Closed-loop identification method, device and electronic equipment

Abstract

The present invention provides a closed-loop identification method, device, and electronic device, relating to the field of control technology. The closed-loop identification method comprises: obtaining an identification excitation signal input to a closed-loop system of a controlled object; decomposing the closed-loop system of the controlled object into a first open-loop system and a second open-loop system based on the identification excitation signal; calculating the power spectral densities of the first open-loop system and the second open-loop system respectively; and determining a frequency domain model of a controlled model in the closed-loop system of the controlled object based on the power spectral densities of the first open-loop system and the second open-loop system. The present invention can be applied to closed-loop identification of micro-motion stages, reducing the impact of noise, lowering computational complexity, and improving the accuracy of closed-loop identification.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and in particular to a closed-loop identification method, device and electronic equipment. Background Art

[0002] The micro-motion stage requires high-precision positioning and high system stability. The mechanical structure of the micro-motion stage is complex, and system identification is required to obtain its accurate transfer function. However, the magnetic levitation structure of the micro-motion stage limits the open-loop identification, and only closed-loop identification can be performed. Closed-loop identification cannot apply the open-loop identification method to avoid unclear or unidentifiable problems.

[0003] The calculation method of the existing closed-loop identification algorithm is relatively complex and difficult to apply to the closed-loop identification of the micro-motion stage with a complex mechanical structure. Therefore, how to perform closed-loop identification on the micro-motion stage has become an urgent problem to be solved. Summary of the Invention

[0004] In view of this, the object of the present invention is to provide a closed-loop identification method, device and electronic equipment that can be applied to the closed-loop identification of the micro-motion stage, reducing the influence of noise, while reducing the computational complexity and improving the accuracy of the closed-loop identification.

[0005] In order to achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows:

[0006] In a first aspect, an embodiment of the present invention provides a closed-loop identification method, comprising: obtaining an identification excitation signal input to a closed-loop system of a controlled object, and decomposing the closed-loop system of the controlled object into a first open-loop system and a second open-loop system based on the identification excitation signal; calculating the power spectral density of the first open-loop system and the second open-loop system respectively; and determining a frequency domain model of a controlled model in the closed-loop system of the controlled object based on the power spectral density of the first open-loop system and the second open-loop system.

[0007] Furthermore, an embodiment of the present invention provides a first possible implementation method of the first aspect, wherein the step of decomposing the closed-loop system of the controlled object into a first open-loop system and a second open-loop system based on the identification excitation signal includes: using the identification excitation signal as the input signal of the first open-loop system, and using the input signal of the controlled model as the output signal of the first open-loop system, to decompose into the first open-loop system; using the identification excitation signal as the input signal of the second open-loop system, and using the output signal of the closed-loop system as the output signal of the second open-loop system, to decompose into the second open-loop system.

[0008] Furthermore, an embodiment of the present invention provides a second possible implementation of the first aspect, wherein the steps of respectively calculating the power spectral density of the first open-loop system and the second open-loop system include: determining the power spectral density functions of the input signal and output signal of the first open-loop system, and the input signal and output signal of the second open-loop system respectively based on the autocorrelation method; determining the power spectral density of the first open-loop system based on the power spectral density function of the input signal and output signal of the first open-loop system; and determining the power spectral density of the second open-loop system based on the power spectral density function of the input signal and output signal of the second open-loop system.

[0009] Furthermore, an embodiment of the present invention provides a third possible implementation of the first aspect, wherein the input signal of the controlled model includes a controller output signal and the identification excitation signal;

[0010] The steps of respectively determining the power spectral density functions of the input signal and output signal of the first open-loop system, and the input signal and output signal of the second open-loop system based on the autocorrelation method include: determining the autocorrelation function of the identification excitation signal, performing Fourier transform on the autocorrelation function, and obtaining the autopower spectral density function of the identification excitation signal; wherein the autopower spectral density function is the power spectral density function of the input signal of the first open-loop system and the second open-loop system; determining the first cross-correlation function between the controller output signal and the identification excitation signal, performing Fourier transform on the first cross-correlation function, and obtaining the power spectral density function of the output signal of the first open-loop system; determining the second cross-correlation function between the output signal of the closed-loop system and the identification excitation signal, performing Fourier transform on the second cross-correlation function, and obtaining the power spectral density function of the output signal of the second open-loop system.

[0011] Furthermore, an embodiment of the present invention provides a fourth possible implementation of the first aspect, wherein the calculation formula for the power spectral density of the first open-loop system is:

[0012]

[0013] The power spectral density of the second open-loop system is:

[0014]

[0015] Among them, T uIdentU (k) is the power spectral density of the first open-loop system, T yIdentU (k) is the power spectral density of the second open-loop system, S IdentU (k) is the auto-power spectral density function of the identification excitation signal, S uIdentU(k) is the power spectral density function of the output signal of the first open-loop system, S yIdentU (k) is the power spectral density function of the output signal of the second open-loop system.

[0016] Furthermore, an embodiment of the present invention provides a fifth possible implementation of the first aspect, wherein the frequency domain model of the controlled model is:

[0017]

[0018] Where G(k) is the transfer function of the controlled model, T uIdentU (k) is the power spectral density of the first open-loop system, T yIdentU (k) is the power spectral density of the second open-loop system.

[0019] Furthermore, an embodiment of the present invention provides a sixth possible implementation of the first aspect, wherein the controlled object is a micro-motion stage.

[0020] In the second aspect, an embodiment of the present invention further provides a closed-loop identification device, comprising: a decomposition module for obtaining an identification excitation signal input to a closed-loop system of a controlled object, and decomposing the closed-loop system of the controlled object into a first open-loop system and a second open-loop system based on the identification excitation signal; a calculation module for respectively calculating the power spectral density of the first open-loop system and the second open-loop system; and a determination module for determining the transfer function of a controlled model in the closed-loop system of the controlled object based on the power spectral density of the first open-loop system and the second open-loop system.

[0021] In a third aspect, an embodiment of the present invention provides an electronic device comprising: a processor and a storage device; the storage device stores a computer program, and when the computer program is executed by the processor, it executes the method as described in any one of the first aspects.

[0022] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any one of the above-mentioned first aspects are executed.

[0023] An embodiment of the present invention provides a closed-loop identification method, device, and electronic device. The closed-loop identification method includes: obtaining an identification excitation signal input to a closed-loop system of a controlled object, decomposing the closed-loop system of the controlled object into a first open-loop system and a second open-loop system based on the identification excitation signal; calculating the power spectrum density of the first open-loop system and the second open-loop system respectively; and determining the frequency domain model of the controlled model in the closed-loop system of the controlled object based on the power spectrum density of the first open-loop system and the second open-loop system. The present invention excites the closed-loop system of the controlled object using the identification excitation signal, decomposing the closed-loop system of the controlled object into two open-loop systems. Based on the power spectrum density of the two open-loop systems obtained by decomposition, the transfer function of the controlled model in the closed-loop system can be accurately identified. The method can be applied to the closed-loop identification of a micro-motion stage, reducing the influence of noise, while reducing computational complexity and improving the accuracy of closed-loop identification.

[0024] Other features and advantages of the embodiments of the present invention will be described in the following description, or some features and advantages can be inferred or determined without doubt from the description, or can be learned by implementing the above-mentioned technologies of the embodiments of the present invention.

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 A flow chart of a closed-loop identification method provided by an embodiment of the present invention is shown;

[0028] Figure 2 A closed-loop system control principle diagram provided by an embodiment of the present invention is shown;

[0029] Figure 3 A schematic structural diagram of a closed-loop identification device provided by an embodiment of the present invention is shown;

[0030] Figure 4 A schematic structural diagram of an electronic device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0032] Currently, the high-precision positioning requirements of the micro-motion stage place higher demands on the stability of the system. Relative stability refers to whether the system has a certain stability margin, which needs to be judged based on the frequency domain response of the transfer function of the controlled object. The complex mechanical structure of the micro-motion stage requires identification to obtain an accurate transfer function. In terms of technical research, although open-loop identification is relatively mature and the results are relatively accurate, the magnetic levitation structure of the micro-motion stage limits the implementation of open-loop identification, and only closed-loop identification can be performed. Compared with the direct and clear open-loop identification, closed-loop identification cannot apply the open-loop identification method, otherwise there will be problems of unclear identification or no identification. However, in actual engineering, the closed-loop identification method is too complex and lacks the conditions for implementation on the engineering site, making it difficult to apply.

[0033] To improve the above-mentioned problems, embodiments of the present invention provide a closed-loop identification method, device, and electronic device. This technology can be applied to closed-loop identification of a micro-motion stage. The embodiments of the present invention are described in detail below.

[0034] This embodiment provides a closed-loop identification method, see Figure 1 The closed-loop identification method flow chart shown in FIG. 1 can be applied to a micro-motion stage. The method mainly includes the following steps S102 to S106:

[0035] Step S102 : obtaining an identification excitation signal inputted by the closed-loop system of the controlled object, and decomposing the closed-loop system of the controlled object into a first open-loop system and a second open-loop system based on the identification excitation signal.

[0036] The controlled object can be a micro-motion stage, see Figure 2 The closed-loop system control principle diagram shown in the figure is as follows: Figure 2 Where C(s) is the controller, G(s) is the controlled model of the micro-motion stage, IdentU is the input identification excitation signal, and y dis the input signal of the closed-loop system (i.e., closed-loop control system), y is the output signal of the closed-loop system, and the application point of the identification excitation signal is selected at the controller output position. The closed-loop system identification excitation signal IdentU, the output signal y of the controlled model G(s), and the input signal u of the controlled model are collected. The collected input and output signals require data preprocessing. Input and output data typically contain DC components or low-frequency components, and no identification method can eliminate their impact on identification accuracy. Therefore, the collected input and output data are preprocessed offline to achieve zero-meaning, which helps improve identification accuracy. Zero-meaning can be achieved using an averaging method. The estimated value of the DC component of the input and output data should be equal to the average value of the observed data.

[0037] According to the collected excitation signal IdentU, output signal y and input signal u of the controlled model, the closed-loop system is decomposed into two open-loop systems for analysis, which are respectively recorded as the first open-loop system and the second open-loop system.

[0038] The above-mentioned identification excitation signal can use a multi-sine signal to excite a single degree of freedom of the micro-motion stage in the full frequency band. The generated multi-sine signal is read into the DSP program. When identifying a single degree of freedom, the excitation signal needs to be multiplied by different amplitude parameters to make the signal-to-noise ratio large enough to obtain sufficient excitation.

[0039] Step S104 , respectively calculating the power spectrum density of the first open-loop system and the second open-loop system.

[0040] The power spectral densities of the first open-loop system and the second open-loop system are calculated respectively by using the correlation analysis method. The power spectral densities of the first open-loop system and the second open-loop system are both the ratio of the cross power spectrum of the output and the input to the auto power spectrum of the input.

[0041] Step S106 : determining a frequency domain model of a controlled model in a closed-loop system of the controlled object based on the power spectrum densities of the first open-loop system and the second open-loop system.

[0042] The frequency domain model of the controlled model is:

[0043]

[0044] Among them, G(k) is the frequency domain model of the controlled model (the frequency domain response of the controlled model), T uIdentU (k) is the power spectrum density of the first open-loop system, T yIdentU (k) is the power spectral density of the second open-loop system. By determining the frequency domain model of the controlled model based on the ratio of the power spectral densities of the two open-loop systems, the influence of the interference term can be effectively reduced, and the frequency domain response of the controlled model can be finally obtained.

[0045] After preprocessing the input and output signals, an indirect method based on a decomposition model can be used to decompose an unstable system into two open-loop stable systems for identification. Sensitivity identification methods are used to obtain sensitivity and process sensitivity functions. Correlation analysis is used for each transfer characteristic, specifically the ratio of the cross-power spectrum of the output and input to the auto-power spectrum of the input. This effectively reduces the influence of interference terms and ultimately yields the frequency domain response of the controlled model.

[0046] When the controlled object is a micro-motion stage, closed-loop identification is performed on the micro-motion stage so that the transfer function of the controlled object can be obtained by fitting the frequency domain model of the identified controlled model, thereby designing a controller to improve the stability and accuracy of the micro-motion stage.

[0047] The closed-loop identification method provided in this embodiment uses an identification excitation signal to excite the closed-loop system of the controlled object, thereby decomposing the closed-loop system of the controlled object into two open-loop systems. Based on the power spectral density of the two decomposed open-loop systems, the transfer function of the controlled model in the closed-loop system can be accurately identified. This method can be applied to the closed-loop identification of micro-motion stages, reducing the impact of noise, while also reducing computational complexity and improving the accuracy of closed-loop identification.

[0048] In a feasible implementation, this embodiment provides an implementation for decomposing a closed-loop system of a controlled object into a first open-loop system and a second open-loop system based on an identification excitation signal. Specifically, the following steps (1) to (2) may be referred to:

[0049] Step (1): using the identification excitation signal as the input signal of the first open-loop system and the input signal of the controlled model as the output signal of the first open-loop system, and decomposing the first open-loop system.

[0050] The above closed-loop control system has a unit feedback link and the system control equation is u(t)=IdentU(t)-C*(y d -y(t)), the closed-loop system is decomposed into two open-loop systems, which are respectively recorded as the first open-loop system and the second open-loop system. The input signal of the first open-loop system is the identification excitation signal IdentU, and the output signal of the first open-loop system is the input signal u of the controlled model. The transfer function of the first open-loop system is

[0051] Step (2): using the identification excitation signal as the input signal of the second open-loop system and the output signal of the closed-loop system as the output signal of the second open-loop system, and decomposing the second open-loop system.

[0052] The input signal of the second open-loop system is the identification excitation signal IdentU, and the output signal of the second open-loop system is the output signal y of the closed-loop system. The transfer function of the second open-loop system is: By comparing the transfer function of the first open-loop system with the transfer function of the second open-loop system, the transfer function T of the controlled object can be obtained. yIdentU / T uIdent U =G.

[0053] In a feasible implementation, this embodiment provides an implementation for respectively calculating the power spectral density of the first open-loop system and the second open-loop system, which can be specifically performed with reference to the following steps 1) to 3):

[0054] Step 1): Based on the autocorrelation method, the power spectral density functions of the input signal and the output signal of the first open-loop system, and the input signal and the output signal of the second open-loop system are determined respectively.

[0055] like Figure 2 As shown, the input signal u of the controlled model includes the controller output signal C(s) and the identification excitation signal IdentU. Based on the autocorrelation method, power spectrum density estimation is performed on the input signal and output signal of the first open-loop system, as well as the input signal and output signal of the second open-loop system. The power spectrum estimation can adopt the autocorrelation method in the classical spectrum estimation method. First, the autocorrelation function is estimated, and then the estimated autocorrelation function is Fourier transformed to obtain an estimated value of the signal power spectrum density.

[0056] The input signal of the above-mentioned controlled model includes the controller output signal and the identification excitation signal; the autocorrelation function of the identification excitation signal is determined, and the autocorrelation function is Fourier transformed to obtain the autopower spectral density function of the identification excitation signal; wherein the autopower spectral density function is the power spectral density function of the input signal of the first open-loop system and the second open-loop system.

[0057] Among them, the autocorrelation function of the above identification excitation signal is:

[0058]

[0059] R IdentU is the autocorrelation of the identification data sequence, N is the length of the identification data sequence, IdentU n To identify the data sequence.

[0060] The discrete Fourier transform of the autocorrelation function gives the autopower spectral density function:

[0061]

[0062] A first cross-correlation function between the controller output signal and the identification excitation signal is determined, and the first cross-correlation function is Fourier transformed to obtain a power spectrum density function of the output signal of the first open-loop system.

[0063] The cross-correlation function between the controller output signal u and the identification excitation signal is recorded as the first cross-correlation function, which is:

[0064]

[0065] R uIdentU To identify the cross-correlation between the data sequence and the controller output sequence, u n is the controller output sequence, and IdentU is the identification data sequence. In practical applications, u n is the output data of the controller obtained in the program control algorithm, and the data is collected every 200 us. IdentU is the identification excitation discrete data input in the program, and the data is input every 200 us.

[0066] Performing Fourier transform on the first cross-correlation function, the power spectrum density function of the output signal of the first open-loop system is obtained as follows:

[0067]

[0068] A second cross-correlation function between the output signal y of the closed-loop system and the identification excitation signal is determined, and the second cross-correlation function is Fourier transformed to obtain a power spectrum density function of the output signal of the second open-loop system.

[0069] The cross-correlation function between the closed-loop system output signal and the identification excitation signal is recorded as the second phase exchange function, and the second cross-correlation function is:

[0070]

[0071] R yIdentU To identify the cross-correlation between the data sequence and the output sequence of the closed-loop system.

[0072] Performing Fourier transform on the second cross-correlation function, the power spectrum density function of the output signal of the second open-loop system is obtained as follows:

[0073]

[0074] Step 2): Based on the power spectral density functions of the input signal and the output signal of the first open-loop system, determine the power spectral density of the first open-loop system.

[0075] The power spectral density of the first open-loop system is calculated based on the ratio of the power spectral density of the output signal of the first open-loop system to the power spectral density of the input signal of the first open-loop system, that is, based on the ratio of the power spectral density function of the output signal of the first open-loop system to the auto-power spectral density function of the identification excitation signal.

[0076] The calculation formula for the power spectrum density of the first open-loop system is:

[0077]

[0078] Step 3): Based on the power spectral density functions of the input signal and the output signal of the second open-loop system, determine the power spectral density of the second open-loop system.

[0079] The power spectral density of the first open-loop system is calculated based on the ratio of the power spectral density of the output signal of the second open-loop system to the power spectral density of the input signal of the second open-loop system, that is, based on the ratio of the power spectral density function of the output signal of the second open-loop system to the self-power spectral density function of the identification excitation signal.

[0080] The power spectral density of the second open-loop system is:

[0081]

[0082] Among them, T uIdentU (k) is the power spectrum density of the first open-loop system, T yIdentU (k) is the power spectrum density of the second open-loop system, S IdentU (k) is the auto-power spectral density function of the identification excitation signal, S uIdentU (k) is the power spectral density function of the output signal of the first open-loop system, S yIdentY (k) is the power spectral density function of the output signal of the second open-loop system.

[0083] The closed-loop identification method provided in this embodiment reduces the influence of noise by decomposing the closed-loop system into two open-loop systems for analysis, making the closed-loop system identification more accurate, realizing closed-loop identification of the micro-motion stage, and improving the stability and accuracy of the micro-motion stage.

[0084] Corresponding to the closed-loop identification method provided in the above embodiment, the embodiment of the present invention provides a closed-loop identification device, see Figure 3 The structure diagram of a closed-loop identification device shown in FIG. 1 includes the following modules:

[0085] The decomposition module 31 is configured to obtain an identification excitation signal input to the closed-loop system of the controlled object, and decompose the closed-loop system of the controlled object into a first open-loop system and a second open-loop system based on the identification excitation signal.

[0086] The calculation module 32 is configured to calculate the power spectrum density of the first open-loop system and the second open-loop system respectively.

[0087] The determination module 33 is configured to determine a transfer function of a controlled model in a closed-loop system of the controlled object based on the power spectrum density of the first open-loop system and the second open-loop system.

[0088] The closed-loop identification device provided in this embodiment excites the closed-loop system of the controlled object using an identification excitation signal, thereby decomposing the closed-loop system of the controlled object into two open-loop systems. Based on the power spectral density of the two decomposed open-loop systems, the transfer function of the controlled model in the closed-loop system can be accurately identified. This device can be applied to the closed-loop identification of micro-motion stages, reducing the impact of noise, lowering computational complexity, and improving the accuracy of closed-loop identification.

[0089] In one embodiment, the above-mentioned decomposition module 31 is further used to use the identification excitation signal as the input signal of the first open-loop system and the input signal of the controlled model as the output signal of the first open-loop system to decompose into the first open-loop system; use the identification excitation signal as the input signal of the second open-loop system and the output signal of the closed-loop system as the output signal of the second open-loop system to decompose into the second open-loop system.

[0090] In one embodiment, the above-mentioned calculation module 32 is further used to determine the power spectral density functions of the input signal and output signal of the first open-loop system, and the input signal and output signal of the second open-loop system based on the autocorrelation method; determine the power spectral density of the first open-loop system based on the power spectral density function of the input signal and output signal of the first open-loop system; and determine the power spectral density of the second open-loop system based on the power spectral density function of the input signal and output signal of the second open-loop system.

[0091] In one embodiment, the input signal of the above-mentioned controlled model includes a controller output signal and an identification excitation signal; the above-mentioned calculation module 32 is further used to determine the autocorrelation function of the identification excitation signal, perform Fourier transform on the autocorrelation function, and obtain the autopower spectral density function of the identification excitation signal; wherein the autopower spectral density function is the power spectral density function of the input signal of the first open-loop system and the second open-loop system; determine the first cross-correlation function of the controller output signal and the identification excitation signal, perform Fourier transform on the first cross-correlation function, and obtain the power spectral density function of the output signal of the first open-loop system; determine the second cross-correlation function of the output signal of the closed-loop system and the identification excitation signal, perform Fourier transform on the second cross-correlation function, and obtain the power spectral density function of the output signal of the second open-loop system.

[0092] In one embodiment, the power spectrum density of the first open-loop system is calculated as follows:

[0093]

[0094] The power spectral density of the second open-loop system is:

[0095]

[0096] Among them, T uIdentU (k) is the power spectrum density of the first open-loop system, T yIdentU (k) is the power spectrum density of the second open-loop system, S IdentU (k) is the auto-power spectral density function of the identification excitation signal, S uIdentU (k) is the power spectral density function of the output signal of the first open-loop system, S yIdentU (k) is the power spectral density function of the output signal of the second open-loop system.

[0097] In one embodiment, the frequency domain model of the controlled model is:

[0098]

[0099] Among them, G(k) is the transfer function of the controlled model, T uIdentU (k) is the power spectrum density of the first open-loop system, T yIdentU (k) is the power spectral density of the second open-loop system.

[0100] In one embodiment, the controlled object is a micro-motion stage.

[0101] The closed-loop identification device provided in this embodiment reduces the influence of noise by decomposing the closed-loop system into two open-loop systems for analysis, making the closed-loop system identification more accurate, realizing closed-loop identification of the micro-motion stage, and improving the stability and accuracy of the micro-motion stage.

[0102] The device provided in this embodiment has the same implementation principle and technical effects as those of the aforementioned embodiments. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0103] An embodiment of the present invention provides an electronic device, such as Figure 4 As shown in the structural diagram of the electronic device, the electronic device includes a processor 41 and a memory 42. The memory stores a computer program that can be run on the processor. When the processor executes the computer program, the steps of the method provided in the above embodiment are implemented.

[0104] See also Figure 4The electronic device further includes a bus 44 and a communication interface 43. The processor 41, the communication interface 43 and the memory 42 are connected via the bus 44. The processor 41 is configured to execute executable modules stored in the memory 42, such as computer programs.

[0105] The memory 42 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The system network element communicates with at least one other network element via at least one communication interface 43 (which may be wired or wireless), and may utilize the Internet, a wide area network, a local area network, a metropolitan area network, or the like.

[0106] The bus 44 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. The bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0107] Among them, the memory 42 is used to store programs, and the processor 41 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 41 or implemented by the processor 41.

[0108] Processor 41 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in processor 41 or software instructions. The above processor 41 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. It may also be a digital signal processing (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in memory 42, and processor 41 reads the information in memory 42 and performs the steps of the above method in conjunction with its hardware.

[0109] An embodiment of the present invention provides a computer-readable medium, wherein the computer-readable medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method described in the above embodiment.

[0110] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the system described above can refer to the corresponding process in the aforementioned embodiment and will not be repeated here.

[0111] The computer program products of the closed-loop identification method, device, and electronic device provided in the embodiments of the present invention include a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the previous method embodiments. For specific implementation, please refer to the method embodiments and will not be repeated here.

[0112] In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0113] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0114] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0115] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A closed-loop identification method, characterized in that: include: obtaining an identification excitation signal inputted by a closed-loop system of a controlled object, and decomposing the closed-loop system of the controlled object into a first open-loop system and a second open-loop system based on the identification excitation signal; Calculating power spectral densities of the first open-loop system and the second open-loop system respectively; determining a frequency domain model of a controlled model in a closed-loop system of the controlled object based on power spectral densities of the first open-loop system and the second open-loop system; The controlled object is a micro-motion stage; The step of respectively calculating the power spectral density of the first open-loop system and the second open-loop system comprises: determining power spectral density functions of the input signal and the output signal of the first open-loop system, and the input signal and the output signal of the second open-loop system respectively based on an autocorrelation method; determining a power spectral density of the first open-loop system based on a power spectral density function of an input signal and an output signal of the first open-loop system; determining a power spectral density of the second open-loop system based on a power spectral density function of an input signal and an output signal of the second open-loop system; The frequency domain model of the controlled model is: Where G(k) is the transfer function of the controlled model, T uIdentU (k) is the power spectral density of the first open-loop system, T yIdenyU (k) is the power spectral density of the second open-loop system.

2. The method according to claim 1, characterized in that The step of decomposing the closed-loop system of the controlled object into a first open-loop system and a second open-loop system based on the identification excitation signal includes: Using the identification excitation signal as the input signal of the first open-loop system and the input signal of the controlled model as the output signal of the first open-loop system, and decomposing the first open-loop system; The identification excitation signal is used as the input signal of the second open-loop system, the output signal of the closed-loop system is used as the output signal of the second open-loop system, and the second open-loop system is decomposed to obtain the second open-loop system.

3. The method according to claim 1, characterized in that The input signal of the controlled model includes the controller output signal and the identification excitation signal; The step of respectively determining the power spectral density functions of the input signal and the output signal of the first open-loop system, and the input signal and the output signal of the second open-loop system based on the autocorrelation method includes: Determining an autocorrelation function of the identification excitation signal, performing Fourier transform on the autocorrelation function, and obtaining an autopower spectral density function of the identification excitation signal; wherein the autopower spectral density function is a power spectral density function of the input signal of the first open-loop system and the second open-loop system; determining a first cross-correlation function between the controller output signal and the identification excitation signal, performing Fourier transform on the first cross-correlation function to obtain a power spectral density function of the output signal of the first open-loop system; A second cross-correlation function between the output signal of the closed-loop system and the identification excitation signal is determined, and the second cross-correlation function is Fourier transformed to obtain a power spectral density function of the output signal of the second open-loop system.

4. The method according to claim 3, characterized in that The calculation formula for the power spectrum density of the first open-loop system is: The power spectral density of the second open-loop system is: Among them, T uIdentU (k) is the power spectral density of the first open-loop system, T yIdentU (k) is the power spectral density of the second open-loop system, S IdentU (k) is the auto-power spectral density function of the identification excitation signal, S uIdentU (k) is the power spectral density function of the output signal of the first open-loop system, S yIdentU (k) is the power spectral density function of the output signal of the second open-loop system.

5. A closed-loop identification device, characterized in that: include: a decomposition module, configured to obtain an identification excitation signal inputted by a closed-loop system of a controlled object, and decompose the closed-loop system of the controlled object into a first open-loop system and a second open-loop system based on the identification excitation signal; a calculation module, configured to calculate power spectral densities of the first open-loop system and the second open-loop system respectively; a determination module, configured to determine a transfer function of a controlled model in a closed-loop system of the controlled object based on power spectral densities of the first open-loop system and the second open-loop system; the controlled object being a micro-motion stage; The calculation module is configured to determine the power spectral density functions of the input signal and output signal of the first open-loop system, and the input signal and output signal of the second open-loop system, respectively, based on an autocorrelation method; determine the power spectral density of the first open-loop system based on the power spectral density functions of the input signal and output signal of the first open-loop system; and determine the power spectral density of the second open-loop system based on the power spectral density functions of the input signal and output signal of the second open-loop system; The frequency domain model of the controlled model is: Where G(k) is the transfer function of the controlled model, T uIdentU (k) is the power spectral density of the first open-loop system, T yIdenyU (k) is the power spectral density of the second open-loop system.

6. An electronic device, characterized in that: include: processors and storage devices; The storage device stores a computer program, which, when executed by the processor, executes the method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are performed.

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