CN-117245652-B - Micro-scale robot deposition system control method based on AFD time-frequency iterative learning

Abstract

The invention relates to a microscale robot deposition system control method based on AFD time-frequency iterative learning, which comprises the steps of S1, iterative learning dynamics model establishment step, S2, initialization step, S3, tracking error self-adaptive Fourier decomposition AFD time-frequency distribution calculation step, S4, feedforward signal self-adaptive Fourier decomposition time-frequency distribution calculation step, S5, critical frequency determination step, S6, learning filter amplitude-frequency characteristic calculation step, S7, robust filter amplitude-frequency characteristic calculation step, S8, iterative learning system input calculation step, S9, iterative learning frequency judgment step, and tracking track output step. The invention combines the time-frequency analysis method based on AFD, the ILC method with advanced phase and the L-Q filter frequency bandwidth adjustment method to realize the high-precision control of the micro-scale robot deposition system.

Inventors

• FU WENYUAN

Assignees

• 华侨大学

Dates

Publication Date

20260512

Application Date

20230913

Claims (1)

1. 1. A micro-scale robot deposition system control method based on AFD time-frequency iterative learning is characterized by comprising the following steps: s1, iterative learning dynamics model establishment; An iterative learning dynamics model of the micro-scale robot deposition system is established as follows: (1); (2); Wherein, the , Indicating the motor running time as The angular position at the time; Indicating the motor running time as The rotational speed at that time; , Indicating the motor running time as The angular position at the time; Indicating the motor running time as The rotational speed at that time; Representing a microscale robotic deposition system at a first Trajectory control dynamics output during iterative learning for the second time; Representing a microscale robotic deposition system at a first Trajectory control dynamics input during iterative learning for the second time; Representing a microscale robotic deposition system at a first High-frequency structure resonance generated during iterative learning for the second time; 、 And Respectively representing system eigenvectors of the micro-scale robot deposition system; , representing a maximum run length of the microscale robotic deposition system; S2, initializing; Setting up The maximum number of iterative learning is Setting an iterative initial input of an axial motion of the micro-scale robotic deposition system as Iterative learning dynamics model of micro-scale robot deposition system Output of the system at the time A kind of electronic device Initial iterative learning control tracking error of (c) And an initial feedforward signal Wherein Representing a desired tracking trajectory of an axial motion of the micro-scale robotic deposition system, Represent the first The system output at the time of the iteration, wherein, The representation of the learning filter is made, , Representing an inverse Z-transform; representation learning filter At the initial frequency bandwidth The amplitude-frequency characteristic of the time; s3, calculating the time-frequency distribution of the self-adaptive Fourier decomposition AFD of the tracking error; Calculation of Is a self-adaptive Fourier decomposition time-frequency distribution ; ; Wherein, the , Representation of Is used for the instantaneous signal amplitude of (a); ; Is that Is the adaptive Fourier decomposition of (1) A sub-decomposition term; Is the maximum decomposition order; Is that The basis functions of the adaptive fourier decomposition, Is that An order orthonormal system; Is that Constant terms of adaptive fourier decomposition; Representing the real part; representing the inner product; Calculation of Instantaneous phase of (2) The following are provided: ; Wherein, the ; Representation pair Performing adaptive Fourier decomposition on complex coefficients in a unit circle during mth decomposition; Representation pair Taking a mould; Representation of A plurality of argument of (2); representing complex coefficients in a unit circle at the ith decomposition of the AFD; Representation pair Taking out the mould, wherein the mould is taken out, Representation of A plurality of argument of (2); Calculation of Is distributed as time frequency of ; Wherein, the , A module representing a plurality of numbers; is a dirac function; s4, calculating adaptive Fourier decomposition time-frequency distribution of the feedforward signal; Calculation of AFD time-frequency distribution of (c) , ; Wherein, the , Representation of Is used for the instantaneous signal amplitude of (a); representing feed-forward signals An mth adaptive fourier decomposed signal component; Is that A basis function of adaptive fourier decomposition; Is that Constant term of adaptive Fourier decomposition Instantaneous phase of (2) The method comprises the following steps: ; Wherein, the ; Representation pair Performing adaptive Fourier decomposition on complex coefficients in a unit circle during mth decomposition; Representation pair Taking a mould; Representation of A plurality of argument of (2); representing complex coefficients in a unit circle at the ith decomposition of the AFD; Representation pair Taking out the mould, wherein the mould is taken out, Representation of A plurality of argument of (2); Calculation of Time-frequency distribution of (a) ; Wherein, the ; S5, determining critical frequency; setting time-frequency distribution Energy amplitude of (2) Time-frequency distribution Energy amplitude of (2) ; Solving inequality And Determining tracking error And a feedforward signal If the critical frequency of (1) Then If (3) Then Wherein, the method comprises the steps of, Representation learning filter First, the Frequency bandwidth of the second iteration; Representing a robust filter First, the Frequency bandwidth of the second iteration; representing the system signal frequency of the kth iterative learning; s6, learning filter amplitude-frequency characteristic calculation; calculation learning filter Amplitude-frequency characteristics of (a) The following are provided: ; Wherein, the ; ; Is the sampling frequency; s7, calculating amplitude-frequency characteristics of the robust filter; Computational robust filter Amplitude-frequency characteristics of (a) The following are provided: ; Wherein, the ; ; S8, inputting a calculation step into the iterative learning system; Calculation of the first using iterative learning control law with advanced phase System input for iterative learning The following are provided: ; Wherein, the Is that Frequency domain representation of (i), i.e ; Is the Z-transform of the data, Is a complex number of variables, which are the same as the variables, In order to be a frequency of the light, In order to sample the period of time, Is an imaginary unit; ; ; And Respectively a robust filter and a learning filter, And The frequency bandwidths of the filters respectively; For a pair of Performing Z inverse transformation to obtain I.e. Wherein Representing an inverse Z transform; s9, iterative learning times judgment; Order the Repeating steps S3-S9 until the iterative learning times are satisfied ; S10, outputting a tracking track; To be obtained Substituting the iterative learning dynamics models (1) and (2) of the established microscale robotic deposition system to obtain the dynamics output of the desired tracking trace 。

Description

Micro-scale robot deposition system control method based on AFD time-frequency iterative learning Technical Field The invention relates to the technical field of iterative learning control, in particular to a microscale robot deposition system control method based on AFD time-frequency iterative learning. Background Among the many motion control systems, the application of feedforward control has significant advantages for achieving high performance trajectory tracking and accurate tracking accuracy. For example, the imaging and measuring device can be used in the fields of nano imaging and measuring of atomic force microscope, control of return device in bearing manufacturing system, photoetching scanning in semiconductor manufacturing process, etc. However, feedforward control requires a high model of the controlled system in practical applications, and obtaining accurate system model information in an industrial production process often has a challenge, and even is difficult to achieve in some cases. In this context, iterative learning Control (ITERATIVE LEARNING Control, ILC) has been developed as a feed-forward Control strategy that is optimized for system performance that repeatedly performs the same task. The ILC uses error information obtained from past learning cycles to generate a feedforward control signal for subsequent iterations to enhance performance of the current learning cycle. Unlike other feed forward control methods, the ILC is able to learn valid information from previous iterations to predict and compensate for external signals, such as repetitive disturbances. Moreover, the external signal to which the ILC is subjected need not be known or measurable, and need only be repeated in iterations. However, transient tracking hysteresis may occur when the feedforward controller responds to input and external disturbances. While feedforward control can eliminate such hysteresis, it is generally only applicable to known or measurable signals, such as a desired reference trajectory, and is limited in coping with disturbances external to the system. Based on its simplicity and ease of implementation, ILCs are widely used in various fields, such as 3D printing, multi-agent systems, intelligent air conditioning and high speed trains. In a micro-scale robotic deposition (MRD) system, the precise deposition of 'ink' is realized by adopting a mechanical arm positioning mode, and the method is used for three-dimensional construction of micro-scale complex parts. High precision X-Y-Z axis positioning of the robotic arm end effector is critical to achieving high precision manufacturing of the workpiece. However, for high precision control of MRD systems, besides the conventional control methods, ILC methods have also attracted considerable interest to researchers. By the ILC method, the MRD system can gradually reduce deposition errors through iterative learning, so that higher deposition accuracy is achieved. This is critical for the fabrication of micro-scale features because of the small dimensional and precision requirements that cause deposition errors that can significantly impact fabrication quality. Therefore, in MRD systems, ILC is a potential high-precision control method, and is expected to significantly improve the control precision of micro-scale components. However, in practical applications, current ILC methods still face a balance between convergence and robustness. Solving the convergence problem may employ methods such as norm optimized ILC, adaptive ILC, etc., but how to achieve high precision manufacturing while maintaining robustness remains a challenging problem. Furthermore, microscale robotic deposition systems tend to involve complex non-linear and time-varying properties, which further increase the challenges of the ILC method. In this case, the introduction of signal processing methods may be an advantageous complement. By analyzing the sensor signals during the movement and deposition of the robot arm, useful information about the dynamics of the system can be obtained, thereby improving the performance of the ILC control. This would provide more possibilities for high precision control of the micro-scale robotic deposition system. In summary, microscale robotic deposition systems face complex control problems as a high-precision manufacturing technique. ILC has potential in improving system performance as a method of feedforward control, but still needs to solve the balance of convergence and robustness. At the same time, the introduction of signal processing methods may also provide a new approach to improving control performance. Disclosure of Invention The invention aims to improve the existing method and provides a microscale robot deposition system control method based on AFD time-frequency iterative learning, which combines an AFD time-frequency analysis method, an ILC method with advanced phase and an L-Q filter frequency bandwidth adjustment method to realize high-p