A chatter suppression method and system for CNC machine tools based on fuzzy sliding mode control

Abstract

This disclosure relates to a method and system for suppressing chatter in CNC machine tools using fuzzy sliding mode control. The method comprises the following steps: constructing a system dynamics model based on parameter uncertainty and external disturbances; designing a sliding mode controller with a switching gain based on the system dynamics model; and constructing fuzzy rules for the sliding mode controller to adjust the switching gain, thereby achieving a rapid response in the feed system while avoiding significant chatter. This invention combines the advantages of fuzzy control and sliding mode control, compensating for their dynamic effects through fuzzy logic regulation, thereby improving the stability of the feed system during cutting.

Description

Technical Field

[0001] The present disclosure relates to the field of dynamic control of numerically controlled machine tools, and in particular to a method and system for suppressing chatter of numerically controlled machine tools based on fuzzy sliding mode control. Background Art

[0002] Regenerative chatter, a self-excited vibration, often occurs during the cutting process of CNC machine tools. Due to the interaction between the tool and the workpiece, chatter can lead to poor surface finish and shortened tool life, making it a bottleneck hindering production efficiency and improving product quality. Therefore, suppressing regenerative chatter has been a key research topic in machining dynamics. During the cutting process, load fluctuations can cause motor speed instability to a certain extent, especially when machining high-hardness materials, where cutting force variations are more pronounced. Furthermore, various interference signals in the machining environment directly affect the performance of the machine tool cutting system. Furthermore, building an accurate model inevitably involves unknown disturbances, including vibration parameters and system uncertainties. Sliding mode control (SMC) is commonly used to provide closed-loop insensitivity to unknown disturbances and ensure finite-time convergence to the desired sliding surface. Although SMC has demonstrated its effectiveness in industry, its application to machine tool dynamics control still presents several challenges:

[0003] (1) The motors of CNC machine tools are typically used to drive feed systems, which are independently controlled by electronic control units to achieve flexible responses in various situations. However, the discontinuous characteristics of SMCs can cause large vibrations in such systems when achieving fast responses.

[0004] (2) The perturbation of model parameters and the interference of the external environment will affect the machining accuracy. Therefore, it is necessary to consider the effect of cutting force on the machining process, otherwise the system stability may be reduced.

[0005] Robust and stable control of the feed system motor is difficult to achieve due to unknown disturbances and parameter uncertainties during machining. To address this challenge, this paper proposes an adaptive fuzzy sliding mode control method for active chatter suppression of feed system dynamics. By adjusting the switching gain of the control law, this method provides a solution for achieving robust cutting force control for the motor drive of the machine tool feed system. Summary of the Invention

[0006] The present disclosure provides a method and system for suppressing chatter in CNC machine tools based on fuzzy sliding mode control, which can solve the problem that the discontinuity characteristics of sliding mode control may cause large-scale chatter in the system when obtaining a rapid response, thereby reducing the stability of the feed system during the cutting process. The present disclosure provides the following technical solutions:

[0007] As one aspect of an embodiment of the present disclosure, a method for suppressing chatter of a CNC machine tool based on fuzzy sliding mode control is provided, comprising the following steps:

[0008] Construct a system dynamics model based on parameter uncertainty and external disturbances;

[0009] Based on the system dynamics model, a sliding mode controller with switching gain is designed;

[0010] A fuzzy rule is constructed for the sliding mode controller to adjust the switching gain.

[0011] Optionally, the system dynamics model is:

[0012]

[0013] Where M, C and K are the modal parameters of the tool, namely the modal mass, damping coefficient and modal stiffness, respectively; ξ(t) is the feed displacement of the tool tip; F is the dynamic cutting force; and f is d For external disturbances, Assume that the external disturbance f d and the model uncertainty g(M,C,D) have unknown boundaries, And both are greater than 0.

[0014] Optionally, the sliding mode controller with switching gain is:

[0015]

[0016] Where k(σ) is the switching gain function, σ(t) is the sliding variable, i.e., the fuzzy input, M, C, and K are the modal parameters of the tool, and λ,A>0 is a constant.

[0017] Optionally, the switching gain is:

[0018]

[0019] Where k fuzzy is the fuzzy rule output, k f <0 is a constant, σ is a sliding variable.

[0020] Optionally, after designing the sliding mode controller including the switching gain, the method further includes: establishing a sliding mode surface and designing a dynamic reaching law.

[0021] Optionally, the sliding surface is:

[0022]

[0023] Where ξ(t) is the feed displacement of the tool tip, and λ>0 is a constant.

[0024] Optionally, the dynamic reaching law is:

[0025] F re =-Aσ(t)-|k(σ)|sign(σ)

[0026] Where A>0 is a constant, σ(t) is the fuzzy input, and k(σ) is the switching gain function.

[0027] Optionally, the fuzzy rule output is:

[0028]

[0029] Where, is the weight of fuzzy rule q, Q is the number of fuzzy rules, is the membership degree of fuzzy rule q, which satisfies the fuzzy membership function is the width of the Gaussian normal function, and α is the median of the Gaussian normal function.

[0030] As another aspect of the embodiments of the present disclosure, a chatter suppression system for a CNC machine tool based on fuzzy sliding mode control is provided, comprising:

[0031] System dynamics model building module, which builds system dynamics models based on parameter uncertainty and external disturbances;

[0032] A sliding mode controller design module, which designs a sliding mode controller with switching gain based on the system dynamics model;

[0033] The fuzzy rule building module builds fuzzy rules for the sliding mode controller to adjust the switching gain.

[0034] As another aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for suppressing chatter of a CNC machine tool based on fuzzy sliding mode control is implemented.

[0035] As another aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the program is executed by a processor, the above-mentioned method for suppressing vibration of CNC machine tools based on fuzzy sliding mode control is implemented.

[0036] Compared with the prior art, the beneficial effects of the present disclosure are:

[0037] 1. The insensitivity of sliding mode control to disturbances is used to reduce the impact of disturbances, and the switching gain is adjusted using the constructed fuzzy rules, so that the feed system can achieve a fast response while avoiding large-scale chatter.

[0038] 2. Dynamic modeling is performed by introducing parameter perturbations and external environmental interference, combining the advantages of fuzzy control and sliding mode control. Fuzzy logic is used to adjust the control action to compensate for their dynamic effects. When moving away from the sliding surface, the gain is increased to reduce the time to reach the sliding surface; when approaching the sliding surface, the gain is reduced to reduce chattering and improve the stability of the feed system during cutting. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Flowchart of the method for suppressing chatter of CNC machine tools based on fuzzy sliding mode control in Example 1;

[0040] Figure 2 This is a flowchart of the implementation process of the method for suppressing chatter of a CNC machine tool based on fuzzy sliding mode control in Example 1;

[0041] Figure 3 This is a control diagram of the turning dynamics model in Example 1;

[0042] Figure 4 This is a schematic block diagram of the CNC machine tool chatter suppression system based on fuzzy sliding mode control in Example 2. DETAILED DESCRIPTION

[0043] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0044] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0045] The term "and / or" herein simply describes an association relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can represent the existence of three situations: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0046] In addition, numerous specific details are provided in the following detailed description to better illustrate the present disclosure. Those skilled in the art will appreciate that the present disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main points of the present disclosure.

[0047] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form combined embodiments without violating the principle logic. Due to space limitations, the present disclosure will not elaborate on them.

[0048] In addition, the present disclosure also provides a method and system for suppressing chatter of CNC machine tools based on fuzzy sliding mode control. The above can be used to implement any method for suppressing chatter of CNC machine tools based on fuzzy sliding mode control provided by the present disclosure. The corresponding technical solutions and descriptions can be found in the corresponding records of the method section and will not be repeated here.

[0049] The method for suppressing chatter of a CNC machine tool based on fuzzy sliding mode control may be executed by a computer or other device capable of implementing chatter suppression of a CNC machine tool based on fuzzy sliding mode control. For example, the method may be executed by a terminal device, a server, or other processing device, wherein the terminal device may be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, an in-vehicle device, a wearable device, etc. In some possible implementations, the method for suppressing chatter of a CNC machine tool based on fuzzy sliding mode control may be implemented by a processor invoking computer-readable instructions stored in a memory.

[0050] Example 1

[0051] This embodiment provides a method for suppressing chatter of CNC machine tools based on fuzzy sliding mode control. Figure 1 As shown, the following steps are included:

[0052] S10, constructing a system dynamics model based on parameter uncertainty and external disturbances;

[0053] S20, designing a sliding mode controller with a switching gain based on the system dynamics model;

[0054] S30: constructing fuzzy rules for the sliding mode controller to adjust the switching gain.

[0055] The present disclosure relates to a method for suppressing chatter of CNC machine tools based on fuzzy sliding mode control. The method comprises the following steps: constructing a system dynamics model based on parameter uncertainty and external disturbances; designing a sliding mode controller with a switching gain based on the system dynamics model; constructing fuzzy rules for the sliding mode controller to adjust the switching gain, and compensating for its dynamic influence through fuzzy logic regulation and control, so that the feed system can achieve a rapid response while avoiding large-scale chatter, thereby improving the stability of the feed system during the cutting process.

[0056] In this embodiment, a method for suppressing chatter of a CNC machine tool based on fuzzy sliding mode control specifically includes the following steps. The implementation process of the algorithm is as follows: Figure 2 As shown, each step of the embodiment of the present disclosure is described in detail below.

[0057] S10, constructing a system dynamics model based on parameter uncertainty and external disturbances;

[0058] In this embodiment, the turning dynamics model control schematic diagram is as follows: Figure 3 As shown in Figure 2, considering the interference of the feed system motor during the cutting process, the simplified single-degree-of-freedom steering system is used in the simulation. The control equation of the feed direction is:

[0059]

[0060] Where M, C, and K are the modal parameters of the tool, namely, the modal mass, damping coefficient, and modal stiffness, respectively; ξ(t) is the feed displacement of the tool tip; F is the dynamic cutting force; and F is d is the combined interference, and it is assumed to be bounded.

[0061] Furthermore, considering the uncertainties of models M, C, and K, the combined effects of model uncertainty and external disturbances mainly affect the controllability, and the two items containing uncertainty can be considered.

[0062]

[0063] Where, are the estimated model parameters of M, C, and K respectively. The deviation between the actual motion model and the nominal motion model is treated as uncertainty. Let g(M, C, K) be the system uncertainty caused by model uncertainty, and f d For external interference.

[0064] In this embodiment, the dynamic model of the feeding system considering model uncertainty is:

[0065]

[0066] in, Assume that the environmental disturbance f d and the model uncertainty g(M,C,D) have unknown boundaries, And both are greater than 0.

[0067] S20, designing a sliding mode controller with a switching gain based on the system dynamics model;

[0068] The sliding mode controller with switching gain is

[0069]

[0070] Where k(σ) is the switching gain function, σ(t) is the sliding variable, i.e., the fuzzy input, M, C, and K are the modal parameters of the tool, and λ,A>0 is a constant.

[0071] In this embodiment, taking into account the above shortcomings of the control law given in the equation, a new proportional control law is introduced, in which a weighted integral and a proportional factor are introduced into the switching gain of the sliding mode control. Proportional control plays a role in stabilizing the system. Properly increasing the proportional factor can reduce the static error, while the sliding mode control with a weighted integral form gain makes the gain system change slowly when in sliding mode. The switching gain is designed to be

[0072]

[0073] Where k fuzzy For fuzzy rule output, it will be designed later; k f <0 is a constant, σ is a sliding variable.

[0074] therefore And the initial value k(0)=0, solving the first-order differential equation yields

[0075]

[0076] Furthermore, the sliding surface is designed as

[0077]

[0078] Where ξ(t) is the feed displacement of the tool tip, is the feed rate, λ>0 is a constant.

[0079] Taking the derivative of the sliding surface, we can get make Get the equivalent control law Dynamic reaching law F re =-Aσ(t)-|k(σ)|sign(σ).

[0080] Where A>0 is a constant, σ(t) is the fuzzy input, and k(σ) is the switching gain function.

[0081] S30: constructing fuzzy rules for the sliding mode controller to adjust the switching gain.

[0082] In this embodiment, the fuzzy input is selected as σ(t), the fuzzy output is k(σ), the fuzzy sets are selected as NB, NM, NS, ZE, PS, PM, PB, the input interval is selected as [-a, a], the output interval is selected as [-b, b], the membership function is selected as the Gaussian basis function, and the fuzzy rule is the if-then rule based on expert experience, that is, if σ(t) is large, then increase k(σ), and if σ(t) is small, then decrease k(σ). The fuzzy rule is designed as follows:

[0083] Table 1 Fuzzy rules

[0084]

[0085]

[0086] Among them, NB is negative and large, NM is negative and medium, NS is negative and small, ZE is zero, PS is positive and small, PM is positive and medium, and PB is positive and large.

[0087] In this embodiment, the fuzzy rule output is:

[0088]

[0089] Where, is the weight of fuzzy rule q, Q is the number of fuzzy rules, is the membership degree of fuzzy rule q, which satisfies the fuzzy membership function is the width of the Gaussian normal function, and α is the median of the Gaussian normal function.

[0090] In this example, its stability is further demonstrated.

[0091] Design the Lyapunov function as

[0092]

[0093] Where, Pick g(M,C,K)+f d The ideal approximation of is the ideal equivalent weight. Therefore

[0094]

[0095] Among them, 0<θ<1, the adaptive law is taken as

[0096]

[0097] Furthermore, according to the designed sliding surface, we can get

[0098]

[0099] Derivative the Lyapunov function and Substituting in, we get:

[0100]

[0101] Further, Substituting in, we get

[0102]

[0103] and So there is

[0104]

[0105] Wherein, θ<A.

[0106] As demonstrated above, this paper proposes a chatter suppression method for CNC machine tools based on fuzzy sliding mode control, targeting a second-order dynamic system model with model uncertainty and uncertain environmental disturbances. Specifically, this method involves establishing a second-order system dynamics model with parameter uncertainty and external disturbances; designing a sliding mode controller with switching gain; and constructing fuzzy rules to adaptively design the switching gain. Fuzzy logic rules are then used to adjust the switching gain to compensate for its dynamic effects, thereby improving the stability of the feed system during the cutting process.

[0107] Example 2

[0108] As another aspect of the embodiment of the present disclosure, a chatter suppression system 100 for a CNC machine tool based on fuzzy sliding mode control is also provided. Figure 4 As shown, the following steps are included:

[0109] System dynamics model construction module 1, based on parameter uncertainty and external disturbances, constructs the system dynamics model;

[0110] Sliding mode controller design module 2, designs a sliding mode controller with switching gain based on the system dynamics model;

[0111] The fuzzy rule construction module 3 constructs fuzzy rules for the sliding mode controller to adjust the switching gain.

[0112] Based on the above modules, the disclosed embodiment constructs a method for suppressing chatter in CNC machine tools using fuzzy sliding mode control. Based on parameter uncertainty and external disturbances, a system dynamics model is constructed. A sliding mode controller with a switching gain is designed based on the system dynamics model. Fuzzy rules are constructed for the sliding mode controller to adjust the switching gain. Fuzzy logic regulation and control are used to compensate for its dynamic effects, enabling the feed system to achieve rapid response while avoiding large-scale chatter, thereby improving the feed system's stability during the cutting process. This implements a chatter suppression system 100 for CNC machine tools based on fuzzy sliding mode control.

[0113] The following describes in detail each module of the embodiment of the present disclosure.

[0114] System dynamics model construction module 1 constructs a system dynamics model based on parameter uncertainty and external disturbances.

[0115] In this embodiment, considering the interference of the motor during the cutting process, the control equation of the feed direction of the simplified single-degree-of-freedom steering system used in the simulation is:

[0116]

[0117] Where M, C, and K are the modal parameters of the tool, namely, the modal mass, damping coefficient, and modal stiffness, respectively; ξ(t) is the feed displacement of the tool tip; F is the dynamic cutting force; and F is d is the combined interference, and it is assumed to be bounded.

[0118] Furthermore, considering the uncertainties of models M, C, and K, the combined effects of model uncertainty and external disturbances mainly affect the controllability, and the two items containing uncertainty can be considered.

[0119]

[0120] Where, are the estimated model parameters of M, C, and K respectively. The deviation between the actual motion model and the nominal motion model is treated as uncertainty. Let g(M, C, K) be the system uncertainty caused by model uncertainty, and f d For external interference.

[0121] In this embodiment, the dynamic model of the feeding system considering model uncertainty is:

[0122]

[0123] in, Assume that the environmental disturbance f d and the model uncertainty g(M,C,D) have unknown boundaries, And both are greater than 0.

[0124] The sliding mode controller design module 2 designs a sliding mode controller with switching gain based on the system dynamics model.

[0125] The sliding mode controller with switching gain is

[0126]

[0127] Where k(σ) is the switching gain function, σ(t) is the sliding variable, i.e., the fuzzy input, M, C, and K are the modal parameters of the tool, and λ,A>0 is a constant.

[0128] In this embodiment, taking into account the above shortcomings of the control law given in the equation, a new proportional control law is introduced, in which a weighted integral and a proportional factor are introduced into the switching gain of the sliding mode control. Proportional control plays a role in stabilizing the system. Properly increasing the proportional factor can reduce the static error, while the sliding mode control with a weighted integral form gain makes the gain system change slowly when in sliding mode. The switching gain is designed to be

[0129]

[0130] Where k fuzzy For fuzzy rule output, it will be designed later; k f <0 is a constant, σ is a sliding variable.

[0131] therefore And the initial value k(0)=0, solving the first-order differential equation yields

[0132]

[0133] Furthermore, the sliding surface is designed as

[0134]

[0135] Where ξ(t) is the feed displacement of the tool tip, and λ>0 is a constant.

[0136] Taking the derivative of the sliding surface, we can get make Get the equivalent control law Dynamic reaching law F re =-Aσ(t)-|k(σ)|sign(σ).

[0137] Where A>0 is a constant, σ(t) is the fuzzy input, and k(σ) is the switching gain function.

[0138] The fuzzy rule construction module 3 constructs fuzzy rules for the sliding mode controller to adjust the switching gain.

[0139] In this embodiment, the fuzzy input is selected as σ(t), the fuzzy output is k(σ), the fuzzy sets are selected as NB, NM, NS, ZE, PS, PM, PB, the input interval is selected as [-a, a], the output interval is selected as [-b, b], the membership function is selected as the Gaussian basis function, and the fuzzy rule is the if-then rule based on expert experience, that is, if σ(t) is large, then increase k(σ), and if σ(t) is small, then decrease k(σ). The fuzzy rule is designed as follows:

[0140] Table 1 Fuzzy rules

[0141]

[0142] Among them, NB is negative and large, NM is negative and medium, NS is negative and small, ZE is zero, PS is positive and small, PM is positive and medium, and PB is positive and large.

[0143] In this embodiment, the fuzzy rule output is:

[0144]

[0145] Where, is the weight of fuzzy rule q, Q is the number of fuzzy rules, is the membership degree of fuzzy rule q, which satisfies the fuzzy membership function is the width of the Gaussian normal function, and α is the median of the Gaussian normal function.

[0146] In this example, its stability is further demonstrated.

[0147] Design the Lyapunov function as

[0148]

[0149] Where, Pick g(M,C,K)+f d The ideal approximation of is the ideal equivalent weight. Therefore

[0150]

[0151] Among them, 0<θ<1, the adaptive law is taken as

[0152]

[0153] Furthermore, according to the designed sliding surface, we can get

[0154]

[0155] Derivative the Lyapunov function and Substituting in, we get:

[0156]

[0157] Further, Substituting in, we get

[0158]

[0159] and So there is

[0160]

[0161] Wherein, θ<A.

[0162] As demonstrated above, this paper proposes a chatter suppression method for CNC machine tools based on fuzzy sliding mode control, targeting a second-order dynamic system model with model uncertainty and uncertain environmental disturbances. Specifically, this method involves establishing a second-order system dynamics model with parameter uncertainty and external disturbances; designing a sliding mode controller with switching gain; and constructing fuzzy rules to adaptively design the switching gain. Fuzzy logic rules are then used to adjust the switching gain to compensate for its dynamic effects, thereby improving the stability of the feed system during the cutting process.

[0163] In some embodiments, the system 100 operates in the following manner during use:

[0164] S1: Run the system dynamics model building module. Build the system dynamics model based on parameter uncertainty and external disturbances.

[0165] S2: Run the sliding mode controller design module. Based on the system dynamics model, design a sliding mode controller with switching gains.

[0166] S3: Run the fuzzy rule building module to build fuzzy rules for the sliding mode controller and adjust the switching gain.

[0167] Based on the description of the above embodiments, it can be seen that the embodiments of the present disclosure can achieve the following technical effects:

[0168] 1. The insensitivity of sliding mode control to disturbances is used to reduce the impact of disturbances, and the switching gain is adjusted using the constructed fuzzy rules, so that the feed system can achieve a fast response while avoiding large-scale chatter.

[0169] 2. Dynamic modeling is performed by introducing parameter perturbations and external environmental interference, combining the advantages of fuzzy control and sliding mode control. Fuzzy logic is used to adjust the control action to compensate for their dynamic effects. When moving away from the sliding surface, the gain is increased to reduce the time to reach the sliding surface; when approaching the sliding surface, the gain is reduced to reduce chattering and improve the stability of the feed system during cutting.

[0170] Example 3

[0171] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for suppressing chatter of a CNC machine tool based on fuzzy sliding mode control in Example 1 is implemented.

[0172] Embodiment 3 of the present disclosure is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0173] The electronic device may be in the form of a general-purpose computing device, for example, a server device. Components of the electronic device may include, but are not limited to, at least one processor, at least one memory, and a bus connecting different system components (including the memory and the processor).

[0174] The bus includes data bus, address bus and control bus.

[0175] The memory may include volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0176] The memory may also include a program tool having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0177] The processor executes computer programs stored in the memory to perform various functional applications and process data.

[0178] The electronic device may also communicate with one or more external devices (e.g., a keyboard, a pointing device, etc.). Such communication may be performed via an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figures, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (RAID) systems, tape drives, and data backup storage systems.

[0179] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, depending on the embodiment of the present application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0180] Example 4

[0181] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for suppressing chatter of a CNC machine tool based on fuzzy sliding mode control in embodiment 1.

[0182] The readable storage medium may include, but is not limited to, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0183] In a possible implementation, the present disclosure can also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of the CNC machine tool vibration suppression method based on fuzzy sliding mode control described in Example 1.

[0184] The program code for executing the present disclosure may be written in any combination of one or more programming languages, and may be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on the remote device.

[0185] Although the embodiments of the present disclosure have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit of the present disclosure, and the scope of the present disclosure is defined by the appended claims and their equivalents.

Claims

1. A method for suppressing chatter of CNC machine tools based on fuzzy sliding mode control, characterized in that: The steps include: Construct a system dynamics model based on parameter uncertainty and external disturbances; Based on the system dynamics model, a sliding mode controller with switching gain is designed; Construct fuzzy rules for the sliding mode controller to adjust the switching gain The system dynamics model is: Where M, C and K are the modal parameters of the tool, namely the modal mass, damping coefficient and modal stiffness, respectively; ξ(t) is the feed displacement of the tool tip; F is the dynamic cutting force; and f is d For external disturbances, Assume that the external disturbance f d and the model uncertainty g(M,C,K) have unknown boundaries, And both are greater than 0; The sliding mode controller with switching gain is: Where k(σ) is the switching gain function, σ(t) is the sliding variable, i.e., the fuzzy input, M, C, and K are the modal parameters of the tool, and λ,A>0 is a constant.

2. The method for suppressing chatter of a CNC machine tool based on fuzzy sliding mode control according to claim 1, characterized in that: The switching gain is: Where k fuzzy is the fuzzy rule output, k f <0 is a constant, σ is a sliding variable.

3. The chatter suppression method for CNC machine tools based on fuzzy sliding mode control according to claim 1, characterized in that: After designing the sliding mode controller with switching gain, the method further includes: establishing a sliding mode surface and designing a dynamic reaching law, wherein the sliding mode surface is: Where ξ(t) is the feed displacement of the tool tip, and λ>0 is a constant.

4. The method for suppressing chatter of a CNC machine tool based on fuzzy sliding mode control according to claim 3, characterized in that: The dynamic reaching law is: F re =-Aσ(t)-|k(σ)|sign(σ) Where A>0 is a constant, σ(t) is the fuzzy input, and k(σ) is the switching gain function.

5. The method for suppressing chatter of a CNC machine tool based on fuzzy sliding mode control according to claim 1, characterized in that: The fuzzy rule output is: Where, is the weight of fuzzy rule q, Q is the number of fuzzy rules, is the membership degree of fuzzy rule q, which satisfies the fuzzy membership function is the width of the Gaussian normal function, and α is the median of the Gaussian normal function.

6. A chatter suppression system for CNC machine tools based on fuzzy sliding mode control, characterized in that: include: System dynamics model building module, which builds system dynamics models based on parameter uncertainty and external disturbances; A sliding mode controller design module, which designs a sliding mode controller with switching gain based on the system dynamics model; A fuzzy rule building module, for building fuzzy rules for the sliding mode controller to adjust the switching gain; The system dynamics model is: Where M, C and K are the modal parameters of the tool, namely the modal mass, damping coefficient and modal stiffness, respectively; ξ(t) is the feed displacement of the tool tip; F is the dynamic cutting force; and f is d For external disturbances, Assume that the external disturbance f d and the model uncertainty g(M,C,K) have unknown boundaries, And both are greater than 0; The sliding mode controller with switching gain is: Where k(σ) is the switching gain function, σ(t) is the sliding variable, i.e., the fuzzy input, M, C, and K are the modal parameters of the tool, and λ,A>0 is a constant.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for suppressing chatter of a CNC machine tool based on fuzzy sliding mode control according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for suppressing chatter of a CNC machine tool based on fuzzy sliding mode control according to any one of claims 1 to 5 is implemented.

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