Balance control method, device, computer equipment and storage medium

Abstract

This application relates to a balance control method, apparatus, computer device, and storage medium. The method includes obtaining target parameters for controlling vehicle motion at different stages and vehicle parameters at a current moment, determining a stable manifold model for the vehicle based on the current vehicle parameters, and then determining vehicle parameters at a subsequent moment based on the stable manifold model and the target parameters. Balance control of the vehicle's motion is then performed based on the vehicle parameters at the subsequent moment. This method can improve the autonomous stability and balance capabilities of unmanned vehicles.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a balance control method, apparatus, computer equipment, and storage medium. Background Art

[0002] With continuous breakthroughs in key technologies in the autonomous driving field, autonomous vehicles, such as motorcycles, have also developed. Motorcycles are single-track, two-wheeled mobile robots with advantages such as high maneuverability and high mobility, allowing them to navigate a wide range of terrains and obstacles. Balance control is a fundamental and crucial task in the movement of autonomous vehicles. Unmanned vehicles must maintain balance at all times to prevent them from tipping over.

[0003] Traditionally, unmanned vehicles are controlled based on a proportional-integral-differential (PID) control method. Specifically, the PID control method typically adds the proportional, integral, and differential values ​​between a target control variable (e.g., a target handlebar angular velocity) and a state feedback variable (e.g., the actual handlebar angular velocity at the current moment) to obtain a final control input variable (e.g., the handlebar angular velocity at the next moment).

[0004] However, PID control methods are only applicable to linear models. Unmanned vehicles exhibit highly nonlinear motion. Therefore, PID control methods are effective only within the range of approximately linear motion, such as small turns at a constant speed. Consequently, PID control methods can produce unreasonable control inputs under nonlinear motion, potentially causing the vehicle to overturn. Consequently, conventional unmanned vehicles exhibit poor autonomous stabilization capabilities. Summary of the Invention

[0005] Based on this, it is necessary to provide a balance control method, device, computer equipment and storage medium that can improve the autonomous stable balance capability of unmanned vehicles in response to the above technical problems.

[0006] In a first aspect, the present application provides a balance control method. The method comprises:

[0007] Obtaining target parameters for controlling different stages of vehicle motion and vehicle parameters at the current moment;

[0008] Determining a steady-state manifold model of the vehicle based on the vehicle parameters at the current moment;

[0009] determining vehicle parameters at the next moment according to the stable state manifold model and the target parameters;

[0010] The movement of the vehicle is balanced and controlled according to the vehicle parameters at the next moment.

[0011] In a second aspect, the present application further provides a balance control device. The device comprises:

[0012] An acquisition module, used to acquire target parameters for controlling different stages of vehicle motion and vehicle parameters at a current moment;

[0013] A first determining module is used to determine a stable state manifold model of the vehicle according to the vehicle parameters at the current moment;

[0014] a second determining module, configured to determine vehicle parameters at a next moment based on the stable state manifold model and the target parameters;

[0015] The control module is used to perform balance control on the movement of the vehicle according to the vehicle parameters at the next moment.

[0016] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0017] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.

[0018] In a fifth aspect, the present application further provides a computer program product, comprising a computer program that implements the steps of any of the above methods when executed by a processor.

[0019] The above-mentioned balance control method, device, computer device, and storage medium obtain target parameters for controlling the different stages of vehicle motion and the vehicle parameters at the current moment, and determine the vehicle's stable state manifold model based on the vehicle parameters at the current moment. Then, based on the stable state manifold model and the target parameters, the vehicle parameters at the next moment are determined, thereby performing balance control on the vehicle's motion based on the vehicle parameters at the next moment. Because the present application can determine the vehicle's stable state manifold model based on the actual vehicle parameters at the current moment, and determine the vehicle parameters at the next moment based on the stable state manifold model and the target parameters, the vehicle parameters at the next moment determined in the present application are reasonable and stable input quantities determined based on the state targets at different stages. Then, based on the vehicle parameters at the next moment, the vehicle's motion can be balanced and controlled, thereby maintaining the vehicle in a stable equilibrium state. Therefore, the balance control method provided by the present application avoids the situation in which unreasonable control input quantities cause the vehicle to overturn in traditional technologies, solves the problem of poor autonomous stable balance capabilities of unmanned vehicles in traditional technologies, and improves the autonomous stable balance capabilities of unmanned vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a diagram of the application environment of the balance control method in the embodiment of the present application;

[0021] Figure 2 Schematic diagram of the flow of the balance control method in the embodiment of the present application;

[0022] Figure 3 This is a schematic diagram of the model of an unmanned motorcycle;

[0023] Figure 4 This is a schematic diagram of a process for determining a stable manifold model of a vehicle in an embodiment of the present application;

[0024] Figure 5 Schematic diagram of the steady-state manifold model in this application;

[0025] Figure 6 This is a schematic diagram of a process for determining vehicle parameters at the next moment in an embodiment of the present application;

[0026] Figure 7 This is a flow chart of a control model for determining the angular velocity of a vehicle's handlebar angle according to an embodiment of the present application;

[0027] Figure 8 This is a flow chart of another control model for determining the angular velocity of a vehicle's handlebar angle according to an embodiment of the present application;

[0028] Figure 9 This is a flow chart of another control model for determining the angular velocity of a vehicle's handlebar angle according to an embodiment of the present application;

[0029] Figure 10 Schematic diagram of the gain coefficient in the startup phase and transfer phase;

[0030] Figure 11 Schematic diagram of the gain coefficient value in the holding stage;

[0031] Figure 12 This is a structural block diagram of the balance control device in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0033] Figure 1 FIG. 1 is an application environment diagram of the balance control method in the embodiment of the present application. The balance control method provided in the embodiment of the present application can be applied to the following situations: Figure 1 In the computer device shown.

[0034] like Figure 1 As shown, a computer device is provided in an embodiment of the present application. The computer device may be a central processing unit (CPU), and may also include a digital signal processor (DSP), a field programmable gate array (FPGA) or other programmable logic devices. The internal structure diagram thereof may be as shown in FIG. Figure 1 As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a balance control method.

[0035] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0036] It should be noted that the vehicle in this application refers to a single-track, two-wheeled unmanned mobile vehicle, such as an unmanned motorcycle or an unmanned electric vehicle. This application is explained using an unmanned motorcycle as an example.

[0037] Figure 2 This is a flow chart of the balance control method in the embodiment of the present application, which can be applied to Figure 1 In the server shown, in one embodiment, as Figure 2 As shown, the following steps are included:

[0038] S201, obtaining target parameters for controlling different stages of vehicle motion and vehicle parameters at the current moment.

[0039] In this embodiment, the computer device obtains target parameters for controlling different stages of vehicle motion and vehicle parameters at the current moment.

[0040] Figure 3 This is a schematic diagram of the model of the unmanned motorcycle. Figure 3 Taking an unmanned motorcycle as an example, the vehicle parameters are shown in Table 1. It is understandable that the parameters of different types of vehicles may vary slightly.

[0041] Specifically, vehicle parameters include vehicle attribute parameters that do not change with time, such as motorcycle mass m, motorcycle center of mass height h, horizontal distance a between the motorcycle front wheel landing point and the center of mass, horizontal distance b between the motorcycle rear wheel landing point and the center of mass, trailing distance c, and front fork angle λ. At the same time, vehicle parameters also include vehicle movement parameters that change with time, such as handlebar angle δ, body inclination angle and heading angle θ.

[0042] Please combine Figure 3 , where the trailing distance c is the distance between the intersection point P of the handlebar axis and the ground and the intersection point of the motorcycle's front wheel and the ground; the fork angle λ is the angle at which the handlebar axis deviates from the YOZ plane; the handlebar angle δ is the angle at which the handlebar rotates about its axis. When the wheel is in the XOZ plane, the handlebar angle δ is 0; the body inclination angle is the angle at which the motorcycle's center of mass deviates from the XOZ plane; the heading angle θ is the angle at which the velocity of the center of mass deviates from the XOZ plane.

[0043] Table 1 Vehicle parameters

[0044]

[0045] Furthermore, the motorcycle is equipped with various sensors, such as a speed sensor, an angle sensor, etc. The computer device is communicatively connected to the sensors on the motorcycle, so that the computer device can directly obtain the vehicle parameters at the current moment through the sensors.

[0046] Furthermore, in order to make the motorcycle move along the desired trajectory, it is necessary to control the state of the motorcycle to transition from one stable state to another. This state change process is the different stages of vehicle motion. In this embodiment, the different stages of vehicle motion are divided into the following three stages of motorcycle motion:

[0047] (1) Start-up phase: The main goal of the start-up phase is to obtain the target vehicle body tilt angle. Reference inclination tracking speed

[0048] (2) Transfer phase: The main goal of the transfer phase is to maintain the reference tilt tracking speed Gradually make the current body tilt angle close to the set target body tilt angle

[0049] (3) Maintaining stage: The main goal of the maintaining stage is to stabilize the vehicle state at the stable state that the target needs to achieve.

[0050] The target parameters are used to control the different stages of vehicle movement and are parameters set according to user needs. The target parameters include at least one parameter, for example, the target parameter may include the target vehicle body tilt angle. The duration of the start-up phase, the range of the state maintenance during the hold phase, etc. Therefore, in order to control the different phases of vehicle motion, the computer device also needs to obtain target parameters. The computer device can obtain the target parameters sent by the terminal or directly read the target parameters from storage, which is not limited in this embodiment.

[0051] S202: Determine a steady-state manifold model of the vehicle based on the vehicle parameters at the current moment.

[0052] In this embodiment, the computer device can determine the steady-state manifold model of the vehicle based on the vehicle parameters at the current moment. The steady-state manifold model is a stable manifold related to the vehicle parameters. The steady-state manifold model can include a plurality of stable range values ​​of the vehicle parameters. When the motorcycle moves using the vehicle parameters within the stable range values, the motorcycle will be in a balanced and stable state of motion. The steady-state manifold model can be a model about the handlebar angle, body inclination angle, and heading angle, or it can be a model about the handlebar angle, body inclination angle, and rear wheel forward speed. It is understandable that the current moment can be an arbitrary moment, that is, the steady-state manifold model of the vehicle can be applicable to the vehicle parameters at any moment.

[0053] The computer device may determine the stable state manifold model using motion data of a simulated or real motorcycle, or may calculate the stable state manifold model by establishing a model, which is not limited in this embodiment.

[0054] S203: Determine the vehicle parameters at the next moment according to the stable state manifold model and the target parameters.

[0055] In this embodiment, the computer determines the vehicle parameters at the next moment based on the steady-state manifold model and the target parameters. The computer can directly determine the vehicle parameters at the next moment, such as the vehicle body lean angle and rear wheel forward speed, as shown in Table 1. Alternatively, the computer can determine the handlebar angular velocity at the next moment by taking the derivative of the vehicle body lean angle.

[0056] S204: Performing balance control on the vehicle's motion according to the vehicle parameters at the next moment.

[0057] In this embodiment, the computer device performs balance control on the vehicle's motion based on the vehicle parameters at the next moment. Specifically, the computer device controls the motorcycle's drive motor based on the vehicle parameters at the next moment, causing the motorcycle to move in accordance with the vehicle parameters at the next moment. Because the vehicle parameters at the next moment are determined based on the steady-state manifold model and the target parameters, the motorcycle's motion based on the vehicle parameters at the next moment is stable and reasonable, thereby achieving balance control of the motorcycle's operation.

[0058] The balance control method provided in this embodiment obtains target parameters for controlling the states of different stages of vehicle motion and the vehicle parameters at the current moment, and determines the vehicle's stable state manifold model based on the vehicle parameters at the current moment. The vehicle parameters at the next moment are then determined based on the stable state manifold model and the target parameters, thereby performing balance control on the vehicle's motion based on the vehicle parameters at the next moment. Because the present application can determine the vehicle's stable state manifold model based on the actual vehicle parameters at the current moment, and determine the vehicle parameters at the next moment based on the stable state manifold model and the target parameters, the vehicle parameters at the next moment determined in this application are reasonable and stable inputs determined based on the state targets at different stages. The vehicle's motion can then be balanced based on the vehicle parameters at the next moment, thereby maintaining the vehicle in a stable equilibrium state. Therefore, the balance control method provided in this application avoids the situation in conventional technologies where unreasonable control inputs can cause the vehicle to overturn, solves the problem of poor autonomous stability and balance capabilities of unmanned vehicles in conventional technologies, and improves the autonomous stability and balance capabilities of unmanned vehicles.

[0059] Figure 4This is a flow chart of determining a stable state manifold model of a vehicle in an embodiment of the present application, with reference to Figure 4 This embodiment relates to an optional implementation method for determining a vehicle's stable state manifold model. Based on the above embodiment, the above S202, based on the current vehicle parameters, determines the vehicle's stable state manifold model, including the following steps:

[0060] S401, determining a vehicle dynamics model and a vehicle stability condition based on vehicle parameters at the current moment; wherein the vehicle stability condition includes that the vehicle's body tilt angular velocity, handlebar turning angular velocity, and rear wheel forward acceleration are all equal to zero.

[0061] In this embodiment, the computer device determines the vehicle dynamics model and vehicle stability conditions based on the current vehicle parameters. One possible implementation is for the computer device to obtain the vehicle dynamics model using a basic modeling method, such as the Lagrangian method. For example, the vehicle dynamics model is a model of the lateral motion of an unmanned motorcycle. Specifically, the computer device determines the vehicle dynamics model as shown in Equation (1).

[0062]

[0063] In formula (1), s λ =sinλ.

[0064] Furthermore, the vehicle stability condition includes that the vehicle's body tilt angular velocity, handlebar turning angular velocity and rear wheel forward acceleration are all equal to zero, that is,

[0065] S402: Determine a stable state manifold model according to the vehicle dynamics model and the vehicle stability condition.

[0066] In this embodiment, the computer device determines the stable state manifold model based on the vehicle dynamics model and the vehicle stability condition. Specifically, the computer device substitutes the vehicle stability condition into the vehicle dynamics model (1) to determine the expression (2) of the stable state manifold model.

[0067] It can be understood that, for any moment, equations (1) and (2) are valid. Therefore, the current moment can be an arbitrary moment, and the steady-state manifold model of the vehicle can be applied to the vehicle parameters at any moment.

[0068]

[0069] In formula (2), The left side of the equation is written as When it is 0, it is recorded as M ss .

[0070] Combining formula (2), we can know that the steady-state manifold model in this embodiment is about the vehicle body inclination angle The model of the handlebar angle δ and the rear wheel forward speed v, the body inclination angle The handlebar angle δ and the rear wheel forward speed v are set as the three coordinate axes of the space coordinate system respectively. According to formula (2), the schematic diagram of the steady-state manifold model can be obtained.

[0071] Figure 5 is a schematic diagram of the steady-state manifold model in this application, such as Figure 5 As shown, the steady-state manifold model can be understood as Figure 5 The manifold surface shown, any point on the manifold surface corresponds to a set of And the group Therefore, when the motorcycle moves using the vehicle parameters on the manifold surface, the motorcycle will be in a balanced and stable motion state.

[0072] This embodiment determines a vehicle dynamics model and vehicle stability conditions based on the current vehicle parameters. The vehicle stability conditions include the vehicle's body roll angular velocity, handlebar angular velocity, and rear wheel forward acceleration all being zero. A stable-state manifold model is then determined based on the vehicle dynamics model and vehicle stability conditions. Because the stable-state manifold model is determined based on the vehicle dynamics model and vehicle stability conditions, determining the vehicle parameters at the next moment based on the stable-state manifold model and target parameters ensures the vehicle is in a stable equilibrium state, achieving balance control and preventing vehicle rollover caused by unreasonable control inputs.

[0073] Figure 6 This is a flow chart of determining vehicle parameters at the next moment in an embodiment of the present application, with reference to Figure 6 This embodiment relates to an optional implementation method for determining the vehicle parameters at the next moment. Based on the above embodiment, the above S203, based on the stable state manifold model and the target parameters, determines the vehicle parameters at the next moment, including the following steps:

[0074] S601: Determine a handlebar angle angular velocity control model of the vehicle according to a steady-state manifold model and target parameters.

[0075] In this embodiment, the vehicle parameters at the next moment include the handlebar angular velocity at the next moment, so the computer device needs to determine the vehicle's handlebar angular velocity control model based on the steady-state manifold model and the target parameters. It can be understood that the vehicle's handlebar angular velocity control model is to control the motorcycle's handlebar angular velocity at the next moment. model.

[0076] S602: Determine the handlebar corner angular velocity at the next moment according to the handlebar corner angular velocity control model.

[0077] In this embodiment, according to the vehicle handlebar angle velocity control model determined in S601, the computer device can determine the handlebar angle velocity at the next moment. Then the computer device calculates the angular velocity of the handlebar at the next moment Control the motorcycle's drive motor, and then the motorcycle will rotate according to the angular velocity of the handlebar at the next moment Do some exercise.

[0078] This embodiment determines a handlebar angle angular velocity control model for the vehicle based on a stable-state manifold model and target parameters, and determines the handlebar angle angular velocity at the next moment based on the handlebar angle angular velocity control model. Therefore, when the vehicle moves using the handlebar angle angular velocity at the next moment, the vehicle will be in a balanced and stable motion state, thereby improving the autonomous stable balancing capability of the unmanned vehicle.

[0079] Figure 7 This is a flow chart of a control model for determining the angular velocity of the vehicle's handlebar angle according to an embodiment of the present application, with reference to Figure 7 This embodiment relates to an optional implementation of how to determine the vehicle's handlebar angle angular velocity control model. Based on the above embodiment, the above S601, based on the steady-state manifold model and the target parameters, determines the vehicle's handlebar angle angular velocity control model, including the following steps:

[0080] S701 : Determine a first condition of a vehicle based on a stable-state manifold model, wherein the first condition is used to restrict the motion state of the vehicle to perform isometric movement based on the stable-state manifold model.

[0081] In this embodiment, the computer device determines the first condition of the vehicle based on the steady-state manifold model. Specifically, taking constant speed control as an example, the computer device determines the first condition as shown in formula (3). As the motorcycle's control input, the motorcycle's motion will maintain isometric movement with respect to the stable manifold. Therefore, the first condition is used to constrain the vehicle's motion to be isometric based on the stable manifold model. It should be noted that the first condition is actually an isometric and perturbation condition for the vehicle's motion.

[0082]

[0083] in, This is a basic control input defined in this embodiment, which sets the handlebar angle control value. The benchmark is is the vehicle body inclination angle at the current moment, is the vehicle body tilt angular velocity at the current moment; K M is the local gradient on the stable manifold model and is always greater than 0, K M Satisfy the following formula (4). when When it is very small, that is and When the handlebar angle is controlled Take the perturbation value, sign(x) is a symbolic expression, when x>0, sign(x)=1; when x=0, sign(x)=0; when x<0, sign(x)=-1. is the target body inclination angle.

[0084]

[0085] In formula (4), yes The vertical projection on the stable manifold model, that is, the vehicle body inclination angle at the current moment and the differential of the projection point of the handlebar angle δ at the current moment on the stable state manifold model.

[0086] If non-constant speed control is used as an example, the computer device only needs to modify the local gradient K on the steady-state manifold model M , the rest is the same as constant speed control, so I will not repeat it here. In the case of non-constant speed control, K M The following formula (5) is satisfied.

[0087]

[0088] where K δ +K v =1 and K δ ≥0,K v ≥0, v proj is the vertical projection of v on the stable manifold model, K δ and K v The value of depends on the direction of the motorcycle's motion state on the stable state manifold, which is actually the distribution weight ratio between the handlebar steering speed control and the rear wheel acceleration control.

[0089] S702: Determine a gain coefficient according to the steady-state manifold model and target parameters.

[0090] In this embodiment, the computer device determines the first condition When the first condition is used to restrict the vehicle's motion state to be isometric based on the stable state manifold model, that is, if the first condition As the control input of the motorcycle, the motorcycle's motion state and the stable state manifold keep moving at equal distances. However, this application requires the motorcycle to move based on the stable state manifold model, so please set Figure 5 , moving based on the steady-state manifold model means that the motorcycle moves based on the vehicle parameters on the manifold surface. Therefore, the computer device also needs to determine the gain coefficient K based on the steady-state manifold model and the target parameters.

[0091] Specifically, the computer device may determine the maximum and minimum values ​​of the gain coefficient K according to the steady-state manifold model and the target parameters, and then determine the gain coefficient K according to a value between the maximum and minimum values ​​of the gain coefficient K.

[0092] S703: Determine a handlebar angle velocity control model according to the first condition and the gain coefficient, wherein the handlebar angle velocity control model is used to control the vehicle to move based on a steady-state manifold model.

[0093] In this embodiment, the computer device determines the handlebar angle velocity control model based on the first condition and the gain coefficient. Specifically, the computer device determines the handlebar angle velocity control model as shown in formula (6).

[0094]

[0095] The computer device determines the handlebar angular velocity at the next moment according to formula (6): And when the motorcycle is turned at the handlebar angle velocity When in motion, a motorcycle is stable and balanced. Therefore, the handlebar angle angular velocity control model is used to control the vehicle's motion based on the steady-state manifold model.

[0096] This embodiment determines a first condition for the vehicle based on a stable manifold model. The first condition is used to constrain the vehicle's motion state to isometric movement based on the stable manifold model. A gain coefficient is determined based on the stable manifold model and target parameters. Furthermore, a handlebar angular velocity control model is determined based on the first condition and the gain coefficient. Because the handlebar angular velocity control model is used to control the vehicle's motion based on the stable manifold model, the handlebar angular velocity ultimately determined at the next moment can maintain a balanced and stable motion state, thereby improving the autonomous balancing capability of the unmanned vehicle.

[0097] Figure 8 This is another flow chart of determining the vehicle handlebar angle angular velocity control model in the embodiment of the present application, referring to Figure 8This embodiment relates to an optional implementation of how to determine the handlebar angle velocity control model of a vehicle. Based on the above embodiment, the above S703, based on the first condition and the gain coefficient, determines the handlebar angle velocity control model, including the following steps:

[0098] S801: Determine a dynamic compensation amount of a handlebar angle velocity control model based on a stable state manifold model.

[0099] In this embodiment, the computer device determines the dynamic compensation amount of the handlebar angle velocity control model based on the stable state manifold model. Among them, the dynamic compensation of the handlebar angle velocity control model is The purpose is to compensate for the unstable factors in the motorcycle's motion, thereby improving the final balance control effect. The computer equipment can substitute the vehicle stability conditions into the vehicle dynamics model (1) to obtain the mathematical derivation of the stable state manifold. The eliminated items are directly used as dynamic compensation quantities. It can also be used as a dynamic compensation after mathematically transforming the eliminated items

[0100] Specifically, the computer device determines the dynamic compensation amount of the handlebar angle velocity control model As shown in the following formula (7).

[0101]

[0102] S802: Determine a handlebar angle angular velocity control model of the vehicle according to the first condition, the gain coefficient, and the dynamic compensation amount.

[0103] In this embodiment, the computer device is based on the first condition Gain coefficient K and dynamic compensation amount Determine the vehicle's handlebar angle angular velocity control model. Specifically, the computer device determines the vehicle's handlebar angle angular velocity control model as shown in formula (8).

[0104]

[0105] This embodiment determines the dynamic compensation amount of the handlebar angle angular velocity control model based on the stable state manifold model, and determines the handlebar angle angular velocity control model of the vehicle based on the first condition, the gain coefficient and the dynamic compensation amount. Since the handlebar angle angular velocity control model also includes the dynamic compensation amount, the dynamic compensation amount further improves the balance control effect of the vehicle. Therefore, the vehicle is ultimately placed in a better stable state based on the vehicle at the next moment, thereby improving the autonomous stability and balance capability of the unmanned vehicle.

[0106] Optionally, the target parameters include a reference tilt tracking speed, a reference tilt tolerance range, and a state maintenance range. The above-mentioned S702 of determining the gain coefficient according to the stable state manifold model and the target parameters can also be implemented as follows:

[0107] The gain coefficient is determined according to the reference inclination tracking speed, the reference inclination allowable range, the state maintenance range and the steady state manifold model.

[0108] In this embodiment, the target parameters include the reference inclination tracking speed Reference tilt allowable range State maintenance range Then the computer equipment tracks the speed according to the reference inclination Reference tilt allowable range State maintenance range and the steady-state manifold model to determine the gain coefficient K.

[0109] Among them, the reference inclination tracking speed Determines the response speed of the state transfer phase, refer to the inclination tracking speed It is proportional to the speed of the state transfer phase. The higher the value, the faster the response speed of the transfer phase. Generally, it refers to the inclination tracking speed. The value is 5~10° / s.

[0110] Reference tilt tolerance range This is to avoid the computer equipment from frequently controlling the gain coefficient within the reference tilt angle allowable range. The gain coefficient K remains unchanged, which means the motorcycle has entered a stable state. Generally, the reference angle tolerance range is The value is 1~2° / s.

[0111] State maintenance range It is a range of the motorcycle state maintenance stage. When the computer device controls the curve corresponding to the gain coefficient K to shrink, the general state maintains the range The value is 0.5~2° / s.

[0112] This embodiment determines the gain coefficient based on the reference tilt angle tracking speed, the reference tilt angle allowable range, the state maintenance range and the stable state manifold model. Therefore, the gain coefficient is a variable parameter set according to the actual control requirements, and the handlebar angle angular velocity at the next moment is finally determined to be predictable. Therefore, the balance control method provided by this embodiment is also beneficial to vehicle trajectory prediction and planning.

[0113] Figure 9This is another flow chart of determining the vehicle handlebar angle angular velocity control model in the embodiment of the present application, referring to Figure 9 This embodiment relates to an optional implementation of how to determine the handlebar angle angular velocity control model for a vehicle. Based on the above embodiment, the gain coefficient is determined based on the reference tilt tracking speed, the reference tilt tolerance range, the state maintenance range, and the stable state manifold model, including the following steps:

[0114] S901: Determine a maximum gain coefficient according to a reference tilt tracking speed, a state maintenance range, and a stable state manifold model.

[0115] In this embodiment, the gain coefficients include the maximum gain coefficient K max and minimum gain coefficient K min The computer first tracks the speed based on the reference inclination State maintenance range and the steady-state manifold model to determine the maximum gain coefficient K max Specifically, the computer device determines the maximum gain coefficient K according to formula (9): max .

[0116]

[0117] Among them, the maximum gain coefficient K max The purpose is to avoid excessive turning speed of the motorcycle when the rate of change of the vehicle body tilt angle is too large, so that the motion state of the motorcycle is closer to the stable state manifold model. In formula (9), the maximum gain coefficient K max It consists of three parts. The first part is the preset reference steering speed. In this embodiment, the first part refers to 1 in formula (9). At any time, the handlebar angular velocity determined by the computer device cannot be less than the reference steering speed, thereby ensuring that the motion state of the motorcycle is along the tangent direction of the stable state manifold model. The second part refers to The second part is used to calculate the angular velocity of the handlebar according to the current moment. The distance d from the manifold surface in the steady-state manifold model M , restricting the motorcycle's motion state to go beyond the stable state manifold model; the third part refers to the formula (9) The third part is the differential required to move toward the target state, and a minimum quantity is introduced into the denominator of the differential To avoid singularity, of course, other extremely small amounts can also be selected to avoid singularity, and this embodiment does not limit this.

[0118] S902: Determine a minimum gain coefficient according to the maximum gain coefficient.

[0119] In this embodiment, the computer device is based on the maximum gain coefficient Kmax Determine the minimum gain factor K min Specifically, the computer device determines the minimum gain coefficient K according to formula (10): min .

[0120] K min =-2K max (10)

[0121] Among them, the minimum gain coefficient K min Considering that when the motorcycle's motion state is completely reversed, it also needs to move quickly based on the stable state manifold model. Since the reverse motion requires double the stroke to return to the stable state manifold model, the minimum gain coefficient K min Take the maximum gain coefficient K max -2 times.

[0122] S903 : Determine a gain coefficient according to the maximum gain coefficient, the minimum gain coefficient, the reference tilt angle tracking speed, and the reference tilt angle allowable range.

[0123] In this embodiment, the computer device is based on the maximum gain coefficient K max , minimum gain coefficient K min , reference inclination tracking speed and reference tilt tolerance Determine the gain factor K. One possible implementation is that the computer device uses As the horizontal axis, the gain coefficient K is used as the vertical axis to determine the maximum gain coefficient K max , minimum gain coefficient K min The position on the vertical axis; and determine At the position of the horizontal axis, according to Determine the position on the vertical axis Position on the vertical axis. Gain coefficient K from the minimum gain coefficient K min Starts to increase monotonically, when in When the gain coefficient K is constant equal to 1, then when Greater than When the gain coefficient K continues to increase monotonically until it is equal to the maximum gain coefficient K max .

[0124] Optionally, the computer device may also determine the vertical intercept b of the gain coefficient K according to formula (11), and further determine the response speed in the startup phase according to the vertical intercept b.

[0125]

[0126] Please refer to Figure 10 and Figure 11One way to achieve this is to use a computer device according to Figure 10 and Figure 11 Determine the gain factor K. Figure 10 Schematic diagram of the gain coefficient values ​​in the startup phase and transfer phase. Figure 11 Schematic diagram of the gain coefficient value in the holding stage.

[0127] like Figure 10 As shown in the figure, the gain coefficient value in the startup phase is represented by the curve before point C (i.e., K < 1). The lower the value, the shorter the startup phase, but the curve must remain monotonically increasing. The gain coefficient value in the transfer phase is represented by the curve after point B (i.e., K > 0). The overlapping area can be regarded as the fuzzy interval between the startup phase and the transfer phase (i.e., 0 < K < 1).

[0128] like Figure 11 As shown, Figure 11 The dotted lines in the figure represent the values ​​of the motorcycle during the starting and transfer phases. Figure 11 The solid line in the figure represents the value diagram of the motorcycle in the holding stage. It can be seen that if the current body inclination angle is close to the target inclination angle, that is, and When it approaches 0, the target angular velocity of the inclination angle Tilt target angular velocity allowable range The curve intercept b is Figure 11 The direction of the arrow in the figure shrinks in the same proportion, that is, Figure 11 After shrinking the dotted line in Figure 11 The solid line in .

[0129] This embodiment determines the maximum gain coefficient based on the reference tilt angle tracking speed, the state maintenance range and the stable state manifold model at the current moment, and determines the minimum gain coefficient based on the maximum gain coefficient, so as to determine the gain coefficient based on the maximum gain coefficient, the minimum gain coefficient, the reference tilt angle tracking speed and the reference tilt angle allowable range. Since the gain coefficient in this application can be adjusted based on actual needs, the balance control of the unmanned motorcycle in this application can strike a balance between fast response and stable control.

[0130] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0131] Based on the same inventive concept, embodiments of the present application also provide a balance control device for implementing the aforementioned balance control method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more balance control device embodiments provided below can be found in the above-described limitations of the balance control method and will not be further elaborated here.

[0132] Figure 12 This is a structural block diagram of the balance control device in the embodiment of the present application. In the embodiment of the present application, Figure 12 As shown, a balance control device 1200 is provided, comprising: an acquisition module 1201, a first determination module 1202, a second determination module 1203 and a control module 1204, wherein:

[0133] The acquisition module 1201 is used to acquire target parameters for controlling the different stages of vehicle motion and vehicle parameters at the current moment.

[0134] The first determining module 1202 is configured to determine a steady-state manifold model of the vehicle according to vehicle parameters at a current moment.

[0135] The second determination module 1203 is configured to determine the vehicle parameters at the next moment according to the stable state manifold model and the target parameters.

[0136] The control module 1204 is used to perform balance control on the movement of the vehicle according to the vehicle parameters at the next moment.

[0137] The balance control device provided in the present application obtains target parameters for controlling the states of different stages of vehicle motion and the vehicle parameters at the current moment, and determines the vehicle's stable state manifold model based on the vehicle parameters at the current moment. The vehicle parameters at the next moment are then determined based on the stable state manifold model and the target parameters, thereby performing balance control on the vehicle's motion based on the vehicle parameters at the next moment. Because the present application can determine the vehicle's stable state manifold model based on the actual vehicle parameters at the current moment, and determine the vehicle parameters at the next moment based on the stable state manifold model and the target parameters, the vehicle parameters at the next moment determined in the present application are reasonable and stable inputs determined based on the state targets at different stages. The vehicle's motion can then be balanced based on the vehicle parameters at the next moment, thereby maintaining the vehicle in a stable equilibrium state. Therefore, the balance control device provided in the present application avoids the situation in conventional technologies where unreasonable control inputs can cause the vehicle to overturn, solves the problem of poor autonomous stability and balance capabilities of unmanned vehicles in conventional technologies, and improves the autonomous stability and balance capabilities of unmanned vehicles.

[0138] Optionally, the vehicle parameter at the next moment includes a handlebar angular velocity at the next moment, and the first determining module 1202 includes:

[0139] The first determination unit is used to determine the vehicle dynamics model and the vehicle stability condition according to the vehicle parameters at the current moment; wherein the vehicle stability condition includes that the vehicle's body tilt angular velocity, handlebar angular velocity and rear wheel forward acceleration are all equal to zero.

[0140] The second determining unit is used to determine a stable state manifold model according to the vehicle dynamics model and the vehicle stability condition.

[0141] Optionally, the second determining module 1203 includes:

[0142] The third determining unit is used to determine a handlebar angle angular velocity control model of the vehicle according to the stable state manifold model and the target parameter.

[0143] The fourth determining unit is configured to determine the handlebar angle velocity at a next moment according to the handlebar angle velocity control model.

[0144] Optionally, the third determining unit includes:

[0145] The first determining subunit is configured to determine a first condition of the vehicle according to the stable state manifold model, wherein the first condition is configured to restrict the motion state of the vehicle to perform isometric movement based on the stable state manifold model.

[0146] The second determining subunit is used to determine the gain coefficient according to the stable state manifold model and the target parameter.

[0147] The third determining subunit is configured to determine a handlebar angle velocity control model according to the first condition and the gain coefficient, wherein the handlebar angle velocity control model is configured to control the vehicle to move based on a steady-state manifold model.

[0148] Optionally, the third determination subunit is specifically used to determine the dynamic compensation amount of the handlebar angle angular velocity control model based on the stable state manifold model; and determine the handlebar angle angular velocity control model of the vehicle based on the first condition, the gain coefficient and the dynamic compensation amount.

[0149] Optionally, the target parameters include a reference inclination tracking speed, a reference inclination allowable range, and a state maintenance range; the third determination subunit is further specifically used to determine the maximum gain coefficient based on the reference inclination tracking speed, the state maintenance range, and the stable state manifold model; determine the minimum gain coefficient based on the maximum gain coefficient; and determine the gain coefficient based on the maximum gain coefficient, the minimum gain coefficient, the reference inclination tracking speed, and the reference inclination allowable range.

[0150] Each module in the aforementioned balance control device may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0151] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0152] Obtaining target parameters for controlling different stages of vehicle motion and vehicle parameters at the current moment;

[0153] determining a steady-state manifold model of the vehicle according to the vehicle parameters at the current moment;

[0154] determining vehicle parameters at a next moment according to the stable state manifold model and the target parameters;

[0155] The movement of the vehicle is balanced and controlled according to the vehicle parameters at the next moment.

[0156] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0157] Determining a vehicle dynamics model and a vehicle stability condition based on the vehicle parameters at the current moment;

[0158] Determining the stable state manifold model according to the vehicle dynamics model and the vehicle stability condition;

[0159] The vehicle stability condition includes that the vehicle's body tilt angular velocity, handlebar turning angular velocity and rear wheel forward acceleration are all equal to zero.

[0160] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0161] determining a handlebar angle angular velocity control model for the vehicle according to the stable state manifold model and the target parameter;

[0162] The handlebar angle velocity at the next moment is determined according to the handlebar angle velocity control model.

[0163] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0164] determining a first condition for the vehicle based on the stable-state manifold model, wherein the first condition is used to restrict the motion state of the vehicle to isometric movement based on the stable-state manifold model;

[0165] determining a gain coefficient based on the steady-state manifold model and the target parameter;

[0166] The handlebar angle angular velocity control model is determined according to the first condition and the gain coefficient, wherein the handlebar angle angular velocity control model is used to control the vehicle to move based on the stable state manifold model.

[0167] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0168] determining a dynamic compensation amount of the handlebar angle angular velocity control model according to the stable state manifold model;

[0169] A handlebar angle angular velocity control model of the vehicle is determined according to the first condition, the gain coefficient, and the dynamic compensation amount.

[0170] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0171] The gain coefficient is determined according to the reference tilt tracking speed, the reference tilt allowable range, the state maintaining range, and the stable state manifold model.

[0172] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0173] determining the maximum gain coefficient according to the reference inclination tracking speed, the state maintenance range, and the stable state manifold model;

[0174] Determining the minimum gain coefficient according to the maximum gain coefficient;

[0175] The gain coefficient is determined according to the maximum gain coefficient, the minimum gain coefficient, the reference tilt angle tracking speed, and the reference tilt angle allowable range.

[0176] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0177] Obtaining target parameters for controlling different stages of vehicle motion and vehicle parameters at the current moment;

[0178] determining a steady-state manifold model of the vehicle according to the vehicle parameters at the current moment;

[0179] determining vehicle parameters at a next moment according to the stable state manifold model and the target parameters;

[0180] The movement of the vehicle is balanced and controlled according to the vehicle parameters at the next moment.

[0181] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0182] Determining a vehicle dynamics model and a vehicle stability condition based on the vehicle parameters at the current moment;

[0183] Determining the stable state manifold model according to the vehicle dynamics model and the vehicle stability condition;

[0184] The vehicle stability condition includes that the vehicle's body tilt angular velocity, handlebar turning angular velocity and rear wheel forward acceleration are all equal to zero.

[0185] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0186] determining a handlebar angle angular velocity control model for the vehicle according to the stable state manifold model and the target parameter;

[0187] The handlebar angle velocity at the next moment is determined according to the handlebar angle velocity control model.

[0188] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0189] determining a first condition for the vehicle based on the stable-state manifold model, wherein the first condition is used to restrict the motion state of the vehicle to isometric movement based on the stable-state manifold model;

[0190] determining a gain coefficient based on the steady-state manifold model and the target parameter;

[0191] The handlebar angle angular velocity control model is determined according to the first condition and the gain coefficient, wherein the handlebar angle angular velocity control model is used to control the vehicle to move based on the stable state manifold model.

[0192] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0193] determining a dynamic compensation amount of the handlebar angle angular velocity control model according to the stable state manifold model;

[0194] A handlebar angle angular velocity control model of the vehicle is determined according to the first condition, the gain coefficient, and the dynamic compensation amount.

[0195] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0196] The gain coefficient is determined according to the reference tilt tracking speed, the reference tilt allowable range, the state maintaining range, and the stable state manifold model.

[0197] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0198] determining the maximum gain coefficient according to the reference inclination tracking speed, the state maintenance range, and the stable state manifold model;

[0199] Determining the minimum gain coefficient according to the maximum gain coefficient;

[0200] The gain coefficient is determined according to the maximum gain coefficient, the minimum gain coefficient, the reference tilt angle tracking speed, and the reference tilt angle allowable range.

[0201] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0202] Obtaining target parameters for controlling different stages of vehicle motion and vehicle parameters at the current moment;

[0203] determining a steady-state manifold model of the vehicle according to the vehicle parameters at the current moment;

[0204] determining vehicle parameters at a next moment according to the stable state manifold model and the target parameters;

[0205] The movement of the vehicle is balanced and controlled according to the vehicle parameters at the next moment.

[0206] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0207] Determining a vehicle dynamics model and a vehicle stability condition based on the vehicle parameters at the current moment;

[0208] Determining the stable state manifold model according to the vehicle dynamics model and the vehicle stability condition;

[0209] The vehicle stability condition includes that the vehicle's body tilt angular velocity, handlebar turning angular velocity and rear wheel forward acceleration are all equal to zero.

[0210] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0211] determining a handlebar angle angular velocity control model for the vehicle according to the stable state manifold model and the target parameter;

[0212] The handlebar angle velocity at the next moment is determined according to the handlebar angle velocity control model.

[0213] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0214] determining a first condition for the vehicle based on the stable-state manifold model, wherein the first condition is used to restrict the motion state of the vehicle to isometric movement based on the stable-state manifold model;

[0215] determining a gain coefficient based on the steady-state manifold model and the target parameter;

[0216] The handlebar angle angular velocity control model is determined according to the first condition and the gain coefficient, wherein the handlebar angle angular velocity control model is used to control the vehicle to move based on the stable state manifold model.

[0217] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0218] determining a dynamic compensation amount of the handlebar angle angular velocity control model according to the stable state manifold model;

[0219] A handlebar angle angular velocity control model of the vehicle is determined according to the first condition, the gain coefficient, and the dynamic compensation amount.

[0220] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0221] The gain coefficient is determined according to the reference tilt tracking speed, the reference tilt allowable range, the state maintaining range, and the stable state manifold model.

[0222] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0223] determining the maximum gain coefficient according to the reference inclination tracking speed, the state maintenance range, and the stable state manifold model;

[0224] Determining the minimum gain coefficient according to the maximum gain coefficient;

[0225] The gain coefficient is determined according to the maximum gain coefficient, the minimum gain coefficient, the reference tilt angle tracking speed, and the reference tilt angle allowable range.

[0226] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0227] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0228] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0229] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A balance control method, characterized in that: The method comprises: Obtaining target parameters for controlling different stages of vehicle motion and vehicle parameters at the current moment; determining a steady-state manifold model of the vehicle according to the vehicle parameters at the current moment; determining vehicle parameters at a next moment according to the stable state manifold model and the target parameters; performing balance control on the movement of the vehicle according to the vehicle parameters at the next moment; Determining the steady-state manifold model of the vehicle based on the vehicle parameters at the current moment includes: Determining a vehicle dynamics model and a vehicle stability condition based on the vehicle parameters at the current moment; Determining the stable state manifold model according to the vehicle dynamics model and the vehicle stability condition; The vehicle stability condition includes that the vehicle's body tilt angular velocity, handlebar angular velocity, and rear wheel forward acceleration are all equal to zero; The vehicle parameters at the next moment include the angular velocity of the handlebar angle at the next moment; and determining the vehicle parameters at the next moment based on the stable state manifold model and the target parameters includes: determining a handlebar angle angular velocity control model for the vehicle according to the stable state manifold model and the target parameter; The handlebar angle velocity at the next moment is determined according to the handlebar angle velocity control model.

2. The method according to claim 1, characterized in that Determining a handlebar angle angular velocity control model of the vehicle according to the stable state manifold model and the target parameter includes: determining a first condition for the vehicle based on the stable-state manifold model, wherein the first condition is used to restrict the motion state of the vehicle to isometric movement based on the stable-state manifold model; determining a gain coefficient based on the steady-state manifold model and the target parameter; The handlebar angle angular velocity control model is determined according to the first condition and the gain coefficient, wherein the handlebar angle angular velocity control model is used to control the vehicle to move based on the stable state manifold model.

3. The method according to claim 2, characterized in that The determining of the handlebar angle velocity control model according to the first condition and the gain coefficient includes: determining a dynamic compensation amount of the handlebar angle angular velocity control model according to the stable state manifold model; A handlebar angle angular velocity control model of the vehicle is determined according to the first condition, the gain coefficient, and the dynamic compensation amount.

4. The method according to claim 2 or 3, characterized in that The target parameters include a reference tilt tracking speed, a reference tilt allowable range, and a state maintenance range; and determining a gain coefficient based on the stable state manifold model and the target parameters includes: The gain coefficient is determined according to the reference tilt tracking speed, the reference tilt allowable range, the state maintaining range, and the stable state manifold model.

5. The method according to claim 4, characterized in that The gain coefficient includes a maximum gain coefficient and a minimum gain coefficient, and determining the gain coefficient according to the reference inclination tracking speed, the reference inclination allowable range, the state maintenance range, and the stable state manifold model includes: determining the maximum gain coefficient according to the reference inclination tracking speed, the state maintenance range, and the stable state manifold model; Determining the minimum gain coefficient according to the maximum gain coefficient; The gain coefficient is determined according to the maximum gain coefficient, the minimum gain coefficient, the reference tilt angle tracking speed, and the reference tilt angle allowable range.

6. A balance control device, characterized in that: The device comprises: An acquisition module, used to acquire target parameters for controlling different stages of vehicle motion and vehicle parameters at a current moment; A first determining module, configured to determine a stable state manifold model of the vehicle according to the vehicle parameters at the current moment; a second determining module, configured to determine vehicle parameters at a next moment based on the stable state manifold model and the target parameters; a control module, configured to perform balance control on the movement of the vehicle according to the vehicle parameters at the next moment; The first determining module includes: a first determining unit, configured to determine a vehicle dynamics model and a vehicle stability condition according to the vehicle parameters at the current moment; a second determining unit, configured to determine the stable-state manifold model based on the vehicle dynamics model and a vehicle stability condition; wherein the vehicle stability condition includes that a body tilt angular velocity, a handlebar turning angular velocity, and a rear wheel forward acceleration of the vehicle are all equal to zero; The vehicle parameters at the next moment include the angular velocity of the handlebar angle at the next moment; the second determining module includes: a third determining unit, configured to determine a handlebar angle angular velocity control model of the vehicle according to the stable state manifold model and the target parameter; The fourth determining unit is configured to determine the handlebar angle velocity at the next moment according to the handlebar angle velocity control model.

7. The device according to claim 6, characterized in that The third determining unit includes: a first determining subunit, configured to determine a first condition of the vehicle according to the stable-state manifold model, wherein the first condition is configured to restrict the motion state of the vehicle to isometric movement based on the stable-state manifold model; a second determining subunit, configured to determine a gain coefficient according to the stable state manifold model and the target parameter; The third determining subunit is configured to determine the handlebar angle angular velocity control model according to the first condition and the gain coefficient, wherein the handlebar angle angular velocity control model is configured to control the vehicle to move based on the stable state manifold model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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