Suspension control system and control method thereof

Abstract

The application discloses a suspension control system and a control method thereof, and belongs to the field of vehicle control technology. The suspension control system comprises a suspension system, a fuzzy adaptive PID controller, a magnetorheological damping force limiter, a magnetorheological damper controller and an inertance coefficient adjusting unit, the inertance container is a variable inertance inertance container; a sprung mass acceleration sensor transmits a signal to the fuzzy controller to obtain a target damping force, and the force is input to the magnetorheological damper controller through the magnetorheological damping force limiter, and the magnetorheological damper controller provides an actual damping force to the suspension system; the optimal controller is designed, the non-sprung mass acceleration x t , displacement x t -x b , x b -x r and the actual damping force are weighted to obtain an optimal inertance coefficient under a current road condition. The system and the method can convert wheel resonance into inertance container resonance, the control method is beneficial to separately controlling the inertance coefficient under different road conditions, can be combined with the magnetorheological damper for joint control, can adjust parameters in real time, and realizes optimal comprehensive performance of the system under all road conditions.

Description

Technical Field

[0001] This invention belongs to the field of vehicle vibration control, and particularly relates to a suspension control system and its control method. Background Technology

[0002] The suspension system is a crucial component of a vehicle, transmitting vertical forces introduced from the road surface and acting between the vehicle body and wheels to dampen and reduce vehicle vibrations caused by road excitation. Based on whether damping and stiffness change with driving conditions, suspension systems are categorized into passive suspension, semi-active suspension, and active suspension. Passive suspensions, due to their fixed spring stiffness and damper damping coefficients, cannot adapt to different road conditions and thus have limited vibration damping performance. Furthermore, traditional passive suspensions dissipate vehicle vibration energy as heat, resulting in significant energy waste. Active suspensions can output control forces in real-time according to road conditions, but require external energy supply, leading to substantial energy consumption. Semi-active suspensions, while not outputting damping force in real-time, can adjust damping or stiffness in real-time, greatly reducing energy consumption. A typical example of a semi-active suspension is the magnetorheological semi-active suspension, which features a simple structure, convenient control, and combines vibration energy recovery with magnetorheological damper technology to minimize energy consumption and improve vehicle dynamics.

[0003] The main control algorithms for current vehicle suspension systems include: control algorithms based on vehicle state determination, control algorithms based on classical control theory, control algorithms based on optimal control theory, and control algorithms based on intelligent optimization theory. The most common control strategies include: fuzzy control, sliding mode variable structure control, ceiling / floor suspension and its derivatives, and optimal control. Linear Quadratic Optimal Control (LQR) in optimal control derives a performance functional from the control objective and then uses variational theory to find the optimal control quantity that minimizes the functional value. Fuzzy control can effectively solve multi-parameter nonlinear problems in semi-active systems, exhibiting good robustness and strong versatility.

[0004] The problem of suppressing the negative effects of vertical vibration in automobiles falls under the vehicle suspension system. The traditional "spring-damper" structure lacks an effective "inertial element," which restricts the overall improvement of the suspension. Although existing technologies introduce inertial containers into vehicle suspensions to form a "inertial container-spring-damper" dynamic inertial suspension structure system, how to achieve active adjustment of its hydraulic inertial capacity through a basic ideal inertial container model, and how to control the "inertial container-spring-damper" dynamic inertial suspension to achieve ideal smoothness and comfort are current technical challenges. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this application proposes a suspension control system and its control method. This system independently controls the variable inertia container whenever road conditions change, or it can be jointly controlled with a magnetorheological damper. A sprung mass acceleration sensor transmits a signal to a fuzzy controller to obtain the desired damping force. This desired damping force is then passed through a magnetorheological damping force limiter and input to the magnetorheological damper controller, which provides the actual damping force to the suspension system. This application also designs an optimal controller for the suspension system. The control objective is to minimize the sum of the squares of the four output variables—unsprung mass acceleration, displacement, and magnetorheological damper control force—with certain weights, and to obtain the optimal inertia coefficient under the current road conditions, which is then transmitted to the inertia container. The suspension system designed in this application can convert wheel resonance into inertia container resonance. Its control method facilitates independent control of the inertia coefficient under different road conditions and can be jointly controlled with the magnetorheological damper to achieve optimal overall system performance across all road conditions.

[0006] The technical solution adopted in this invention is as follows:

[0007] A suspension control system, including

[0008] A suspension system comprising an inertia container, a spring, and a damper connected in parallel between the unsprung mass and the tire's equivalent spring, as well as a suspension spring, a zero-magnetic-field damper, and a magnetorheological damper connected in parallel between the unsprung mass and the sprung mass; the inertia container is a variable inertia container.

[0009] A fuzzy adaptive PID controller receives the vertical vibration acceleration of the vehicle body on the sprung mass in the suspension system; the fuzzy adaptive PID controller internally adjusts the k... p k i k d The desired damping force is calculated by real-time nonlinear adjustment of these three parameters;

[0010] A magnetorheological damping force limiter receives the desired damping force output by a fuzzy adaptive PID controller, performs damping force limitation processing within the magnetorheological damping force limiter, and outputs the limited desired damping force.

[0011] A magnetorheological damper controller, wherein the magnetorheological damper controller limits the desired damping force, and outputs the actual damping force to the magnetorheological damper of the suspension system according to the limited desired damping force;

[0012] The inertia coefficient adjustment unit includes a road condition identification unit and an optimal controller. The road condition identification unit is used to obtain the unsprung mass acceleration x″. t Displacement x t -xb x b -x r x t For unsprung mass displacement, x b For the displacement of the inertial container, x r For road surface excitation; the optimal controller obtains x″ t x t -x b x b -x r The actual damping force is calculated, and the squares of these four parameters are weighted to obtain the optimal inertial capacity coefficient under the current road surface conditions. This coefficient is then sent to the inertial container to adjust the inertial capacity coefficient of the inertial container.

[0013] Furthermore, the two chambers on both sides of the inertial container are connected to a hydraulic motor through a stacked hydraulic control check valve. A stacked double check valve is installed on each of the two hydraulic delivery branches equipped with the stacked hydraulic control check valve, and a proportional valve is connected between the two hydraulic delivery branches.

[0014] Furthermore, the self-adjustment formulas for the three parameters in the fuzzy adaptive PID controller are expressed as follows:

[0015]

[0016] Where, k p k i k d For the parameters of the fuzzy adaptive PID controller; k p0 k i0 k d0 For parameters of a standard PID controller; Δk p Δk i Δk d q is the adjustment value of the PID controller; p q i q d These are the correction coefficients for the fuzzy adaptive PID controller.

[0017] Furthermore, in the fuzzy adaptive PID controller, the state error e and the rate of change ec of the state error of the suspension system are used as inputs to the fuzzy adaptive PID controller.

[0018] Furthermore, consider k in a standard PID controller p k i k d The interrelationships and constraints among these three parameters correspond to different values ​​of e and ec. Therefore, the specific principles for PID controller parameter tuning are as follows:

[0019] If e×ec>0, it means that the system state error is changing in the direction of increasing absolute value;

[0020] If e×ec < 0, it means the error is changing in the direction of decreasing absolute value. When e×ec < 0, k p k must be greater than e×ec>0 p k when e×ec < 0 i The value must also be less than k when e×ec < 0. p To prevent differential saturation and avoid significant overshoot in the system response, the differential action should be removed, i.e., k d =0.

[0021] Furthermore, the two input variables in the fuzzy adaptive PID controller are described using seven fuzzy language subsets, namely negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB; at the same time, the three output variables of the fuzzy controller are also described using seven fuzzy language subsets, represented as negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB, with the basic universe of discourse being the normalized interval [-1,1].

[0022] Furthermore, the magnetorheological damper controller employs an integral separation PI algorithm for its internal control.

[0023] Furthermore, the performance metrics for the LQR controller are established as follows:

[0024]

[0025] Where q1, q2, q3, and r are x″ t x t -x b x b -x r The weighting coefficients corresponding to the four performance indicators, including actual damping force; q is a diagonal matrix composed of q1, q2, and q3, expressed as q = diag[q1 q2 q3].

[0026] Furthermore, in the optimal control law design, the state space of the variable inertia-capacity ISD semi-active suspension model, y = Cx + Du, is substituted into the performance index of the LQR controller to obtain the following extension:

[0027]

[0028] Wherein, the weighting matrix of the state variables Q = C T qC is a positive semi-definite symmetric constant matrix, and the weighting matrix of the control input is R = D. T qD+r and cross term N=C T If qD is a positive definite symmetric constant, then the optimal control law is: u = -Kx;

[0029] The feedback gain matrix is ​​expressed as: K = R -1 (B T (P+N), where P is a solution to the Riccati equation.

[0030] P satisfies the Riccati equation: A T P+PA-(PB+N)R -1 (B T P+N)+Q=0

[0031] The optimal feedback gain matrix K can be obtained using the LQR function provided by Matlab software, and thus the optimal inertia coefficient can be obtained.

[0032] A suspension control method includes the following steps:

[0033] Step 1: Construct a suspension system with a variable inertia container;

[0034] Step 2: Based on the suspension system in the suspension control system of Step 1, collect the vertical vibration acceleration of the vehicle body and the unsprung mass acceleration x″ from the suspension system. t Displacement x t -x b x b -x r x t For unsprung mass displacement, x b For the displacement of the inertial container, x r For road surface excitation;

[0035] Step 3: The fuzzy adaptive PID controller outputs the desired damping force based on the vertical vibration acceleration of the vehicle body; the magnetorheological damping force limiter limits the desired damping force and inputs the limited desired damping force into the magnetorheological damper controller; the magnetorheological damper controller outputs the actual damping force to the magnetorheological damper 4 of the suspension system according to the limited desired damping force, thereby realizing the adjustment of the damping force in the suspension system.

[0036] Step 4: The inertia coefficient adjustment unit is based on x″ t x t -x b x b -x r The actual damping force is calculated, and the squares of these four parameters are weighted to obtain the optimal inertial capacity coefficient under the current road surface conditions. This coefficient is then sent to the inertial container to adjust the inertial capacity coefficient of the inertial container.

[0037] The beneficial effects of this invention are:

[0038] This invention proposes a novel suspension system control method to address the problem of suppressing the negative effects of vertical vibration in automobiles. This novel suspension system can convert wheel resonance into inertial container resonance. Its control method is conducive to controlling the inertial capacity coefficient separately under different road conditions, and can be jointly controlled with the magnetorheological damper to adjust parameters in real time and achieve optimal overall system performance under all road conditions. Attached Figure Description

[0039] Figure 1 This is a flowchart of the suspension system control of the present invention;

[0040] Figure 2 This is a schematic diagram of the suspension system structure of the present invention;

[0041] Figure 3 This is a structural diagram of the variable inertia capacity container of the present invention;

[0042] Figure 4 This is the Simulink control diagram of the fuzzy adaptive controller of this invention;

[0043] Figure 5 This is the fuzzy control rule diagram of the present invention;

[0044] Figure 6 This is a diagram illustrating the control effect of the present invention;

[0045] In the figure, 1: sprung mass, 2: suspension spring, 3: zero magnetic field damping, 4: magnetorheological damper, 5: unsprung mass, 6: inertial container, 7: spring, 8: damper, 9: tire equivalent spring, 10: hydraulic motor, 11: proportional valve, 12: stacked double one-way throttle valve, 13: stacked hydraulic control one-way valve. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0047] The suspension control system described in this invention has the following overall system structure block diagram: Figure 1 It includes: suspension system, fuzzy adaptive PID controller, inertia coefficient adjustment unit, magnetorheological damping force limiter, and magnetorheological damper controller.

[0048] 1. Suspension system

[0049] Combined with appendix Figure 2As shown, the suspension system includes an inertia container 6, a spring 7, a damper 8, a suspension spring 2, a zero magnetic field damper 3, and a magnetorheological damper 4; wherein, the inertia container 6 is a variable inertia container, and the inertia container 6, spring 7, and damper 8 are connected in parallel between the unsprung mass 5 and the tire equivalent spring 9, and the suspension spring 2, zero magnetic field damper 3, and magnetorheological damper 4 are connected in parallel between the unsprung mass 5 and the sprung mass 1.

[0050] In this application, the inertia container 6 is placed between the unsprung mass 5 and the tire equivalent spring 9, so that the wheel resonance is converted into the resonance of the inertia container 6, thereby eliminating the wheel resonance and improving the wheel dynamic deformation.

[0051] The structure of the inertial container 6 designed in this application is as follows: Figure 3 As shown, the device includes an inertial container 6 and its hydraulic circuit. The two chambers of the inertial container 6 are connected to a hydraulic motor 10 via stacked hydraulically controlled check valves 13. Stacked double check valves 12 are installed on each of the two hydraulic delivery branches equipped with the stacked hydraulically controlled check valves 13. A proportional valve 11 connects the two hydraulic delivery branches. The inertial container 6 designed in this application is based on an existing constant-inertia hydraulic inertial container. The inertial capacity coefficient of the inertial container is changed by altering the flow rate through the stacked double check valves 12. The stacked hydraulically controlled check valves 13 maintain the circuit pressure, and the proportional valve 11 adjusts the inertial capacity coefficient of the inertial container.

[0052] The equation of motion for the suspension system designed in this application is expressed as follows:

[0053]

[0054]

[0055]

[0056] In the formula: b s Let c be the inertia coefficient of a variable inertia container. ground F is the damping coefficient of the ground canopy. f For the output damping force of the magnetorheological damper, x s For the displacement of the spring-loaded mass, These are the sprung mass velocity and sprung mass acceleration, respectively; x t For non-sprung mass displacement, These represent the velocity and acceleration of the unsprung mass, respectively; x b For the displacement of the inertial container, These are the velocity and acceleration of the inertial container, respectively; x r For road surface excitation; m s For the sprung mass, m t The unsprung mass is c0; the suspension damping coefficient is k. sFor suspension stiffness, k t This refers to tire stiffness.

[0057] 2. Fuzzy Adaptive PID Controller

[0058] The vertical vibration acceleration of the vehicle body is obtained from the sprung mass 1 using an accelerometer, and the vertical vibration acceleration of the vehicle body is used as the output of the suspension system; the actual damping force is used as the input of the suspension system.

[0059] A fuzzy adaptive PID controller is constructed. The fuzzy adaptive PID controller receives the vertical vibration acceleration data of the vehicle body from the acceleration sensor, that is, the output of the suspension system is input into the fuzzy adaptive PID controller.

[0060] In this application, the vertical vibration acceleration of the vehicle body is defined as the state error of the suspension system, and the state error and the rate of change of the state error of the suspension system are used as inputs to the fuzzy adaptive PID controller. The state error of the suspension system is denoted as e, and the rate of change of the state error of the suspension system is denoted as ec.

[0061] Using a fuzzy controller to evaluate the three parameters (k) of a traditional PID controller p k i k d The desired damping force is calculated through real-time nonlinear adjustment. The fuzzy controller structure is as follows: Figure 4 As shown, the self-tuning formulas for the three parameters in the fuzzy adaptive PID controller are expressed as follows:

[0062]

[0063] Where, k p k i k d For the parameters of the fuzzy adaptive PID controller; k p0 k i0 k d0 For parameters of a standard PID controller; Δk p Δk i Δk d q is the adjustment value of the PID controller; p q i q d These are the correction coefficients for the fuzzy adaptive PID controller.

[0064] Taking into account the mutual influence and constraints among the three parameters of a standard PID controller, and corresponding to different values ​​of e and ec, the specific tuning principles for PID controller parameters are as follows:

[0065] If e×ec>0, it means that the system state error is changing in the direction of increasing absolute value.

[0066] If e×ec < 0, it means the error is changing in the direction of decreasing absolute value. When e×ec < 0, k p k must be greater than e×ec>0 p k when e×ec < 0 i The value must also be less than k when e×ec < 0. p To prevent differential saturation and avoid significant overshoot in the system response, the differential action should be removed, i.e., k d =0.

[0067] In fuzzy control, the two input variables of the fuzzy adaptive PID controller are described by seven fuzzy language subsets, namely negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB. At the same time, the three output variables of the fuzzy controller are also described by seven fuzzy language subsets, namely negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB. The basic universe of discourse is the normalized interval [-1,1].

[0068] The universes of discourse for the system state error e and the rate of change of error ec after fuzzy discretization are:

[0069] e={-1,-0.833,-0.667,-0.5,-0.333,-0.167,0,0.167,0.333,0.5,0.667,0.833,1}

[0070] ec={-1,-0.833,-0.667,-0.5,-0.333,-0.167,0,0.167,0.333,0.5,0.667,0.833,1}

[0071] Set the input to the fuzzy controller—the adjustment value Δk of the PID controller. p Δk i and Δk d If the fundamental domain is the normalized interval [-1, 1], then the adjustment amount Δk p Δk i and Δk d The domain of discourse is:

[0072] Δk p ={-1,-0.833,-0.667,-0.5,-0.333,-0.167,0,0.167,0.333,0.5,0.667,0.833,1}

[0073] Δk i={-1,-0.833,-0.667,-0.5,-0.333,-0.167,0,0.167,0.333,0.5,0.667,0.833,1}

[0074] Δk d ={-1,-0.833,-0.667,-0.5,-0.333,-0.167,0,0.167,0.333,0.5,0.667,0.833,1}

[0075] Referring to the PID controller parameter tuning principles given above, and taking into account the vibration control characteristics of the vehicle suspension system and expert knowledge and experience, such as... Figure 4 Formulate a fuzzy controller for e and ec to Δk p Δk i and Δk d The fuzzy control rules stipulate that when the error is large, the control quantity should be selected to eliminate the error as quickly as possible; when the error is small, the control quantity should be selected to prevent overshoot and prioritize system stability.

[0076] 3. Magnetorheological damping force limiter

[0077] The magnetorheological damping force limiter receives the desired damping force output from the fuzzy adaptive PID controller, performs damping force limitation processing within the magnetorheological damping force limiter, and outputs the limited desired damping force.

[0078] 4. Magnetorheological damper controller

[0079] The magnetorheological damper controller receives the desired damping force after constraint. Its internal control law outputs a control current corresponding to the desired damping force based on this constraint, thus obtaining the actual damping force. The internal control of the magnetorheological damper controller employs an integral-separated PI algorithm, which is referenced in "Sun Dong. Design and Experimental Study of Magnetorheological Semi-Active Suspension Controller Based on Feedback Linearized Kalman Observer [D]. Jiangsu University, 2020."

[0080] 5. Inertia coefficient adjustment unit

[0081] The inertia coefficient adjustment unit includes a road condition recognition unit and an optimal controller. Details are as follows:

[0082] The road condition recognition unit includes an acceleration sensor and a displacement sensor, which are used to acquire information on unsprung mass acceleration, unsprung mass displacement, and inertial container displacement, respectively; thereby obtaining the unsprung mass acceleration x″. t Displacement x t -x b x b -x r .

[0083] The optimal controller receives information from the road condition identification unit and the actual damping force output by the magnetorheological damper, and then adjusts the optimal controller for x″. t Displacement x t -x b x b -x r The squares of the four parameters—the actual damping force output by the magnetorheological damper, the inertial capacity coefficient under the current road conditions—are weighted to obtain the optimal inertial capacity coefficient, which is then sent to the inertial container to achieve variable inertial capacity adjustment.

[0084] In this application, the method for obtaining the optimal inertia coefficient is as follows:

[0085] The state variables and output variables of the suspension system are selected as follows:

[0086]

[0087]

[0088] The system input u is the actual damping force F output by the magnetorheological damper. f The disturbance is the road surface excitation w = q', where q is a diagonal matrix composed of q1, q2, and q3; the state space of the variable inertia capacities ISD semi-active suspension model is as follows:

[0089]

[0090] y = Cx + Du

[0091] Where matrices A, B, G, C, and D are represented as follows:

[0092]

[0093]

[0094]

[0095]

[0096]

[0097] The performance metrics for the LQR controller are as follows:

[0098]

[0099] Where q1, q2, q3, and r are x″ t x t -x b x b -x rThe weighting coefficients corresponding to the four performance indicators, including actual damping force; q is a diagonal matrix composed of q1, q2, and q3, expressed as q = diag[q1 q2 q3].

[0100] In the design of optimal control laws, substituting y = Cx + Du into the performance index of the LQR controller yields the following extension:

[0101]

[0102] Wherein, the weighting matrix of the state variables Q = C T qC is a positive semi-definite symmetric constant matrix, and the weighting matrix of the control input is R = D. T qD+r and cross term N=C T If qD is a positive definite symmetric constant, then the optimal control law is: u = -Kx;

[0103] The feedback gain matrix is ​​expressed as: K = R -1 (B T (P+N), where P is a solution to the Riccati equation.

[0104] P satisfies the Riccati equation: A T P+PA-(PB+N)R -1 (B T P+N)+Q=0

[0105] The optimal feedback gain matrix K can be obtained using the LQR function provided by Matlab software, and the optimal inertia coefficient can be obtained from this.

[0106] exist Figure 6 In the diagram, 6a, 6b, and 6c correspond to comparisons of wheel dynamic load, vehicle body vertical vibration acceleration, and suspension dynamic travel, respectively. Figure 6 In a, 6b, and 6c, the solid line represents a suspension system proposed in this patent, and the dashed line represents a passive suspension. By comparing the control effects of the suspension system proposed in this patent and the passive suspension, it can be seen that, compared with the passive suspension, the suspension system and its control effect designed in this invention have a significant effect on suppressing the vertical vibration acceleration of the vehicle body, reducing the suspension travel, and suppressing the dynamic deformation of the wheels.

[0107] The above embodiments are only used to illustrate the design concept and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications made based on the principles and design ideas disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A suspension control system, characterized in that, include The suspension system includes an inertia container (6), a spring (7), and a damper (8) connected in parallel between the unsprung mass (5) and the tire equivalent spring (9), and a suspension spring (2), a zero magnetic field damper (3), and a magnetorheological damper (4) connected in parallel between the unsprung mass (5) and the sprung mass (1); the inertia container (6) is a variable inertia container; A fuzzy adaptive PID controller receives the vertical vibration acceleration of the vehicle body on the sprung mass in the suspension system; the fuzzy adaptive PID controller internally... , , The desired damping force is calculated by real-time nonlinear adjustment of these three parameters; the self-adjustment formulas for the three parameters in the fuzzy adaptive PID controller are expressed as follows: ; in, , , These are the parameters for the fuzzy adaptive PID controller; , , These are the parameters for a standard PID controller; , , This is the adjustment value of the PID controller; , , These are the correction coefficients for the fuzzy adaptive PID controller; The state error of the suspension system and the rate of change of state error As input to the fuzzy adaptive PID controller; Considering standard PID controllers , , The interrelationships and constraints among these three parameters correspond to different state errors e and the rate of change of state error ec. Therefore, the specific principles for PID controller parameter tuning are as follows: like This indicates that the system state error is changing in the direction of increasing absolute value; like This indicates that the error is changing in the direction of decreasing absolute value. time Must be greater than of ,and time The value must also be less than time To prevent differential saturation and avoid significant overshoot in the system response, the differential action should be removed. ; The two input variables in the fuzzy adaptive PID controller are described by seven fuzzy language subsets: negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB. At the same time, the three output variables of the fuzzy controller are also described by seven fuzzy language subsets: negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB. The basic universe of discourse is the normalized interval [-1,1]. A magnetorheological damping force limiter receives the desired damping force output by a fuzzy adaptive PID controller, performs damping force limitation processing within the magnetorheological damping force limiter, and outputs the limited desired damping force. Magnetorheological damper controller, the magnetorheological damper controller limits the desired damping force, and outputs the actual damping force to the magnetorheological damper of the suspension system (4) according to the limited desired damping force. The inertia coefficient adjustment unit includes a road condition recognition unit and an optimal controller. The road condition recognition unit is used to acquire the unsprung mass acceleration. Displacement , , For non-sprung mass displacement, For the displacement of the inertial container, For road surface excitation; optimal controller acquisition , , And the actual damping force, and on , , The optimal inertial capacity coefficient under the current road surface conditions is obtained by weighting the squares of the four parameters: actual damping force, actual damping force, etc., and then sent to the inertial container to adjust the inertial capacity coefficient of the inertial container.

2. A suspension control system according to claim 1, characterized in that, The two chambers of the inertial container (6) are connected to the hydraulic motor (10) through the superimposed hydraulic control check valve (13). The two hydraulic transmission branches equipped with the superimposed hydraulic control check valve (13) are each equipped with a superimposed double check valve (12). A proportional valve (11) is connected between the two hydraulic transmission branches.

3. A suspension control system according to claim 1, characterized in that, The magnetorheological damper controller uses an integral separation PI algorithm for internal control.

4. A suspension control system according to claim 1, characterized in that, The performance metrics for the LQR controller are as follows: ; in, They are respectively , , and the weighting coefficient corresponding to the actual damping force; For the reason The resulting diagonal matrix is ​​represented as , Let y be the system input and y be the model state space. This represents the actual damping force output by the magnetorheological damper.

5. A suspension control system according to claim 4, characterized in that, In the design of optimal control laws, the model state space of the variable inertia-capacity ISD semi-active suspension is... Substituting this into the performance metrics of the LQR controller, we can expand it to: ; Wherein, the weighting matrix of the state variables It is a semi-positive definite symmetric constant matrix, and the weighting matrix controls the input quantities. and cross terms If the constant is positive definite and symmetric, then the optimal control law is: ; The feedback gain matrix is ​​expressed as: , This is a solution to the Riccati equation. P satisfies the Riccati equation: ; The optimal feedback gain matrix K is obtained using the LQR function provided by Matlab software, and the optimal inertia coefficient is obtained from it. A, B, C, and D are matrices.

6. A suspension control method, characterized in that, Includes the following steps: Step 1: Build a suspension control system as described in claim 1; Step 2: Based on the suspension system in the suspension control system of Step 1, collect the vertical vibration acceleration and unsprung mass acceleration of the vehicle body from the suspension system. Displacement , , For non-sprung mass displacement, For the displacement of the inertial container, For road surface excitation; Step 3: The fuzzy adaptive PID controller outputs the desired damping force based on the vertical vibration acceleration of the vehicle body; the magnetorheological damping force limiter limits the desired damping force and inputs the limited desired damping force into the magnetorheological damper controller. The magnetorheological damper controller outputs the actual damping force to the magnetorheological damper (4) of the suspension system according to the limited desired damping force, thereby realizing the adjustment of the damping force in the suspension system. Step 4, the inertia coefficient adjustment unit is based on , , And the actual damping force, and on , , The optimal inertial capacity coefficient under the current road surface conditions is obtained by weighting the squares of the four parameters, including the actual damping force, and then sent to the inertial container to adjust the inertial capacity coefficient of the inertial container.

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