In-wheel motor automobile active suspension control method and system under complex road conditions

Abstract

The present application relates to the technical field of automobile active suspension control, more particularly to an active suspension control method and system for an in-wheel motor automobile under complex road conditions. The control method comprises: obtaining the current driving state of the vehicle; and switching the output force of the active suspension actuator according to the driving information. The present application combines the extension LQG control and the preview MPC control to construct a complex road switching control scheme, and introduces a smoothing coefficient to ensure smooth transition during control switching, thereby improving the ride comfort of the vehicle under random road conditions containing discrete impacts. The present application further improves the driving smoothness and suspension working safety, especially the comprehensive control performance of the suspension under the influence of discrete impacts.

Description

Technical Field

[0001] This invention relates to the field of active suspension control technology for automobiles, and more specifically, to an active suspension control method and system for in-wheel motor automobiles under complex road conditions. Background Technology

[0002] Currently, suspension control algorithms mainly include PID control, sliding mode control, and adaptive control, all of which have achieved good results. However, a single control algorithm is difficult to meet the control objectives under different road conditions and cannot adapt to the performance requirements of the suspension system under complex road conditions (i.e., different grades of road surface and road surface containing multiple discrete impacts). Therefore, the method of combining and switching multiple controllers has been introduced to improve the control effect of the system and enhance the overall performance of the vehicle.

[0003] Chinese invention patent CN116533698A proposes an active suspension control method based on adaptive inversion fast terminal sliding mode. This method takes the vehicle's vertical and pitch motion trajectories on a smooth road surface as ideal states and establishes a non-singular integral sliding mode surface. Based on inversion sliding mode control and adaptive control methods, a vehicle vertical motion control law is designed, which can effectively improve vehicle ride comfort. However, this method only optimizes ride comfort and does not consider the vehicle's suspension safety.

[0004] Chinese invention patent CN110722950A proposes a semi-active suspension hybrid damping extension switching control method. It uses extension theory to dynamically match ceiling damping and floor damping, coordinating suspension safety and ride comfort to improve overall suspension performance. However, ceiling and floor damping control has limited application in active suspension control due to the limitation of adjustable damping range; furthermore, this method only considers random road surfaces and does not study the impact of discrete impacts such as speed bumps and potholes on the suspension system, making it difficult to adapt to suspension control requirements under complex road conditions. Summary of the Invention

[0005] Based on this, it is necessary to address the problems of poor coordination of suspension performance under operating conditions and insufficient consideration of discrete impact effects in existing active suspension control methods. Therefore, an active suspension control method and system for in-wheel motor vehicles under complex road conditions is provided.

[0006] This invention is achieved using the following technical solution:

[0007] In a first aspect, this invention discloses an active suspension control method for a hub motor vehicle under complex road conditions, comprising the following steps:

[0008] Obtain the vehicle's current driving status. Driving information includes: real-time vehicle speed and real-time forward collision warning information. Real-time forward collision warning information includes: whether there is a discrete impact ahead of the vehicle, and the real-time distance between the vehicle and the discrete impact.

[0009] The output force of the active suspension actuator is switched according to the driving information.

[0010] The switching rules include:

[0011] If there is no discrete impact in front of the vehicle, extensional LQG control is performed to make the output force of the active suspension actuator F. LQG ;

[0012] If there is a discrete impact in front of the vehicle, and this discrete impact does not appear within the MPC prediction domain, then extensional LQG control is performed to make the output force of the active suspension actuator F. LQG If the discrete impact occurs within the MPC prediction domain, then switch from extensional LQG control to pre-aiming MPC control, making the output force of the active suspension actuator F. t Before reaching the discrete impact, switch completely to pre-aiming MPC control to make the output force of the active suspension actuator F. MPC If the discrete impact disappears from the MPC prediction domain, then switch from preview MPC control to extensional LQG control, making the output force of the active suspension actuator F. t After the vehicle passes through the discrete impact, it fully switches to extensional LQG control, making the output force of the active suspension actuator F. LQG ;

[0013] Among them, F t F t The formula for calculating ′ is:

[0014]

[0015] In the formula, F t This indicates the transition output force from extensional LQG control to pre-aiming MPC control;

[0016] F t ′ represents the transition output force when switching from pre-aiming MPC control to extensional LQG control;

[0017] F LQG This represents the output force of the active suspension actuator calculated using the extension LQG algorithm based on the half-vehicle dynamics model of the suspension system.

[0018] F MPC This represents the output force of the active suspension actuator calculated using the pre-aiming MPC algorithm based on the half-vehicle dynamics model of the suspension system;

[0019] Δt represents the smoothing coefficient; Δt represents F. t From F LQG Switch to F MPC The required time, or F t From F MPC Switch to F LQG The time required.

[0020] This in-wheel motor vehicle's active suspension control method under complex road conditions implements the method or process according to embodiments of this disclosure.

[0021] Secondly, the present invention discloses an active suspension control system for a hub motor vehicle under complex road conditions, which uses the active suspension control method for a hub motor vehicle under complex road conditions disclosed in the first aspect.

[0022] The active suspension control system for in-wheel motor vehicles under complex road conditions includes: a driving information acquisition module, an output force calculation module, and a switching control module.

[0023] The driving information acquisition module is used to acquire the vehicle's driving information. The output force calculation module is used to calculate F. LQG F t F MPC F t The switching control module is used to switch the output force of the hub motor based on driving information.

[0024] The in-wheel motor vehicle's active suspension control system for complex road conditions implements the method or process according to embodiments of this disclosure.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. This invention combines extensional LQG control and forward-looking MPC control to construct a control scheme for complex road condition switching, and introduces a smoothing coefficient. This ensures a smooth transition during control switching, thereby improving ride comfort in random road conditions with discrete impacts.

[0027] 2. This invention takes into account the two conflicting indicators in active suspension—ride comfort and suspension safety—and optimizes the weighting coefficients of the extension LQG control based on the performance requirements of vehicles under different road grades to design output forces that meet both ride comfort and safety requirements. Furthermore, based on extension theory, a weighting allocation strategy is designed, and the actual output force is controlled by extension LQG to ensure coordinated control of vehicle ride comfort and safety according to different road conditions, thereby improving the overall performance of the vehicle.

[0028] 3. This invention addresses discrete impacts in roads by designing a pre-aiming MPC control that considers vehicle-front aiming information. This complements the shortcomings of extension LQG control, which does not consider discrete impacts, and can more effectively deal with discrete impacts, reduce the vertical acceleration and pitch acceleration of the vehicle when passing through discrete impacts, and improve the overall performance of the vehicle when passing through discrete impacts. Attached Figure Description

[0029] Figure 1 This is a simplified flowchart of the active suspension control method for a hub motor vehicle under complex road conditions in Embodiment 1 of the present invention;

[0030] Figure 2 This is a diagram of the half-vehicle dynamics model of the suspension system constructed in Embodiment 1 of the present invention;

[0031] Figure 3 for Figure 1 Schematic diagram of LQG control in Zhongketop;

[0032] Figure 4 for Figure 3 A schematic diagram of the extension domain and the classical domain in extensional LQG control;

[0033] Figure 5 This is a graph of the smoothing coefficient in Embodiment 1 of the present invention;

[0034] Figure 6 This is a comparative diagram of the experiment of extension LQG control and traditional LQG control on a Class B road surface in Embodiment 3 of the present invention;

[0035] Figure 7 This is a comparative diagram of the experiment of extension LQG control and traditional LQG control on a Class C road surface in Embodiment 3 of the present invention;

[0036] Figure 8 This is a comparison diagram of the vehicle pitch angle acceleration changes in Embodiment 4 of the present invention, performed on a Class B random road surface containing one speed bump and one pothole, using the control method of Embodiment 1 and the instantaneous control method.

[0037] Figure 9 This is a comparison diagram of the vertical acceleration of the vehicle body in Embodiment 4 of the present invention, performed on a Class B random road surface containing one speed bump and one pothole, using the control method of Embodiment 1 and the instantaneous control method.

[0038] Figure 10 This is a comparison diagram of the vehicle pitch angle acceleration changes in Embodiment 4 of the present invention under Class B random road conditions containing two speed bumps, using the control method of Embodiment 1 and a single extended LQG control.

[0039] Figure 11This is a comparison chart of the vertical acceleration of the vehicle body under the control method of Example 1 and the single extension LQG control in Example 4 of the present invention under a Class B random road condition containing two speed bumps.

[0040] Figure 12 This is a comparison diagram of the vehicle pitch angle acceleration changes in Embodiment 4 of the present invention under Class B random road conditions containing one speed bump and one pothole, using the control method of Embodiment 1 and a single extensible LQG control.

[0041] Figure 13 This is a comparison diagram of the vertical acceleration of the vehicle body under the control method of Example 1 and the single extensible LQG control in Example 4 of the present invention under a Class B random road condition containing one speed bump and one pothole.

[0042] Figure 14 This is a comparison graph showing the changes in vehicle pitch angle acceleration under the control method of Example 1 and the single extensible LQG control in Example 4 of the present invention on a Class B random road condition containing two potholes.

[0043] Figure 15 This is a comparison graph showing the changes in vehicle vertical acceleration under the control method of Example 1 and the single extensible LQG control in Example 4 of the present invention on a Class B random road surface condition containing two potholes. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] It should be noted that when a component is said to be "installed on" another component, it can be directly on the other component or it may be in a component that is centered on it. When a component is said to be "set on" another component, it can be directly set on the other component or it may also be in a component that is centered on it. When a component is said to be "fixed to" another component, it can be directly fixed to the other component or it may also be in a component that is centered on it.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0047] Example 1

[0048] Please see Figure 1 This is a schematic diagram of an active suspension control method for a hub motor vehicle under complex road conditions, as disclosed in Embodiment 1. Referring to the background art, complex road conditions refer to road surfaces of different grades and road surfaces containing multiple discrete impacts.

[0049] In summary, the control concept of this invention is:

[0050] Based on the half-vehicle dynamics model of the suspension system, extensional LQG control and anti-impact MPC control are combined. The switching timing is based on whether there is a discrete impact in the MPC prediction domain, and the output force is adjusted in real time. A smoothing coefficient is introduced during the switching. This reduces additional disturbances caused by control switching and improves the overall ride comfort of the vehicle.

[0051] Specifically, such as Figure 1 As shown, an active suspension control method for a hub motor vehicle under complex road conditions includes the following steps:

[0052] Acquire vehicle driving information. This driving information includes: real-time vehicle speed and real-time forward collision warning information. The real-time forward collision warning information includes: whether there is a discrete impact in front of the vehicle, and the real-time distance between the vehicle and the discrete impact.

[0053] Specifically, if there is a discrete impact in front of the vehicle, and the real-time distance between them is L, and the real-time vehicle speed is v, then the time required for the vehicle to reach the discrete impact, t0 = L / v, can be calculated. t0 is then compared with a preset MPC prediction domain, where the time domain length of the MPC prediction domain is (N... P -1)*T,N P This represents the number of prediction steps in MPC, and T represents the control step size: if 0 < t0 < (N P If t0 ≤ 0, it means that the discrete shock appears within the MPC prediction domain; if t0 ≤ 0, it means that the discrete shock disappears from the MPC prediction domain.

[0054] The output force of the active suspension actuators is switched based on driving information. The switching rules include:

[0055] If there is no discrete impact in front of the vehicle, extensional LQG control is performed to make the output force of the active suspension actuator F. LQG ;

[0056] If there is a discrete impact in front of the vehicle, and this discrete impact does not appear within the MPC prediction domain, then extensional LQG control is performed to make the output force of the active suspension actuator F. LQGIf the discrete impact occurs within the MPC prediction domain, then switch from extensional LQG control to pre-aiming MPC control, making the output force of the active suspension actuator F. t Before reaching the discrete impact, switch completely to pre-aiming MPC control to make the output force of the active suspension actuator F. MPC If the discrete impact disappears from the MPC prediction domain, then switch from preview MPC control to extensional LQG control, making the output force of the active suspension actuator F. t After the vehicle passes through the discrete impact, it fully switches to extensional LQG control, making the output force of the active suspension actuator F. LQG .

[0057] Among them, F t F t The formula for calculating ′ is:

[0058]

[0059] In the formula, F t This indicates the transition output force from extensional LQG control to pre-aiming MPC control;

[0060] F t ′ represents the transition output force when switching from pre-aiming MPC control to extensional LQG control;

[0061] F LQG This represents the output force of the active suspension actuator calculated using the extension LQG algorithm based on the half-vehicle dynamics model of the suspension system.

[0062] F MPC This represents the output force of the active suspension actuator calculated using the pre-aiming MPC algorithm based on the half-vehicle dynamics model of the suspension system;

[0063] Indicates the smoothing coefficient;

[0064] Δt represents F t From F LQG Switch to F MPC The required time, or F t From F MPC Switch to F LQG The time required.

[0065] Referring to the formulas above, both the extension LQG control and the anti-sighting MPC control are based on the half-vehicle dynamics model of the suspension system.

[0066] See Figure 2 For the half-vehicle dynamics model of the suspension system, it is actually a four-degree-of-freedom planar model of the vehicle's active suspension, and its construction process is as follows:

[0067] Based on the Lagrange equations, a system of model differential equations is established:

[0068]

[0069] In the formula, m s Indicates the vehicle's mass;

[0070] I sl This represents the pitch inertia of the vehicle body;

[0071] m uf Indicates the unsprung mass of the front axle;

[0072] c sf Indicates the front axle suspension damping;

[0073] k sf Indicates the front axle suspension stiffness;

[0074] k tf Indicates the front axle tire stiffness;

[0075] L f Indicates the distance from the front axle to the vehicle's center of gravity;

[0076] m ur Indicates the unsprung mass of the rear axle;

[0077] c sr Indicates the rear axle suspension damping;

[0078] k sr Indicates the rear axle suspension stiffness;

[0079] k tr Indicates the tire stiffness of the rear axle;

[0080] L r Indicates the distance from the rear axle to the vehicle's center of gravity;

[0081] F af Indicates the active control force of the front axle;

[0082] F ar Indicates the active control force of the rear axle;

[0083] This indicates the vertical acceleration of the vehicle body; For z s The second derivative of z; s This indicates the vertical displacement at the vehicle's center of gravity.

[0084] This indicates the vehicle's pitch angle acceleration; express The second derivative; Indicates the vehicle's pitch angle;

[0085] Indicate z sf The first derivative; z sf This indicates the vertical displacement at the front axle of the vehicle body;

[0086] Indicate z sr The first derivative; z sr This indicates the vertical displacement at the rear axle of the vehicle body;

[0087] Indicate z uf The first derivative; z uf This indicates the vertical displacement of the unsprung mass on the front axle;

[0088] Indicate z ur The first derivative; z ur This indicates the vertical displacement of the unsprung mass on the rear axle;

[0089] q f Indicates road surface excitation on the front axle;

[0090] q r Indicates the road surface excitation on the rear axle;

[0091] m f Indicates the mass of the front axle hub motor;

[0092] m r This indicates the mass of the rear axle hub motor.

[0093] It should be noted that some of the above parameters can be obtained from factory calibration data, while others can be collected using onboard sensors.

[0094] For z sf z sr Taking the second derivative and combining it with the above system of differential equations, we get:

[0095]

[0096]

[0097] Define the following input vector:

[0098]

[0099] U = [F af F ar ] T ;

[0100] Where X represents the system state variable; W represents the road surface excitation variable; and U represents the system control variable.

[0101] The system of differential equations of the model can be written as state equations:

[0102]

[0103] In the formula, A represents the system state matrix; B represents the control input matrix; and E represents the excitation input matrix.

[0104]

[0105]

[0106]

[0107] a1, a2, and a3 are proportioning coefficients:

[0108]

[0109] Set the output vector as follows:

[0110]

[0111] Construct the relationship between the output vector and the input vector:

[0112] Y = CX + DU;

[0113] Where C and D represent the appropriate dimension matrices:

[0114]

[0115]

[0116] Combining the above equations, we obtain the expression for a four-degree-of-freedom planar model of a vehicle's active suspension:

[0117]

[0118] The following sections will provide detailed explanations of extension LQG control and aiming MPC control:

[0119] ①Extensional LQG control is based on the half-vehicle dynamics model of the suspension system to obtain the output force F that adapts to different road surface levels and balances ride comfort and safety. LQG .

[0120] This is because tire dynamic displacement and suspension dynamic deflection are closely related to vehicle handling and safety, while vehicle pitch angle acceleration and vertical acceleration directly affect ride smoothness and passenger comfort. Therefore, various performance indicators should be considered according to different road conditions.

[0121] In summary, see Figure 3 The design process of extensional LQG control (i.e., F LQGThe calculation method is as follows:

[0122] Based on the half-vehicle dynamics model of the suspension system, calculate F s F h Among them, F s To control output force for smoothness; F h For safety control of output force;

[0123] Based on the state of the target vehicle's active suspension, ε is calculated; where ε is the output force trade-off factor; the process of calculating ε can be used to design an extension controller.

[0124] Calculate F based on Fs, Fh, and ε. LQG Among them, F LQC =εF s +(1-ε)F h ; Calculate F LQG The process can be used to design an LQG controller. First, let's look at F. s F h The calculation method is as follows:

[0125] The LQG objective function is constructed based on the half-vehicle dynamics model of the suspension system; whereby the LQG objective function is:

[0126]

[0127] In the formula, J LQG Let q1, q2, q3, q4, q5, and q6 represent the LQG objective function; q1, q2, q3, q4, q5, and q6 represent the weight coefficients.

[0128] Of course, J LQG You can refer to the rewriting of the suspension system's half-vehicle dynamics model into a standard quadratic form:

[0129]

[0130] Among them, Q L R and N are well-defined matrices: Q L =C T Q0C;R=D T Q0D;N=C T Q0D;

[0131] Q0 represents the weight coefficient matrix:

[0132]

[0133] Since the performance of the extensional LQG control depends on the values ​​of q1, q2, q3, q4, q5, and q6, a genetic algorithm is used to optimize the weight coefficients under different constraints to obtain a smoothness index (i.e., vehicle vertical acceleration). Vehicle pitch acceleration The smoothness control output force F is mainly based on the smoothness of the ride. s Safety indicators (i.e., suspension dynamic deflection f) d Front axle tire dynamic deformation z uf -q f Rear axle tire dynamic deformation z ur -q r The main safety control output force F h .

[0134] In other words, based on the genetic algorithm, the optimal weight coefficients under the constraints of the smoothness control strategy are calculated and used as the first set of weight coefficients. This first set of weight coefficients is then substituted into the LQG objective function, and the LQG objective function is solved to obtain the optimal feedback gain matrix K under the smoothness control strategy. s And thus obtain F s Among them, F s =-K s X.

[0135] Based on a genetic algorithm, the optimal weight coefficients under the constraints of the safety control strategy are calculated and used as the second set of weight coefficients. Substituting the second set of weight coefficients into the LQG objective function and solving the LQG objective function yields the optimal feedback gain matrix K under the safety control strategy. h And thus obtain F h Among them, F h =-K h X.

[0136] More specifically, the process of solving for weight coefficient group one and weight coefficient group two is as follows:

[0137] Genetic algorithm constructs fitness function:

[0138]

[0139] In the formula, α1, α2, α3, α4, α5, and α6 represent the weights of each performance index of the active suspension.

[0140] RMS[.] represents the root mean square value of each performance index of the active suspension;

[0141] RMS[.] p This represents the root mean square value of various performance indicators of the passive suspension.

[0142] The constraints of the smoothness control strategy are:

[0143]

[0144] The purpose of the ride comfort control strategy constraints is to find the optimal weight coefficients on a flat road surface (i.e., Class A road surface). The goal is to achieve the best ride comfort with the minimum output force of the active suspension actuators, that is, to focus on reducing the vertical acceleration and pitch acceleration of the vehicle body.

[0145] The constraints of the security control strategy are:

[0146]

[0147] Among them, f d Indicates the dynamic deflection of the suspension; f lim This indicates the maximum dynamic travel of the suspension.

[0148] The purpose of the safety control strategy constraints is to find the optimal weight coefficients on bumpy roads (i.e., Class D roads). The goal is to improve vehicle ride comfort while ensuring suspension safety—that is, to avoid suspension impacts with limit blocks and wheel lift-off.

[0149] Under the above two constraints, by finding the weight coefficients corresponding to the minimum value of the fitness function, we can obtain weight coefficient group one and weight coefficient group two, respectively.

[0150] Based on the obtained weight coefficient group one and weight coefficient group two, respectively, they are substituted into the LQG objective function for solving, and K is obtained respectively. s K h And thus obtain F s F h .

[0151] Specifically: Based on weight coefficient group one, substitute back to J LQG A set of Q is obtained from L R, N, and then the group Q L Substituting R and N into the Riccati algebraic equation, we can solve for K, which is then used as K. s .

[0152] Based on the second set of weighted coefficients, substitute back to J LQG Another set of Q was obtained from it. L R, N, and then the group Q L Substituting R and N into the Riccati algebraic equation, we can solve for K, which is then used as K. h .

[0153] The Ricardi algebraic equation is:

[0154]

[0155] P is the symmetric positive definite solution to be found. That is, based on the known Q... L Given R and N, first solve for P, then solve for K.

[0156] Based on the obtained K s Substitute Find F s ;in, F represents s The front axle active control force component; F represents s The rear axle active control force component.

[0157] Based on the obtained K h Substitute Find F h .in, F represents h The front axle active control force component; F represents h The rear axle active control force component.

[0158] Next, let's look at ε. ε is used to balance the ride comfort and operational safety of the suspension system.

[0159] The methods for calculating ε include:

[0160] Determine the state of the target vehicle's active suspension;

[0161] If the target vehicle's active suspension is in the classical domain, ε = 1; if the target vehicle's active suspension is in the extended domain, ε = -k(S); if the vehicle's active suspension is in the non-domain, ε = 0.

[0162] Where k(S) is the correlation function, expressed as:

[0163]

[0164] In the formula, |OS| represents the norm operator; A0 represents the classical domain boundary; and A1 represents the extensional domain boundary.

[0165] |OS| reflects the degree of closeness to the ideal state point (0,0). See also Figure 4 This demonstrates the distribution of classical, extended, and non-domains: a classical domain is a circular region with (0,0) as the center and A0 as the radius; an extended domain is a ring-shaped region with (0,0) as the center, A1 as the outer radius, and A0 as the inner radius; other regions larger than A1 are non-domains.

[0166] Specifically, the formula for calculating |OS| is:

[0167]

[0168] In the formula, e1 represents the ride comfort deviation; e2 represents the suspension operation safety deviation; z uf -q fz ur -q r It consists of 4 characteristic quantities; This indicates the vertical acceleration of the vehicle body; For z s The second derivative of z; s This indicates the vertical displacement at the vehicle's center of gravity. This indicates the vehicle's pitch angle acceleration; express The second derivative; Indicates the vehicle's pitch angle; z uf -q f Indicates the dynamic deformation of the front axle tire; z uf q represents the vertical displacement of the unsprung mass on the front axle; f Indicates front axle road surface excitation; z ur -q r Indicates the dynamic deformation of the rear axle tire; z ur q represents the vertical displacement of the unsprung mass on the rear axle; r This represents the road surface excitation on the rear axle; k1, k2, k3, and k4 represent the relative influence factors of the four characteristic quantities, respectively.

[0169] The formula for calculating A0 is:

[0170]

[0171] In the formula, e 1s e 2s This indicates the tolerance range for ride comfort control deviations; The standard deviations of the four smoothness control characteristics; k 1s k 2s k 3s k 4s This represents the relative influence factor of the standard deviation of the four smoothness control characteristics.

[0172] The formula for calculating A1 is:

[0173]

[0174] In the formula, e 1h e 2h Indicates the tolerance range for safety control deviations; The standard deviations of the four safety control characteristics; k 1h k 2h k 3h k 4h This represents the relative influence factor of the standard deviation of the four safety control characteristics.

[0175] It should be noted that since the suspension response parameters under random road surface inputs follow a normal distribution, the above standard deviation is calculated according to the "3σ rule" of random signals.

[0176] F calculated based on the above process LQG It can adapt to different road surfaces and take into account both smoothness and safety.

[0177] ② The anti-collision MPC control is based on the half-vehicle dynamics model of the suspension system to obtain the output force F used when passing discrete impacts. MPC .

[0178] This is because if F is still used when passing through discrete impacts... LQG This can easily lead to a higher smoothness index. Introducing F... MPC It can compensate for deficiencies and lower the smoothness index.

[0179] In summary, the design process of the pre-aiming MPC control (i.e., F) MPC The calculation method is as follows:

[0180] Construct a system of quadratic programming equations; the system of quadratic programming equations includes: the objective quadratic form function of MPC and the MPC constraint equations.

[0181] Solving the quadratic programming equations yields... And as F MPC ; Calculate F MPC The process can be used to design a pre-aiming MPC controller.

[0182] The objective quadratic function of MPC is:

[0183]

[0184] The MPC constraint equations are:

[0185]

[0186] In the formula, minJ MPC Let Q represent the quadratic form function of the MPC objective; Q and R are weight matrices; Represents the optimal set of control forces; I represents the appropriate dimension matrix; I represents the appropriate dimension identity matrix. Let L represent the set of road surface excitations; x(k|k) represent the actual state of the system at time k; L y U y L represents the upper and lower limit constraint sets for suspension dynamic deflection and tire dynamic deformation, respectively; u U u These represent the upper and lower limit constraint sets of the output force of the active suspension actuator, respectively.

[0187] The quadratic programming equations aim to predict the future state of a controlled object using a discrete model, and obtain the optimal control quantity by solving an optimization problem in the finite time domain. The process of constructing the quadratic programming equations includes the following:

[0188] First, let's look at the MPC constraint equations:

[0189] The half-vehicle dynamics model of the suspension system is discretized to obtain a discrete dynamics model; the expression of the discrete dynamics model is:

[0190]

[0191] In the formula, x(k+1|k) represents the system state quantity predicted at time k+1; x(k|k) represents the actual state of the system at time k; u(k|k) represents the control quantity predicted by the system at time k; w(k|k) represents the actual road surface excitation quantity of the system at time k; y(k|k) represents the output quantity predicted by the system at time k; A d B d E d C and D represent the appropriate dimension matrix.

[0192] Among them, A d =e AT , T represents the control step size.

[0193] The future state equations of the target vehicle's active suspension are constructed based on the discrete dynamics model; where the future state equations are:

[0194]

[0195] In the formula, Represents the state quantity matrix of the system in the prediction domain; This represents the output matrix of the system in the prediction domain;

[0196] Represents an appropriate dimension matrix;

[0197] Represents the optimal set of control forces; This represents the set of road surface excitations.

[0198] It should be noted that this future state equation reflects the MPC prediction domain (corresponding to an MPC prediction step number of N). p The system output in the MPC control domain (corresponding to MPC control steps of N) c The system control variables in ).

[0199] therefore, x(k+θ|k) represents the system state quantity predicted at time k+θ; θ∈[1,N]p ].

[0200] w(k+λ|k) represents the road surface excitation predicted at time k+λ; λ∈[1,N] p -1].

[0201] u(k+θ|k) represents the system control quantity predicted at time k+θ.

[0202] y(k+θ|k) represents the system output at time k+θ predicted at time k.

[0203] Therefore, there are:

[0204]

[0205]

[0206]

[0207]

[0208]

[0209] Adding constraints to the future state equations yields the MPC constraint equations; specifically, adding L to the future state equations... y U y L u U u As a constraint, the future state equation can be transformed into the MPC constraint equation, which is:

[0210] Let's look at the quadratic function of the MPC objective:

[0211] Construct the initial MPC objective function; where the initial MPC objective function is:

[0212]

[0213] In the formula, minJ′ MPC N represents the initial MPC objective function; P N represents the number of MPC prediction steps; y(k+i|k) represents the system output at time k+i predicted at time k; c denoted by MPC control steps; u(k+j|k) represents the system control quantity predicted at time k+j at time k; Q and R are weight matrices.

[0214] Expanding the initial MPC objective function in conjunction with the future state equations, and ignoring constant terms unrelated to the system control inputs, we obtain the quadratic form of the MPC objective function, which is:

[0215]

[0216] By combining the quadratic form function of the MPC objective with the MPC constraint equations, we obtain a system of quadratic programming equations.

[0217] It should be noted that the solution obtained from the quadratic programming equations... in, express The front axle active control force component; express The rear axle active control force component.

[0218] Because of the differences between extensional LQG control and aiming MPC control, therefore F LQG F MPC Significant differences often exist. A sudden control switch can cause a large instantaneous disturbance to the system, leading to a decrease in suspension system performance during the switch. Therefore, as mentioned above, a balance coefficient is introduced.

[0219] See Figure 5 Balance coefficient In fact, it is a sigmoid function, which derives the transition output force F from the extensional LQG control to the pre-aiming MPC control. t The transition output force F is derived from the pre-aiming MPC control and the extension LQG control. t This achieves a balanced transition in control switching, reducing additional disturbances caused by control switching.

[0220] Example 2

[0221] This embodiment 2 discloses an active suspension control system for a hub motor vehicle under complex road conditions, which uses the active suspension control method for a hub motor vehicle under complex road conditions from embodiment 1.

[0222] The active suspension control system for in-wheel motor vehicles under complex road conditions includes: a driving information acquisition module, an output force calculation module, and a switching control module.

[0223] The driving information acquisition module is used to acquire the vehicle's driving information. The output force calculation module is used to calculate F. LQG F t F MPC F t The switching control module is used to switch the output force of the active suspension actuators based on driving information.

[0224] This embodiment 2 also discloses a hub motor vehicle, which uses the active suspension control method for hub motor vehicles under complex road conditions in embodiment 1.

[0225] Example 3

[0226] This embodiment 3 uses Class B and Class C road surfaces to verify the extension LQG control effect in embodiment 1 and compares it with traditional LQG control.

[0227] The test plan is as follows:

[0228] 1. Passing through Class B roads at a real-time speed of 60km / h, using both extended LQG control and traditional LQG control respectively.

[0229] 2. The vehicle travels at a real-time speed of 60 km / h through a Class C road surface, and adopts both extended LQG control and traditional LQG control.

[0230] The performance indicators of the vehicles under the two schemes are compared; in order to intuitively reflect the test results, the root mean square value of each indicator is used for comparison.

[0231] See results Figure 6 , Figure 7 It can be seen that, compared with traditional LQG control, extended LQG control has a significant improvement in vehicle vertical acceleration and vehicle pitch angle acceleration on both Class B and Class C roads.

[0232] On relatively smooth Class B roads, the suspension is less prone to safety issues. In this case, the Extensible LQG control sacrifices some safety performance indicators to further improve ride comfort. On relatively bumpy Class C roads, the Extensible LQG control balances vehicle ride comfort while ensuring suspension safety.

[0233] In summary, the extended LQG control in Example 1 meets the expectation of coordinated control of ride comfort and suspension safety according to different vehicle operating conditions.

[0234] Example 4

[0235] In this embodiment 4, the control method of embodiment 1 is first verified using a Class B random road surface containing multiple discrete impacts, and then compared with the instantaneous control method.

[0236] The test plan is as follows:

[0237] 1. At a real-time vehicle speed of 30 km / h, the vehicle passes through a Class B random road surface containing one speed bump and one pothole, and the control method of Example 1 (i.e., with a smoothing coefficient) is applied. Instantaneous control method (i.e., no smoothing coefficient) To control.

[0238] Compare the vehicle pitch acceleration under the two control methods; see the results below. Figure 8 Compare the vertical acceleration of the vehicle body under the two control methods; see the results below. Figure 9 It can be seen that the vehicle pitch acceleration and vertical acceleration under the instantaneous control method are significantly greater than those under the control method of Example 1. This indicates that the introduction of a smoothing coefficient... It can effectively reduce the additional disturbances introduced when switching control strategies.

[0239] Example 5

[0240] This embodiment 5 also uses a Class B random road surface with multiple discrete impacts to verify the control method of embodiment 1, and compares it with a single extension LQG control.

[0241] The test plan is as follows:

[0242] 1. The vehicle travels at a real-time speed of 30 km / h through a Class B random road surface containing two speed bumps, and is controlled using the control method of Example 1 and a single extension LQG control respectively.

[0243] 2. The vehicle travels at a real-time speed of 30 km / h through a Class B random road surface containing one speed bump and one pothole, and is controlled using the control method of Example 1 and a single extension LQG control.

[0244] 3. The vehicle travels at a real-time speed of 30 km / h through a Class B random road surface containing two potholes, and is controlled using the control method of Example 1 and a single extension LQG control.

[0245] Comparing the vehicle pitch acceleration and vertical acceleration under the above three road conditions: See the results for the first road condition. Figure 10 , Figure 11 The results for the second road surface condition are as follows: Figure 12 , Figure 13 The results for the third road surface condition are as follows: Figure 14 , Figure 15 It can be seen that the vertical acceleration and pitch acceleration of the vehicle body under the control method of Example 1 are significantly less than those of the single extension LQG control. This indicates that the control method of Example 1, by incorporating the anticipation MPC, compensates for the deficiency of extension LQG control in not considering discrete impacts, thereby effectively coping with complex road conditions and improving the overall performance of the active suspension.

[0246] Example 6

[0247] This embodiment 6 discloses a readable storage medium storing computer program instructions. When the computer program instructions are read and executed by a processor, the steps of the active suspension control method for a hub motor vehicle under complex road conditions in embodiment 1 are performed.

[0248] When applying the method of Example 1, it can be applied in the form of software, such as a program designed to run independently on a computer-readable storage medium, which can be a USB flash drive or a USB security token, and designed to be a program that starts the entire method through an external trigger.

[0249] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0250] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. An active suspension control method for in-wheel motor vehicles under complex road conditions, characterized in that, Includes the following steps: Acquire vehicle driving information; wherein, the driving information includes: real-time vehicle speed, real-time forward-looking information; the real-time forward-looking information includes: whether there is a discrete impact in front of the vehicle, and the real-time distance between the vehicle and the discrete impact; The output force of the active suspension actuators is switched based on driving information; the switching rules include: If there is no discrete impact in front of the vehicle, extensional LQG control is applied to make the output force of the active suspension actuators equal to... F LQG ; If a discrete impact exists in front of the vehicle and this impact is not within the MPC prediction domain, then extensional LQG control is implemented to make the output force of the active suspension actuators equal to... F LQG If the discrete impact occurs within the MPC prediction domain, then switch from extensional LQG control to pre-aiming MPC control, making the output force of the active suspension actuator... F t Before reaching the discrete impact, switch fully to pre-aiming MPC control to make the output force of the active suspension actuators... F MPC If the discrete impact disappears from the MPC prediction domain, then the extensional LQG control is switched from the preview MPC control to make the output force of the active suspension actuator... After the vehicle passes through the discrete impact, it fully switches to extensional LQG control, making the output force of the active suspension actuators... F LQG ; in, F t , The calculation formula is: ； In the formula, F t This indicates the transition output force from extensional LQG control to pre-aiming MPC control; This indicates the transition output force when switching from pre-aiming MPC control to extensional LQG control; F LQG This represents the output force of the active suspension actuator calculated using the extension LQG algorithm based on the half-vehicle dynamics model of the suspension system. F MPC This represents the output force of the active suspension actuator calculated using the pre-aiming MPC algorithm based on the half-vehicle dynamics model of the suspension system; The smoothing coefficient is represented by the sigmoid function; Δ t express F t from F LQG Switch to F MPC The time required, or from F MPC Switch to F LQG Time required; F MPC The calculation methods include: Construct a system of quadratic programming equations; wherein the system of quadratic programming equations includes: an MPC objective quadratic form function and MPC constraint equations; The objective quadratic function of the MPC is: ； The MPC constraint equation is as follows: ； In the formula, min J MPC Represent the quadratic form function representing the objective of MPC; Q , R This is the weight matrix; Represents the optimal set of control forces; , , Represents an appropriate dimension matrix; I Represents an identity matrix of appropriate dimension; Represents the set of road surface excitations; This represents the actual state of the system at time k; , These represent the upper and lower limit constraint sets for suspension dynamic deflection and tire dynamic deformation, respectively. , These represent the upper and lower limit constraint sets of the output force of the active suspension actuator, respectively; Solving the system of quadratic programming equations yields the following results. And as F MPC ; The method for constructing the objective quadratic form function of MPC is as follows: Construct an initial MPC objective function; wherein, the initial MPC objective function is: ； In the formula, N represents the initial MPC objective function; P Indicates the number of prediction steps in MPC; N represents the predicted system output at time k+i, calculated at time k; c Indicates the number of MPC control steps; This represents the system control quantity predicted at time k+j from time k. Q , R This is the weight matrix; The initial MPC objective function is expanded by combining it with the future state equations, and constant terms that are irrelevant to the system control quantity are ignored, resulting in a quadratic form function of the MPC objective.

2. The active suspension control method for a hub motor vehicle under complex road conditions according to claim 1, characterized in that, The suspension system half-vehicle dynamics model is a four-degree-of-freedom planar model of the target vehicle's active suspension; wherein, the expression of the four-degree-of-freedom planar model of the vehicle's active suspension is: ； In the formula, express X The first derivative; X Represents system state variables; ; z sf This indicates the vertical displacement at the front axle of the vehicle body. z uf This indicates the vertical displacement of the unsprung mass on the front axle. z sr This indicates the vertical displacement at the rear axle of the vehicle body. z ur This indicates the vertical displacement of the unsprung mass on the rear axle. q f This indicates road surface excitation on the front axle. q r Indicates the road surface excitation on the rear axle; Y Indicates the system output quantity; ; This indicates the vertical acceleration of the vehicle body; for z s The second derivative; z s This indicates the vertical displacement at the vehicle's center of gravity. This indicates the vehicle's pitch angle acceleration; express The second derivative; Indicates the vehicle's pitch angle; A Represents the system state matrix; B Represents the control input matrix; C, D Represents an appropriate dimension matrix; E Represents the excitation input matrix; U Indicates system control variables; W This indicates the amount of road surface excitation.

3. The active suspension control method for in-wheel motor vehicles under complex road conditions according to claim 2, F LQG The calculation methods include: Based on the half-vehicle dynamics model of the suspension system, calculate F s , F h ; in, F s To control output force for smoothness; F h For safety control of output force; Based on the state of the target vehicle's active suspension, calculate ε ;in, ε This is the output force trade-off factor; based on F s , F h , ε ,calculate F LQG ;in, .

4. The active suspension control method for a hub motor vehicle under complex road conditions according to claim 3, characterized in that, F s , F h The calculation method is as follows: An LQG objective function is constructed based on the half-vehicle dynamics model of the suspension system; wherein, the LQG objective function is: ； In the formula, J LQG Describe the LQG objective function; q 1. q 2. q 3 、q 4 、q 5 、q 6 represents the weighting coefficient; Based on the genetic algorithm, the optimal weight coefficients under the constraints of the smoothness control strategy are calculated and used as the first weight coefficient group; the optimal weight coefficients under the constraints of the safety control strategy are calculated and used as the second weight coefficient group. Substituting the weighting coefficients into the LQG objective function and solving the LQG objective function yields the optimal feedback gain matrix under the smoothness control strategy. K s And thus obtain F s ;in, ; Substituting the second set of weight coefficients into the LQG objective function and solving the LQG objective function yields the optimal feedback gain matrix under the safety control strategy. K h And thus obtain F h ;in, .

5. The active suspension control method for a hub motor vehicle under complex road conditions according to claim 3, characterized in that, ε The calculation methods include: Determine the state of the vehicle's active suspension; If the vehicle's active suspension is in the classic domain ε =1; if the vehicle's active suspension is in the extension region. ε =- k ( S If the vehicle's active suspension is in a non-domain, ε =0; in, k ( S ) is an associative function, and its expression is: ； In the formula, Represents the norm operator; A 0 represents the boundary of a classical field; A 1 represents the boundary of the extended domain.

6. The active suspension control method for a hub motor vehicle under complex road conditions according to claim 5, characterized in that, The calculation formula is: ； In the formula, e 1 indicates a deviation in ride comfort; e 2 indicates a deviation in suspension operating safety; , , , There are 4 characteristic quantities; This indicates the vertical acceleration of the vehicle body; for z s The second derivative; z s This indicates the vertical displacement at the vehicle's center of gravity. This indicates the vehicle's pitch angle acceleration; express The second derivative; Indicates the vehicle's pitch angle; This indicates the dynamic deformation of the front axle tire; This indicates the vertical displacement of the unsprung mass on the front axle; Indicates road surface excitation on the front axle; This indicates the dynamic deformation of the rear axle tires; This indicates the vertical displacement of the unsprung mass on the rear axle; Indicates the road surface excitation on the rear axle; k 1. k 2. k 3. k 4 represents the relative influence factors of the four characteristic quantities; A The formula for calculating 0 is: ； In the formula, e 1s , e 2s This indicates the tolerance range for ride comfort control deviations; , , , The standard deviations of the four smoothness control characteristics; k 1s , k 2s , k 3s , k 4s The relative influence factors of the standard deviations of the four smoothness control characteristics; A The formula for calculating 1 is: ； In the formula, e 1h , e 2h Indicates the tolerance range for safety control deviations; , , , The standard deviations of the four safety control characteristics; k 1h , k 2h , k 3h , k 4h This represents the relative influence factor of the standard deviation of the four safety control characteristics.

7. The active suspension control method for a hub motor vehicle under complex road conditions according to claim 1, characterized in that, The method for constructing the quadratic programming equation system includes: The half-vehicle dynamics model of the suspension system is discretized to obtain a discrete dynamics model; wherein the expression of the discrete dynamics model is: ； In the formula, This represents the system state quantity predicted at time k+1 from time k. This represents the actual state of the system at time k; This represents the control quantity predicted by the system at time k; This represents the actual road surface excitation quantity of the system at time k; This represents the predicted output of the system at time k; A d , B d , E d , C, D Represents an appropriate dimension matrix; The future state equations of the target vehicle's active suspension are constructed based on the discrete dynamics model; wherein, the future state equations are: ； In the formula, Represents the state quantity matrix of the system in the prediction domain; This represents the output matrix of the system in the prediction domain; , , , , , Represents an appropriate dimension matrix; Represents the set of optimal control forces in the prediction domain; Represents the set of road surface excitations in the prediction domain; By adding constraints to the future state equations, we obtain the MPC constraint equations. Construct a quadratic form function of the MPC objective and combine it with the MPC constraint equations to obtain a system of quadratic programming equations.

8. An active suspension control system for in-wheel motor vehicles under complex road conditions, characterized in that, It uses the active suspension control method for in-wheel motor vehicles under complex road conditions as described in any one of claims 1-7; The active suspension control system for the in-wheel motor vehicle under complex road conditions includes: The driving information acquisition module is used to acquire the vehicle's driving information; Output force calculation module, which is used to calculate F LQG 、F t 、F MPC , ; as well as The switching control module is used to switch the output force of the active suspension actuators based on driving information.

Images