Active suspension system control method based on beneficial disturbance, coupling and nonlinearity

Abstract

The present invention discloses a control method for an active suspension system based on beneficial disturbance, coupling and nonlinearity, comprising: constructing a bionic nonlinear reference model of the active suspension system, taking the output of the bionic nonlinear reference model as the expected trajectory; constructing a fuzzy disturbance observer to estimate the collective disturbance in the bionic nonlinear reference model; defining a state coupling effect index and a disturbance effect index, judging whether the state coupling and collective disturbance in the bionic nonlinear reference model are beneficial according to the two effect indexes, thereby constructing a tracking controller that considers beneficial state coupling and beneficial disturbance, so that the suspension stroke of the active suspension system tracks the expected trajectory. Based on the evaluation of the potential influence of state coupling, disturbance and nonlinear factors on the system, a robust tracking control method is designed to suppress only the negative influence of coupling, disturbance and nonlinear factors, while retaining the positive influence, and effectively deal with the influence of disturbance, state coupling and inherent nonlinearity of the system.

Description

Technical Field

[0001] The invention relates to the technical field of active suspension system tracking control, and in particular to an active suspension system control method based on beneficial disturbance, coupling and nonlinearity. Background Art

[0002] As one of the key components of the vehicle chassis, the vehicle suspension system has an important impact on the vehicle's ride comfort, stability, and road handling. Compared with the traditional suspension system, the active suspension system has better vibration isolation performance when the vehicle encounters irregular roads. Therefore, for the active suspension system, researchers have designed a series of control methods to improve the performance of the suspension system, mainly including linear control methods and nonlinear control methods.

[0003] Active suspension systems have several common problems in practical applications, such as unavoidable disturbances (parameter uncertainty, uncertainty of unmodeled dynamics, and external disturbances), inherent nonlinear factors, coupling effects, energy consumption, etc. These problems need to be considered in the design of the controller. In response to the above practical problems, researchers have reported a series of control methods, which are summarized as follows:

[0004] 1. In order to reduce the impact of internal and external disturbances, many robust control methods and control methods based on disturbance observers are proposed. For example, disturbance observers are used to estimate the impact of system uncertainty and unknown roads, and on this basis, a sliding mode control method that can reduce the acceleration of sprung mass is proposed. The collective disturbance of the active suspension system is compensated by the extended tracking control method (ESO) based on nonlinear observers. The impact of mismatched disturbances is handled by extending nonlinear disturbance observers and modified sliding surfaces. Fuzzy disturbance observers are used to compensate for disturbances caused by nonlinear coupling, road changes, and nonlinear dynamic uncertainties.

[0005] However, the above control methods all regard disturbances as factors that have a negative impact on the system dynamic response, and thus directly eliminate them. However, the impact of disturbance effects on system performance has both good and bad sides, but most of the existing control methods are designed only under the assumption that the impact of disturbance effects is bad. Although the negative impact of disturbances can be directly eliminated, the positive impact is also eliminated.

[0006] 2. Existing control methods usually regard the inherent coupling term as an uncertainty factor of the model and eliminate it directly. Specifically, a robust sliding mode control method is designed for the active suspension system to make it insensitive to the uncertainty of the model. The influence of uncertain parameters is suppressed by introducing an adaptive estimator. The stability of the closed-loop system is guaranteed by designing a robust gain scheduling control method, and disturbances are suppressed. The influence of model uncertainty is resolved by using the membership function of the fuzzy system. The coupling effect is divided into beneficial and harmful. The control performance of the system can be improved by eliminating harmful coupling effects and retaining useful coupling effects.

[0007] 3. In order to improve control performance and reduce energy consumption, the nonlinear stiffness and damping generated by the biological reference model are introduced in the vehicle suspension control. For example, Pan et al. designed a bionic reference model inspired by the limb kinematics of biological systems. Although the model only takes one layer and does not consider the damping effect, its results still provide a solution to the energy-saving nonlinear control problem. In addition, a more realistic bionic reference model is designed, including two layers and nonlinear damping effects. Damping effects and nonlinear stiffness can provide better control performance (not only vibration isolation). Therefore, the influence of these nonlinear factors needs to be considered in the design of robust controllers.

[0008] In summary, the above existing control methods are not perfect in solving the uncertainty of system parameters / models, state coupling and inherent nonlinear factors, and there is no control method that can solve these problems at the same time. Summary of the invention

[0009] In order to solve the above problems, the present invention proposes an active suspension system control method based on beneficial disturbances, coupling and nonlinearity. On the basis of evaluating the potential influence of state coupling, disturbance and nonlinear factors on the system, a robust tracking control method is designed to suppress only the negative influence of coupling, disturbance and nonlinear factors while retaining the positive influence, thereby effectively coping with the influence of disturbances, state coupling and inherent nonlinearity of the system.

[0010] In order to achieve the above object, the present invention adopts the following technical solution:

[0011] In a first aspect, the present invention provides an active suspension system control method based on beneficial disturbance, coupling and nonlinearity, comprising:

[0012] Construct a bionic nonlinear reference model of the active suspension system and use the output of the bionic nonlinear reference model as the desired trajectory;

[0013] A fuzzy disturbance observer is constructed to estimate the collective disturbance in the biomimetic nonlinear reference model;

[0014] The state coupling effect index and disturbance effect index are defined. Whether the state coupling and collective disturbance in the bionic nonlinear reference model are beneficial is judged according to the two effect indices. A tracking controller considering beneficial state coupling and beneficial disturbance is constructed so that the suspension travel of the active suspension system tracks the expected trajectory.

[0015] As an optional implementation, the collective disturbance includes parameter uncertainty, unmodeled dynamic uncertainty and external disturbance; the collective disturbance Λ is expressed as:

[0016]

[0017] Among them, m s and m u are the sprung mass and unsprung mass, respectively, z ed is the expected trajectory, e is the suspension travel tracking error; D is the unknown disturbance;

[0018] The unknown disturbance D is:

[0019]

[0020] Among them, k s1 and k s2 are stiffness coefficients, k d is the damping coefficient, k t and k b are the stiffness coefficient and damping coefficient of the tire, z e is the suspension travel, Δ s1 , Δ s2 , Δ t , Δ d and Δ b are the coefficient disturbance ranges, d1 and d2 are the unmodeled dynamic disturbance and external disturbance respectively, z t For tire travel.

[0021] As an optional implementation, the fuzzy disturbance observer is:

[0022]

[0023] Where σ is the positive control gain, k s1 and k s2 are stiffness coefficients, k d is the damping coefficient, k t and k b are the stiffness coefficient and damping coefficient of the tire, e is the suspension travel tracking error, m s and m u are the sprung mass and unsprung mass, respectively, z t is the tire travel, z ed is the desired trajectory, u(t) represents the control input, is an auxiliary function, is the estimated value of the collective disturbance.

[0024] As an optional implementation, the state coupling effect index is:

[0025]

[0026] Where e is the suspension travel tracking error, α is a positive number for assistance, and k t and k b are the stiffness coefficient and damping coefficient of the tire, z t For tire travel.

[0027] As an optional implementation, the disturbance effect index is:

[0028]

[0029] Where, e is the suspension travel tracking error; is the estimated value of the collective disturbance.

[0030] As an optional implementation, when the state coupling effect index is less than zero, it indicates that the state coupling is beneficial, when it is greater than zero, it indicates that the state coupling is harmful, and when it is equal to zero, it indicates that the state coupling has no effect;

[0031] When the disturbance effect index is less than zero, it means the disturbance is beneficial; when it is greater than zero, it means the disturbance is harmful; when it is equal to zero, it means the disturbance has no effect.

[0032] As an optional implementation, the tracking controller is:

[0033]

[0034] Among them, k P , k D , k S is a positive control gain, e is the suspension travel tracking error, α is a positive number for assistance, k s1 and k s2 are stiffness coefficients, k d is the damping coefficient, z ed is the expected trajectory, m is the auxiliary function, and m s and m u are the sprung mass and unsprung mass, k t and k b are the stiffness coefficient and damping coefficient of the tire, z t is the tire travel, is the estimated value of the collective disturbance, J1 is the state coupling effect index, and J2 is the disturbance effect index.

[0035] In a second aspect, the present invention provides an active suspension system control system based on beneficial disturbance, coupling and nonlinearity, comprising:

[0036] A model building module is configured to build a bionic nonlinear reference model of the active suspension system, using the output of the bionic nonlinear reference model as the desired trajectory;

[0037] a disturbance estimation module configured to construct a fuzzy disturbance observer to estimate the collective disturbance in the biomimetic nonlinear reference model;

[0038] The tracking control module is configured to define a state coupling effect index and a disturbance effect index, and judge whether the state coupling and collective disturbance in the bionic nonlinear reference model are beneficial according to the two effect indexes, so as to construct a tracking controller that considers beneficial state coupling and beneficial disturbance, so that the suspension travel of the active suspension system tracks the expected trajectory.

[0039] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in the first aspect is performed.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The active suspension system control method based on beneficial disturbances, coupling and nonlinearity proposed in the present invention accurately estimates the collective disturbances including parameter uncertainty, unmodeled dynamics and external disturbances through a fuzzy disturbance observer. It does not require the precise values ​​of system parameters (such as sprung / unsprung mass, stiffness and damping coefficient), thereby greatly improving the robustness of the active suspension system.

[0043] The active suspension system control method based on beneficial disturbance, coupling and nonlinearity proposed in the present invention introduces a state coupling effect index for the first time to analyze the influence of state coupling on the performance of the active suspension system, which not only eliminates harmful state coupling effects but also retains beneficial state coupling effects, thereby obtaining better tracking control performance and stability performance.

[0044] The active suspension system control method based on beneficial disturbance, coupling and nonlinearity proposed in the present invention innovatively constructs a new type of disturbance effect index based on whether the disturbance direction is consistent with the expected movement direction, which is used to distinguish the favorable and unfavorable effects of the disturbance and further improve the tracking performance.

[0045] The active suspension system control method based on beneficial disturbance, coupling and nonlinearity proposed in the present invention adopts ideal bionic nonlinear stiffness and damping characteristics to improve the control performance in order to solve the inherent nonlinearity problem of the suspension system, thereby suppressing the negative impact of inherent nonlinearity while also making better use of the positive effects of inherent nonlinearity.

[0046] The active suspension system control method based on beneficial disturbance, coupling and nonlinearity proposed in the present invention designs a robust tracking control method on the basis of evaluating the potential influence of state coupling, disturbance and nonlinear factors on the system, which only suppresses the negative influence of coupling, disturbance and nonlinear factors while retaining the positive influence, effectively suppresses the transient response of the vehicle suspension and significantly saves the energy cost of active control.

[0047] The active suspension system control method based on beneficial disturbance, coupling and nonlinearity proposed in the present invention utilizes the robust control of beneficial state coupling, disturbance and nonlinear effects for the first time. It has strong robustness to parameter uncertainty, uncertainty of unmodeled dynamics and external disturbances, which can not only reduce control energy consumption but also improve vehicle smoothness. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0049] Figure 1 A schematic diagram of an active suspension system based on a bionic nonlinear reference model provided in Example 1 of the present invention;

[0050] Figure 2 (a) is a schematic diagram of a bird leg provided in Example 1 of the present invention;

[0051] Figure 2 (b) is a schematic diagram of a bionic nonlinear reference model provided in Example 1 of the present invention;

[0052] Figure 2 (c) Deformation analysis provided for Example 1 of the present invention (number of layers n=2);

[0053] Figure 3 The control method provided in Embodiment 1 of the present invention is different from the traditional robust control method;

[0054] Figure 4 A schematic diagram of a sinusoidal road surface provided in Example 1 of the present invention;

[0055] Figure 5 A schematic diagram of a random road surface provided in Example 1 of the present invention;

[0056] Figure 6(a)-6(b)A schematic diagram of vehicle acceleration and its frequency components on a sinusoidal road surface provided in Example 1 of the present invention;

[0057] Figure 7(a)-7(b) A schematic diagram of the control input and its frequency components under a sinusoidal road surface provided by Embodiment 1 of the present invention;

[0058] Figure 8(a)-8(b) A schematic diagram of suspension travel and tire travel on a sinusoidal road surface provided by Embodiment 1 of the present invention;

[0059] Figure 9(a)-9(b) A schematic diagram of vehicle acceleration and its frequency components on a random road surface provided by Embodiment 1 of the present invention;

[0060] Figure 10(a)-10(b) A schematic diagram of the control input and its frequency components under random road conditions provided by Embodiment 1 of the present invention;

[0061] Figure 11(a)-11(b) This is a schematic diagram of suspension travel and tire travel on a random road surface provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0062] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0063] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in this embodiment have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0064] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0065] Example 1

[0066] This embodiment provides an energy-saving robust control method for an active suspension system based on beneficial disturbance, coupling and nonlinearity. Different from the existing control method, this embodiment considers robustness, disturbance, inherent nonlinear factors, state coupling effect and energy consumption, makes full use of nonlinear stiffness and damping effect, builds a bionic nonlinear reference model, and accurately estimates the collective disturbance including parameter uncertainty, unmodeled dynamic uncertainty and external disturbance through a fuzzy disturbance observer; designs a state coupling effect index and a disturbance effect index to judge the influence of state coupling and disturbance factors; finally, Lyapunov's theorem is used to ensure the asymptotic stability of the active suspension system. It has good transient response characteristics and significant energy-saving effects.

[0067] The specific steps include:

[0068] Construct a bionic nonlinear reference model of the active suspension system and use the output of the bionic nonlinear reference model as the desired trajectory;

[0069] A fuzzy disturbance observer is constructed to estimate the collective disturbance in the biomimetic nonlinear reference model;

[0070] The state coupling effect index and disturbance effect index are defined. Whether the state coupling and collective disturbance in the bionic nonlinear reference model are beneficial is judged according to the two effect indices. A tracking controller considering beneficial state coupling and beneficial disturbance is constructed so that the suspension travel of the active suspension system tracks the expected trajectory.

[0071] like Figure 1 As shown, the dynamic model of the active suspension system is described as:

[0072]

[0073]

[0074] Among them, m s and m u denote the sprung mass and unsprung mass respectively, z s , z u , z r denote the sprung mass displacement, unsprung mass displacement, and road displacement, respectively; d1 and d2 denote the disturbances caused by unmodeled dynamics, external disturbances, etc., respectively; u(t) denotes the control input; and F s and F d Denote the spring and damping forces, respectively, F t and F b They represent tire elasticity and damping force respectively.

[0075] F s , F d , F t and F b Expressed as:

[0076] F s =k s1 (z s -z u )+k s2 (z s -z u ) 3 (3)

[0077]

[0078] F t =k t (z u -z r ) (5)

[0079]

[0080] Among them, k s1 and k s2 represents the stiffness coefficient, k d represents the damping coefficient, k t and k b They represent the stiffness coefficient and damping coefficient of the tire respectively, and their expressions are:

[0081]

[0082]

[0083]

[0084]

[0085] in, as well as Indicates nominal value, Δ si , Δ d , Δ t and Δ b Represents the coefficient perturbation range.

[0086] Analyzing equation (1)-equation (2), we get:

[0087]

[0088] Among them, z t =z u -z r Indicates tire travel; m s and m u Denote the nominal values ​​of sprung mass and unsprung mass respectively; D denotes the unknown disturbance:

[0089]

[0090] From formula (2), we get:

[0091]

[0092] The suspension travel tracking error is defined as:

[0093] e=z e -z ed (14)

[0094] Substituting formula (14) into formula (11), we can get:

[0095]

[0096] in, represents the auxiliary function; Λ represents the collective perturbation:

[0097]

[0098] From formula (13)-formula (14), we can get:

[0099]

[0100] In the error dynamics defined in equations (15) and (17), Represents the state coupling effect produced by the tire travel system.

[0101] The control goal of this embodiment is to construct a robust tracking controller that considers both state coupling and disturbance effects so that the suspension travel z of the active suspension system is e =z s -z u Accurately track the desired trajectory z ed ,Right now:

[0102]

[0103] At the same time, the designed robust tracking controller consumes less energy than other existing control methods under the condition of better riding comfort. In order to better evaluate the control performance, this embodiment introduces the following two indicators: the root mean square value of the vehicle body acceleration and the energy consumption of the actuator:

[0104]

[0105]

[0106] Where T represents the experimental time and P is expressed as:

[0107]

[0108] In this embodiment, the beneficial nonlinear dynamics of the active suspension system are considered and a bionic nonlinear reference model is constructed; Figure 2 (a)- Figure 2 (c) The multilayer bionic structure shown, the corresponding system parameters are shown in Table 1;

[0109] Table 1 Nonlinear system model of bionic X-type structure

[0110]

[0111]

[0112] z ed It can be calculated as:

[0113]

[0114] Among them, b1 and b2 represent auxiliary functions of the following form:

[0115]

[0116] Where E represents an auxiliary function of the following form:

[0117]

[0118] Then, to ensure that ed , Always within the allowed range, modify formula (22) as follows:

[0119]

[0120]

[0121]

[0122] In this embodiment, a fuzzy disturbance observer is designed to accurately estimate the unknown / uncertain collective disturbance; the collective disturbance Λ is estimated by the fuzzy disturbance observer:

[0123]

[0124] in, represents the estimated value of the collective perturbation Λ, represents the fuzzy variable, represents the adjustable parameter vector, r is the number of fuzzy rules; ξ i is the fuzzy basis function, and its expression is:

[0125]

[0126] in, is the membership function.

[0127] Therefore, the following observation system is proposed:

[0128]

[0129] in, indicates a positive control gain;

[0130] The disturbance observation error ζ is defined as:

[0131]

[0132] From equations (15), (30)-(31), we can get:

[0133]

[0134] Adjustable parameter vector for:

[0135]

[0136] in, Indicates a positive control gain.

[0137] The fuzzy disturbance observer can estimate the disturbance very well, and the collective disturbance Λ is expressed as:

[0138]

[0139] where ε represents the observation error, and It represents the upper bound of ε, which decreases gradually as the number of fuzzy rules increases.

[0140] Since the state coupling term is highly nonlinear, existing control methods usually assume that the state is fully measurable, and on this basis treat the state coupling term as a known disturbance term, thereby directly eliminating it. In fact, in the presence of external disturbances and measurement noise, state coupling has an important impact on the stability and overall control performance of the controlled system. Most existing active suspension control methods do not analyze the state coupling effect, which may limit the further improvement of their control performance. Therefore, it is important to quantitatively evaluate the impact of state coupling on the performance of active suspension systems.

[0141] Importantly, the magnitude and direction of the disturbance can have a critical impact on the performance of the active suspension system. The magnitude of the disturbance is closely related to the stability of the system, which can be addressed through boundedness analysis. The directionality of the disturbance is closely related to the tracking performance, but the directionality of the disturbance is rarely studied. If the sign of the time-varying disturbance is consistent with the desired motion, the time-varying disturbance has the potential to improve the tracking performance. Therefore, it is necessary to consider the relationship between the disturbance effect and the tracking performance.

[0142] Then, in order to make full use of the beneficial state coupling and disturbance information, this embodiment defines a state coupling effect index and a disturbance effect index;

[0143] Specifically, the state coupling effect index is:

[0144]

[0145] The state coupling effect is defined as:

[0146]

[0147] in, Auxiliary positive number.

[0148] The disturbance effect index is:

[0149]

[0150] in,

[0151]

[0152] The role of the state coupling effect indicator is to reflect the influence of one subsystem on another subsystem. More precisely, the potential benefit of the state coupling effect depends on whether the state coupling helps guide the Lyapunov function to move toward the origin;

[0153] Then, choose the Lyapunov function as:

[0154]

[0155] By designing the control input u(t), the derivative of the Lyapunov function with respect to time can be organized as:

[0156]

[0157] Where k1 represents the positive control gain, State coupling effect It is obvious that The symbol and The size of the

[0158] In the case of J1<0, It can be chosen to be 0, the state coupling effect is beneficial, so this state coupling can be introduced in the tracking controller.

[0159] In the case of J1 ≥ 0, Can be selected as On this basis, we can get State coupling effects are detrimental / inconsequential and should be eliminated.

[0160] In this embodiment, the disturbance effect index provides a new perspective to identify the impact of concentrated disturbances on the error system. If a useful disturbance effect appears, it should be fully utilized in the tracking controller design instead of directly eliminating it.

[0161] For the error dynamics model described by equations (15) and (17), the robust control method based on fuzzy disturbance observer designed in this embodiment is:

[0162]

[0163] Among them, k P , k D , k S Indicates positive control gain, the expressions of F(J1) and F(J2) are:

[0164]

[0165]

[0166] If the control gain needs to meet the following conditions:

[0167]

[0168] Among them, α and ω represent auxiliary positive numbers, then the tracking error and the first-order derivative of the tracking error with respect to time converge asymptotically to 0, that is:

[0169]

[0170] Proof: First construct the Lyapunov candidate function as:

[0171]

[0172] Next, we prove that V in equation (44) is non-negative:

[0173] Calculating the second term on the right side of formula (44) yields:

[0174]

[0175] Among them, the calculation process used nature.

[0176] Then, using Young's inequality, the third term on the right side of equation (44) can be rearranged as:

[0177]

[0178] Substituting the conclusions of formula (45) and formula (46) into formula (44), we can obtain:

[0179]

[0180] Substituting the conditions of formula (42) into the formula, we can see that the Lyapunov candidate function is non-negative.

[0181] Then, we take the derivative of both ends of equation (44) with respect to time, and substitute the conclusions of equation (15) and equation (39) into the obtained result, and we can know that:

[0182]

[0183] Next we will discuss and Symbols;

[0184] When J1<0, this indicates that state coupling is beneficial. Based on the definitions of J1 and F(J1), we can obtain

[0185] If J1 ≥ 0, this indicates that the state coupling is harmful / has no effect, and we can get

[0186] In general, the following relationship always holds:

[0187]

[0188] In discussion Before studying the symbols scope;

[0189] If J2<0, this indicates that the disturbance is beneficial, and we can get

[0190] If J2 ≥ 0, this indicates that the disturbance is harmful, and we can get

[0191] In general, Always holds true.

[0192] Therefore, the following conclusions hold:

[0193]

[0194] Then, according to the nature as well as The second and eighth terms of formula (48) can be calculated as:

[0195]

[0196]

[0197] The sixth term of formula (48) can be calculated as:

[0198]

[0199] Similarly, the seventh term of formula (48) can be derived as:

[0200]

[0201] Based on the conclusions of formula (49)-formula (54), formula (48) can be further organized as:

[0202]

[0203] It is not difficult to obtain the condition of formula (42) if the following inequality holds:

[0204]

[0205] Available To this end, consider the following two scenarios.

[0206] Scenario 1: In this case, equation (56) always holds true. Case 2: In this case, equation (56) can be simplified as:

[0207]

[0208] The following inequality always holds:

[0209]

[0210] To ensure that formula (57) always holds, we select k D So that:

[0211]

[0212] According to the condition of formula (42), we can get:

[0213]

[0214] This shows that:

[0215]

[0216] Substituting the conclusion of formula (61) into formula (59), we can derive:

[0217]

[0218] According to the above analysis, if equation (62) holds, It can be calculated as:

[0219]

[0220] Among them, η1 and η2 represent two positive numbers. The controlled system is Lyapunov stable, and the following conclusions can be directly derived:

[0221]

[0222] and

[0223]

[0224] In summary, the design of the robust control method is proven.

[0225] like Figure 3 As shown, unlike the traditional active suspension control method which sets the reference trajectory to 0, the control method proposed in this embodiment selects the bionic nonlinear reference model as the ideal output tracking trajectory, avoiding the problem of eliminating the beneficial nonlinear factors of the active suspension system. Therefore, the control method proposed in this embodiment has a significant energy-saving effect.

[0226] In addition, the conventional robust control method directly eliminates and compensates for disturbances and state coupling, while the robust tracking control method of this embodiment makes full use of the beneficial disturbances and beneficial state coupling of the active suspension system. By defining the state coupling effect index and the disturbance effect index, the influence of the disturbance and state coupling effects on the active suspension system is judged, and by introducing the trigger condition realized by the s function, the harmful disturbance and state coupling effects are eliminated, and the beneficial disturbance and beneficial state coupling effects are retained.

[0227] In this embodiment, in order to verify the performance and energy-saving effect of the proposed control method, two road surface excitations, sinusoidal road surface and random road surface, are considered. Figure 4-Figure 5 As shown, the relevant system parameters of the quarter active suspension test platform are shown in Table 2;

[0228] In order to better verify the excellent control performance of the proposed control method, the method in this embodiment is compared with the passive suspension system and the extended state observer (ESO) by experiments; the control gains of the ESO control method and the control method designed in this embodiment are shown in Table 3, and the membership function of the proposed control method is selected as:

[0229]

[0230]

[0231] In addition, the relevant parameters of the bionic nonlinear reference model are shown in Table 4.

[0232] Table 2 Parameters of quarter active suspension system

[0233]

[0234]

[0235] Table 3 Control gain

[0236] Control Methods Control gain ESO control method <![CDATA[b o =0.4,w0=120,k1=20,k2=20]]> Proposed control method <![CDATA[k P =60.k D =20.k S =20,γ=50]]>

[0237] Table 4 Parameters of bionic nonlinear reference model

[0238] parameter value parameter value M 22kg <![CDATA[μ1]]> 5Ns / m <![CDATA[θ1]]> π / 6rad <![CDATA[μ2]]> 0.15Ns / m <![CDATA[l1]]> 0.1m <![CDATA[s h ]]> 500N / m <![CDATA[l2]]> 0.2m <![CDATA[s v ]]> 350N / m <![CDATA[λ1]]> -0.038m <![CDATA[λ2]]> 0.038m <![CDATA[δ1]]> -1m / s <![CDATA[δ1]]> 1m / s <![CDATA[β1]]> <![CDATA[-1m / s 2 ]]> <![CDATA[β2]]> <![CDATA[1m / s 2 ]]>

[0239] like Figure 6(a)-Figure 6(b) and Figure 9(a)-Figure 9(b) The time response and frequency response of the vehicle acceleration under sinusoidal and random road conditions are compared for the passive suspension system, ESOT controller, and the designed robust tracking controller. Table 5 summarizes the RMS acceleration of the vehicle under two different road conditions. Figure 6(a)-Figure 6(b) and Figure 9(a)-Figure 9(b) It can be seen that compared with the passive suspension system, the vehicle acceleration of the ESOT control method and the method of this embodiment is greatly reduced. At the same time, this also shows that the ESOT controller and the tracking controller designed in this embodiment can effectively isolate the model parameter uncertainty and the vibration caused by external disturbances. Correspondingly, the ride comfort of the ESOT controller and the designed tracking controller has been significantly improved. Specifically, it can be seen from Table 5 that compared with the passive suspension system, the ESOT controller reduces the root mean square value of the vehicle acceleration for sinusoidal and random roads by about 52.99% and 24.81%, respectively; while the designed tracking controller reduces the root mean square value of the vehicle acceleration for sinusoidal and random roads by 60.83% and 35.93%, respectively.

[0240] Table 5 RMS acceleration of vehicle body under different controllers (m / s 2 )

[0241] Control Methods Sine road surface Random road surface Passive suspension 0.2974 0.0270 ESO control method 0.1398(↓52.99％) 0.0203(↓24.81％) Proposed control method 0.1165(↓60.83％) 0.0173(↓35.93％)

[0242] Figures 7(a) and 10(a) are the time response diagrams of the control inputs of the ESOT controller and the designed tracking controller under sinusoidal and random roads, respectively, and the corresponding frequency responses are shown in Figures 7(b) and 10(b), respectively. Table 6 gives the root mean square values ​​of the control inputs of the ESOT controller and the designed tracking controller. It can be seen from Figures 7(a) and 10(a) that the control input of the designed tracking controller is much smaller than that of the ESOT controller. In addition, the high-frequency component of the ESOT controller is much larger than that of the designed tracking controller. It is well known that the high bandwidth requirement of the actuator will increase the cost of the controlled active suspension system and the possibility of input saturation. And in practical applications, the bandwidth of the actuator is always limited. Therefore, compared with the ESOT controller, the designed tracking controller has higher energy efficiency and lower actuator bandwidth characteristics. As shown in Table 6, compared with the ESOT controller, the root mean square value of the energy consumption of the designed tracking control method for random roads and sinusoidal roads is reduced by about 67.13% and 86.52%, respectively.

[0243] Table 6 RMS of control input of different controllers (W)

[0244] Control Methods Sine road surface Random road surface ESO control method 0.0502 0.0089 Proposed control method 0.0165(↓67.13％) 0.0012(↓86.52％)

[0245] Depend on Figure 8(a)-Figure 8(b) and Figure 11(a)-Figure 11(b) It can be seen that the suspension travel and tire travel of the passive suspension system, ESOT controller and the designed tracking controller are all kept within the allowable range. These results all show that the designed tracking controller can ensure the safe driving of the driver and passengers.

[0246] Example 2

[0247] This embodiment provides an active suspension system control system based on beneficial disturbance, coupling and nonlinearity, including:

[0248] A model building module is configured to build a bionic nonlinear reference model of the active suspension system, using the output of the bionic nonlinear reference model as the desired trajectory;

[0249] a disturbance estimation module configured to construct a fuzzy disturbance observer to estimate the collective disturbance in the biomimetic nonlinear reference model;

[0250] The tracking control module is configured to define a state coupling effect index and a disturbance effect index, and judge whether the state coupling and collective disturbance in the bionic nonlinear reference model are beneficial according to the two effect indexes, so as to construct a tracking controller that considers beneficial state coupling and beneficial disturbance, so that the suspension travel of the active suspension system tracks the expected trajectory.

[0251] It should be noted that the above modules correspond to the steps described in Example 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above Example 1. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer executable instructions.

[0252] In further embodiments, there is also provided:

[0253] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is completed. For the sake of brevity, it will not be described in detail here.

[0254] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0255] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0256] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method described in Example 1 is completed.

[0257] The method in Example 1 can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it is not described in detail here.

[0258] Those skilled in the art will appreciate that the units, i.e., algorithm steps, of the various examples described in conjunction with this embodiment can be implemented in electronic hardware or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0259] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. An active suspension system control method based on beneficial disturbance, coupling and nonlinearity, characterized in that: include: Construct a bionic nonlinear reference model of the active suspension system and use the output of the bionic nonlinear reference model as the desired trajectory; A fuzzy disturbance observer is constructed to estimate the collective disturbance in the biomimetic nonlinear reference model; Define the state coupling effect index and the disturbance effect index, and judge whether the state coupling and collective disturbance in the bionic nonlinear reference model are beneficial according to the two effect indices, so as to construct a tracking controller that considers beneficial state coupling and beneficial disturbance, so that the suspension travel of the active suspension system tracks the expected trajectory; The state coupling effect index is: in, is the suspension travel tracking error, is an auxiliary positive number, and are the stiffness coefficient and damping coefficient of the tire respectively, is the tire travel; The disturbance effect index is: in, is the suspension travel tracking error; is the estimated value of the collective disturbance; When the state coupling effect index is less than zero, it means that the state coupling is beneficial; when it is greater than zero, it means that the state coupling is harmful; when it is equal to zero, it means that the state coupling has no effect; When the disturbance effect index is less than zero, it means the disturbance is beneficial; when it is greater than zero, it means the disturbance is harmful; when it is equal to zero, it means the disturbance has no effect.

2. The active suspension system control method based on beneficial disturbance, coupling and nonlinearity as claimed in claim 1, characterized in that: The collective disturbance includes parameter uncertainty, unmodeled dynamic uncertainty and external disturbance; the collective disturbance It is expressed as: in, and are the sprung mass and unsprung mass, is the expected trajectory, is the suspension travel tracking error; D is an unknown disturbance; Unknown disturbance D for: in, and are stiffness coefficients, is the damping coefficient, and are the stiffness coefficient and damping coefficient of the tire respectively, is the suspension travel, , , , and are the coefficient perturbation ranges, and are the unmodeled dynamic disturbance and external disturbance, respectively. For tire travel.

3. The active suspension system control method based on beneficial disturbance, coupling and nonlinearity as claimed in claim 1, characterized in that: The fuzzy disturbance observer is: in, is the positive control gain, and are stiffness coefficients, is the damping coefficient, and are the stiffness coefficient and damping coefficient of the tire respectively, is the suspension travel tracking error, and are the sprung mass and unsprung mass, is the tire travel, is the expected trajectory, represents the control input, is an auxiliary function, is the estimated value of the collective disturbance.

4. The active suspension system control method based on beneficial disturbance, coupling and nonlinearity as claimed in claim 1, characterized in that: The tracking controller is: in, , , is a positive control gain, is the suspension travel tracking error, is an auxiliary positive number, and are stiffness coefficients, is the damping coefficient, is the expected trajectory, is an auxiliary function, and are the sprung mass and unsprung mass, and are the stiffness coefficient and damping coefficient of the tire respectively, is the tire travel, is the estimated value of the collective perturbation, is the state coupling effect indicator, It is an indicator of disturbance effect.

5. Active suspension system control system based on beneficial disturbance, coupling and nonlinearity, characterized in that: include: A model building module is configured to build a bionic nonlinear reference model of the active suspension system, using the output of the bionic nonlinear reference model as the desired trajectory; a disturbance estimation module configured to construct a fuzzy disturbance observer to estimate the collective disturbance in the biomimetic nonlinear reference model; A tracking control module is configured to define a state coupling effect index and a disturbance effect index, and judge whether the state coupling and the collective disturbance in the bionic nonlinear reference model are beneficial according to the two effect indexes, so as to construct a tracking controller that considers the beneficial state coupling and the beneficial disturbance, so as to make the suspension travel of the active suspension system track the expected trajectory; The state coupling effect index is: in, is the suspension travel tracking error, is an auxiliary positive number, and are the stiffness coefficient and damping coefficient of the tire respectively, is the tire travel; The disturbance effect index is: in, is the suspension travel tracking error; is the estimated value of the collective disturbance; When the state coupling effect index is less than zero, it means that the state coupling is beneficial; when it is greater than zero, it means that the state coupling is harmful; when it is equal to zero, it means that the state coupling has no effect; When the disturbance effect index is less than zero, it means the disturbance is beneficial; when it is greater than zero, it means the disturbance is harmful; when it is equal to zero, it means the disturbance has no effect.

6. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 4 is completed.

7. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 4.

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