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Workpiece stage micro MIMO robust fuzzy neural network sliding mode control method
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A technology of fuzzy neural network and control method, which is applied in the field of MIMO robust fuzzy neural network sliding mode control of lithography workpiece table micro-motion, and can solve the problems of reducing the performance of traditional decoupling control system, model uncertainty, external disturbance, etc.
Inactive Publication Date: 2015-12-23
HEILONGJIANG UNIV
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[0004] The purpose of the present invention is to solve the problem that the performance of the traditional decoupling control system is reduced by factors such as the coupling effect of the fretting part of the lithography machine workpiece table, model uncertainty, and external disturbances, and to provide a MIMO robust fuzzy neural network for the fretting of the workpiece table. Network sliding mode control method
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specific Embodiment approach 1
[0041] Specific implementation mode one: combine figure 1 Describe this embodiment, a kind of work table micro-motion MIMO robust fuzzy neural network sliding mode control method described in this embodiment, the control method includes the following steps:
[0042] Step 1. According to the six-degree-of-freedom system of workpiece table micro-motion, establish a workpiece table micro-motion six-degree-of-freedom coupling model with disturbance items M q ·· + C q · = f - f e x ;
[0043] Among them, M is the inertia matrix, C is the Kelvin matrix, f is the voice coil motor thrust, f ex is the disturbance item;
[0044] Step 2. For the six-degree-of-freedom coupling model of workpiece table micro-motion with disturbance term established in step 1, determine the uncert...
specific Embodiment approach 2
[0052] Specific embodiment 2: This embodiment is a further limitation of the micro-motion MIMO robust fuzzy neural network sliding mode control method of the workpiece table described in specific embodiment 1. In the step 3, the input of the neural network input θ is :
[0055] Among them, Γ is a positive definite diagonal matrix, e and are the tracking position error and tracking speed error of the workpiece platform micro-moving six-degree-of-freedom system, respectively.
specific Embodiment approach 3
[0056] Specific embodiment three: this embodiment is a further limitation of the micro-movement MIMO robust fuzzy neural network sliding mode control method of the workpiece table described in specific embodiment one or two: in step four, the fuzzy control item u involved The rules, fuzzy membership function, value and adaptive law of σ are as follows:
[0057] Fuzzy rules:
[0058] IFs i isNB,Thenu if isNB, description: if s i is negative, then u if is negative;
[0059] IFs i isN,Thenu if isN, description: if s i is negative, then u if is negative;
[0060] IFs i isZ,Thenu if isZ, description: if s i is zero, then u if is zero;
[0061] IFs i isP,Thenu if isP, description: if s i is positive, then u if It is positive
[0062] IFs i isPB,Thenu if isPB, description: if s i is Chia, then u if is Chia Tai;
[0063] Among them, u if is the output of the i-th degree of freedom of the fuzzy system, s i is the component of the i-th degree of freedom of the ...
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Abstract
The invention discloses a workpiece stage micro MIMO robust fuzzy neural network sliding mode control method, which belongs to the field of ultra precisionmotion control and aims at solving problems of approximation and neglect operation for realizing a complete diagonal decoupling form by a lithography workpiece stage micro part and unmodelled coupling features in horizontal and vertical directions. The method of the invention comprises steps: 1, a workpiece stage micro six-degree of freedom coupling model with disturbance term is built; 2, as for the workpiece stage micro six-degree of freedom coupling model with disturbance term, model parameter uncertainty is determined; 3, according to the first step and the second step, a neural network is adopted for estimating a nonlinear function, and an estimation result is acquired; 4, according to the first step and the third step, a workpiece stage micro MIMO robust fuzzy neural network sliding mode control law is determined; and 5, according to the control law determined by the fourth step, a workpiece stage micro six-degree of freedom system is controlled. The method of the invention is used for the workpiece stage micro six-degree of freedom control system.
Description
technical field [0001] The invention belongs to the field of ultra-precise motion control, and mainly relates to a micro-motion MIMO robust fuzzy neural network sliding mode control method for a workpiece table of a lithographymachine. Background technique [0002] A lithographymachine is a device for manufacturing large-scale integrated circuits. The workpiece table system is a key component of the lithography machine, and its main function is to carry the siliconwafer to be exposed to achieve ultra-precision movement. The motion accuracy and speed of the workpiece table have a direct impact on the resolution and productivity of the lithography machine. The ultra-precise dynamic tracking and positioning of the workpiece table is the key technology for the development of lithography machines. Because long-stroke linear motors cannot guarantee nanometer-level motion accuracy, voice coil motors are usually used as actuators, but the stroke of voice coil motors is very lim...
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