Sliding mode control method and system for CNC vertical lathe servo system based on multi-objective optimization

Abstract

The present disclosure relates to a sliding mode control method and system for a CNC vertical lathe servo system based on multi-objective optimization, the method comprising the following steps: designing a new sliding mode control law based on the nonlinear uncertain system state equation of the servo system, constructing a sliding mode controller model, and constructing a multi-objective evaluation mechanism for the servo system according to system error information and step response performance indicators; setting corresponding preference information according to the control requirements of the servo system operating state to adjust the weight coefficient of the multi-objective optimization algorithm, and solving the optimal control parameters using an improved multi-objective optimization algorithm; substituting the optimal control parameters into the sliding mode controller to obtain the control information at that moment, and controlling the servo system. The present disclosure realizes the online real-time adjustment of the control parameters of the sliding mode controller, which improves the tracking accuracy while reducing the jitter of the system.

Description

Technical Field

[0001] The present disclosure relates to the technical field related to the control of a CNC vertical lathe servo system, and in particular to a sliding mode control method and system for a CNC vertical lathe servo system based on multi-objective optimization. Background Art

[0002] In automatic control systems, servo systems have been widely used in many fields such as mechanical manufacturing, industrial robots, electronic manufacturing equipment, and metallurgical industry. In CNC machine tools, servo systems are important connecting parts between CNC devices and mechanical transmission components of machine tools. The dynamic performance of servo feed systems (such as rapid responsiveness, stability, dynamic and static tracking accuracy, etc.) will directly affect the technical indicators such as accuracy and efficiency of CNC machine tools. Therefore, the use of suitable servo system controllers will play an important role in the performance of the entire CNC machine tool. For CNC vertical lathe servo systems, the control algorithms that are currently successfully applied mainly include: proportional integral differential control, fuzzy control, sliding mode control, model predictive control, etc. Among them, sliding mode control mainly achieves rapid response to input transformation by designing the required sliding surface and equivalent control law. The robustness and fast response speed of sliding mode control have attracted more and more attention.

[0003] At present, with the increasing demand for speed and precision of CNC machine tools, the traditional sliding mode control cannot meet this demand. Therefore, the improvement of sliding mode control has received widespread attention from researchers at home and abroad. It is mainly based on the controller to adaptively adjust the control parameters or improve the control law according to the changes in the environment to meet the servo control needs. However, the current adaptive adjustment is limited to a specific single performance indicator, and it is difficult to balance the multi-objective performance requirements. Therefore, it is necessary to optimize the system multi-objective, but the offline adjustment of control parameters cannot accurately adapt to environmental changes. Therefore, for the CNC vertical lathe servo system, it is necessary to explore how to combine the multi-objective optimization algorithm with the controller and realize online adjustment of control parameters to improve control accuracy. Summary of the invention

[0004] The present disclosure provides a sliding mode control method and system for a CNC vertical lathe servo system based on multi-objective optimization, which adaptively adjusts control parameters according to environmental changes to meet servo control requirements and realizes control of the servo drive system of a CNC machine tool, thereby solving the problem that offline adjustment of control parameters cannot accurately adapt to environmental changes, the tracking accuracy is not high, and system chattering is prone to occur. To solve the above technical problems, the present disclosure provides the following technical solutions:

[0005] As one aspect of the embodiments of the present disclosure, a sliding mode control method for a CNC vertical lathe servo system based on multi-objective optimization is provided. The method comprises the following steps:

[0006] S10, constructing a sliding mode controller model and designing a new sliding mode control law based on the nonlinear uncertain system state equation of the servo system;

[0007] S20, designing a parameter setting trigger mechanism, wherein the parameter setting trigger mechanism includes: setting an error information threshold, determining whether the system error information exceeds the threshold, and if so, triggering the parameter setting mechanism to adjust the parameters of the sliding mode controller; if not, maintaining the original parameters for sliding mode control;

[0008] S30, combining the sliding mode control law, constructing a multi-objective evaluation mechanism for the servo system according to system error information and step response performance indicators;

[0009] S40, setting corresponding preference information according to the control requirements of the servo system operation state, and adjusting the weight coefficient of the multi-objective optimization algorithm;

[0010] S50, improving the multi-objective optimization algorithm and solving to obtain the optimal control parameters of the servo system;

[0011] S60, substituting the optimal control parameters into the sliding mode controller, solving to obtain the control information at that moment, controlling the servo system, and designing a delay trigger mechanism for adjusting the control parameters according to the step response performance index.

[0012] Optionally, the sliding mode control law is:

[0013]

[0014] Among them, q, μ are controller design parameters, K a ,K b is the control parameter adjusted by the multi-objective optimization algorithm, x1 is the state information of the servo system, s is the sliding surface function, sgn() is the sign function, and tanh() is the hyperbolic tangent function.

[0015] Optionally, the system error information is:

[0016] x e (k) = x(k) - x q (k)

[0017] In the formula, k represents the current time, x e (k) represents the system error at the current moment, x(k) represents the current system state, and x q (k) is the expected state, where the system state variable is x(k) = [i q (k) w(k)] T .

[0018] Optionally, the servo system multi-objective evaluation mechanism is:

[0019]

[0020] Where J1, J2, and J3 are multi-objective optimization functions, which are overshoot, adjustment time, and time-weighted integral absolute error, respectively; y m is the step response peak value, y s is the steady-state value, t s The output step response enters the steady-state value y s The time corresponding to the ±Δ error band, Δ = 0.02, e(t) is the difference between the step response and the steady-state value, e(t) = y(t)-y s .

[0021] Optionally, the weight coefficient is expressed as:

[0022] ω=[σ1,σ2,σ3]

[0023] Where ω represents the weight vector of the multi-objective optimization functions J1, J2, and J3, σ1 represents the weight of the overshoot optimization function J1, σ2 represents the weight of the adjustment time optimization function J2, and σ3 represents the weight of the adjustment time optimization function J3.

[0024] Optionally, the setting of the corresponding preference information according to the control requirements of the servo system operation state includes: when the error is large, a faster response speed is preferred, and the weight of the optimization function J2 representing the adjustment time is amplified; when the error is small, the weight of the optimization function J1 representing the overshoot is amplified;

[0025]

[0026] Where σ1 represents the weight of the optimization function J1 of the overshoot, σ2 represents the weight of the optimization function J2 of the adjustment time, m represents the system error evaluation coefficient, ξ1, ξ2 are the amplification coefficients, and k represents the current moment.

[0027] Optionally, the delay trigger mechanism is:

[0028]

[0029] Wherein, d is the adjustment time obtained by solving the multi-objective optimization algorithm, Q is the delayed trigger signal, k represents the current moment, and the controller is set to maintain the currently adjusted control parameters before the moment k+d. After the moment k+d, the parameter setting trigger mechanism will be restarted to enter step S20.

[0030] As another aspect of the embodiment of the present disclosure, a sliding mode control system of a CNC vertical lathe servo system based on multi-objective optimization is provided, comprising:

[0031] Sliding mode control law design module, builds a sliding mode controller model, and designs a new sliding mode control law based on the nonlinear uncertain system state equation of the servo system;

[0032] Triggering the parameter setting module, setting the error information threshold, and judging whether the system error information exceeds the threshold. If it exceeds the threshold, the parameter setting mechanism is triggered to adjust the parameters of the sliding mode controller. If it does not exceed the threshold, the original parameters are maintained for sliding mode control.

[0033] A multi-objective evaluation construction module, combining the sliding mode control law, constructs a multi-objective evaluation mechanism for the servo system according to system error information and step response performance indicators;

[0034] The optimal control parameter solving module sets the corresponding preference information according to the control requirements of the servo system operation state, adjusts the weight coefficient of the multi-objective optimization algorithm, improves the multi-objective optimization algorithm, and solves the optimal control parameters of the servo system;

[0035] The servo system control module substitutes the optimal control parameters into the sliding mode controller, solves and obtains the control information at that moment, controls the servo system, and designs a delayed trigger mechanism for adjusting the control parameters according to the step response performance index.

[0036] 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 sliding mode control method of a CNC vertical lathe servo system based on multi-objective optimization is implemented.

[0037] 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 sliding mode control method of a CNC vertical lathe servo system based on multi-objective optimization is implemented.

[0038] Compared with the prior art, the beneficial effects of the present disclosure are:

[0039] 1. The present invention uses system status information as a basis for judgment and adopts a multi-objective optimization algorithm to adjust the control parameters of the sliding mode controller online in real time. It is no longer limited to a specific single performance indicator. At the same time, it also avoids the problem that the offline algorithm cannot accurately adapt to environmental changes, thereby achieving the best control effect in different application scenarios and expanding the scope of application.

[0040] 2. The present disclosure converts system error information into a step signal, which simplifies the calculation of the multi-objective optimization algorithm. The step response performance index is used as a multi-objective optimization function to continuously balance the performance requirements of multiple objectives, thereby accurately solving the optimal solution of the algorithm.

[0041] 3. An improved multi-objective optimization algorithm is used to transform the multi-objective problem into multiple single problems through weight vectors, and the weight vector is adjusted by setting the corresponding preference information according to the system operation status, which greatly reduces the computational complexity and improves the system operation efficiency.

[0042] 4. The weight vector of the multi-objective optimization algorithm is adaptively adjusted according to the congestion degree and the number of iterations, and the computing resources are reasonably allocated to achieve an adaptive balance between convergence speed and convergence accuracy, and to find a variety of feasible solutions for the sliding mode controller parameters to select the best one. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of the sliding mode control method of the CNC vertical lathe servo system based on multi-objective optimization in Example 1;

[0044] Figure 2 It is a flow chart of the multi-objective sliding mode control method of the servo system of a numerical control machine tool in Example 1;

[0045] Figure 3 This is a flow chart of improving the decomposition-based multi-objective optimization algorithm in Example 1;

[0046] Figure 4 It is a schematic block diagram of the sliding mode control system of the CNC vertical lathe servo system based on multi-objective optimization in Example 2. DETAILED DESCRIPTION

[0047] 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 specified.

[0048] 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.

[0049] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0050] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0051] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not go into details.

[0052] In addition, the present disclosure also provides a sliding mode control method and system for a CNC vertical lathe servo system based on multi-objective optimization. The above can be used to implement any sliding mode control method for a CNC vertical lathe servo system based on multi-objective optimization provided by the present disclosure. The corresponding technical solutions and descriptions are referred to the corresponding records in the method part and will not be repeated here.

[0053] The execution subject of the sliding mode control method of the CNC vertical lathe servo system based on multi-objective optimization can be a computer or other sliding mode control device of the CNC vertical lathe servo system based on multi-objective optimization. For example, the method can be executed by a terminal device or a server or other processing device, wherein the terminal device can 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, a vehicle-mounted device, a wearable device, etc. In some possible implementations, the workflow engine method based on the low-code platform can be implemented by a processor calling a computer-readable instruction stored in a memory.

[0054] Example 1

[0055] This embodiment provides a sliding mode control method for a CNC vertical lathe servo system based on multi-objective optimization. Figure 1 As shown, the following steps are included:

[0056] S10, constructing a sliding mode controller model and designing a new sliding mode control law based on the nonlinear uncertain system state equation of the servo system;

[0057] S20, designing a parameter setting trigger mechanism, wherein the parameter setting trigger mechanism includes: setting an error information threshold, determining whether the system error information exceeds the threshold, and if so, triggering the parameter setting mechanism to adjust the parameters of the sliding mode controller; if not, maintaining the original parameters for sliding mode control;

[0058] S30, combining the sliding mode control law, constructing a multi-objective evaluation mechanism for the servo system according to system error information and step response performance indicators;

[0059] S40, setting corresponding preference information according to the control requirements of the servo system operation state, and adjusting the weight coefficient of the multi-objective optimization algorithm;

[0060] S50, improving the multi-objective optimization algorithm and solving to obtain the optimal control parameters of the servo system;

[0061] S60, substituting the optimal control parameters into the sliding mode controller, solving to obtain the control information at that moment, controlling the servo system, and designing a delay trigger mechanism for adjusting the control parameters according to the step response performance index.

[0062] The present invention relates to a sliding mode control method for a CNC vertical lathe servo system based on multi-objective optimization. A new sliding mode control law is designed based on a nonlinear uncertain system state equation of the servo system. A multi-objective evaluation mechanism of the servo system is constructed according to system error information and step response performance indicators, thereby improving the solution accuracy and reducing the system chattering. According to the operating state of the servo system, corresponding preference information is set to adjust the weight coefficient of the multi-objective optimization algorithm, and an improved multi-objective optimization algorithm is used to solve the optimal control parameters to realize the control of the servo system, thereby reducing the calculation complexity and improving the operating efficiency of the system.

[0063] Flow chart of the multi-objective sliding mode control method for CNC machine tool servo system, as shown in Figure 2 As shown, each step of the embodiment of the present disclosure is described in detail below.

[0064] S10, construct a sliding mode controller model and design a new sliding mode control law based on the nonlinear uncertain system state equation of the servo system.

[0065] In this embodiment, a permanent magnet synchronous motor is used as a driving source, and a mathematical model under the dq coordinate axis is established as follows:

[0066]

[0067] In the formula, u d ,u q are the dq axis components of the stator voltage, i d ,i q is the dq axis component of the stator current, L is the stator inductance, R is the stator resistance, ψ f is the permanent magnet flux, w m is the mechanical angular velocity of the motor, p is the number of pole pairs, T L is the load torque and J is the moment of inertia.

[0068] When the control strategy id =0, the above mathematical model can be transformed into:

[0069]

[0070] Define the state variables of the servo system as:

[0071]

[0072] In the formula, w ref is the reference speed of the motor.

[0073] Derivative the above formula:

[0074]

[0075] Design a sliding mode controller and define the sliding surface function as:

[0076] s=cx1+x2

[0077] Where c is the design parameter.

[0078] Derivative of the sliding surface function:

[0079]

[0080] Where D is the control gain and u is the control input vector.

[0081] In order to eliminate the steady-state error caused by external interference and reduce system chattering, a new sliding mode control law is designed as follows:

[0082]

[0083] Where q, μ are the design parameters of the sliding mode controller, K a ,K b is the control parameter adjusted by the multi-objective optimization algorithm, x1 is the state information of the servo system, s is the sliding surface function, sgn() is the sign function, and tanh() is the hyperbolic tangent function.

[0084] The sliding mode controller expression can be obtained as:

[0085]

[0086] So the reference current of q axis is:

[0087]

[0088] S20, designing a parameter setting trigger mechanism, the parameter setting trigger mechanism includes: setting an error information threshold, determining whether the system error information exceeds the threshold, if it exceeds the threshold, triggering the parameter setting mechanism to adjust the parameters of the sliding mode controller, if it does not exceed the threshold, maintaining the original parameters for sliding mode control.

[0089] The system error information is:

[0090] x e (k) = x(k) - x q (k)

[0091] In the formula, k represents the current time, x e (k) represents the system error at the current moment, x(k) represents the current system state, and x q (k) is the expected state, where the system state variable is x(k) = [i q (k) w(k)] T .

[0092] In this embodiment, an error threshold n is set to determine whether the system error information exceeds the threshold. If it exceeds the threshold, the parameter setting mechanism is triggered. If it does not exceed the threshold, the original control parameters are maintained for sliding mode control.

[0093]

[0094] Wherein, M is a signal that triggers the parameter tuning mechanism. If M=1, the parameters of the sliding mode controller are adjusted; if M=0, the control parameters of the previous moment are used for sliding mode control.

[0095] S30, combining the sliding mode control law, and constructing a multi-objective evaluation mechanism for the servo system according to system error information and step response performance indicators.

[0096] In this embodiment, the multi-objective evaluation mechanism of the servo system is:

[0097]

[0098] Where J1, J2, and J3 are multi-objective optimization functions, which are overshoot, adjustment time, and time-weighted integral absolute error, respectively; y m is the step response peak value, y s is the steady-state value, t s The output step response enters the steady-state value y s The time corresponding to the ±Δ error band, Δ = 0.02, e(t) is the difference between the step response and the steady-state value, e(t) = y(t)-y s .

[0099] S40, setting corresponding preference information according to the control requirements of the servo system operation status, and adjusting the weight coefficient of the multi-objective optimization algorithm.

[0100] The weight coefficient is expressed as:

[0101] ω=[σ1,σ2,σ3]

[0102] Where ω represents the weight vector of the multi-objective optimization functions J1, J2, and J3, σ1 represents the weight of the overshoot optimization function J1, σ2 represents the weight of the adjustment time optimization function J2, and σ3 represents the weight of the adjustment time optimization function J3.

[0103] In this embodiment, in order to obtain the optimal solution of the controller parameters more quickly and avoid wasting resources by calculating solutions with large differences in the solution set, the corresponding preference information is set according to the control requirements of the servo system operation state, so as to adjust the weight coefficient.

[0104] When the error is large, a faster response speed is preferred, and the weight of the optimization function J2 representing the adjustment time is amplified; when the error is small, the weight of the optimization function J1 representing the overshoot is amplified;

[0105]

[0106] Where σ1 represents the weight of the optimization function J1 of the overshoot, σ2 represents the weight of the optimization function J2 of the adjustment time, m represents the system error evaluation coefficient, ξ1, ξ2 are the amplification coefficients, and k represents the current moment.

[0107] In this embodiment, after adjusting the initial weights based on the decomposition-based multi-objective optimization algorithm, constraints are imposed on the range of variable weights in the multi-objective optimization algorithm.

[0108]

[0109] In the formula, σ min1 ,σ max1 They represent the minimum and maximum values ​​of the adjustable weights of the optimization function J1, σ min2 ,σ max2 Respectively represent the minimum and maximum values ​​of the adjustable weights of the optimization function.

[0110] S50, improving the multi-objective optimization algorithm and solving to obtain the optimal control parameters of the servo system;

[0111] In this embodiment, a decomposition-based multi-objective optimization algorithm is selected to decompose MOPs into N sub-problems, and then an evolutionary algorithm is used to solve these single-objective sub-problems simultaneously, wherein the objective function of each single-objective sub-problem is an aggregate function of the various objective functions. The population of the multi-objective optimization algorithm is composed of the current optimal solution of each sub-problem (i.e., the aggregate function), and the optimization of each sub-problem is completed through evolutionary operations between its neighboring sub-problems, and the neighboring relationship between sub-problems is determined by the distance between the weight vectors of the sub-problems. The improved decomposition-based multi-objective optimization algorithm successfully introduces the decomposition method commonly used in mathematical programming into the field of multi-objective evolution, and can directly use the evolutionary algorithm to solve the fitness allocation and diversity retention strategy when solving single-objective optimization problems.

[0112] Based on the decomposition multi-objective optimization algorithm, the Chebyshev decomposition method is used to transform the multi-objective optimization problem into a scalar optimization problem, which can be described as:

[0113]

[0114] In the formula, z * =(z1 * ,z2 * ,…,z m * ) is the reference point, and z i * =min{f i (x)|x∈Ω},i=1,2,...,m, m is the target dimension, Ω is the constraint set of the solution variable, g te is the fitness function. For each Pareto optimal solution x * , there exists a weight vector ω such that x * An optimal solution to the corresponding problem. Accordingly, the Pareto optimal solution set can be obtained by changing the weight vector.

[0115] Weight vector ω i The neighborhood of {ω 1 ,ω 2 ,...,ω S} i The nearest weight vectors, and the neighborhood of subproblem i is the weight vector ω i The population of each generation is composed of the current optimal solutions of each subproblem.

[0116] In this embodiment, a flowchart of improving the decomposition-based multi-objective optimization algorithm according to the relationship between congestion and iteration is shown in FIG. Figure 3 As shown, the calculation steps of the improved algorithm are as follows:

[0117] S501, initialization: Initialize the weight vector ω = [σ1, σ2, σ3] and divide the neighborhood. For i = 1, 2, ..., N, set ω(i) = {ω 1 ,ω 2 ,…,ω S} is the neighborhood of the i-th reference vector; initialize the evolution population x = {x 1 ,x 2 ,…x N}, and calculate the target value F i =F(x i );Calculate the optimal value of each problem and record it as z * =(z1 * ,z2 * ,…,z m * );

[0118] S502, Evolution: For i = 1, 2, ..., N, randomly select two individuals x from the neighborhood l and x k , after differential evolution, a new individual y is generated and improved to y′ through the corresponding problem constraints;

[0119] S503, Update: Update the optimal value z. For j = 1, 2, ..., m, if z j >f j (y′), then z j =f j (y′); Update the neighborhood solution. For i∈1,2,…,S, use the Chebyshev decomposition method to calculate the aggregate function value of y′. If the aggregate function value of y′ is not greater than x a The aggregate function value is replaced by the response target value F i = F(y′);

[0120] S504, adjusting the weight vector: uniformly adjusting the weight vector using the crowding degree, and calculating the crowding degree of the individual as follows:

[0121]

[0122] Where Spa(i) is the degree of crowding at the location of individual i, A is the neighborhood of individual i, R(i,j) is the neighborhood radius, d(i,j) is the Euclidean distance between individuals i and j, and r is the median of the distances from all individuals in the set to their pth nearest solution.

[0123] In this embodiment, the congestion of different weights is deleted and the weight vector is added. For the smallest congestion Spa value, it means that the neighborhood under the weight vector is saturated, so the corresponding weight vector is deleted; for the larger congestion Spa value, it means that further exploration is needed nearby. Therefore, combined with the current number of iterations, the exploration function is realized by adding a new weight vector. Then the new weight vector is added as:

[0124]

[0125] In the formula, ω new To add a new weight vector, g is the number of reference weight vectors determined according to the number of iterations, ω spai are the first g weight vectors with larger congestion in the vicinity, g current is the current iteration number, g max is the predetermined number of iterations, and c is the proportional coefficient.

[0126] S505, stop iteration: if all the predetermined number of iterations are met, then stop iteration and output the optimal solution, otherwise continue to execute S502.

[0127] S60, substituting the optimal control parameters into the sliding mode controller, solving to obtain the control information at that moment, controlling the servo system, and designing a delay trigger mechanism for adjusting the control parameters according to the step response performance index.

[0128] In this embodiment, the delay trigger mechanism is:

[0129]

[0130] Wherein, d is the adjustment time obtained by solving the multi-objective optimization algorithm, Q is the delayed trigger signal, k represents the current moment, and the controller is set to maintain the currently adjusted control parameters before the moment k+d. After the moment k+d, the parameter setting trigger mechanism will be restarted to enter step S20.

[0131] In this embodiment, the multi-objective optimization algorithm and sliding mode control law and other technologies are combined to consider the impact of multiple performance indicators on the control system to achieve online adjustment of control parameters. A new sliding mode control law is designed to improve control accuracy and reduce system jitter. The improved multi-objective optimization algorithm is used to solve the optimal solution of Pareto control online, and the corresponding preference information is set according to the control requirements of the servo system operating state, so as to adjust the initial weight and constraint orientation range of the multi-objective optimization algorithm, thereby reducing the computational complexity. During the algorithm iteration process, the weight vector is adjusted using the congestion degree and the number of iterations, so as to reasonably allocate computing resources and obtain the optimal solution of controller parameters more quickly.

[0132] Example 2

[0133] As another aspect of the embodiment of the present disclosure, a sliding mode control system 100 of a CNC vertical lathe servo system based on multi-objective optimization is also provided. Figure 4 As shown, the following steps are included:

[0134] Sliding mode control law design module 1, builds a sliding mode controller model and designs a new sliding mode control law based on the nonlinear uncertain system state equation of the servo system;

[0135] Trigger parameter setting module 2, set the error information threshold, determine whether the system error information exceeds the threshold, if it exceeds the threshold, trigger the parameter setting mechanism, adjust the parameters of the sliding mode controller, if it does not exceed the threshold, keep the original parameters for sliding mode control;

[0136] A multi-objective evaluation construction module 3, combining the sliding mode control law, constructs a multi-objective evaluation mechanism for the servo system according to system error information and step response performance indicators;

[0137] The optimal control parameter solving module 4 sets the corresponding preference information according to the control requirements of the servo system operation state, adjusts the weight coefficient of the multi-objective optimization algorithm, improves the multi-objective optimization algorithm, and solves to obtain the optimal control parameters of the servo system;

[0138] The servo system control module 5 substitutes the optimal control parameters into the sliding mode controller, solves and obtains the control information at that moment, controls the servo system, and designs a delay trigger mechanism for adjusting the control parameters according to the step response performance index.

[0139] Based on the above modules, the disclosed embodiment of the present invention constructs a sliding mode control method for a CNC vertical lathe servo system based on multi-objective optimization, designs a new sliding mode control law based on the nonlinear uncertain system state equation of the servo system, and constructs a multi-objective evaluation mechanism for the servo system based on system error information and step response performance indicators, thereby improving the solution accuracy while reducing the system chattering; according to the operating state of the servo system, corresponding preference information is set to adjust the weight coefficient of the multi-objective optimization algorithm, and the improved multi-objective optimization algorithm is used to solve the optimal control parameters, control the servo system, reduce the computational complexity, and improve the system's operating efficiency. A sliding mode control system 100 for a CNC vertical lathe servo system based on multi-objective optimization is implemented.

[0140] The following is a detailed description of each module in the embodiment of the present disclosure.

[0141] Sliding mode control law design module 1, builds a sliding mode controller model and designs a new sliding mode control law based on the nonlinear uncertain system state equation of the servo system;

[0142] In this embodiment, a permanent magnet synchronous motor is used as a driving source, and a mathematical model under the dq coordinate axis is established as follows:

[0143]

[0144] In the formula, u d ,u q are the dq axis components of the stator voltage, i d ,i q is the dq axis component of the stator current, L is the stator inductance, R is the stator resistance, ψ f is the permanent magnet flux, w m is the mechanical angular velocity of the motor, p is the number of pole pairs, T L is the load torque and J is the moment of inertia.

[0145] When the control strategy i d =0, the above mathematical model can be transformed into:

[0146]

[0147] Define the state variables of the servo system as:

[0148]

[0149] In the formula, w ref is the reference speed of the motor.

[0150] Derivative the above formula:

[0151]

[0152] Design a sliding mode controller and define the sliding surface function as:

[0153] s=cx1+x2

[0154] Where c is the design parameter.

[0155] Derivative of the sliding surface function:

[0156]

[0157] Where D is the control gain and u is the control input vector.

[0158] In order to eliminate the steady-state error caused by external interference and reduce system chattering, a new sliding mode control law is designed as follows:

[0159]

[0160] Where q, μ are the design parameters of the sliding mode controller, K a ,K bis the control parameter adjusted by the multi-objective optimization algorithm, x1 is the state information of the servo system, s is the sliding surface function, sgn() is the sign function, and tanh() is the hyperbolic tangent function.

[0161] The sliding mode controller expression can be obtained as:

[0162]

[0163] So the reference current of q axis is:

[0164]

[0165] Trigger parameter setting module 2, set the error information threshold, determine whether the system error information exceeds the threshold, if it exceeds the threshold, trigger the parameter setting mechanism, adjust the parameters of the sliding mode controller, if it does not exceed the threshold, keep the original parameters for sliding mode control;

[0166] The system error information is:

[0167] x e (k) = x(k) - x q (k)

[0168] In the formula, k represents the current time, x e (k) represents the system error at the current moment, x(k) represents the current system state, and x q (k) is the expected state, where the system state variable is x(k) = [i q (k)w(k)] T .

[0169] In this embodiment, an error threshold n is set to determine whether the system error information exceeds the threshold. If it exceeds the threshold, the parameter setting mechanism is triggered. If it does not exceed the threshold, the original control parameters are maintained for sliding mode control.

[0170]

[0171] Wherein, M is a signal that triggers the parameter tuning mechanism. If M=1, the parameters of the sliding mode controller are adjusted; if M=0, the control parameters of the previous moment are used for sliding mode control.

[0172] A multi-objective evaluation construction module 3, combining the sliding mode control law, constructs a multi-objective evaluation mechanism for the servo system according to system error information and step response performance indicators;

[0173] In this embodiment, the multi-objective evaluation mechanism of the servo system is:

[0174]

[0175] Where J1, J2, and J3 are multi-objective optimization functions, which are overshoot, adjustment time, and time-weighted integral absolute error, respectively; y m is the step response peak value, y s is the steady-state value, t s The output step response enters the steady-state value y s The time corresponding to the ±Δ error band, Δ = 0.02, e(t) is the difference between the step response and the steady-state value, e(t) = y(t)-y s .

[0176] The optimal control parameter solving module 4 sets the corresponding preference information according to the control requirements of the servo system operation state, adjusts the weight coefficient of the multi-objective optimization algorithm, improves the multi-objective optimization algorithm, and solves to obtain the optimal control parameters of the servo system;

[0177] The weight coefficient is expressed as:

[0178] ω=[σ1,σ2,σ3]

[0179] Where ω represents the weight vector of the multi-objective optimization functions J1, J2, and J3, σ1 represents the weight of the overshoot optimization function J1, σ2 represents the weight of the adjustment time optimization function J2, and σ3 represents the weight of the adjustment time optimization function J3.

[0180] In this embodiment, in order to obtain the optimal solution of the controller parameters more quickly and avoid wasting resources by calculating solutions with large differences in the solution set, the corresponding preference information is set according to the control requirements of the servo system operation state, so as to adjust the weight coefficient.

[0181] When the error is large, a faster response speed is preferred, and the weight of the optimization function J2 representing the adjustment time is amplified; when the error is small, the weight of the optimization function J1 representing the overshoot is amplified;

[0182]

[0183] Where σ1 represents the weight of the optimization function J1 of the overshoot, σ2 represents the weight of the optimization function J2 of the adjustment time, m represents the system error evaluation coefficient, ξ1, ξ2 are the amplification coefficients, and k represents the current moment.

[0184] In this embodiment, after adjusting the initial weights based on the decomposition-based multi-objective optimization algorithm, constraints are imposed on the range of variable weights in the multi-objective optimization algorithm.

[0185]

[0186] In the formula, σ min1 ,σ max1They represent the minimum and maximum values ​​of the adjustable weights of the optimization function J1, σ min2 ,σ max2 Respectively represent the minimum and maximum values ​​of the adjustable weights of the optimization function.

[0187] In this embodiment, a decomposition-based multi-objective optimization algorithm is selected to decompose MOPs into N sub-problems, and then an evolutionary algorithm is used to solve these single-objective sub-problems simultaneously, wherein the objective function of each single-objective sub-problem is an aggregate function of the various objective functions. The population of the multi-objective optimization algorithm is composed of the current optimal solution of each sub-problem (i.e., the aggregate function), and the optimization of each sub-problem is completed through evolutionary operations between its neighboring sub-problems, and the neighboring relationship between sub-problems is determined by the distance between the weight vectors of the sub-problems. The improved decomposition-based multi-objective optimization algorithm successfully introduces the decomposition method commonly used in mathematical programming into the field of multi-objective evolution, and can directly use the evolutionary algorithm to solve the fitness allocation and diversity retention strategy when solving single-objective optimization problems.

[0188] Based on the decomposition multi-objective optimization algorithm, the Chebyshev decomposition method is used to transform the multi-objective optimization problem into a scalar optimization problem, which can be described as:

[0189]

[0190] In the formula, z * =(z1 * ,z2 * ,…,z m * ) is the reference point, and z i * =min{f i (x)|x∈Ω},i=1,2,…,m, m is the target dimension, Ω is the constraint set of the solution variable, g te is the fitness function. For each Pareto optimal solution x * , there exists a weight vector ω such that x * An optimal solution to the corresponding problem. Accordingly, the Pareto optimal solution set can be obtained by changing the weight vector.

[0191] Weight vector ω i The neighborhood of {ω 1 ,ω 2 ,…,ω S} i The nearest weight vectors, and the neighborhood of subproblem i is the weight vector ω i The population of each generation is composed of the current optimal solutions of each subproblem.

[0192] In this embodiment, a flowchart of improving the decomposition-based multi-objective optimization algorithm according to the relationship between congestion and iteration is provided. The calculation steps of the improved algorithm are as follows:

[0193] S501, initialization: Initialize the weight vector ω = [σ1, σ2, σ3] and divide the neighborhood. For i = 1, 2, ..., N, set ω(i) = {ω 1 ,ω 2 ,…,ω S} is the neighborhood of the i-th reference vector; initialize the evolution population x = {x 1 ,x 2 ,…x N}, and calculate the target value F i =F(x i );Calculate the optimal value of each problem and record it as z * =(z1 * ,z2 * ,…,z m * );

[0194] S502, Evolution: For i = 1, 2, ..., N, randomly select two individuals x from the neighborhood l and x k , after differential evolution, a new individual y is generated and improved to y′ through the corresponding problem constraints;

[0195] S503, Update: Update the optimal value z. For j = 1, 2, ..., m, if z j >f j (y′), then z j =f j (y′); Update the neighborhood solution. For i∈1,2,…,S, use the Chebyshev decomposition method to calculate the aggregate function value of y′. If the aggregate function value of y′ is not greater than x a The aggregate function value is replaced by the response target value F i = F(y′);

[0196] S504, adjusting the weight vector: uniformly adjusting the weight vector using the crowding degree, and calculating the crowding degree of the individual as follows:

[0197]

[0198] Where Spa(i) is the degree of crowding at the location of individual i, A is the neighborhood of individual i, R(i,j) is the neighborhood radius, d(i,j) is the Euclidean distance between individuals i and j, and r is the median of the distances from all individuals in the set to their pth nearest solution.

[0199] In this embodiment, the congestion of different weights is deleted and the weight vector is added. For the smallest congestion Spa value, it means that the neighborhood under the weight vector is saturated, so the corresponding weight vector is deleted; for the larger congestion Spa value, it means that further exploration is needed nearby. Therefore, combined with the current number of iterations, the exploration function is realized by adding a new weight vector. Then the new weight vector is added as:

[0200]

[0201] In the formula, ω new To add a new weight vector, g is the number of reference weight vectors determined according to the number of iterations, ω spai are the first g weight vectors with larger congestion in the vicinity, g current is the current iteration number, g max is the predetermined number of iterations, and c is the proportional coefficient.

[0202] S505, stop iteration: if all the predetermined number of iterations are met, then stop iteration and output the optimal solution, otherwise continue to execute S502.

[0203] The servo system control module 5 substitutes the optimal control parameters into the sliding mode controller, solves and obtains the control information at that moment, controls the servo system, and designs a delay trigger mechanism for adjusting the control parameters according to the step response performance index.

[0204] In this embodiment, the delay trigger mechanism is:

[0205]

[0206] Wherein, d is the adjustment time obtained by solving the multi-objective optimization algorithm, Q is the delayed trigger signal, k represents the current moment, and the controller is set to maintain the currently adjusted control parameters before the moment k+d. After the moment k+d, the parameter setting trigger mechanism will be restarted to enter step S20.

[0207] In this embodiment, the multi-objective optimization algorithm and sliding mode control law and other technologies are combined to consider the impact of multiple performance indicators on the control system to achieve online adjustment of control parameters. A new sliding mode control law is designed to improve control accuracy and reduce system jitter. The improved multi-objective optimization algorithm is used to solve the optimal solution of Pareto control online, and the corresponding preference information is set according to the control requirements of the servo system operating state, so as to adjust the initial weight and constraint orientation range of the multi-objective optimization algorithm, thereby reducing the computational complexity. During the algorithm iteration process, the weight vector is adjusted using the congestion degree and the number of iterations, so as to reasonably allocate computing resources and obtain the optimal solution of controller parameters more quickly.

[0208] In some embodiments, the system 100 operates in the following manner during use:

[0209] S1: Run the sliding mode control law design module 1, build a sliding mode controller model, and design a new sliding mode control law based on the nonlinear uncertain system state equation of the servo system;

[0210] S2: Run the trigger parameter setting module 2, set the error information threshold, and determine whether the system error information exceeds the threshold. If it exceeds the threshold, the parameter setting mechanism is triggered to adjust the parameters of the sliding mode controller. If it does not exceed the threshold, the original parameters are maintained for sliding mode control.

[0211] S3: Run the multi-objective evaluation construction module 3, combine the sliding mode control law, and construct a multi-objective evaluation mechanism for the servo system according to the system error information and the step response performance index;

[0212] S4: running the optimal control parameter solving module 4, setting corresponding preference information according to the control requirements of the servo system operation state, adjusting the weight coefficient of the multi-objective optimization algorithm, improving the multi-objective optimization algorithm, and solving to obtain the optimal control parameters of the servo system;

[0213] S5: Run the servo system control module 5, substitute the optimal control parameters into the sliding mode controller, solve and obtain the control information at that moment, control the servo system, and design a delay trigger mechanism for adjusting the control parameters according to the step response performance index.

[0214] 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:

[0215] (1) The present invention uses system status information as a basis for judgment and adopts a multi-objective optimization algorithm to adjust the control parameters of the sliding mode controller online in real time. It is no longer limited to a specific single performance indicator. At the same time, it also avoids the problem that the offline algorithm cannot accurately adapt to environmental changes. It can achieve the best control effect in different application scenarios and expand the scope of application.

[0216] (2) The present disclosure converts system error information into a step signal, which simplifies the calculation of the multi-objective optimization algorithm. The step response performance index is used as a multi-objective optimization function to continuously balance the performance requirements of multiple objectives, thereby accurately solving the optimal solution of the algorithm.

[0217] (3) An improved multi-objective optimization algorithm is used to transform the multi-objective problem into multiple single problems through weight vectors, and the weight vectors are adjusted by setting the corresponding preference information according to the system operation status, which greatly reduces the computational complexity and improves the system operation efficiency.

[0218] (4) The weight vector of the multi-objective optimization algorithm is adaptively adjusted according to the congestion degree and the number of iterations, and the computing resources are reasonably allocated to achieve an adaptive balance between convergence speed and convergence accuracy, thereby finding a variety of feasible solutions for the sliding mode controller parameters and selecting the best one.

[0219] Example 3

[0220] An electronic device comprises 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 sliding mode control method for a CNC vertical lathe servo system based on multi-objective optimization in Example 1 is implemented.

[0221] Embodiment 3 of the present disclosure is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0222] The electronic device may be in the form of a general-purpose computing device, for example, it may be a server device. The 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).

[0223] The bus includes data bus, address bus and control bus.

[0224] The memory may include volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0225] 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 the implementation of a network environment.

[0226] The processor executes various functional applications and data processing by running computer programs stored in the memory.

[0227] The electronic device can also communicate with one or more external devices (e.g., keyboard, pointing device, etc.). Such communication can be performed through an input / output (I / O) interface. In addition, the electronic device can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN) and / or public network, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

[0228] 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, according to the embodiments 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 into multiple units / modules to be embodied.

[0229] Example 4

[0230] A computer-readable storage medium stores a computer program, and when the program is executed by a processor, the steps of the sliding mode control method of a CNC vertical lathe servo system based on multi-objective optimization in embodiment 1 are implemented.

[0231] 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 of the above.

[0232] In a possible implementation manner, the present disclosure can also be implemented in the form of a program product, which includes a 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 sliding mode control method for a CNC vertical lathe servo system based on multi-objective optimization described in Example 1.

[0233] Among them, the program code for executing the present disclosure can be written in any combination of one or more programming languages, and the program code can be executed completely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or completely on the remote device.

[0234] Although 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 variations 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 sliding mode control method for a CNC vertical lathe servo system based on multi-objective optimization, characterized in that: The steps include: S10, constructing a sliding mode controller model and designing a new sliding mode control law based on the nonlinear uncertain system state equation of the servo system; S20, designing a parameter setting trigger mechanism, wherein the parameter setting trigger mechanism includes: setting an error information threshold, determining whether the system error information exceeds the threshold, and if so, triggering the parameter setting mechanism to adjust the parameters of the sliding mode controller; if not, maintaining the original parameters for sliding mode control; S30, combining the sliding mode control law, and constructing a multi-objective evaluation mechanism for the servo system according to system error information and step response performance indicators; S40, setting corresponding preference information according to the control requirements of the servo system operation state, and adjusting the weight coefficient of the multi-objective optimization algorithm; S50, improving the multi-objective optimization algorithm and solving to obtain the optimal control parameters of the servo system; S60, substituting the optimal control parameters into the sliding mode controller, solving and obtaining the control information of the servo system at the current moment, controlling the servo system, and designing a delay trigger mechanism for adjusting the control parameters according to the step response performance index; The sliding mode control law is: Among them, q, μ are controller design parameters, K a ,K b is the control parameter adjusted by the multi-objective optimization algorithm, x1 is the state information of the servo system, s is the sliding surface function, sgn() is the sign function, and tanh() is the hyperbolic tangent function; The sliding surface function is: s=cx1+x2, where c is a design parameter, and the state variable of the servo system is: Among them, w m is the mechanical angular velocity of the motor, x2 is the derivative of x1, w ref is the reference speed of the motor; The multi-objective evaluation mechanism of the servo system is: Where J1, J2, and J3 are multi-objective optimization functions, which are overshoot, adjustment time, and time-weighted integral absolute error, respectively; y m is the step response peak value, y s is the steady-state value, t s The output step response enters the steady-state value y s The time corresponding to the ±Δ error band, Δ = 0.02, e(t) is the difference between the step response and the steady-state value, e(t) = y(t)-y s .

2. The sliding mode control method for a CNC vertical lathe servo system based on multi-objective optimization according to claim 1 is characterized in that: The system error information is: x e (k)=x(k)-x q (k) In the formula, k represents the current time, x e (k) represents the system error at the current moment, x(k) represents the current system state, and x q (k) is the expected state.

3. The sliding mode control method for a CNC vertical lathe servo system based on multi-objective optimization according to claim 1 is characterized in that: The weight coefficient is expressed as: ω=[σ1,σ2,σ3] Where ω represents the weight vector of the multi-objective optimization functions J1, J2, and J3, σ1 represents the weight of the overshoot optimization function J1, σ2 represents the weight of the adjustment time optimization function J2, and σ3 represents the weight of the adjustment time optimization function J3.

4. The sliding mode control method for a CNC vertical lathe servo system based on multi-objective optimization according to claim 1 is characterized in that: The corresponding preference information is set according to the control requirements of the servo system operation state, including: when the error is large, a faster response speed is preferred, and the weight of the optimization function J2 representing the adjustment time is amplified; when the error is small, the weight of the optimization function J1 representing the overshoot is amplified; Where σ1 represents the weight of the optimization function J1 of the overshoot, σ2 represents the weight of the optimization function J2 of the adjustment time, m represents the system error evaluation coefficient, ξ1, ξ2 are the amplification coefficients, and k represents the current moment.

5. The sliding mode control method for a CNC vertical lathe servo system based on multi-objective optimization according to claim 1 is characterized in that: The delay trigger mechanism is: Wherein, d is the adjustment time obtained by solving the multi-objective optimization algorithm, Q is the delayed trigger signal, k represents the current moment, and the controller is set to maintain the currently adjusted control parameters before the moment k+d. After the moment k+d, the parameter setting trigger mechanism will be restarted to enter step S20.

6. A self-learning solution system for numerical control machining parameters, characterized in that: include: Sliding mode control law design module, builds a sliding mode controller model, and designs a new sliding mode control law based on the nonlinear uncertain system state equation of the servo system; Triggering the parameter setting module, setting the error information threshold, and judging whether the system error information exceeds the threshold. If it exceeds the threshold, the parameter setting mechanism is triggered to adjust the parameters of the sliding mode controller. If it does not exceed the threshold, the original parameters are maintained for sliding mode control. A multi-objective evaluation construction module, combining the sliding mode control law, constructs a multi-objective evaluation mechanism for the servo system according to system error information and step response performance indicators; The optimal control parameter solving module sets the corresponding preference information according to the control requirements of the servo system operation state, adjusts the weight coefficient of the multi-objective optimization algorithm, improves the multi-objective optimization algorithm, and solves the optimal control parameters of the servo system; The servo system control module substitutes the optimal control parameters into the sliding mode controller, obtains the control information of the servo system at the current moment, controls the servo system, and designs a delay trigger mechanism for adjusting the control parameters according to the step response performance index; The sliding mode control law is: Among them, q, μ are controller design parameters, K a ,K b is the control parameter adjusted by the multi-objective optimization algorithm, x1 is the state information of the servo system, s is the sliding surface function, sgn() is the sign function, and tanh() is the hyperbolic tangent function; The sliding surface function is: s=cx1+x2, where c is a design parameter, and the state variable of the servo system is: Among them, w m is the mechanical angular velocity of the motor, x2 is the derivative of x1, w ref is the reference speed of the motor; The multi-objective evaluation mechanism of the servo system is: Where J1, J2, and J3 are multi-objective optimization functions, which are overshoot, adjustment time, and time-weighted integral absolute error, respectively; y m is the step response peak value, y s is the steady-state value, t s The output step response enters the steady-state value y s The time corresponding to the ±Δ error band, Δ = 0.02, e(t) is the difference between the step response and the steady-state value, e(t) = y(t)-y s .

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the sliding mode control method for a CNC vertical lathe servo system based on multi-objective optimization 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 sliding mode control method for a CNC vertical lathe servo system based on multi-objective optimization described in any one of claims 1 to 5 is implemented.

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