：Present embodiment is to a kind of workpiece platform micro-motion MIMO Shandongs described in embodiment one The further restriction of rod fuzzy neural network sliding-mode control, in the step 3, neutral net input θ input is：
Estimate weight matrixAdaptive law be：
Wherein, Γ is positive definite diagonal matrix, e andRespectively the track position error of workpiece platform micro-motion six degree of freedom system and Tracking velocity error.
Embodiment three：Present embodiment is to a kind of workpiece platform micro-motion described in embodiment one or two The further restriction of MIMO robust fuzzy neural networks sliding mode control methods：In step 4, fuzzy rule that fuzzy control item u is related to Then, fuzzy membership function, value and σ adaptive law are specially：
Fuzzy rule：
IF si is NB,Then uifIs NB, explanation：If siIt is negative big, then uifIt is negative big；
IF si is N,Then uifIs N, explanation：If siIt is negative, then uifIt is negative；
IF si is Z,Then uifIs Z, explanation：If siIt is zero, then uifIt is zero；
IF si is P,Then uifIs P, explanation：If siIt is just, then uifIt is just
IF si is PB,Then uifIs PB, explanation：If siIt is honest, then uifIt is honest；
Wherein, uifFor the output of the free degree of fuzzy system i-th, siFor the component of the free degree of sliding formwork function i-th, NB, N, Z, P Represented respectively with PB it is negative it is big, negative, zero, just with honest, i ∈ 1,2,3,4,5,6；
The Indistinct Input membership function that fuzzy control item u is related to is triangle membership function, specific value such as Fig. 2 institutes Show；
The fuzzy output membership function that fuzzy control item u is related to is monodrome membership function, specific value such as Fig. 3 institutes Show；
Fuzzy control item u value is u=η uf；
Wherein, u=[u1 u2 u3 u4 u5 u6]T, u1、u2、u3、u4、u5And u6U six degree of freedom component, mould are represented respectively The output u of paste systemf=[u1f u2f u3f u4f u5f u6f]T, u1f、u2f、u3f、u4f、u5fAnd u6fU is represented respectivelyfSix freely Spend component, gain coefficient η=diag { η1 η2 η3 η4 η5 η6, η1、η2、η3、η4、η5And η6The six of gain coefficient η is represented respectively Free degree component, η adaptive law are Represent ηiDifferential, γiFor postiive gain；Fuzzy control item u is related to σ adaptive law Represent σiDifferential, KiFor postiive gain, subscript i=1,2 ... 6.
Present embodiment effect：
In view of photo-etching machine work-piece platform fine motion parts of traditional uneoupled control mode, to realize, diagonally decoupling form is entered completely Capable approximation and ignore operational issue and ignore horizontal, vertical direction the coupled characteristic that do not model can be to workpiece platform micro-motion part The problem of kinetic control system performance has an impact.The workpiece platform micro-motion MIMO robust fuzzy nerve nets that present embodiment is proposed Network sliding-mode control can solve the above problems well.Compared with traditional work stage uneoupled control mode, this embodiment party Formula proposed method need not carry out decoupling and model simplification operation and can utilize neutral net real-time online to disturbance with it is not true It is qualitative to be estimated, it can effectively mitigate sliding formwork buffeting effect pair by introducing fuzzy logic present embodiment proposed method The robustness for influenceing and further improving system of workpiece platform micro-motion part control system.

Claims (3)

  1. A kind of 1. workpiece platform micro-motion MIMO robust fuzzies neural networks sliding mode control method, it is characterised in that the control method Comprise the following steps：
    Step 1: according to workpiece platform micro-motion six degree of freedom system, establish the workpiece platform micro-motion six degree of freedom with disturbance term and couple Model；
    Step 2: the workpiece platform micro-motion six degree of freedom coupling model with disturbance term established for step 1 determines model parameter It is uncertain；
    Step 3: for step 1 foundation with the workpiece platform micro-motion six degree of freedom coupling model of disturbance term and the mould of step 2 Shape parameter is uncertain, and nonlinear function is estimated using neutral net, obtains estimated result；
    Step 4: according to step 1 foundation with the workpiece platform micro-motion six degree of freedom coupling model of disturbance term and the god of step 3 Workpiece platform micro-motion MIMO robust fuzzy neural networks sliding mode control laws are determined through network-evaluated result；
    Step 5: the control law determined according to step 4 is controlled to workpiece platform micro-motion six degree of freedom system；
    In the step 1, establishing the workpiece platform micro-motion six degree of freedom coupling model with disturbance term is：
    <mrow> <mi>M</mi> <mover> <mi>q</mi> <mo>&amp;CenterDot;&amp;CenterDot;</mo> </mover> <mo>+</mo> <mi>C</mi> <mover> <mi>q</mi> <mo>&amp;CenterDot;</mo> </mover> <mo>=</mo> <mi>f</mi> <mo>-</mo> <msub> <mi>f</mi> <mrow> <mi>e</mi> <mi>x</mi> </mrow> </msub> <mo>;</mo> </mrow>
    Wherein, M is inertial matrix, and C is kirschner matrix, and f is voice coil motor thrust, fexFor disturbance term；
    In the step 3, using neutral net to nonlinear functionEstimated；
    Estimated result is：
    Wherein,WithThe respectively output speed and acceleration of workpiece platform micro-motion six degree of freedom system,For function T nerve It is network-evaluated,Estimating weight matrix for neutral net, a (θ) is activation vector, and θ inputs for neutral net,For inertial matrix Nominal value, △ M are the Additive Generator parameter of inertial matrix；
    In the step 4, workpiece platform micro-motion MIMO robust fuzzy neural networks sliding mode control laws are：
    <mrow> <mtable> <mtr> <mtd> <mrow> <mi>f</mi> <mo>=</mo> <mover> <mi>M</mi> <mo>^</mo> </mover> <mrow> <mo>(</mo> <msub> <mover> <mi>q</mi> <mo>&amp;CenterDot;&amp;CenterDot;</mo> </mover> <mi>d</mi> </msub> <mo>-</mo> <mi>&amp;alpha;</mi> <mover> <mi>e</mi> <mo>&amp;CenterDot;</mo> </mover> <mo>-</mo> <msub> <mi>u</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mover> <mi>C</mi> <mo>^</mo> </mover> <mover> <mi>q</mi> <mo>&amp;CenterDot;</mo> </mover> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>u</mi> <mn>0</mn> </msub> <mo>=</mo> <msup> <mover> <mi>w</mi> <mo>^</mo> </mover> <mi>T</mi> </msup> <mi>a</mi> <mrow> <mo>(</mo> <mi>&amp;theta;</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>u</mi> <mo>+</mo> <mrow> <mo>(</mo> <mi>c</mi> <mo>+</mo> <mi>&amp;sigma;</mi> <mo>)</mo> </mrow> <mi>s</mi> </mrow> </mtd> </mtr> </mtable> <mo>;</mo> </mrow>
    Wherein, u is fuzzy control item, c=diag { c1c2c3c4c5c6And σ=diag { σ1 σ2 σ3 σ4 σ5 σ6It is positive definite Diagonal matrix, c1、c2、c3、c4、c5And c6Represent c six degree of freedom component, σ1、σ2、σ3、σ4、σ5And σ6Represent σ six degree of freedom Component, sliding formwork item s=diag { s1s2s3s4s5s6, s1、s2、s3、s4、s5And s6S six degree of freedom component, diag generations are represented respectively Table diagonal matrix；Represent reference acceleration,Tracking velocity error is represented, α represents proportionality coefficient matrix.
  2. 2. workpiece platform micro-motion MIMO robust fuzzies neural networks sliding mode control method according to claim 1, its feature exist In in step 2, model parameter uncertainty is：
    <mrow> <mi>M</mi> <mo>=</mo> <mover> <mi>M</mi> <mo>^</mo> </mover> <mo>+</mo> <mi>&amp;Delta;</mi> <mi>M</mi> <mo>,</mo> <mi>C</mi> <mo>=</mo> <mover> <mi>C</mi> <mo>^</mo> </mover> <mo>+</mo> <mi>&amp;Delta;</mi> <mi>C</mi> <mo>;</mo> </mrow>
    Wherein,For the nominal value of kirschner matrix, △ C are the Additive Generator parameter of kirschner matrix.
  3. 3. workpiece platform micro-motion MIMO robust fuzzies neural networks sliding mode control method according to claim 1, its feature exist In in step 4, fuzzy rule, fuzzy membership function, value and σ that fuzzy control item u is related to adaptive law are specially：
    Fuzzy rule：
    IF si is NB,Then uif is NB
    IF si is N,Then uif is N
    IF si is Z,Then uif is Z
    IF si is P,Then uif is P
    IF si is PB,Then uif is PB
    Wherein, uifFor the output of the free degree of fuzzy system i-th, siFor the component of the free degree of sliding formwork item i-th, NB, N, Z, P and PB divide Dai Biao not bear it is big, negative, zero, just with honest, i ∈ 1,2,3,4,5,6；
    The Indistinct Input membership function that fuzzy control item u is related to is triangle membership function；
    The fuzzy output membership function that fuzzy control item u is related to is monodrome membership function；Fuzzy control item u value is u =η uf；
    Wherein, u=[u1 u2 u3 u4 u5 u6]T, u1、u2、u3、u4、u5And u6U six degree of freedom component is represented respectively, obscures system The output u of systemf=[u1f u2f u3f u4f u5f u6f]T, u1f、u2f、u3f、u4f、u5fAnd u6fU is represented respectivelyfSix degree of freedom point Amount, gain coefficient η=diag { η1 η2 η3 η4 η5 η6, η1、η2、η3、η4、η5And η6The six of expression gain coefficient η is free respectively Component is spent, η adaptive law is Represent ηiDifferential, γiFor postiive gain；The σ's that fuzzy control item u is related to Adaptive law Represent σiDifferential, KiFor postiive gain, subscript i=1,2 ... 6.
CN201510628029.9A 2015-09-28 2015-09-28 A kind of workpiece platform micro-motion MIMO robust fuzzies neural networks sliding mode control method Expired - Fee Related CN105182753B (en)

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