Distributed driving automobile dynamic inertia suspension model construction and control method

Abstract

The invention discloses a method for constructing and controlling a dynamic inertia suspension model of a distributed driving automobile in the technical field of vibration isolation of vehicle suspensions. The method comprises the following working steps: step 1, constructing an ideal reference model and an actual controlled model; 2, establishing a generalized error vector; step 3, establishing a sliding mode switching surface function; step 4, establishing a sliding mode control law; and 5, analyzing the performance of the controllable dynamic inertia suspension of the distributed driving automobile. According to the method, the dynamic response performance and the comprehensive control capability of the dynamic inertia suspension system of the distributed driving automobile can be improved under the operating environment of high nonlinearity, strong uncertainty and multi-disturbance.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle suspension vibration isolation, and in particular to a distributed drive vehicle dynamic inertia suspension model construction and control method. Background Art

[0002] In-wheel motor-driven vehicles are widely considered to be the ideal direction for the development of electric vehicles due to their high degree of electrification, simplified mechanical structure and significantly improved energy efficiency. However, they have problems such as a significant increase in unsprung mass and radial unbalanced electromagnetic force disturbances, which seriously affect the vertical dynamic performance of the vehicle, especially the difficulty in balancing ride comfort and handling stability, which has become a bottleneck in the current technological development. In order to effectively address the above problems, dynamic inertia suspension technology has gradually become an important way to improve the vertical performance of vehicles. Among them, the inertia container, as a new type of inertial adjustment element, can be introduced into the suspension system to construct an "inertia container-spring-damper" topology structure, which significantly enhances the ability to control frequency domain vibrations. Although passive suspension has an irreplaceable position in the vehicle suspension system due to its simple structure and reliable operation. However, the following problems still exist: 1. Although traditional sliding mode control has good robustness, it is still difficult to effectively suppress nonlinear disturbances under complex road conditions by relying solely on passive structures under complex working conditions, which can easily cause significant vibration, affecting system stability and mechanical durability;

[0003] 2. Although conventional fuzzy control has adaptive capabilities, it has certain limitations in response speed and accuracy. Summary of the Invention

[0004] The purpose of the present invention is to solve the above problems and provide a distributed drive vehicle dynamic inertia suspension model construction and control method, which can improve the dynamic response performance and comprehensive control capability of the distributed drive vehicle dynamic inertia suspension system in a highly nonlinear, highly uncertain and multi-disturbance operating environment.

[0005] In order to achieve the above-mentioned purpose, the distributed drive vehicle dynamic inertia suspension model construction and control method of the present invention adopts the following technical solutions:

[0006] A distributed drive vehicle dynamic inertia suspension model construction and control method includes the following steps:

[0007] Step 1: Build an ideal reference model, analyze its performance, and then select a controllable inertial suspension system consisting of an electromechanical inertia capacitor and a parallel spring to establish the actual controlled model;

[0008] Step 2: Analyze the state vectors of the ideal reference model and the actual controlled model to obtain a generalized error vector, and introduce the generalized error dynamics into the asymptotically stable sliding mode dynamics. Based on the ideal reference model and the actual controlled model of the two-degree-of-freedom quarter-suspension, the velocity error, displacement error, and their integrals of the sprung mass are used as the generalized error vectors for sliding mode control, and the error dynamics equations are established.

[0009] Step 3: Perform sliding mode variable structure control on the generalized error vector, establish the sliding mode switching surface function and the sliding mode control system motion equation;

[0010] Step 4: When the system motion trajectory reaches the sliding mode surface, the equivalent control law is activated. Then, based on the conditions for the sliding mode to be established, the constant velocity reaching law is used to obtain the output control force of the sliding mode control system, and finally the sliding mode control law is established.

[0011] Step 5: Use an electromechanical inertia capacitor to execute the output control force and perform performance analysis on the controllable dynamic inertia suspension of the distributed drive vehicle under random road input.

[0012] Preferably, the kinetic equation of the ideal reference model constructed in step 1 is:

[0013]

[0014] In formula (1), m s is the sprung mass, z s is the vertical displacement of the controlled model body, z u is the vertical displacement of the controlled model wheel, z r is the road roughness displacement, k is the suspension spring stiffness, k t is the equivalent stiffness of the tire, c is the suspension damping coefficient, F r_Z is the unbalanced radial electromagnetic force, b is the inertia constant, s is the complex frequency variable in Laplace transform, m us is the mass of the motor stator, m es is the mass of the motor rotor.

[0015] Preferably, the dynamic equation of the actual controlled model constructed in step 1 is as follows:

[0016]

[0017] In formula (2), m u is the unsprung mass, F b is the control force of the electromechanical inertia vessel.

[0018] Preferably, the generalized error vector in step 2 is:

[0019]

[0020] Where e is the generalized error vector, is the first-order derivative of e, z s is the vertical displacement of the controlled model body, z sr For z s Reference value of

[0021] And the error dynamics equation is established as:

[0022]

[0023] In formula (5),

[0024]

[0025] Among them, A e is the error system matrix, B e is the error input matrix, H e is the error model matrix, G e is the input matrix of the drive system, u is the control signal of the sliding mode controller, X is the input variable, is the state variable, z ur For z u The reference value of M is the total mass of the system, T(s) is the transfer function of the suspension system, and k r is the spring stiffness of the reference model, c v is the velocity-dependent damping coefficient.

[0026] Preferably, the sliding mode switching function in step 3 is: s = Ce, then And C=[1c1 c2]; thus the motion equation of the sliding mode control system is:

[0027] C(s)=c2+c1s+s 2 (6)

[0028] Where s is the sliding mode switching surface function, C is the coefficient matrix of the sliding mode switching surface function, and c1 and c2 are constants in the function.

[0029] Preferably, c1=4, c2=8, so that the characteristic roots of the sliding mode motion equation are located in the left half plane of the complex frequency domain.

[0030] Preferably, in step 4, the equivalent control law is started, s=0 and Get the equivalent control law u dep for:

[0031]

[0032] At this time, the sliding mode motion equation is

[0033] Preferably, the conditions for the establishment of the sliding mode in step 4 are The constant velocity reaching law is used to improve the dynamic quality of the sliding mode motion segment. The constant velocity reaching law is:

[0034]

[0035] In formula (9), s is the sliding mode switching surface function, is the first-order derivative of s, ε is a positive constant that satisfies ε>0, and sgn(s) is a sign function;

[0036] Take the Lyapunov function but sgn(s)≤0, the system is stable;

[0037] The output control force of the sliding mode control system is obtained as:

[0038] u=u dep +ε sgn(s) (10) The sliding mode control law is established as:

[0039]

[0040] Preferably, the fuzzy sliding mode control quantity is obtained by using a two-dimensional fuzzy controller with the sliding mode switching surface function and its derivative as input and the absolute value of the output replacing the approach speed of the moving point in the sliding mode control system.

[0041] Preferably, the electromechanical inertia device in step 5 is a ball screw inertia device, and the performance of the distributed drive vehicle controllable dynamic inertial suspension is analyzed from a time domain perspective. The electromechanical inertia device of the present invention is structured in a parallel coupling configuration with a rotating motor. The rotating motor drives the screw through a coupling to rotate. To achieve good response speed and high control accuracy, this study uses a Mitsubishi HG-KR73(B) servo motor. The time domain analysis method of the present invention is as follows: the dynamic performance of the designed distributed drive vehicle controllable dynamic inertial suspension is simulated and analyzed under random road input. Specifically, the vehicle body acceleration, suspension dynamic travel, and tire dynamic load are used as research objects, and the performance response differences at a vehicle speed of 20 m / s are compared among a traditional passive suspension of a distributed drive vehicle, a passive inertial suspension of a distributed drive vehicle, a generalized skyhook dynamic inertial suspension with controllable inertia and damping, and the controllable dynamic inertial suspension of the distributed drive vehicle of the present invention.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. This invention effectively combines the robustness of fuzzy control with the rapid response of sliding mode control, significantly improving the suspension system's dynamic tracking accuracy and anti-interference capabilities under complex operating conditions. It also suppresses the chattering phenomenon associated with traditional sliding mode control through adaptive fuzzy rules, thereby enhancing the overall performance of the suspension while maintaining a balance between ride comfort, vehicle stability, and mechanical durability.

[0044] 2. The present invention adopts a distributed drive vehicle dynamic inertia suspension design, especially for the nonlinear time-varying parameter optimization problem in a multi-source uncertain disturbance environment. It innovatively constructs a collaborative framework of switching surface function and fuzzy reasoning mechanism, breaking through the adaptability limitations of traditional linear control to high-order dynamic systems and providing a new solution for the design of distributed drive vehicle dynamic inertia suspension. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of the model construction and control method of a distributed drive vehicle dynamic inertia suspension.

[0046] Figure 2 is the actual controlled model diagram;

[0047] Figure 3 is the ideal reference model diagram;

[0048] Figure 4 This is a time domain comparison diagram of vehicle acceleration under random roads;

[0049] Figure 5 This is a time domain comparison diagram of suspension dynamic travel under random roads;

[0050] Figure 6 This is a time domain comparison diagram of tire dynamic load under random road conditions. DETAILED DESCRIPTION

[0051] The present invention will be further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, modifications of various equivalent forms of the present invention made by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0052] like Figure 1-6 As shown, a distributed drive vehicle dynamic inertia suspension model construction and control method includes the following steps:

[0053] Step 1: Build an ideal reference model, analyze its performance, and then select a controllable inertial suspension system consisting of an electromechanical inertia capacitor and a parallel spring to establish the actual controlled model;

[0054] The dynamic equation of the ideal reference model is:

[0055]

[0056] In formula (1), m s is the sprung mass, z s is the vertical displacement of the controlled model body, z u is the vertical displacement of the controlled model wheel, z r is the road roughness displacement, k is the suspension spring stiffness, k t is the equivalent stiffness of the tire, c is the suspension damping coefficient, F r_Z is the unbalanced radial electromagnetic force, b is the inertia constant, s is the complex frequency variable in Laplace transform, m us is the mass of the motor stator, m es is the mass of the motor rotor.

[0057] The dynamic equation of the actual controlled model is as follows:

[0058]

[0059] In formula (2), m u is the unsprung mass, F b is the control force of the electromechanical inertia vessel.

[0060] Step 2: Analyze the state vectors of the ideal reference model and the actual controlled model to obtain a generalized error vector, and introduce the generalized error dynamics into the asymptotically stable sliding mode dynamics. Based on the ideal reference model and the actual controlled model of the two-degree-of-freedom quarter-suspension, the velocity error, displacement error, and their integrals of the sprung mass are used as the generalized error vectors for sliding mode control, and the error dynamics equations are established.

[0061] Where the generalized error vector is:

[0062]

[0063] Where e is the generalized error vector, is the first-order derivative of e, z s is the vertical displacement of the controlled model body, z sr For z s Reference value of

[0064] And the error dynamics equation is established as:

[0065]

[0066] In formula (5),

[0067]

[0068] Among them, A e is the error system matrix, B e is the error input matrix, He is the error model matrix, G e is the input matrix of the drive system, u is the control signal of the sliding mode controller, X is the input variable, is the state variable, z ur For z u The reference value of M is the total mass of the system, T(s) is the transfer function of the suspension system, and k r is the spring stiffness of the reference model, c v is the velocity-dependent damping coefficient.

[0069] Step 3: Perform sliding mode variable structure control on the generalized error vector, establish the sliding mode switching surface function and the sliding mode control system motion equation;

[0070] The sliding mode switching function is: s = Ce, then And C=[1 c1 c2]; thus the motion equation of the sliding mode control system is:

[0071] C(s)=c2+c1s+s 2 (6)

[0072] Where s is the sliding mode switching surface function, C is the coefficient matrix of the sliding mode switching surface function, c1 and c2 are constants in the function, which are usually set by design requirements.

[0073] In order to ensure the asymptotic stability of the system and its excellent dynamic quality, it is necessary to ensure that all the characteristic roots of the motion equation are on the left half plane of the complex frequency domain (s domain), and the values ​​of c1 and c2 are: c1 = 4, c2 = 8.

[0074] Step 4: When the system motion trajectory reaches the sliding surface, the equivalent control law starts. At this time, s = 0 and Get the equivalent control law u dep for:

[0075]

[0076] At this time, the sliding mode motion equation is

[0077] The existence and accessibility of the sliding mode determine the controller parameters. According to the establishment conditions of the sliding mode, The constant velocity reaching law is used to improve the dynamic quality of the sliding mode motion segment. The constant velocity reaching law is:

[0078]

[0079] In formula (9), s is the sliding mode switching surface function, is the first-order derivative of s, ε is a positive constant that satisfies ε>0, and sgn(s) is a sign function;

[0080] Take the Lyapunov function but sgn(s)≤0, the system is stable;

[0081] The output control force of the sliding mode control system is obtained as:

[0082] u=u deq +εsgn(s) (10) The sliding mode control law is established as:

[0083]

[0084] Furthermore, a two-dimensional fuzzy controller is used as the input of the sliding mode switching surface function and its derivative, and the absolute value of the output replaces the approaching speed of the moving point in the sliding mode control system to obtain the fuzzy sliding mode control quantity.

[0085] Step 5: Use an electromechanical inertial device (e.g., a ball screw inertial device) to generate output control force. The performance of the distributed drive vehicle's controllable inertial suspension is analyzed from a time domain perspective. The electromechanical inertial device in this invention is a parallel coupling between the inertial device and a rotating motor. The rotating motor drives the screw through a coupling. To achieve good response speed and high control accuracy, this study uses a Mitsubishi HG-KR73(B) servo motor.

[0086] The time domain analysis method of the present invention is as follows: under random road input, the dynamic performance of the designed distributed drive vehicle controllable dynamic inertia suspension is simulated and analyzed;

[0087] Among them, the random road surface input is:

[0088]

[0089] Where v is the vehicle speed, w(t) is the white noise signal, G q (n0) is the road roughness coefficient.

[0090] The present invention uses vehicle body acceleration, suspension dynamic travel, and tire dynamic load as research objects. The performance responses of a conventional passive suspension for a distributed drive vehicle, a passive inertial suspension for a distributed drive vehicle, a generalized skyhook dynamic inertial suspension with controllable inertia and damping for a distributed drive vehicle, and the controllable dynamic inertial suspension of the present invention at a vehicle speed of 20 m / s are compared. A comparison table of the root mean square values ​​is shown in Table 1.

[0091] Table 1 Comparison of RMS values ​​of various suspension performance indicators and optimization effects

[0092]

[0093] Through Table 1 and Figure 4-6Time-domain analysis shows that compared to traditional passive suspension in distributed drive vehicles, the passive inertial suspension reduces the RMS values ​​of body acceleration and suspension travel by 2% and 0%, respectively. The controllable inertial suspension reduces the RMS values ​​of body acceleration and suspension travel by 24.5% and 32.6%, respectively. At this time, the RMS values ​​of tire dynamic loads for both the passive inertial suspension and the controllable inertial suspension do not show significant improvement compared to traditional passive suspension. Therefore, in terms of time-domain indicators, the controllable inertial suspension of distributed drive vehicles shows significant performance improvements compared to both the passive inertial suspension and traditional passive suspension of distributed drive vehicles.

[0094] In summary, from a time domain perspective, the controllable dynamic inertia suspension of a distributed drive vehicle with adaptive fuzzy sliding mode control can effectively reduce the deterioration of the RMS acceleration of the vehicle body and the increase in suspension travel caused by the increase in unsprung mass and unbalanced radial electromagnetic force.

[0095] The embodiments described are preferred implementations of the present invention, but the present invention is not limited to the above implementations. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essence of the present invention are within the scope of protection of the present invention.

Claims

1. A distributed drive vehicle dynamic inertia suspension model construction and control method, characterized in that: The following steps are included: Step 1: Build an ideal reference model, analyze its performance, and then select a controllable inertial suspension system consisting of an electromechanical inertia capacitor and a parallel spring to establish the actual controlled model; Step 2: Analyze the state vectors of the ideal reference model and the actual controlled model to obtain the generalized error vector, and introduce the generalized error dynamics into the asymptotically stable sliding mode dynamics; Based on the ideal reference model and actual controlled model of a two-degree-of-freedom quarter-suspension, the velocity error, displacement error and their integrals of the sprung mass are used as generalized error vectors for sliding mode control, and the error dynamics equation is established. Step 3: Perform sliding mode variable structure control on the generalized error vector, establish the sliding mode switching surface function and the sliding mode control system motion equation; Step 4: When the system motion trajectory reaches the sliding mode surface, the equivalent control law is activated. Then, based on the conditions for the sliding mode to be established, the constant velocity reaching law is used to obtain the output control force of the sliding mode control system, and finally the sliding mode control law is established. Step 5: Use an electromechanical inertia capacitor to execute the output control force and perform performance analysis on the controllable dynamic inertia suspension of the distributed drive vehicle under random road input.

2. The method for constructing and controlling a dynamic inertia suspension model of a distributed drive vehicle according to claim 1, characterized in that: The dynamic equation of the ideal reference model constructed in step 1 is: In formula (1), m s is the sprung mass, z s is the vertical displacement of the controlled model body, z u is the vertical displacement of the controlled model wheel, z r is the road roughness displacement, k is the suspension spring stiffness, k t is the equivalent stiffness of the tire, c is the suspension damping coefficient, F r_Z is the unbalanced radial electromagnetic force, b is the inertia constant, s is the complex frequency variable in Laplace transform, m us is the mass of the motor stator, m es is the mass of the motor rotor.

3. The method for constructing and controlling a dynamic inertia suspension model of a distributed drive vehicle according to claim 2, characterized in that: The dynamic equation of the actual controlled model constructed in step 1 is as follows: In formula (2), m u is the unsprung mass, F b is the control force of the electromechanical inertia vessel.

4. The method for constructing and controlling a dynamic inertia suspension model of a distributed drive vehicle according to claim 3, wherein: The generalized error vector in step 2 is: Where e is the generalized error vector, is the first-order derivative of e, z s is the vertical displacement of the controlled model body, z sr For z s Reference value of And the error dynamics equation is established as: In formula (5), Among them, A e is the error system matrix, B e is the error input matrix, H e is the error model matrix, G e is the input matrix of the drive system, u is the control signal of the sliding mode controller, X is the input variable, is the state variable, z ur For z u The reference value of M is the total mass of the system, T(s) is the transfer function of the suspension system, and k r is the spring stiffness of the reference model, c v is the velocity-dependent damping coefficient.

5. The method for constructing and controlling a dynamic inertia suspension model of a distributed drive vehicle according to claim 4, characterized in that: The sliding mode switching function in step 3 is: s = Ce, then And C = [1c1c2]; the motion equation of the sliding mode control system is obtained as follows: C(s)=c2+c1s+s 2 (6) Where s is the sliding mode switching surface function, C is the coefficient matrix of the sliding mode switching surface function, and c1 and c2 are constants in the function.

6. The method for constructing and controlling a dynamic inertia suspension model of a distributed drive vehicle according to claim 5, characterized in that: c1=4,c2=8, so that the characteristic roots of the sliding mode motion equation are located in the left half plane of the complex frequency domain.

7. The method for constructing and controlling a dynamic inertia suspension model of a distributed drive vehicle according to claim 6, characterized in that: In step 4, the equivalent control law starts, s = 0 and Get the equivalent control law u dep for: At this time, the sliding mode motion equation is 8. The method for constructing and controlling a dynamic inertia suspension model of a distributed drive vehicle according to claim 7, characterized in that: The establishment condition of sliding mode in step 4 is The constant velocity reaching law is used to improve the dynamic quality of the sliding mode motion segment. The constant velocity reaching law is: In formula (9), s is the sliding mode switching surface function, is the first-order derivative of s, ε is a positive constant that satisfies ε>0, and sgn(s) is a sign function; Take the Lyapunov function but sgn(s)≤0, the system is stable; The output control force of the sliding mode control system is obtained as: u=u deq +εsgn(s) (10) The sliding mode control law is established as:

9. The method for constructing and controlling a dynamic inertia suspension model of a distributed drive vehicle according to claim 8, characterized in that: By using a two-dimensional fuzzy controller as the input of the sliding mode switching surface function and its derivative, the absolute value of the output replaces the approaching speed of the moving point in the sliding mode control system to obtain the fuzzy sliding mode control quantity.

10. The method for constructing and controlling a dynamic inertia suspension model of a distributed drive vehicle according to claim 1, characterized in that: In step five, the electromechanical inertia device is a ball screw inertia device, and the performance of the controllable dynamic inertia suspension of the distributed drive vehicle is analyzed from the time domain perspective.

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