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CN107662208A - Flexible joint mechanical arm finite time self-adaptive backstepping control method based on neural network - Google Patents

Flexible joint mechanical arm finite time self-adaptive backstepping control method based on neural network Download PDF

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Publication number
CN107662208A
CN107662208A CN201710732672.5A CN201710732672A CN107662208A CN 107662208 A CN107662208 A CN 107662208A CN 201710732672 A CN201710732672 A CN 201710732672A CN 107662208 A CN107662208 A CN 107662208A
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formula
error
follows
mechanical arm
flexible joint
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CN107662208B (en
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陈强
施卉辉
孙明轩
何熊熊
庄华亮
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Zhejiang University of Technology ZJUT
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B25J9/1605—Simulation of manipulator lay-out, design, modelling of manipulator
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J17/00—Joints
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1628—Programme controls characterised by the control loop
    • B25J9/1638—Programme controls characterised by the control loop compensation for arm bending/inertia, pay load weight/inertia

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  • Engineering & Computer Science (AREA)
  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Feedback Control In General (AREA)

Abstract

A flexible joint mechanical arm finite time self-adaptive backstepping control method based on a neural network is designed by aiming at a flexible joint mechanical arm containing unknown uncertain items and utilizing the neural network and the finite time control method. In each step of the backstepping control, the self-adaptive finite time virtual controller is provided to realize that the system tracking error converges to the area near the balance point in a finite time. Two simple neural networks are applied to approximate and compensate the uncertain items of the system, and a large amount of calculation in the traditional backstepping control is reduced. The invention provides a control method which can compensate unknown uncertainty of a system, solve the problem of large calculation amount of the traditional backstepping control and realize the convergence of the tracking error of the system in limited time and the tracking of the system in limited time.

Description

A kind of adaptive contragradience control of flexible joint mechanical arm finite time based on neutral net Method processed
Technical field
The present invention relates to a kind of adaptive backstepping control method of flexible joint mechanical arm finite time based on neutral net, Particular with the flexible joint mechanical arm control method of unknown indeterminate.
Background technology
Mechanical arm due to its flexible movements, motional inertia is small, operating efficiency is high, reliable and stable the advantages that in real life Effect it is outstanding day by day, using quite varied especially in high-precision field, such as industrial design, Aero-Space, medical treatment Apparatus etc..With the development of science and technology, required precision of the people to mechanical arm also more and more higher, but physical presence in mechanical arm Complicated uncertain factor drastically influence the control performance of mechanical arm and the limitation of technical merit.In order to reach higher essence Degree and performance requirement, consider the flexibility of joint of mechanical arm, flexible joint machine are used in model and design control method is established Tool arm.For flexible joint mechanical arm, researchers propose many control methods, such as Self Adaptive Control, fuzzy control, sliding formwork Control, Reverse Step Control etc..
Backstepping control method is a kind of Nonlinear System Design method, and its basic thought is by the nonlinear system point of complexity Then solution separately designs Virtual Controller in each subsystem, " retreats " arrive always into the subsystem no more than systematic education Whole system, the design until completing whole control law., can using Reverse Step Control Technology design flexible joint mechanical arm controller Solve the problems, such as the mismatched uncertainties in system.
Although many control strategies can efficiently solve the tracing control of flexible joint mechanical arm, it is most of all only It can guarantee that system mode error ultimately uniform boundary.Reach stable to ensure system in finite time, using finite time Control.Finite-time control has been successfully applied to mechanical arm system, spacecraft system, multi-agent system and permanent-magnet synchronous Many control fields such as electric system, it is a kind of control technology theoretical based on stability in finite time, it can improve system Robust performance, guarantee system reach stable state in finite time.Neutral net can approach a position in arbitrary accuracy Function is put, therefore it is widely used in solving the problems, such as systematic uncertainty.The control method application of above-mentioned control strategy has one Fixed limitation, or cannot be guaranteed each state error variable in Finite-time convergence, or system model must be Know.
The content of the invention
In order to overcome the unknown uncertain problem of flexible joint mechanical arm, the present invention provides a kind of based on neutral net The adaptive backstepping control method of flexible joint mechanical arm finite time, the Virtual Controller designed in Reverse Step Control and reality Controller can ensure field of the error variance near Finite-time convergence to equalization point, utilize neutral net approximation system Indeterminate, and solve the problems, such as that traditional Reverse Step Control is computationally intensive, ensure system tracking error in Finite-time convergence.
In order to solve the above-mentioned technical problem the technical scheme proposed is as follows：
A kind of adaptive backstepping control method of flexible joint mechanical arm finite time based on neutral net, the controlling party Method comprises the following steps：
Step 1, the dynamic model of flexible joint mechanical arm, initialization system mode, sampling time and control ginseng are established Number, process are as follows：
The dynamic model expression-form of 1.1 1 n rank flexible joint mechanical arms is：
Wherein q ∈ Rn,θ∈RnIt is joint position vector sum motor position vector respectively, n is the order of system；It is Joint velocity vector；It is motor acceleration vector；M(q)∈Rn×nTo represent the unknown nonsingular symmetrical of joint inertia Positive definite matrix；J∈Rn×nTo represent the unknown nonsingular symmetric positive definite matrix of motor inertia；K∈Rn×nIt is an expression joint bullet The unknown diagonal positive definite matrix of spring rigidity；h(q,θ)∈Rn×nTo represent the function of centripetal force, coriolis force and acceleration of gravity；u∈ RnRepresent control torque vector；
1.2 redefine variable, are write formula (1) as state space equation form：
Define x1=q,x3=θ,Formula (1) is write as following state space form：
Wherein xi, i=1,2,3,4 be all measurable, M (x1), h (x1,x2), K and J are unknown items；
Step 2, computing system tracking error, its process are as follows：
It is as follows to define system tracking error：
z1=x1-xd (3)
Wherein xdIt is the reference locus of a given smooth bounded；
Formula (3) derivation is obtained：
WhereinIt is the first derivative of error,It is the first derivative of reference locus；
Step 3, error variance is defined, designs Virtual Controller, its process is as follows：
3.1, which define error variance, is：
zj=xj-aj-1, j=2,3,4 (5)
Wherein, aj-1, j=2,3,4 is to design the Virtual Controller during controller；
Derivation is carried out to formula (5), obtained：
WhereinIt is the first derivative of error,J=2,3,4 be design controller during Virtual Controller one Order derivative；
Formula (2) and formula (5) are substituted into formula (4) and formula (6), obtained：
3.2 definitionWherein To approachWithDesign following two neutral nets：
DefinitionFor neutral net ideal weight matrix, m is the number of neuron；ThenApproached as following form：
WhereinIt is nerve net The basic function of network；ε1,ε2Represent the approximate error and satisfaction of neutral net | | ε1||≤ε1N,||ε2||≤ε2N；ε1N, ε2NIt is positive Constant, | | | | two norms of expression value；WithForm it is as follows：
Wherein al,bl,cl,dl, l=1,2 is constant parameter；
3.3 design neural network weights and evaluated error more new law：
WhereinIt is positive definite diagonal matrix；σ1,σ2,ρ1,ρ2It is suitable parameter；WithIt is respectivelyWithEstimate；WithIt is ε respectively1NAnd ε2NEstimate；
3.4 design Virtual Controllers, it is as follows：
Wherein h1,h2,h3,k1,k2,k3It is normal number；
The 3.5 actual controllers of design are as follows：
Wherein h4, k4It is normal number；
3.6 wushu (8), formula (9), formula (16) and formula (17) are updated in formula (7), are obtained：
Step 4, it is following form to design liapunov function：
Wherein
Derivation is carried out to formula (19), and (18) are substituted into, is obtained：
If formula (20) is write as
Wherein η1=Min { 2h1,2h2λmin{M-1(x1)K},2h3,2h4λmin{J-1,
η2=Min { 2αk1,(2M-1(x1)K)αk2,2αk3,(2J-1)αk4,
δ=Z2NW1NΦ1N+Z4NW2NΦ2N+Z2NE1μ+Z4NE2μ,
Wherein Min { } represents minimum value, λmin{ } represents minimum characteristic value；Z2NAnd Z4NRepresent respectively | | z2| | and ||z4| | maximum；W1NAnd W2NRepresent respectivelyWithMaximum；Φ1NAnd Φ2NRepresent respectivelyWithMaximum；E1μAnd E2μRepresent respectivelyWithMaximum；
Then in the field near decision-making system tracking error finite time convergence control to equalization point.
The present invention is based on the flexible joint mechanical arm containing unknown indeterminate, combining adaptive Reverse Step Control, nerve net Network, finite-time control method, devise the adaptive Reverse Step Control of flexible joint mechanical arm finite time based on neutral net Method, solve uncertain problem in system, reduce the amount of calculation in traditional Reverse Step Control, realize that system tracking error has Limit time Convergence.
The present invention technical concept be：For the flexible joint mechanical arm containing unknown indeterminate, design is adaptive anti- Step control, using neutral net and finite-time control method, designing a kind of flexible joint mechanical arm based on neutral net has Adaptive backstepping control method between in limited time.In each step of Reverse Step Control, propose that adaptive finite time Virtual Controller is real Existing system tracking error is in the field near Finite-time convergence to equalization point.Forced using two simple neutral nets Near and compensation system does not know the unknown, and reduces substantial amounts of amount of calculation in traditional Reverse Step Control.The invention provides a kind of energy The unknown indeterminate of compensation system, solve the problems, such as that traditional Reverse Step Control is computationally intensive, realize system tracking error when limited Interior convergence, the control method of system finite time tracking.
Beneficial effects of the present invention are：The unknown indeterminate of compensation system, the substantial amounts of amount of calculation of traditional Reverse Step Control is reduced, System tracking error is realized in Finite-time convergence, realizes that system finite time tracks.
Brief description of the drawings
Fig. 1 is the tracking effect figure of the present invention；
Fig. 2 is the tracking error figure of the present invention；
The neutral net that Fig. 3 is the present invention approaches unknown term diagram；
Fig. 4 is the weights norm figure that the neutral net of the present invention is approached；
Fig. 5 is the state-Variable Diagram of the present invention；
Fig. 6 is the control input figure of the present invention；
Fig. 7 is the control flow schematic diagram of the present invention.
Embodiment
The present invention will be further described below in conjunction with the accompanying drawings.
Reference picture 1- Fig. 7, a kind of adaptive Reverse Step Control side of flexible joint mechanical arm finite time based on neutral net Method, the control method comprise the following steps：
Step 1, the dynamic model of flexible joint mechanical arm, initialization system mode, sampling time and control ginseng are established Number, process are as follows：
The dynamic model expression-form of 1.1 1 n rank flexible joint mechanical arms is：
Wherein q ∈ Rn,θ∈RnIt is joint position vector sum motor position vector respectively, n is the order of system；It is Joint velocity vector；It is motor acceleration vector；M(q)∈Rn×nTo represent the unknown nonsingular symmetrical of joint inertia Positive definite matrix；J∈Rn×nTo represent the unknown nonsingular symmetric positive definite matrix of motor inertia；K∈Rn×nIt is an expression joint bullet The unknown diagonal positive definite matrix of spring rigidity；h(q,θ)∈Rn×nTo represent the function of centripetal force, coriolis force and acceleration of gravity；u∈ RnRepresent control torque vector；
1.2 redefine variable, are write formula (1) as state space equation form：
Define x1=q,x3=θ,Formula (1) is write as following state space form：
Wherein xi, i=1,2,3,4 be all measurable, M (x1), h (x1,x2), K and J are unknown items；
Step 2, computing system tracking error, its process are as follows：
It is as follows to define system tracking error：
z1=x1-xd (3)
Wherein xdIt is the reference locus of a given smooth bounded；
Formula (3) derivation is obtained：
WhereinIt is the first derivative of error,It is the first derivative of reference locus；
Step 3, error variance is defined, designs Virtual Controller, its process is as follows：
3.1, which define error variance, is：
zj=xj-aj-1, j=2,3,4 (5)
Wherein, aj-1, j=2,3,4 is to design the Virtual Controller during controller；
Derivation is carried out to formula (5), obtained：
WhereinIt is the first derivative of error,J=2,3,4 be design controller during Virtual Controller one Order derivative；
Formula (2) and formula (5) are substituted into formula (4) and formula (6), obtained：
3.2 definitionWhereinTo approachWithDesign following two nerve nets Network：
DefinitionFor neutral net ideal weight matrix, m is the number of neuron；ThenApproached as following form：
WhereinIt is nerve net The basic function of network；ε1,ε2Represent the approximate error and satisfaction of neutral net | | ε1||≤ε1N,||ε2||≤ε2N；ε1N, ε2NIt is positive Constant, | | | | two norms of expression value；WithForm it is as follows：
Wherein al,bl,cl,dl, l=1,2 is constant parameter；
3.3 design neural network weights and evaluated error more new law：
WhereinIt is positive definite diagonal matrix；σ1,σ2,ρ1,ρ2It is suitable parameter；WithIt is respectivelyWithEstimate；WithIt is ε respectively1NAnd ε2NEstimate；
3.4 design Virtual Controllers, it is as follows：
Wherein h1,h2,h3,k1,k2,k3It is normal number；
The 3.5 actual controllers of design are as follows：
Wherein h4, k4It is normal number；
3.6 wushu (8), formula (9), formula (16) and formula (17) are updated in formula (7), are obtained：
Step 4, it is following form to design liapunov function：
Wherein
Derivation is carried out to formula (19), and (18) are substituted into, is obtained：
If formula (20) is write as
Wherein η1=Min { 2h1,2h2λmin{M-1(x1)K},2h3,2h4λmin{J-1,
η2=Min { 2αk1,(2M-1(x1)K)αk2,2αk3,(2J-1)αk4,
δ=Z2NW1NΦ1N+Z4NW2NΦ2N+Z2NE1μ+Z4NE2μ,
Wherein Min { } represents minimum value, λmin{ } represents minimum characteristic value；Z2NAnd Z4NRepresent respectively | | z2| | and ||z4| | maximum；W1NAnd W2NRepresent respectivelyWithMaximum；Φ1NAnd Φ2NRepresent respectivelyWithMaximum；E1μAnd E2μRepresent respectivelyWithMaximum；
Then in the field near decision-making system tracking error finite time convergence control to equalization point.
The validity of extracting method in order to verify, this method carry out emulation for the flexible joint mechanical arm in a joint and tested Card.System initialization parameter setting is as follows：
The parameter of Base Function is as follows：a1=10, b1=2, c1=1, d1=-1.8, a2=5, b2=0.2, c2 =1, d2The more new law of=- 4, neural network weight and estimation error is as follows：Γ1=0.05, Γ2=0.1, σ1=0.1, σ2= 0.1,γ1=1, γ2=0.5, ρ1=5, ρ2=5, the coefficient of Virtual Controller is as follows：h1=4, h2=0.8, h3=2, h4=5, k1=2.2, k2=0.3, k3=3, k4=5, α=13/15, reference locus equation are：xd=sin (t)；System initial value gives xd(0)=0, x1(0)=x2(0)=x3(0)=x4(0)=0, weights and evaluated error initial value, which are elected as, is distributed in [- 1,1] Any vector.
What Fig. 1 and Fig. 2 was represented is system tracking performance and corresponding tracking error respectively, it can be seen that output x1Can be very Preferable track x is tracked welld, and tracking error is converged in zero domain；What Fig. 3 and Fig. 4 was represented is neutral net Approximation effect, it can be seen that neutral net can approach unknown function well under the weights norm of bounded.In Fig. 5 and Fig. 6 In, it is shown that other several state variables and control input.
Therefore, the present invention can provide a kind of energy compensation system and not know the unknown, reduce traditional Reverse Step Control and largely count The control method of calculation amount, realize that system tracking error tracks in Finite-time convergence and system finite time.
Described above is the excellent effect of optimization that one embodiment that the present invention provides is shown, it is clear that the present invention is not only Above-described embodiment is limited to, without departing from essence spirit of the present invention and the premise without departing from scope involved by substantive content of the present invention Under it can be made it is a variety of deformation be carried out.

Claims (1)

  1. A kind of 1. adaptive backstepping control method of flexible joint mechanical arm finite time based on neutral net, it is characterised in that： The control method comprises the following steps：
    Step 1, the dynamic model of flexible joint mechanical arm, initialization system mode, sampling time and control parameter, mistake are established Journey is as follows：
    The dynamic model expression-form of 1.1 1 n rank flexible joint mechanical arms is：
    Wherein q ∈ Rn,θ∈RnIt is joint position vector sum motor position vector respectively, n is the order of system；It is joint Vector acceleration；It is motor acceleration vector；M(q)∈Rn×nTo represent the unknown nonsingular symmetric positive definite of joint inertia Matrix；J∈Rn×nTo represent the unknown nonsingular symmetric positive definite matrix of motor inertia；K∈Rn×nIt is one and represents that joint spring is firm The unknown diagonal positive definite matrix of degree；h(q,θ)∈Rn×nTo represent the function of centripetal force, coriolis force and acceleration of gravity；u∈RnTable Show control torque vector；
    1.2 redefine variable, are write formula (1) as state space equation form：
    Define x1=q,x3=θ,Formula (1) is write as following state space form：
    Wherein xi, i=1,2,3,4 be all measurable, M (x1), h (x1,x2), K and J are unknown items；
    Step 2, computing system tracking error, its process are as follows：
    It is as follows to define system tracking error：
    z1=x1-xd (3)
    Wherein xdIt is the reference locus of a given smooth bounded；
    Formula (3) derivation is obtained：
    WhereinIt is the first derivative of error,It is the first derivative of reference locus；
    Step 3, error variance is defined, designs Virtual Controller, its process is as follows：
    3.1, which define error variance, is：
    zj=xj-aj-1, j=2,3,4 (5)
    Wherein, aj-1, j=2,3,4 is to design the Virtual Controller during controller；
    Derivation is carried out to formula (5), obtained：
    WhereinIt is the first derivative of error,J=2,3,4 is that the single order of Virtual Controller designed during controller is led Number；
    Formula (2) and formula (5) are substituted into formula (4) and formula (6), obtained：
    3.2 definitionWherein To approachWithDesign following two neutral nets：
    DefinitionFor neutral net ideal weight matrix, m is the number of neuron；Then Approached as following form：
    WhereinIt is neutral net Basic function；ε1,ε2Represent the approximate error and satisfaction of neutral net | | ε1||≤ε1N,||ε2||≤ε2N；ε1N, ε2NIt is positive normal Number, | | | | two norms of expression value；WithForm it is as follows：
    Wherein al,bl,cl,dl, l=1,2 is constant parameter；
    3.3 design neural network weights and evaluated error more new law：
    WhereinIt is positive definite diagonal matrix；σ1,σ2,ρ1,ρ2It is suitable parameter；WithPoint It is notWithEstimate；WithIt is ε respectively1NAnd ε2NEstimate；
    3.4 design Virtual Controllers, it is as follows：
    Wherein h1,h2,h3,k1,k2,k3It is normal number；
    The 3.5 actual controllers of design are as follows：
    Wherein h4, k4It is normal number；
    3.6 wushu (8), formula (9), formula (16) and formula (17) are updated in formula (7), are obtained：
    Step 4, it is following form to design liapunov function：
    Wherein
    Derivation is carried out to formula (19), and (18) are substituted into, is obtained：
    If formula (20) is write as
    Wherein η1=Min { 2h1,2h2λmin{M-1(x1)K},2h3,2h4λmin{J-1,
    η2=Min { 2αk1,(2M-1(x1)K)αk2,2αk3,(2J-1)αk4,
    δ=Z2NW1NΦ1N+Z4NW2NΦ2N+Z2NE1μ+Z4NE2μ,
    Wherein Min { } represents minimum value, λmin{ } represents minimum characteristic value；Z2NAnd Z4NRepresent respectively | | z2| | and | | z4| | maximum；W1NAnd W2NRepresent respectivelyWithMaximum；Φ1NAnd Φ2NRepresent respectivelyWith's Maximum；E1μAnd E2μRepresent respectivelyWithMaximum；
    Then in the field near decision-making system tracking error finite time convergence control to equalization point.
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CN113858198A (en) * 2021-09-28 2021-12-31 南京邮电大学 Mechanical arm force tracking impedance control method considering joint motor dynamics
CN114851198A (en) * 2022-05-17 2022-08-05 广州大学 Consistent tracking fixed time stability control method for multi-single-link mechanical arm
CN115008456A (en) * 2022-06-14 2022-09-06 中国科学院数学与系统科学研究院 Tracking control method and system for flexible single-chain mechanical arm multi-agent
CN118357919A (en) * 2024-05-16 2024-07-19 辽宁工业大学 Flexible joint mechanical arm preset time event trigger control method under error constraint

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003271923A (en) * 2002-03-15 2003-09-26 Sony Corp Recorder and recording method, and robot device
US20050119791A1 (en) * 2002-10-11 2005-06-02 Fujitsu Limited Robot control algorithm constructing apparatus, robot control algorithm constructing program storage medium, robot controller, robot control program storage medium, and robot
CN104932271A (en) * 2015-06-08 2015-09-23 浙江工业大学 Neural network full order slip form control method for mechanical arm servo system
CN104950678A (en) * 2015-06-17 2015-09-30 浙江工业大学 Neural network inversion control method for flexible manipulator system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003271923A (en) * 2002-03-15 2003-09-26 Sony Corp Recorder and recording method, and robot device
US20050119791A1 (en) * 2002-10-11 2005-06-02 Fujitsu Limited Robot control algorithm constructing apparatus, robot control algorithm constructing program storage medium, robot controller, robot control program storage medium, and robot
CN104932271A (en) * 2015-06-08 2015-09-23 浙江工业大学 Neural network full order slip form control method for mechanical arm servo system
CN104950678A (en) * 2015-06-17 2015-09-30 浙江工业大学 Neural network inversion control method for flexible manipulator system

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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