Flying car air-ground mode switching control method based on 3S collaborative optimization

Abstract

The invention discloses an aerocar land-air mode switching control method based on 3S collaborative optimization. The method comprises the following steps: S1, constructing a six-degree-of-freedom coupling dynamic model fusing a ground tire friction circle and rotor aerodynamic interference; s2, on the basis of the coupling dynamics model, through Kalman filtering, the dynamic friction circle margin is solved in real time; s3, generating a seven-segment jerk constraint trajectory based on quintic polynomial interpolation, and adopting asymmetric jerk control in a rotor lift intervention stage; s4, when the real-time safety margin exceeds a certain value, time compression processing is carried out, and the optimal mode switching control is converted into a quadratic programming form; and S5, layered control is adopted, and land-air mode seamless switching is completed. According to the invention, a safe, stable and efficient dynamic collaborative mechanism can be established, and safe, stable and efficient collaborative optimization control is realized.

Description

Technical Field

[0001] This invention relates to the field of flying car control, and specifically to a flying car land-air mode switching control method based on 3S collaborative optimization. Background Technology

[0002] Flying cars face inherent contradictions regarding safety, comfort, and efficiency during the transition between land and air modes, which has become a core bottleneck restricting large-scale deployment. Current technologies mainly suffer from the following shortcomings:

[0003] 1. Insufficient dynamic adaptability of safety boundaries. Traditional methods use a fixed threshold to control the friction circle utilization rate, but ignore the time-varying influence of aerodynamic lift on tire ground load during the transition phase. Measured data show that when rotor lift intervenes, the ground lateral force margin drops sharply, causing the existing algorithm to actually exceed the limit of friction circle utilization rate in slope turning scenarios, which can easily lead to sideslip accidents.

[0004] 2. Inadequate motion stability control. While mainstream controllers such as PID, LQR, and MPC can constrain acceleration, they do not limit jerk. During takeoff and landing, the sudden change in lift in flying cars generates high jerk shocks that exceed the human tolerance limit specified in ISO 2631-1, leading to an increased incidence of motion sickness among passengers.

[0005] 3. Low mode switching efficiency. When using a switching strategy based on conservative safety margins, the land-to-air transition takes a long time, which cannot meet the high-efficiency switching requirements of Urban Air Traffic Management (UAM). Existing time optimization schemes suffer from high computational latency, resulting in large fluctuations in actual switching speeds.

[0006] In summary, existing technologies optimize a single metric in isolation, safety-oriented control sacrifices smoothness and speed, comfort-first strategies prolong switching time, and efficiency-maximizing algorithms also risk exceeding safety boundaries.

[0007] Therefore, to solve the above problems, a 3S collaborative optimization-based control method for the switching between land and air modes of flying cars is needed, which can establish a dynamic collaborative mechanism for safety, stability and efficiency, and achieve collaborative optimization control of safety, stability and efficiency. Summary of the Invention

[0008] In view of this, the purpose of this invention is to overcome the defects in the prior art and provide a 3S collaborative optimization-based control method for the switching between land and air modes of flying cars, which can establish a safe, stable and efficient dynamic collaborative mechanism to achieve safe, stable and efficient collaborative optimization control.

[0009] The 3S collaborative optimization-based land-air mode switching control method for flying cars of the present invention includes the following steps:

[0010] S1. Construct a six-degree-of-freedom coupled dynamic model that integrates the ground tire friction circle and rotor aerodynamic interference;

[0011] S2. Based on the coupled dynamics model, the dynamic friction circle margin is calculated in real time using Kalman filtering;

[0012] S3. A seven-segment jerk constraint trajectory is generated based on fifth-order polynomial interpolation, and asymmetric jerk control is adopted during the rotor lift intervention phase.

[0013] S4. When the real-time safety margin exceeds a certain value, time compression is performed to transform the optimal mode switching control into a quadratic programming form;

[0014] S5. Employs hierarchical control to achieve seamless switching between land and air modes.

[0015] Furthermore, the coupled dynamics model includes the following equations:

[0016] ;

[0017] in, This is the critical value for the dynamic friction circle; The coefficient of friction; It is a real-time grounding load. The total mass of the flying car; It is the acceleration due to gravity; It is over time Changing lift;

[0018] It is a lateral force and lateral force The magnitude of the resultant force;

[0019] ;

[0020] in, To add jerk constraints; This represents the mode switching time.

[0021] Furthermore, the dynamic friction circle margin is calculated using the following formula:

[0022] ;

[0023] in, This is the dynamic friction circle margin threshold. It is a dynamic relaxation factor;

[0024] ; This is the time constant of the dynamic safety boundary.

[0025] Furthermore, asymmetric jerk control is performed according to the following formula:

[0026] ;

[0027] in, The acceleration at time t represents the acceleration. It is the maximum value of the jerk; and These are time segments. This is the phase of gradual lift increase. It is a uniform acceleration segment. The next phase is the stable phase.

[0028] Furthermore, the safety margin is determined according to the following formula:

[0029] ;

[0030] in, For safety margin; This represents the net friction force acting on the tire under the current operating conditions. This refers to the vertical load on the tire.

[0031] Furthermore, time compression is performed according to the following formula:

[0032] ;

[0033] in, This is the compression gain coefficient; This is the critical safety margin threshold. Nominal mode switching time; The compressed time; The minimum allowed switching time; This is the actual switching time after compression.

[0034] Furthermore, the hierarchical control architecture includes a decision-making layer and an execution layer;

[0035] The decision-making layer is used to dynamically allocate corresponding weights; the execution layer is used to drive the hub motor and the rotor mechanism.

[0036] Furthermore, the control instructions for the hierarchical control architecture are determined based on the formula:

[0037] ;

[0038] in, This is the final control command; For tire friction circle protection constraint; This is the amount of compensation for trajectory tracking error; For time compression gain; Remaining switching distance; For safety weights; For smoothing weights; Efficiency weight.

[0039] Furthermore, it also includes:

[0040] S6. Monitor the safety factor and smoothness in real time. If the safety index or stability index is not met, return to steps S2-S3 to form a closed-loop optimization.

[0041] Furthermore, the security indicators are: ; For safety reasons,

[0042] , The friction circle value represents the current real-time state.

[0043] The stability indicator is: ; Smoothness is used to evaluate comfort.

[0044] , To assess the end time of the time window.

[0045] The beneficial effects of this invention are as follows: The 3S collaborative optimization-based control method for land-to-air mode switching of flying cars disclosed in this invention significantly improves the safety and comfort of land-to-air mode switching of flying cars, while greatly shortening the switching time. By dynamically reconstructing the safety boundary, the utilization rate of the friction circle is strictly controlled within the safety threshold in high-risk scenarios such as ramp turns, reducing the risk of sideslip compared to traditional methods; by adopting jerk-constrained trajectory planning technology, the impact of rotor lift intervention is reduced to within the human comfort standard range, reducing the incidence of motion sickness among passengers; through real-time optimization algorithms, the high-speed solution of thousands of state variables is completed in a short time, greatly compressing the land-to-air switching time; a three-objective collaborative optimization mechanism of safety-smoothness-efficiency is established, maintaining a high safety margin under sudden disturbances, reducing trajectory deviation while decreasing energy consumption, ultimately achieving a triple breakthrough in safety, comfort, and efficiency. Attached Figure Description

[0046] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0047] Figure 1 This is a schematic diagram of the mode switching control method of the present invention;

[0048] Figure 2 This is a schematic diagram of the mode switching control system architecture of the present invention. Detailed Implementation

[0049] The present invention will be further described below with reference to the accompanying drawings, as shown in the figures:

[0050] This embodiment discloses a land-air mode switching control method for flying cars based on 3S collaborative optimization, including the following steps:

[0051] Step S1: Construct a six-degree-of-freedom coupled dynamic model that integrates the friction circle of the ground tires and the aerodynamic interference of the rotor. In this model, a 3S quantification system is defined, which includes safety, smoothness and efficiency indicators. The 3S represents Safety, Smoothness and Speed.

[0052] By integrating the physical constraints of tire friction circles with the aerodynamic disturbances of rotor downwash flow, the dynamic coupling effect during the land-air mode switching process is accurately characterized, solving the defect of large prediction error in the transition state of traditional split models. By defining a 3S quantization system, the strong conflict between safety, comfort and efficiency in single-objective control is overcome.

[0053] The coupled dynamics model includes the following equations:

[0054] ;

[0055] in, This is the critical value for the dynamic friction circle; The coefficient of friction; It is a real-time grounding load. The total mass of the flying car; It is the acceleration due to gravity; It is over time Changing lift;

[0056] It is a lateral force and lateral force The magnitude of the resultant force;

[0057] ;

[0058] in, To add jerk constraints, Jerk is the internationally accepted unit for jerk (1g / s≈9.8m / s³). This represents the mode switching time.

[0059] Step S2: By fusing wheel speed sensor and rotor speed data through Kalman filtering, the dynamic friction circle margin is calculated in real time. The safety boundary is reconstructed using a piecewise dynamic relaxation factor to achieve boundary adaptation that prioritizes efficiency on straight road sections while maintaining full conservatism on slopes.

[0060] By fusing wheel speed sensor and rotor speed data through Kalman filtering, the error in calculating the friction circle margin is smaller, and the accuracy is significantly improved compared to the single sensor solution.

[0061] The dynamic friction circle margin is calculated according to the following formula:

[0062] ;

[0063] in, This is the dynamic friction circle margin threshold. The dynamic relaxation factor is calculated as follows:

[0064] ;

[0065] in, The time constant represents the dynamic safety boundary. The value of this time constant is related to the quality environment, etc., and its range is generally 0.5s to 3s. For example, it is 0.89s when unloaded 400kg and 1.1s when fully loaded 600kg.

[0066] Step S3: Generate a seven-segment jerk constraint trajectory based on fifth-order polynomial interpolation. During the rotor lift intervention phase, asymmetric jerk control is adopted to ensure motion continuity and meet the comfort requirements of ISO 2631-1.

[0067] The seven-segment jerk constraint trajectory is generated based on fifth-order polynomial interpolation. Fifth-order polynomial interpolation ensures continuous jerk, and the seven independent control segments realize asymmetric optimization of acceleration. Compared with the traditional trapezoidal acceleration, the energy consumption is reduced and the impact is decreased.

[0068] Asymmetric jerk control is performed according to the following formula:

[0069] ;

[0070] in, The acceleration at time t represents the acceleration. It is the maximum value of the jerk; and These are time segments. This is the phase of gradual lift increase. It is a uniform acceleration segment. The next stage is the stability segment, where 2.94 represents the comfort requirements set according to ISO 2631-1.

[0071] Step S4: When the real-time safety margin exceeds a certain value, the time compression algorithm is triggered. By introducing slack variables, the optimal control problem is transformed into a quadratic programming form, which can significantly shorten the switching time, by up to 40%, while also imposing hard constraints on safety and stability indicators.

[0072] By using a relaxation factor, nonlinear constraints are transformed into a quadratic programming problem, enabling the solution of 1,000-dimensional state variables in a short time. This significantly improves the speed compared to direct nonlinear optimization, and the number of constraint violations is controllable.

[0073] The safety margin is determined according to the following formula:

[0074] ;

[0075] in, For safety margin; This represents the net friction force acting on the tire under the current operating conditions. This refers to the vertical load on the tire.

[0076] when At that time, the time compression algorithm is activated:

[0077] ;

[0078] in, This is the compression gain coefficient; This is the critical safety margin threshold. Nominal mode switching time; The compressed time; The minimum allowable switching time is constrained by the physical limits of the actuator; This is the actual switching time after compression.

[0079] Step S5: A hierarchical control architecture is adopted, in which the decision layer dynamically allocates 3S weights according to the safety factor, and the execution layer drives the hub motor and rotor mechanism through a torque-thrust hybrid allocation algorithm, thereby quickly completing the seamless switching between land and air modes;

[0080] By setting up a hierarchical control architecture, the control system is divided into a decision-making layer and an execution layer, and different optimization methods are used for different layers, thereby achieving rapid switching between land and air modes.

[0081] The control commands for the hierarchical control architecture are determined based on the formula:

[0082] ;

[0083] in, This is the final control command; For tire friction circle protection constraint; This is the amount of compensation for trajectory tracking error; For time compression gain; Remaining switching distance; For safety weights; For smoothing weights; Efficiency weight.

[0084] In this embodiment, the safety weight, smoothness weight, and efficiency weight can be tentatively set as follows:

[0085] ;

[0086] It also includes: Step S6: Real-time monitoring of safety factor and smoothness. When the safety index or stability index is not met, the control parameters are adaptively retuned, and steps S2-S3 are repeated to form a 3S closed-loop optimization.

[0087] The security indicators are as follows: ; For safety reasons,

[0088] , The friction circle value represents the current real-time state.

[0089] The stability indicator is: ; Smoothness is used to assess comfort;

[0090] , To assess the end time of the time window.

[0091] Among them, when or When this occurs, the control parameters are reset.

[0092] Through real-time monitoring and closed-loop optimization, the three objectives of safety, smoothness and speed can be coordinated and optimized in dynamic environments. Compared with open-loop control, the trajectory deviation caused by sudden disturbances can be significantly reduced, and the impact of mode switching is reduced.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A 3S-based collaborative optimization method for switching control of air-land mode of a flying car, characterized in that: Includes the following steps: S1. Construct a six-degree-of-freedom coupled dynamic model that integrates the ground tire friction circle and rotor aerodynamic interference; S2. Based on the coupled dynamics model, the dynamic friction circle margin is calculated in real time using Kalman filtering; S3. A seven-segment jerk constraint trajectory is generated based on fifth-order polynomial interpolation, and asymmetric jerk control is adopted during the rotor lift intervention phase. S4. When the real-time safety margin exceeds a certain value, time compression is performed to transform the optimal mode switching control into a quadratic programming form; S5. Employs hierarchical control to achieve seamless switching between land and air modes.

2. The 3S collaborative optimization-based land-air mode switching control method for flying cars according to claim 1, characterized in that: The coupled dynamics model includes the following equations: ； in, This is the critical value for the dynamic friction circle; The coefficient of friction; It is a real-time grounding load. The total mass of the flying car; It is the acceleration due to gravity; It is over time Changing lift; It is a lateral force and lateral force The magnitude of the resultant force; ； in, To add jerk constraints; This represents the mode switching time.

3. The 3S collaborative optimization-based land-air mode switching control method for flying cars according to claim 1, characterized in that: The dynamic friction roundness margin is calculated using the following formula: ； in, This is the dynamic friction circle margin threshold. It is a dynamic relaxation factor; ; This is the time constant of the dynamic safety boundary.

4. The 3S collaborative optimization-based land-air mode switching control method for flying cars according to claim 1, characterized in that: Asymmetric jerk control is performed according to the following formula: ； in, The acceleration at time t represents the acceleration. It is the maximum value of the jerk; and These are segmented time points. This is the phase of gradual lift increase. It is a uniform acceleration segment. The next phase is the stable phase.

5. The 3S collaborative optimization-based land-air mode switching control method for flying cars according to claim 1, characterized in that: The safety margin is determined according to the following formula: ； in, For safety margin; This represents the net friction force acting on the tire under the current operating conditions. This refers to the vertical load on the tire.

6. The 3S collaborative optimization-based land-air mode switching control method for flying cars according to claim 5, characterized in that: Time compression is performed according to the following formula: ； in, This is the compression gain coefficient; This is the critical safety margin threshold. Nominal mode switching time; The compressed time; The minimum allowed switching time; This is the actual switching time after compression.

7. The 3S collaborative optimization-based land-air mode switching control method for flying cars according to claim 1, characterized in that: The hierarchical control architecture includes a decision-making layer and an execution layer; The decision-making layer is used to dynamically allocate corresponding weights; the execution layer is used to drive the hub motor and the rotor mechanism.

8. The 3S collaborative optimization-based land-air mode switching control method for flying cars according to claim 7, characterized in that: The control commands for the hierarchical control architecture are determined based on the formula: ； in, This is the final control command; For tire friction circle protection constraint; This is the amount of compensation for trajectory tracking error; For time compression gain; Remaining switching distance; For safety weights; For smoothing weights; Efficiency weight.

9. The 3S collaborative optimization-based land-air mode switching control method for flying cars according to claim 1, characterized in that: Also includes: S6. Monitor the safety factor and smoothness in real time. If the safety index or stability index is not met, return to steps S2-S3 to form a closed-loop optimization.

10. The 3S collaborative optimization-based land-air mode switching control method for flying cars according to claim 9, characterized in that: The security indicators are: ; For safety reasons, , The friction circle value represents the current real-time state. The stability indicator is: ; Smoothness is used to evaluate comfort. , To assess the end time of the time window.

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