Intelligent chassis dual-disturbance observer control method

Abstract

This invention discloses a smart chassis dual-disturbance observer control method, belonging to the field of disturbance control technology, including the following steps: S1, decomposing the control system into a non-matched disturbance subsystem and a matched disturbance subsystem; S2, using the IUDE method to perform disturbance compensation and disturbance estimation on the decomposed matched disturbance subsystem to obtain an equivalent system; S3, determining the disturbance boundedness of the equivalent system; S4, using the weight matrix of state control and the weight matrix of disturbance estimation to perform disturbance estimation and compensation on the equivalent system after determining the disturbance boundedness. This invention is applied to the ride comfort control of a 1 / 4 car model with active suspension. The IUDE algorithm and LMID algorithm effectively achieve the estimation of the original disturbance and the equivalent disturbance, respectively, indicating that this method has good performance in both stabilizing the control system and achieving disturbance estimation.

Description

Technical Field

[0001] This invention belongs to the field of disturbance control technology, specifically relating to a smart chassis dual disturbance observer control method. Background Technology

[0002] Dynamic control of intelligent chassis systems typically refers to the use of different control algorithms to control the transmission system, driving system, steering system, braking system, and suspension system, thereby achieving intelligent chassis control.

[0003] Since these systems typically include a certain degree of disturbance, which acts on the dynamic system in the form of force or torque, the control of the system needs to possess a certain degree of robustness. Generally, the robustness of the system can be improved in two ways: first, by employing robust control algorithms to suppress disturbances; and second, by employing disturbance observer algorithms to estimate and compensate for disturbances.

[0004] However, robust control algorithms can only mitigate disturbances to a certain extent, and disturbance observer algorithms inevitably introduce errors during the estimation process. In other words, both schemes have certain shortcomings in disturbance suppression, which affects the improvement of key vehicle performance. Summary of the Invention

[0005] To address the above problems, this invention proposes a smart chassis dual-disturbance observer control method.

[0006] The technical solution of this invention is: a smart chassis dual-disturbance observer control method comprising the following steps:

[0007] S1. The control system is divided into a non-matched disturbance subsystem and a matched disturbance subsystem;

[0008] S2. The IUDE method is used to perform perturbation compensation and perturbation estimation on the split matched perturbation subsystem to obtain the equivalent system;

[0009] S3. Determine the boundedness of the disturbance of the equivalent system;

[0010] S4. Using the weight matrix of state control and the weight matrix of disturbance estimation, perform disturbance estimation and compensation on the equivalent system after determining the boundedness of the disturbance.

[0011] Furthermore, in S1, the expression for separating the unmatched perturbation subsystem and the matched perturbation subsystem is:

[0012] ;

[0013] in, Represents the state of the non-matched disturbance subsystem The derivative, Indicates the state of the matched disturbance subsystem The derivative, This represents the steady system matrix of the unmatched perturbation subsystem. This represents the steady system matrix of the matched perturbation subsystem. Represents the system's state variables. This represents the steady-state input matrix of the unmatched perturbation subsystem. This represents the steady-state input matrix of the matched perturbation subsystem. Indicates the system's control input, This represents the known terms of the non-matched perturbation subsystem. This represents the known terms of the matched perturbation subsystem. This indicates a non-matching perturbation. This indicates a matching perturbation.

[0014] Furthermore, S2 includes the following sub-steps:

[0015] S21. Perform disturbance compensation on the split matching disturbance subsystem;

[0016] S22. Perform disturbance estimation on the matched disturbance subsystem after disturbance compensation;

[0017] S23. Use the control quantity to perform equivalent processing on the matched disturbance subsystem after disturbance compensation and disturbance estimation to obtain the equivalent system.

[0018] Furthermore, in S21, the expression for perturbation compensation of the split matched perturbation subsystem is:

[0019] ;

[0020] in, This represents the steady system matrix of the matched perturbation subsystem. Represents the system's state variables. This represents the steady-state input matrix of the matched perturbation subsystem. Indicates reference input. This represents the known terms of the matched perturbation subsystem. This indicates a matching perturbation. This represents the compensation value for matching disturbances. Indicates the system's control input;

[0021] In S22, the discretized expression for perturbation estimation is:

[0022] ;

[0023] in, Indicates the matching perturbation exist The estimated value at time, Indicates to The first step in organizing the quantity. Indicates the matching perturbation The estimated value, Representing discrete time, Indicates to The second process quantity of sorting, Indicates to The third process quantity of sorting exist The value at time, Indicates to The third process quantity of sorting exist The value at time;

[0024] In S23, the control quantity The expression is:

[0025] ;

[0026] ;

[0027] in, Indicates reference control, Indicates a false reversal. This represents the compensation value for matching disturbances. , This represents the proportion of partial disturbance compensation. A simplified representation of a diagonal matrix.

[0028] Furthermore, in S3, the expression for the equivalent system is:

[0029] ;

[0030] in, Represents the state variables of the control system The derivative, The matrix representing the steady system of the control system. Represents the state variables of the control system. Represents the steady input matrix of the control system. Indicates reference control, Represents the known terms of the equivalent system. It represents the disturbance of the equivalent system.

[0031] Furthermore, in S3, when the original perturbation is bounded and the derivative of the matched perturbation is bounded, then the perturbation of the equivalent system is bounded.

[0032] Furthermore, in S41, the weight matrix for state control... The expression is:

[0033] ;

[0034] in, A simplified representation of a diagonal matrix. Represents state variables The weights of the components in the first dimension. Represents state variables No. The weights of the components in each dimension. Represents the real number field. Represents state variables Dimensions.

[0035] In S41, the weight matrix for perturbation estimation The expression is:

[0036] ;

[0037] in, Indicates disturbance The weights of the components in the first dimension. Indicates disturbance No. The weights of the components in each dimension. A simplified representation of a diagonal matrix.

[0038] The beneficial effects of this invention are:

[0039] (1) The LMID-IUDE algorithm of the present invention uses both the robust control algorithm (LMID algorithm) and the disturbance observer algorithm (IUDE algorithm). The IUDE algorithm is used to process the matching disturbance of the original system once and obtain the equivalent system and disturbance. Then the LMID algorithm is used to control the equivalent system and obtain a disturbance observer at the same time, which can realize the compensation of the matching disturbance of the equivalent system.

[0040] (2) This invention extends the relevant theory of the IUDE algorithm in two aspects: first, it provides a discretized expression of the disturbance estimation to facilitate practical application; second, it explains the boundedness of the equivalent system disturbance obtained by the algorithm.

[0041] (3) This invention extends the relevant theory of the LMID algorithm and introduces two weight matrices to control the system state and estimate the disturbance of the equivalent system, respectively, thereby enhancing the flexibility of the algorithm. At the same time, since the LMID algorithm deals with the equivalent system, the disturbance observer in the LMID algorithm can estimate the disturbance of the equivalent system and compensate for the matching disturbance of the equivalent system.

[0042] (4) The present invention is applied to the ride comfort control of a 1 / 4 car model with active suspension. Compared with the IUDE algorithm or the LMID algorithm alone, the LMID-IUDE algorithm can effectively reduce the sprung mass acceleration, thereby improving the ride comfort. Moreover, the IUDE algorithm and the LMID algorithm effectively estimate the original disturbance and the equivalent disturbance, respectively, indicating that the method has good performance in both stabilizing the control system and realizing disturbance estimation. Attached Figure Description

[0043] Figure 1 A flowchart of the intelligent chassis dual-disturbance observer control method;

[0044] Figure 2 The structure of the designed LMID-IUDE control algorithm is shown.

[0045] Figure 3 This is a 1 / 4 scale model with active suspension.

[0046] Figure 4 The diagram shows the comparative control effects of the designed control system on three performance indicators: sprung acceleration, wheel dynamic deformation, and suspension dynamic deflection on a random road surface.

[0047] Figure 5 A comparison chart showing the observation results of the IUDE algorithm on matched perturbations and the LMID algorithm on equivalent matched perturbations. Detailed Implementation

[0048] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0049] like Figure 1 As shown, the present invention provides a smart chassis dual-disturbance observer control method, comprising the following steps:

[0050] S1. The control system is divided into a non-matched disturbance subsystem and a matched disturbance subsystem;

[0051] S2. The IUDE method is used to perform perturbation compensation and perturbation estimation on the split matched perturbation subsystem to obtain the equivalent system;

[0052] S3. Determine the boundedness of the disturbance of the equivalent system;

[0053] S4. Using the weight matrix of state control and the weight matrix of disturbance estimation, perform disturbance estimation and compensation on the equivalent system after determining the boundedness of the disturbance.

[0054] This invention uses both the Robust Control Algorithm (LMID algorithm) and the Disturbance Observer Algorithm (IUDE algorithm) to enhance the robustness of the system. The LMID algorithm also has the effect of a disturbance observer, and the whole system includes two disturbance observers.

[0055] This invention applies the IUDE algorithm to estimate and compensate for matching perturbations, provides the discretized form of the algorithm for practical application, and analyzes the boundedness of the perturbations in the equivalent system.

[0056] This invention applies the LMID algorithm to control the equivalent system obtained by the IUDE algorithm and to estimate the disturbances of the equivalent system. Simultaneously, weights are introduced into the algorithm to enable flexible adjustment of state control and disturbance estimation.

[0057] The controller structure designed in this invention is as follows: Figure 2 As shown, the algorithm is named LMID-IUDE and includes the following four components.

[0058] First, regarding the original system The perturbations are split into unmatched and matched perturbations.

[0059] Second, the IUDE algorithm is used to control the matched disturbance subsystem of the original system, to estimate and compensate for the matched disturbance, and to obtain the discretized expression of the matched disturbance estimate as follows: This facilitates the development of actual controllers.

[0060] Third, we analyze the equivalent system obtained by the IUDE algorithm. The matching perturbation compensation of the IUDE algorithm is expressed as follows: And analyze the boundedness of the disturbance in the equivalent system.

[0061] Fourth, the LMID algorithm is used to control the equivalent system, and is introduced into the Lyapunov function. and The two weight matrices correspond to system state control and disturbance estimation, respectively. Furthermore, the LMID algorithm also functions as a disturbance observer, capable of estimating disturbances in the equivalent system and compensating for matching disturbances in the equivalent system.

[0062] In this embodiment of the invention, in S1, the expression for splitting the unmatched perturbation subsystem and the matched perturbation subsystem is:

[0063] ;

[0064] in, Represents the state of the non-matched disturbance subsystem The derivative, Indicates the state of the matched disturbance subsystem The derivative, This represents the steady system matrix of the unmatched perturbation subsystem. This represents the steady system matrix of the matched perturbation subsystem. Represents the system's state variables. This represents the steady-state input matrix of the unmatched perturbation subsystem. This represents the steady-state input matrix of the matched perturbation subsystem. Indicates the system's control input, This represents the known terms of the non-matched perturbation subsystem. This represents the known terms of the matched perturbation subsystem. This indicates a non-matching perturbation. This indicates a matching perturbation.

[0065] In this embodiment of the invention, S2 includes the following sub-steps:

[0066] S21. Perform disturbance compensation on the split matching disturbance subsystem;

[0067] S22. Perform disturbance estimation on the matched disturbance subsystem after disturbance compensation;

[0068] S23. Use the control quantity to perform equivalent processing on the matched disturbance subsystem after disturbance compensation and disturbance estimation to obtain the equivalent system.

[0069] In this embodiment of the invention, in S21, the expression for perturbation compensation of the split matched perturbation subsystem is:

[0070] ;

[0071] in, This represents the steady system matrix of the matched perturbation subsystem. Represents the system's state variables. This represents the steady-state input matrix of the matched perturbation subsystem. Indicates reference input. This represents the known terms of the matched perturbation subsystem. This indicates a matching perturbation. This represents the compensation value for matching disturbances. Indicates the system's control input;

[0072] In S22, the discretized expression for perturbation estimation is:

[0073] ;

[0074] in, Indicates the matching perturbation exist The estimated value at time, Indicates to The first step in organizing the quantity. Indicates the matching perturbation The estimated value, Representing discrete time, Indicates to The second process quantity of sorting, Indicates to The third process quantity of sorting exist The value at time, Indicates to The third process quantity of sorting exist The value at time;

[0075] In S23, the control quantity The expression is:

[0076] ;

[0077] ;

[0078] in, Indicates reference control, Indicates a false reversal. This represents the compensation value for matching disturbances. , This represents the proportion of partial disturbance compensation. A simplified representation of a diagonal matrix.

[0079] In this embodiment of the invention, in S3, the expression for the equivalent system is:

[0080] ;

[0081] in, Represents the state variables of the control system The derivative, The matrix representing the steady system of the control system. Represents the state variables of the control system. Represents the steady input matrix of the control system. Indicates reference control, Represents the known terms of the equivalent system. It represents the disturbance of the equivalent system.

[0082] In this embodiment of the invention, in S3, when the original perturbation is bounded and the derivative of the matched perturbation is bounded, the perturbation of the equivalent system is bounded.

[0083] In this embodiment of the invention, in S41, the weight matrix of state control... The expression is:

[0084] ;

[0085] in, A simplified representation of a diagonal matrix. Represents state variables The weights of the components in the first dimension. Represents state variables No. The weights of the components in each dimension. Represents the real number field. Represents state variables Dimensions.

[0086] In S41, the weight matrix for perturbation estimation The expression is:

[0087] ;

[0088] in, Indicates disturbance The weights of the components in the first dimension. Indicates disturbance No. The weights of the components in each dimension. A simplified representation of a diagonal matrix.

[0089] In this embodiment of the invention, the intelligent chassis system includes a transmission system, a driving system, a steering system, a braking system, and a suspension system. Different control algorithms are typically used for the dynamic control of the intelligent chassis system, tailored to the characteristics of different systems, to achieve chassis intelligence and improve key vehicle performance.

[0090] Generally, to simplify the analysis process, all disturbances in the system can be integrated into a lumped disturbance form, and the chassis dynamics control system can be expressed in the form of the following state-space equations:

[0091] (1);

[0092] In the formula: To control the system status; Input for the control system; For known terms, known nonlinearities can be expressed; For aggregated disturbances, External disturbance, and ; , , It is a constant matrix.

[0093] In intelligent chassis systems, lumped disturbances act on the dynamic system in the form of forces or torques, posing a challenge to the robustness of control algorithms. Generally, the robustness of the system can be improved in two ways.

[0094] One approach is to employ robust control algorithms to suppress disturbances. These generally include the following: H2 / H ∞Control, controlled by H2 and H ∞ The control system consists of two algorithms. H2 control aims to reduce the L2 norm of the transfer function from the disturbance to the controlled output, that is, to reduce the average response of the controlled output to the disturbance. ∞ Control aims to reduce the infinity norm of the transfer function, i.e., reduce the maximum response. Sliding Mode Control (SMC) defines a sliding surface to reduce the system's dimensionality, designs a suitable Lyapunov function based on the bounds of the actual disturbance, and uses a sign function to design the control law to stabilize the system. Adaptive control is generally suitable for slow, time-varying disturbances. It combines the Lyapunov function with an integral approach to suppress disturbances, but often fails to obtain accurate disturbance estimates. Control based on Linear Matrix Inequality (LMI) transforms the Lyapunov function's conditions into solving linear matrix inequalities, taking into account the characteristics of the disturbance.

[0095] Second, disturbance observer algorithms are used to estimate and compensate for disturbances. These generally include the following: Extended State Observer (ESO), a component of ADRC control, which extends the disturbance into the system's state variables and estimates them; a series of disturbance observers designed based on the Lyapunov method; and algorithms such as DOB, NDOB, UDE, and GPIO.

[0096] Robust control algorithms can only mitigate disturbances to a certain extent, and when the disturbance is too large, H2 / H ∞ Inappropriate parameter settings may lead to solution failure or result in a large feedback gain that causes the control quantity to become extremely large, resulting in system divergence. Therefore, it is usually necessary to select a compromise controller parameter.

[0097] The perturbation observer algorithm inevitably introduces errors during the estimation process. These errors manifest as lag in perturbation estimation, difficulty in tracking high-frequency perturbations, and the limitation of perturbation compensation to matching perturbations. These estimation errors become new equivalent perturbations to the system, still affecting system stability.

[0098] Therefore, both schemes have certain shortcomings in terms of disturbance suppression.

[0099] To address the limitations of the single method mentioned above, this invention uses both methods simultaneously to enhance the robustness of the system. The disturbance observer algorithm is the IUDE algorithm, and the robustness control algorithm is the LMID algorithm. Furthermore, the LMID algorithm also has the effect of a disturbance observer, meaning that the whole system includes two disturbance observers.

[0100] The IUDE algorithm, derived from the classic UDE algorithm, is applied first to estimate and compensate for the matching perturbation, and a discretized expression of the algorithm is given for practical application. Furthermore, the boundedness of the equivalent system perturbation after applying the IUDE algorithm is analyzed.

[0101] The LMID algorithm, derived from the classic LMI algorithm, is applied to control the equivalent system obtained by the IUDE algorithm. This algorithm falls under the category of robust control algorithms and can also act as a disturbance observer, enabling the estimation of disturbances in the equivalent system. Weights are introduced to flexibly adjust the state control and disturbance estimation for this algorithm.

[0102] When controlling an intelligent chassis dynamics system, lumped disturbances It can be used to describe unknown system states, sensor errors, unmodeled dynamic characteristics, or changes in system parameters. For example, in a suspension dynamics system, depending on the form of dynamic modeling, unknown disturbances can be the vertical velocity of the unsprung mass, road surface excitation, or nonlinearity or dead zone of the actuator.

[0103] The algorithm designed in this invention is named the LMID-IUDE algorithm, and the controller structure is as follows: Figure 2 As shown. The algorithm is described in four parts: First, the system is defined according to unmatched / matched disturbances. Then, the IUDE algorithm, a disturbance observer algorithm, is introduced to handle matched disturbances. Subsequently, the disturbance form of the equivalent system after the introduction of the IUDE algorithm is analyzed. Finally, the LMID algorithm, a robust control algorithm, is introduced to control the equivalent system and estimate the disturbance of the equivalent system.

[0104] The robustness of the system is enhanced by estimating and compensating for disturbances. However, since disturbance compensation is achieved through control inputs... Therefore, this approach is only applicable to compensation for matched perturbations. Thus, when introducing algorithms related to perturbation observers, the system must first be decomposed.

[0105] For the system in equation (1), without loss of generality, we define ,in The channel in question is experiencing a mismatched disturbance. To match the perturbation, the matrix is ​​split along the same row dimension. , Known terms and aggregate disturbance ,get , , and The system (1) is split into unmatched / matched perturbation subsystems:

[0106] (2);

[0107] In the formula: unmatched / matched disturbances are generally classified according to whether the channel contains control variables, that is, generally there are .

[0108] For the matched perturbation subsystem (second equation) in equation (2), the IUDE algorithm design reference system is as follows:

[0109] (3);

[0110] In the formula: For reference input; This is the compensation value for the matching disturbance; defined. This is an estimate of the matching perturbation; This is a partial disturbance compensation ratio, and .

[0111] definition , , , , , They are respectively , , , , , Laplace transform.

[0112] design ,in And a single diagonal element satisfies , This type of filter It has two adjustable parameters and Among the parameters Parameters that affect the cutoff frequency and amplitude-frequency response of a filter It can directly affect the amplitude-frequency response without affecting the cutoff frequency. Reduce or Both can reduce the amplitude-frequency response of the filter and improve disturbance estimation. The filter parameters are defined as follows: and .

[0113] Applying a Laplace transform to the reference system of equation (2), we obtain:

[0114] (4);

[0115] Define the inverse Laplace transform as ,pass and The design of the reference system can be completed in two calculations. However, the structure of equation (4) is difficult to simplify to obtain a feasible perturbation observer expression, so the following equation is introduced for processing:

[0116] (5);

[0117] definition for Laplace transform, and Make It is easy to simplify and can be expressed as:

[0118] (6);

[0119] because and Since all matrices are diagonal matrices, the order of matrix multiplication can be interchanged, thus yielding the following relationship:

[0120] (7);

[0121] Right now It can be regarded as After filtering The filtered result. In controller design, the z-transform can be introduced to obtain a discretized expression, which can then be substituted into a simplified form. ,get:

[0122] (8);

[0123] Therefore, the discretized expression is:

[0124] (9)

[0125] The above analysis is for a reference system. When the matrix When the order is full, a pseudo-inverse can be defined. It can be controlled by the amount Make the reference system equivalent to the actual system, and design it as follows:

[0126] (10);

[0127] The IUDE algorithm can be used to redesign existing algorithms, which can be described as applying existing algorithms to reference control. The design uses the IUDE algorithm to estimate and compensate for disturbances, and obtains the actual control law of equation (9).

[0128] For the equivalent system obtained after introducing the IUDE algorithm, the perturbation form of the equivalent system is given below, and the known terms are handled. This transforms the equivalent system into a linear system, facilitating the design of subsequent algorithms.

[0129] Considering the known terms In the middle, in the matched perturbation channel It can be done Direct compensation is performed, while those in unmatched perturbation channels... Therefore, it is not possible, so it will Classified as an equivalent mismatched perturbation. Definition and and define and Then the equivalent system of equation (1) can be rearranged as:

[0130] (11);

[0131] In the formula: , .

[0132] The following is about and Organize and analyze the disturbance of the equivalent system equation (11). .

[0133] because , , , , , These are all diagonal matrices, allowing for individual analysis of each term and the definition of scalar terms. , , and define the matching perturbation compensation term and disturbance estimation term The sub-items are and .right By organizing the sub-items, we can obtain:

[0134] (12);

[0135] definition and We can obtain:

[0136] (13);

[0137] From equation (6), we can obtain Therefore, we can obtain:

[0138] (14)

[0139] That is when and When bounded, due to the diagonal matrix The diagonal elements are positive, therefore regardless of Values, and It must be bounded. Correspondingly, when When it is bounded, It is also bounded.

[0140] Therefore, when the original disturbance Derivative of bounded and matched perturbation When bounded, after processing by the IUDE algorithm, the disturbance of the equivalent system represented by equation (11) It is bounded.

[0141] For the equivalent system represented by equation (11) obtained after processing by the IUDE algorithm, the perturbation of the new equivalent system... The perturbation estimate is defined as ,and The error of the perturbation estimation is defined as... The design control rate is:

[0142] (15);

[0143] At this time there is ,and .

[0144] The disturbance estimate is defined as:

[0145] (16);

[0146] From the first expression in equation (16), we can obtain:

[0147] (17);

[0148] Combining the second equation in equation (16), we can obtain:

[0149] (18);

[0150] And by get .

[0151] For system state Error of equivalent disturbance estimate Define weights respectively and ,definition:

[0152] (19);

[0153] definition , , And define a symmetric positive definite matrix. Combining equation (18), we can obtain:

[0154] (20);

[0155] because , where the diagonal matrix Since the diagonal elements are positive, equation (20) can be rearranged as follows:

[0156] (twenty one);

[0157] In the formula, , This indicates the transpose of the preceding term, which here means... .

[0158] Define scalar and define The following can be compiled:

[0159] (twenty two);

[0160] In the formula, , , , , express .

[0161] By solving equation (22), the feedback control rate is obtained. and This allows for further estimation of the disturbance.

[0162] In addition, definition , , obtain system ,and This indicates that the system is uniformly bounded.

[0163] This part of the algorithm is named the LMID algorithm, and the overall algorithm is named the LMID-IUDE algorithm. Since the LMID algorithm also includes a perturbation observer in its design, it is also named the dual-perturbation observer algorithm.

[0164] The following description is based on specific embodiments.

[0165] In the intelligent chassis system, a 1 / 4 vehicle active suspension control system is selected as the control use case, and its structure is as follows: Figure 2 and Figure 3As shown, it includes two subsystems: the sprung system and the unsprung system, which are related to ride comfort and handling stability, respectively. The dynamic equations are expressed as follows:

[0166] (twenty three);

[0167] In the formula, The vertical displacement of the sprung mass; The vertical displacement of the unsprung mass; For vertical excitation of the road surface; This refers to the dynamic deflection of the suspension. For wheel dynamic deformation; For the sprung mass; Unsprung mass; For spring stiffness; For tire stiffness; For damping of the shock absorber; For tire damping; The actuating force of the actuator, ranging from [-2500, 2500]. Speed Take 20 .

[0168] In the control of the sprung system related to ride comfort, the sprung system equation in equation (23) is usually processed, corresponding to the state variable defined in equation (1) as follows: The control quantity is The disturbance term is And there are: , , , .

[0169] Five comparison algorithms were designed for this system. Algorithm 1 is an uncontrolled algorithm, i.e. Algorithm 2 is based on LMI control. Algorithm 3 uses the IUDE algorithm alone, which only compensates for matched disturbances once and relies on the inherent properties of the system to ensure stability. Algorithm 4 uses the LMID algorithm alone, which considers both matched and unmatched disturbances to solve for feedback gain. It ensures system stability by controlling the pole placement of the control system and estimating and compensating for matched disturbances. Algorithm 5 is the proposed LMID-IUDE algorithm. This algorithm first estimates and compensates for a disturbance once using the IUDE algorithm to obtain the equivalent system, and then processes the equivalent system using the LMID algorithm. It will simultaneously provide the estimation results of the IUDE algorithm for matched disturbances and the estimation results of the LMID algorithm for disturbances in the equivalent system.

[0170] For Algorithm 2, the basic LMI control, its design process is as follows: This system defines a symmetric positive definite matrix. Design steady feedback gain Control rate Design Lyapunov functions. Define scalar ,make ,get:

[0171] (twenty four);

[0172] Equation (24) is about and For nonlinear systems, in order to solve the above equation, we define... , Multiplying both sides of the inequality by the left and right sides ,get:

[0173] (25);

[0174] Thus, it transforms into something about and The linear matrix inequalities, combined with the given... Solving this equation, we get .

[0175] Furthermore, Algorithms 3 and 5 use the same set of IUDE algorithm parameters, and take... To achieve partial disturbance compensation and prevent significant deterioration in the control of suspension dynamic deflection and wheel dynamic load, Algorithm 4 and Algorithm 5 use the same set of LMID algorithm parameters.

[0176] Regarding sprung acceleration on a Class C random road surface. Wheel deformation Suspension dynamic deflection The simulation results for the three performance indicators are as follows: Figure 4 As shown, the root-mean-square (rms) values ​​are presented in Table 1. In Algorithm 5, the observed performance of the IUDE algorithm on matching perturbations and the observed performance of the LMID algorithm on equivalent matching perturbations are shown in Table 1. Figure 5 As shown.

[0177] Table 1

[0178] Control Algorithm Algorithm 1: Passive Suspension 0.459 1.761 5.169 Algorithm 2: Basic LMI Control 0.324 1.991 5.075 Algorithm 3: IUDE Algorithm 0.377 1.897 5.279 Algorithm 4: LMID Algorithm 0.269 2.735 6.935 Algorithm 5: LMID-IUDE Algorithm 0.162 4.185 7.646

[0179] Combining RMS value and Figure 4 For sprung acceleration The control performance of the five algorithms, ranked from worst to best, is Algorithm 1, Algorithm 3, Algorithm 2, Algorithm 4, and Algorithm 5, demonstrating the effectiveness of the LMID-IUDE algorithm. The LMID-IUDE algorithm simultaneously utilizes a robust control algorithm (LMID algorithm) and a disturbance observer (IUDE algorithm), and the LMID algorithm also provides disturbance estimation capabilities, achieving dual disturbance observer control. This algorithm combines the characteristics of both approaches, exhibiting a significant suppression effect on disturbances compared to a single control method, effectively ensuring system stability and significantly improving system smoothness.

[0180] For wheel dynamic deformation and suspension dynamic deflection The degradation of these two metrics in the designed algorithm stems from the inherent conflict between smoothness and handling stability control, while also reflecting the good improvement in smoothness achieved by the designed algorithm.

[0181] Depend on Figure 5 It can be seen that the IUDE algorithm effectively achieves the estimation and compensation of the matching disturbance, but there is still a certain estimation error. This estimation error will be transformed into the equivalent system matching disturbance. The disturbance observer designed by the LMID algorithm also achieves the estimation of the equivalent system disturbance better.

[0182] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A smart chassis dual perturbation observer control method, characterized in that, Includes the following steps: S1. The control system is divided into a non-matched disturbance subsystem and a matched disturbance subsystem; S2. The IUDE method is used to perform perturbation compensation and perturbation estimation on the split matched perturbation subsystem to obtain the equivalent system; S3. Determine the boundedness of the disturbance of the equivalent system; S4. Using the weight matrix of state control and the weight matrix of disturbance estimation, perform disturbance estimation and compensation on the equivalent system after determining the boundedness of the disturbance. S2 includes the following sub-steps: S21. Perform disturbance compensation on the split matching disturbance subsystem; S22. Perform disturbance estimation on the matched disturbance subsystem after disturbance compensation; S23. Use the control quantity to perform equivalent processing on the matched disturbance subsystem after disturbance compensation and disturbance estimation to obtain the equivalent system; In step S21, the expression for perturbation compensation of the split matched perturbation subsystem is: ； in, This represents the steady system matrix of the matched perturbation subsystem. Represents the system's state variables. This represents the steady-state input matrix of the matched perturbation subsystem. Indicates reference input. This represents the known terms of the matched perturbation subsystem. This indicates a matching perturbation. This represents the compensation value for matching perturbations. Indicates the system's control input; In step S22, the discretized expression for perturbation estimation is: ； in, Indicates the matching perturbation exist The estimated value at time, Indicates to The first step in organizing the quantity. Indicates the matching perturbation The estimated value, Representing discrete time, Indicates to The second process quantity of sorting, Indicates to The third process quantity of sorting exist The value at time, Indicates to The third process quantity of sorting exist The value at time; In S23, the control quantity The expression is: ； ； in, Indicates reference control, Indicates a false reversal. This represents the compensation value for the matching disturbance.

2. The intelligent chassis dual-disturbance observer control method according to claim 1, characterized in that, In S1, the expression for separating the unmatched perturbation subsystem and the matched perturbation subsystem is: ； in, Represents the state of the non-matched disturbance subsystem The derivative of Indicates the state of the matched disturbance subsystem The derivative of This represents the steady system matrix of the unmatched perturbation subsystem. This represents the steady system matrix of the matched perturbation subsystem. Represents the system's state variables. This represents the steady-state input matrix of the unmatched perturbation subsystem. This represents the steady-state input matrix of the matched perturbation subsystem. Indicates the system's control input, This represents the known terms of the non-matched perturbation subsystem. This represents the known terms of the matched perturbation subsystem. This indicates a non-matching perturbation. This indicates a matching perturbation.

3. The intelligent chassis dual-disturbance observer control method according to claim 1, characterized in that, In S3, the equivalent system is expressed as follows: ； in, Represents the state variables of the control system The derivative of The matrix representing the steady system of the control system. Represents the state variables of the control system. Represents the steady input matrix of the control system. Indicates reference control, Represents the known terms of the equivalent system. It represents the disturbance of the equivalent system.

4. The intelligent chassis dual-disturbance observer control method according to claim 1, characterized in that, In S3, when the original disturbance is bounded and the derivative of the matched disturbance is bounded, the disturbance of the equivalent system is bounded.

5. The intelligent chassis dual-disturbance observer control method according to claim 1, characterized in that, In S4, the weight matrix for state control The expression is: ； in, A simplified representation of a diagonal matrix. Represents state variables The weights of the components in the first dimension. Represents state variables No. The weights of the components in each dimension. Represents the real number field. Represents state variables The dimension; In S4, the weight matrix for perturbation estimation The expression is: ； in, Indicates disturbance The weights of the components in the first dimension. Indicates disturbance No. The weights of the components in each dimension. A simplified representation of a diagonal matrix.

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