A method and device for real-time optimization of suspension control performance based on PID controller

Abstract

The present application discloses a method, device, equipment and computer-readable storage medium for real-time optimization of suspension control performance based on a PID controller. In the process of using a PID controller to perform suspension control on an electromagnetic suspension system, and when the model of the electromagnetic suspension system is unknown, in order to ensure the suspension control performance, the present application does not adjust the parameters of the PID controller like the traditional solution, but uses an output observer and a first-order inertia link to generate another control signal, and sums the control signal with the control signal output by the PID as the control signal for the electromagnetic suspension system. In this way, the defects of the parameter adjustment process being cumbersome, time-consuming and even requiring shutdown during adjustment are overcome, and the real-time online optimization of the PID controller is achieved to achieve the purpose of optimizing the suspension control performance.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device, equipment and computer-readable storage medium for real-time optimization of suspension control performance based on a PID controller. Background Art

[0002] The electromagnetic suspension system is a key subsystem of the maglev train. The electromagnetic suspension controller generates control variables based on the real-time measured suspension gap, adjusts the excitation coil current to achieve electromagnetic force regulation, and thus suspends the controlled object to a specified position. The performance of the electromagnetic suspension controller directly affects the suspension performance of the electromagnetic suspension system. Therefore, the design of the electromagnetic suspension controller is very important.

[0003] During the all-weather operation of the electromagnetic suspension system, it will face model parameter uncertainties such as various unmodeled disturbances and changes in system component parameters. It is an important requirement to ensure the excellent suspension performance of the electromagnetic suspension system under the above factors. However, when the electromagnetic suspension system is shipped out of the factory, due to the unknown model of the electromagnetic suspension system, the parameter setting of the electromagnetic suspension controller is cumbersome and time-consuming, and it is difficult to ensure high suspension performance for different track sections, and even requires manual re-setting. How to achieve real-time reconstruction of the electromagnetic suspension controller under model parameter uncertainty to improve suspension performance is another need to be solved in the electromagnetic suspension control system.

[0004] The existing technical solutions for implementing electromagnetic suspension controllers are mainly divided into the following two categories:

[0005] One type is a classic PID controller or an improved PID controller, in which a PID controller is obtained by using a classic offline debugging, or a PID controller is obtained by adding a neural network, an adaptive control law, etc. to the classic PID controller to perform online tuning of the PID controller parameters. The advantages of this controller are that the structure is simple and acceptable suspension performance can be obtained when the model is unknown. However, when the electromagnetic suspension system is shipped from the factory, the PID controller parameters have the defects of being cumbersome and time-consuming to tune, and it is difficult to ensure high suspension performance for different track sections, and even requires manual re-tuning.

[0006] The other type is a robust controller or a nonlinear controller, which uses prior knowledge of model parameter uncertainty to design a robust controller, or designs a nonlinear controller based on system model information. Although a variety of complex electromagnetic suspension controllers have been proposed, such as robust controllers and nonlinear controllers, these methods often make specific assumptions about uncertainty or require electromagnetic suspension system model information. Compared with the classic PID controller, the structure of this type of controller is more complex, and the electrical and mechanical structure of the electromagnetic suspension system involves many parameters and is affected by factors such as the changing operating environment. Therefore, this type of control method has not been effectively applied in engineering.

[0007] At present, the most widely used electromagnetic suspension controller in engineering is still the classic PID controller. How to achieve efficient tuning of the PID controller to optimize the suspension performance when the electromagnetic suspension system model is unknown is an urgent problem to be solved in the electromagnetic suspension control system. Summary of the invention

[0008] The purpose of this application is to provide a method, device, equipment and computer-readable storage medium for real-time optimization of suspension control performance based on a PID controller, so as to solve the problem that the implementation effect of the current suspension control scheme based on a PID controller is not ideal when the electromagnetic suspension system model is unknown. The specific scheme is as follows:

[0009] In a first aspect, the present application provides a method for real-time optimization of suspension control performance based on a PID controller, comprising:

[0010] During the stable operation of the electromagnetic suspension system, the output observer construction algorithm is run to obtain the output observer;

[0011] At each sampling moment, running the output observer to obtain a residual signal;

[0012] In each time window, the first-order inertia link parameter update algorithm is run to update the first-order inertia link parameters;

[0013] At each sampling moment, generating a first control variable according to the residual signal and the first-order inertia link parameter and a first-order inertia link expression;

[0014] Summing the first control variable and the second control variable output by the PID controller to obtain a final control variable;

[0015] The electromagnetic suspension system is subjected to suspension control according to the final control amount.

[0016] Optionally, the output observer is as follows:

[0017] x o (k+1)=A1x o (k)+B1u(k)+L1y(k)

[0018] r(k)=gy(k)-c1x o (k)

[0019] Where k, k+1 represent the current sampling time and the next sampling time; x orepresents the state variable of the output observer; A1, B1, g, c1 represent the parameter matrix of the output observer, L1 represents the observer gain of the output observer; r represents the residual signal; u represents the final control amount; y represents the suspension gap of the electromagnetic suspension system.

[0020] Optionally, the running the output observer construction algorithm to obtain the output observer includes:

[0021] In the first time period and the second time period, the second reference signal, the final control value and the suspension gap of the electromagnetic suspension system are respectively collected to obtain four matrices. The first time period is (k-2s-N c ,ks-1), the second time period is (ksN c +1,k), where k represents the current time, s and N c are all specified time parameters and N c >>s;

[0022] Arrange the four matrices, and perform LQ decomposition on the arrangement result;

[0023] Perform singular value decomposition on the LQ decomposition result; determine the intermediate matrix according to the singular value decomposition result;

[0024] extracting a first intermediate parameter from the intermediate matrix;

[0025] According to the first intermediate parameters, a parameter matrix and an observer gain of an output observer are determined; and according to the parameter matrix and the observer gain, a corresponding output observer is constructed.

[0026] Optionally, determining a parameter matrix and an observer gain of an output observer according to the first intermediate parameter includes:

[0027] According to the first intermediate parameter, the parameter matrix and observer gain of the output observer are determined according to the target formula, and the target formula is as follows:

[0028] g=q s+1 ,c1=[0 … 0 1] 1×s

[0029]

[0030] Among them, p1, p2, ..., p s and q1,q2,...,q s+1 is the first intermediate parameter.

[0031] Optionally, the first-order inertia link expression is as follows:

[0032]

[0033] Among them, z is the discrete domain defined in the digital control system, u1 is the first control quantity, r represents the residual signal, and θ1, θ2 are the first-order inertia link parameters.

[0034] Optionally, the running of the first-order inertia link parameter update algorithm to update the first-order inertia link parameters includes:

[0035] S1. Set the time window, the window size is a positive integer multiple of the sampling period; initialize the first-order inertia link parameters;

[0036] S2. Calculate a second intermediate parameter at each sampling moment in the time window;

[0037] S3. At the end of each time window, updating the first-order inertia link parameter according to the second intermediate parameter;

[0038] S4, determining whether the updated first-order inertia link parameter exceeds a threshold value, and if so, resetting the updated first-order inertia link parameter to be equal to the threshold value;

[0039] S5. Determine whether the stop updating condition is met. If so, output the final first-order inertia link parameters. Otherwise, return to S2.

[0040] In a second aspect, the present application provides a real-time optimization device for suspension control performance based on a PID controller, comprising:

[0041] An output observer construction module is used to run an output observer construction algorithm to obtain an output observer during the stable operation of the electromagnetic suspension system;

[0042] A residual signal generating module, used for running the output observer at each sampling time to obtain a residual signal;

[0043] A first-order inertia link parameter update module is used to run a first-order inertia link parameter update algorithm in each time window to update the first-order inertia link parameters;

[0044] A first control quantity generating module, used for generating a first control quantity according to the residual signal and the first-order inertia link parameter at each sampling moment and according to a first-order inertia link expression;

[0045] A final control quantity generating module, used for summing the first control quantity and the second control quantity output by the PID controller to obtain a final control quantity;

[0046] The suspension control module is used to perform suspension control on the electromagnetic suspension system according to the final control amount.

[0047] In a third aspect, the present application provides a real-time optimization device for suspension control performance based on a PID controller, comprising:

[0048] Memory: used to store computer programs;

[0049] Processor: used to execute the computer program to implement the real-time optimization method of suspension control performance based on PID controller as described above.

[0050] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and the computer program, when executed by a processor, is used to implement the real-time optimization method for suspension control performance based on a PID controller as described above.

[0051] The present application provides a real-time optimization method for suspension control performance based on a PID controller, comprising: during the stable operation of an electromagnetic suspension system, running an output observer construction algorithm to obtain an output observer; at each sampling moment, running the output observer to obtain a residual signal; at each time window, running a first-order inertia link parameter update algorithm to update the first-order inertia link parameters; at each sampling moment, generating a first control quantity according to a first-order inertia link expression based on the residual signal and the first-order inertia link parameters; summing the first control quantity and a second control quantity output by the PID controller to obtain a final control quantity; and performing suspension control on the electromagnetic suspension system according to the final control quantity.

[0052] It can be seen that in the process of using the PID controller to control the electromagnetic suspension system, and when the model of the electromagnetic suspension system is unknown, in order to ensure the suspension control performance, this method does not adjust the PID controller parameters like the traditional solution, but uses the output observer and the first-order inertia link to generate another control signal, and sums the control signal with the PID output control signal as the control signal for the electromagnetic suspension system. In this way, the defects of the parameter setting process being cumbersome, time-consuming, and even requiring shutdown during setting are overcome, and the purpose of achieving suspension control performance by optimizing the PID controller in real time online is achieved without changing the parameters of the existing PID controller.

[0053] In addition, the present application also provides a device, equipment and computer-readable storage medium for real-time optimization of suspension control performance based on a PID controller, the technical effects of which correspond to the technical effects of the above-mentioned method and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 This is a flow chart of Example 1 of a real-time optimization method for suspension control performance based on a PID controller provided in this application;

[0056] Figure 2 This is a schematic diagram of the principle of the real-time optimization method for suspension control performance based on a PID controller provided in this application;

[0057] Figure 3 This is a functional block diagram of an embodiment of a device for real-time optimization of suspension control performance based on a PID controller provided in this application;

[0058] Figure 4 This is a schematic structural diagram of an embodiment of a device for real-time optimization of suspension control performance based on a PID controller provided in this application. DETAILED DESCRIPTION

[0059] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0060] In the field of electromagnetic suspension control, the most widely used electromagnetic suspension controller is still based on the classic PID controller. In view of this, the present application proposes a method, device, equipment and computer-readable storage medium for real-time optimization of suspension control performance based on a PID controller, which can reconstruct the PID controller for the existing PID controller when the electromagnetic suspension system model is unknown, and further optimize the suspension performance without changing the parameters of the existing PID controller.

[0061] The following is an introduction to the first embodiment of the real-time optimization method for suspension control performance based on the PID controller provided by the present application. Figure 1 , Embodiment 1 includes:

[0062] S11, during the stable operation of the electromagnetic suspension system, running the output observer construction algorithm to obtain the output observer;

[0063] During the stable operation of the electromagnetic suspension system, the PID controller has enabled the electromagnetic suspension system to achieve suspension control. At this time, the electromagnetic suspension system operates stably at the working point.

[0064] S12, at each sampling time, running the output observer to obtain a residual signal;

[0065] S13, in each time window, running the first-order inertia link parameter update algorithm to update the first-order inertia link parameters;

[0066] S14, at each sampling moment, generating a first control variable according to a first-order inertia link expression based on the residual signal and the first-order inertia link parameter;

[0067] S15, summing the first control variable and the second control variable output by the PID controller to obtain a final control variable;

[0068] As described above, the PID controller has enabled the electromagnetic suspension system to achieve suspension control, that is, the PID controller has been outputting the above-mentioned second control quantity. This embodiment uses the first control quantity to optimize the suspension control performance of the PID controller, wherein the generation scheme of the first control quantity can be added to the system at any time, and there is no need to suspend the operation of the PID controller when adding it. Moreover, since this embodiment optimizes the suspension control performance of the PID controller through another control signal, namely the second control quantity, this embodiment does not need to adjust the parameters of the PID controller.

[0069] S16. Perform suspension control on the electromagnetic suspension system according to the final control amount.

[0070] It is understandable that, during the suspension control process, a corresponding control amount is generated at each sampling moment, so the above steps S12 to S16 are actually executed repeatedly. This embodiment only takes the generation process of a single control amount as an example to illustrate the overall implementation process.

[0071] The present embodiment provides a real-time optimization method for suspension control performance based on a PID controller. In the process of using a PID controller to perform suspension control on an electromagnetic suspension system, and when the model of the electromagnetic suspension system is unknown, in order to ensure the suspension control performance, this method does not adjust the parameters of the PID controller like the traditional solution, but uses an output observer and a first-order inertia link to generate another control signal, and sums the control signal with the control signal output by the PID as the control signal for the electromagnetic suspension system. In this way, the defects of the parameter adjustment process being cumbersome, time-consuming, and even requiring shutdown during adjustment are overcome, and the purpose of real-time online optimization of the PID controller to achieve suspension control performance is achieved without changing the parameters of the existing PID controller.

[0072] The following is a detailed introduction to the second embodiment of the real-time optimization method for suspension control performance based on the PID controller provided by the present application. Based on the first embodiment, the second embodiment provides a detailed description of the suspension control process.

[0073] The principle of this embodiment is as follows Figure 2 As shown in Figure 1, it mainly includes four parts: output observer, first-order inertia link, output observer construction algorithm, and first-order inertia link parameter update algorithm, namely Figure 2 The contents in the dotted box and their working modes with each other constitute the following. According to engineering practice, the current loop, suspension electromagnet and (car body) load are second-order systems, that is, the order 2 is known.

[0074] First, some parameters involved in this embodiment are described:

[0075] w(k) is the reference signal, i.e. the specified suspension gap value;

[0076] u0(k) is the output signal of the PID controller, i.e. the second control variable mentioned above, with a dimension of 1;

[0077] u1(k) is the output signal of the first-order inertia link, that is, the first control quantity mentioned above, with a dimension of 1;

[0078] u(k) is the sum of the output signal u0(k) of the PID controller and the output signal u1(k) of the first-order inertia link, that is, u1(k)+u0(k);

[0079] y(k) is the sensor measurement value of the electromagnetic suspension system, that is, the suspension gap, with a dimension of 1;

[0080] r(k) is the output value of the output observer, that is, the above residual signal, with a dimension of 1;

[0081] k refers to the current time (i.e., the time when the kth sampling period starts);

[0082] The sampling period is denoted by T in the following text and is a known quantity.

[0083] Overall, Figure 2 The working mode between the four parts in the dotted box is:

[0084] Step 1: The PID controller enables the electromagnetic suspension system to achieve suspension control. At this time, the electromagnetic suspension system operates stably at the working point;

[0085] Step 2: Run the output observer construction algorithm to obtain the output observer;

[0086] Step 3: At each sampling time, run the output observer to obtain the residual signal;

[0087] Step 4: In each time window, run the first-order inertia link parameter update algorithm to update the first-order inertia link parameters;

[0088] Step 5: Generate the first control quantity at each sampling time according to the residual signal and the first-order inertia parameter and the first-order inertia link expression;

[0089] Step 6: Sum the second control quantity output by the PID controller and the first control quantity output by the first-order inertia link as the final control quantity. Figure 2 The structure output in ;

[0090] Step 7: Repeat Step 3 to Step 6.

[0091] The PID controller, output observer and output observer construction algorithm, first-order inertia link and first-order inertia link parameter update algorithm are introduced below respectively.

[0092] First, the PID controller

[0093] The PID controller in the z domain, i.e. the discrete domain, has the following form:

[0094]

[0095] The z-domain is a well-defined discrete domain in digital control systems.

[0096] Among them, K p ,K i ,K d , N are proportional, integral, differential and filter parameters respectively, which are known quantities and obtained through debugging.

[0097] It should be noted that Figure 2 In the above figure, if the first-order inertia link and output observer are removed, the remaining part can be realized by PID controller to control the suspension of electromagnetic suspension system. The introduction of output observer and first-order inertia link is to further improve the suspension performance on the basis of the already achieved suspension.

[0098] Second, the output observer

[0099] For the electromagnetic suspension system, the output observer is as follows:

[0100] x o (k+1)=A1x o (k)+B1u(k)+L1y(k)

[0101] r(k)=gy(k)-c1x o (k)

[0102] Where k, k+1 represent the current sampling time and the next sampling time; xo represents the state variable of the output observer; A1, B1, g, c1 represent the parameter matrix of the output observer, L1 represents the observer gain of the output observer; r represents the residual signal; u represents the final control amount; y represents the suspension gap of the electromagnetic suspension system.

[0103] The above parameter matrix and observer gain are obtained as follows:

[0104] g=q s+1 ,c1=[0 … 0 1] 1×s

[0105]

[0106] Among them, p1, p2, ..., p s and q1,q2,...,q s+1 is the first intermediate parameter, s is the specified time parameter, and is a scalar.

[0107] The first intermediate parameter is obtained according to the following output observer construction algorithm:

[0108] Step 1: As mentioned above, the PID controller is as follows:

[0109]

[0110] The PID controller is coprime decomposed according to the classic method of coprime decomposition of transfer function, namely the Euclidean Algorithm, and the coprime decomposition expression of the PID controller is obtained as follows:

[0111] u0=V -1 (z)U(z)(w(z)-y(z))

[0112] Where V(z) and U(z) are relatively prime transfer functions.

[0113] The second reference signal is constructed by V(z) and the reference signal w(z) in the above formula as follows:

[0114]

[0115] in, That is the second reference signal.

[0116] Step 2: Collect data over a period of time i=k-2s-N c ,…,k, forming the following matrix:

[0117]

[0118]

[0119] Collect u(i) and y(i) at the same time, i = ksN c ,···,k, forming the following matrix:

[0120]

[0121]

[0122] Among them, N c >>s and is specified by the implementer.

[0123] Step 2: Arrange the above four matrices as follows and perform LQ decomposition on them:

[0124]

[0125] Among them, Q1, Q2, Q3 are unit orthogonal bases.

[0126] Step 3: Perform singular value decomposition on the result obtained in step 2:

[0127]

[0128] Among them, U1, U2, V1, V2 are unitary matrices obtained after singular value decomposition. ∑1 is the singular value matrix corresponding to U1 and V1, and ∑2 is the singular value matrix corresponding to U2 and V2.

[0129] The number of rows of U2 is 2(s+1), and the number of columns is s-1. According to the dimension of U2, U2 is obtained as follows: for [U1 U2] obtained by singular value decomposition, all rows corresponding to the last s-1 columns are extracted.

[0130] Define the intermediate matrix Where * represents the transpose of the matrix.

[0131] Step 4: From the intermediate matrix K d,s Extract the first intermediate parameters p1, p2, ..., p s and q1,q2,...,q s+1 The specific extraction method is: select K d,s The last row of the row, the 1st to sth columns of the row are p1, p2, ..., p s , take the columns s+2 to 2(s+1) of the row as q1,q2,...,q s+1 .

[0132] Step 5: According to the p1, p2, ..., p obtained in step 4 s and q1,q2,...,q s+1Construct the parameter matrix A1, B1, g, c1 of the output observer and the observer gain L1 of the output observer to obtain the output observer.

[0133] Third, the first-order inertia link

[0134] The first-order inertia link expression is as follows:

[0135]

[0136] Among them, the initial state of the first-order inertia link is 0, and θ1 and θ2 are the first-order inertia link parameters that need to be calculated online in real time. The reconstruction of the PID controller is achieved by optimizing θ1 and θ2 online in real time.

[0137] Among them, θ2∈[-b,b], b<1, and the range of θ1 is not restricted.

[0138] The process of real-time optimization of θ1, θ2 is implemented by the first-order inertia link parameter update algorithm. The specific process of the algorithm is as follows:

[0139] Step 1: The implementer specifies the optimization time window W, which is a positive integer multiple of the sampling period; initialize θ1=0, θ2=0.

[0140] Step 2: At each sampling moment, calculate the second intermediate parameter according to the following formula:

[0141]

[0142]

[0143] in,

[0144]

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151] The relevant parameters have been defined in the PID controller and are known quantities.

[0152] Step 3: At the end of each time window, update θ1, θ2 according to the second intermediate parameter. The specific implementation process is as follows:

[0153]

[0154]

[0155] in, represents the j+1th update of θ1, ε1 is the update step size, which is selected by the implementer; k j Refers to the sampling time corresponding to the j+1th update; λ u ,λ y are weights, respectively, which are selected by the implementer; for θ2, the symbols have the same definition.

[0156] Step 4: If Then discard the calculated in step 3 make like Update

[0157] Step 5: Repeat steps 2 to 5 until θ1 and θ2 meet the stop update condition. The stop update condition is defined by the implementer. For example, it can be defined as a stop command input from an external source; for another example, it can be:

[0158]

[0159] Wherein, j represents the jth update of the parameter vector; |·| is the absolute value operation; m1 and m2 are the judgment windows for judging whether the parameter vectors θ1 and θ2 meet the stop updating conditions, which can be set by themselves; δ1 and δ2 are the judgment thresholds for stopping the update, which can also be set by themselves.

[0160] In summary, the present embodiment provides a real-time optimization method for suspension control performance based on a PID controller, which can ensure the excellent suspension control performance of the PID controller when the model parameters are unknown. The implementation structure is simple, and the suspension control performance is improved by real-time optimization of the first-order inertia link, which is convenient for engineering implementation, improves the efficiency of PID controller parameter setting, and ensures the good suspension performance of the electromagnetic suspension system under different working conditions.

[0161] The following is an introduction to the real-time optimization device for suspension control performance based on a PID controller provided in an embodiment of the present application. The real-time optimization device for suspension control performance based on a PID controller described below and the real-time optimization method for suspension control performance based on a PID controller described above can refer to each other.

[0162] like Figure 3As shown, the real-time optimization device for suspension control performance based on PID controller of this embodiment includes:

[0163] The output observer construction module 31 is used to run the output observer construction algorithm to obtain the output observer during the stable operation of the electromagnetic suspension system;

[0164] A residual signal generating module 32, used for running the output observer at each sampling time to obtain a residual signal;

[0165] A first-order inertia link parameter updating module 33 is used to run a first-order inertia link parameter updating algorithm in each time window to update the first-order inertia link parameters;

[0166] A first control variable generating module 34 is used to generate a first control variable according to the residual signal and the first-order inertia link parameter at each sampling moment and in accordance with a first-order inertia link expression;

[0167] A final control quantity generating module 35, configured to sum the first control quantity and the second control quantity output by the PID controller to obtain a final control quantity;

[0168] The suspension control module 36 is used to perform suspension control on the electromagnetic suspension system according to the final control amount.

[0169] The real-time optimization device for suspension control performance based on PID controller of this embodiment is used to implement the aforementioned real-time optimization method for suspension control performance based on PID controller. Therefore, the specific implementation method of the device can be seen in the embodiment part of the real-time optimization method for suspension control performance based on PID controller in the previous text, and will not be introduced in detail here.

[0170] In addition, the present application also provides a real-time optimization device for suspension control performance based on a PID controller, such as Figure 4 As shown, including:

[0171] Memory 100: used for storing computer programs;

[0172] Processor 200: used to execute the computer program to implement the real-time optimization method of suspension control performance based on PID controller as described above.

[0173] Finally, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, it is used to implement the real-time optimization method of suspension control performance based on a PID controller as described above.

[0174] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0175] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0176] The above is a detailed introduction to the scheme provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A real-time optimization method for suspension control performance based on PID controller, characterized in that: include: During the stable operation of the electromagnetic suspension system, the output observer construction algorithm is run to obtain the output observer; At each sampling moment, running the output observer to obtain a residual signal; In each time window, the first-order inertia link parameter update algorithm is run to update the first-order inertia link parameters; At each sampling moment, generating a first control variable according to the residual signal and the first-order inertia link parameter and a first-order inertia link expression; Summing the first control variable and the second control variable output by the PID controller to obtain a final control variable; The electromagnetic suspension system is subjected to suspension control according to the final control amount.

2. The method according to claim 1, characterized in that The output observer is as follows: x o (k+1)=A1x o (k)+B1u(k)+L1y(k) r(k)=gy(k)-c1x o (to) Where k, k+1 represent the current sampling time and the next sampling time; x o represents the state variable of the output observer; A1, B1, g, c1 represent the parameter matrix of the output observer, L1 represents the observer gain of the output observer; r represents the residual signal; u represents the final control amount; y represents the suspension gap of the electromagnetic suspension system.

3. The method according to claim 2, characterized in that The step of running the output observer construction algorithm to obtain the output observer includes: In the first time period and the second time period, data collection is performed on the second reference signal, the final control value and the suspension gap of the electromagnetic suspension system respectively to obtain four matrices. The first time period is (k-2s-N c ,ks-1), the second time period is (ksN c +1,k), where k represents the current time, s and N c are all specified time parameters and N c >>s; Arrange the four matrices, and perform LQ decomposition on the arrangement result; Perform singular value decomposition on the LQ decomposition result; determine the intermediate matrix according to the singular value decomposition result; extracting a first intermediate parameter from the intermediate matrix; According to the first intermediate parameters, a parameter matrix and an observer gain of an output observer are determined; and according to the parameter matrix and the observer gain, a corresponding output observer is constructed.

4. The method according to claim 3, characterized in that The step of determining a parameter matrix and an observer gain of an output observer according to the first intermediate parameter comprises: According to the first intermediate parameter, the parameter matrix and observer gain of the output observer are determined according to the target formula, and the target formula is as follows: g=q s+1 ,c1=[0 … 0 1] 1×s Among them, p1, p2, ..., p s and q1,q2,...,q s+1 is the first intermediate parameter.

5. The method according to claim 1, characterized in that The first-order inertia link expression is as follows: Among them, z is the discrete domain defined in the digital control system, u1 is the first control quantity, r represents the residual signal, and θ1, θ2 are the first-order inertia link parameters.

6. The method according to claim 5, characterized in that The step of running the first-order inertia link parameter update algorithm to update the first-order inertia link parameters includes: S1. Set the time window, the window size is a positive integer multiple of the sampling period; initialize the first-order inertia link parameters; S2. Calculate a second intermediate parameter at each sampling moment in the time window; S3. At the end of each time window, updating the first-order inertia link parameter according to the second intermediate parameter; S4, determining whether the updated first-order inertia link parameter exceeds a threshold value, and if so, resetting the updated first-order inertia link parameter to be equal to the threshold value; S5. Determine whether the stop updating condition is met. If so, output the final first-order inertia link parameters. Otherwise, return to S2.

7. A real-time optimization device for suspension control performance based on PID controller, characterized in that: include: An output observer construction module is used to run an output observer construction algorithm to obtain an output observer during the stable operation of the electromagnetic suspension system; A residual signal generating module, used for running the output observer at each sampling time to obtain a residual signal; A first-order inertia link parameter update module is used to run a first-order inertia link parameter update algorithm in each time window to update the first-order inertia link parameters; A first control quantity generating module, used for generating a first control quantity according to the residual signal and the first-order inertia link parameter at each sampling moment and according to a first-order inertia link expression; A final control quantity generating module, used for summing the first control quantity and the second control quantity output by the PID controller to obtain a final control quantity; The suspension control module is used to perform suspension control on the electromagnetic suspension system according to the final control amount.

8. A real-time optimization device for suspension control performance based on a PID controller, characterized in that: include: Memory: used to store computer programs; Processor: used to execute the computer program to implement the real-time optimization method of suspension control performance based on PID controller as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, is used to implement the real-time optimization method for suspension control performance based on a PID controller as described in any one of claims 1 to 6.

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