Active fault-tolerant control method and device for intelligent hovercar

Abstract

The invention provides an active fault-tolerant control method and device for an intelligent hovercar, and the method comprises the steps: obtaining the state quantity of the hovercar at the current moment and the rotating speed vectors of four motor rotors, and obtaining the fault-tolerant control of the hovercar based on the state quantity of the hovercar at the current moment and the rotating speed vectors of the four motor rotors; obtaining the state quantity of the flying car at the next moment by using a pre-established aerodynamic model; based on the state quantity and the expected state quantity of the hovercar at the next moment, a thrust vector of a motor at the next moment is obtained through calculation by using an NMPC controller; based on the thrust vector of the motor at the next moment, determining the total lift force of the motor at the next moment and the angular acceleration of the hovercar at the next moment; and based on the total lift force of the motor at the next moment, the total torque of the motor at the next moment and the rotating speed vectors of the four motor rotors at the current moment, determining the rotating speed vectors of the four motor rotors at the next moment. The stability, the accuracy, the anti-interference capability and the motion sickness capability of the hovercar are improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent flying vehicles, and in particular to an active fault-tolerant control method and device for an intelligent flying vehicle. Background Art

[0002] At present, the controllers of flying cars can be divided into three categories: non-model-based controllers, model-based controllers and learning-based controllers. Among the non-model-based controllers, PID has excellent performance and good control performance, but the traditional PID controller will become difficult to adjust parameters when dealing with high-speed nonlinear systems, and the robustness shown under sudden disturbances is not strong. Moreover, it does not have a planning function and the adjustment accuracy is not high.

[0003] The linear quadratic regulator (LQR) is a type of optimal controller. LQR is robust and has been successfully applied to quadrotor control. However, since the dynamic characteristics of the actuator are not considered, the LQR controller optimizes the control by converting the nonlinear model of the drone into a linear state space model and has no prediction function, the dynamic response capability is not strong in high-speed systems.

[0004] The MPC controller is a model-based optimization control method that can meet the planning constraints of high-speed systems. The MPC controller uses the state space model to perform rolling optimization control on the original aircraft's nonlinear model. There is a model linearization relationship, and the prediction results will have a certain error.

[0005] In the intelligent controller based on learning, there is no need to build a dynamic model of the UAV. Instead, the system is trained through the data obtained from the UAV flight experiment. Flight controllers based on fuzzy logic and neural networks have been applied to UAVs. Although the intelligent controller based on learning has been successfully verified through many experiments. However, it is still difficult to analyze the stability and robustness of these methods. Summary of the invention

[0006] In view of this, the present application provides an active fault-tolerant control method and device for an intelligent flying car to overcome the above-mentioned technical defects.

[0007] In a first aspect, an embodiment of the present application provides an active fault-tolerant control method for an intelligent flying car, which is applied to a flying car, wherein the flying car includes four motors, each of which includes a motor rotor; including:

[0008] Obtain the current state of the flying car and the speed vectors of the four motor rotors, wherein the state includes: position, speed, Euler angle and angular velocity;

[0009] Based on the state quantity of the flying car at the current moment and the speed vectors of the four motor rotors, the state quantity of the flying car at the next moment is obtained by using a pre-established aerodynamic model;

[0010] Based on the state quantity and expected state quantity of the flying car at the next moment, the thrust vector of the motor at the next moment is calculated using the NMPC controller;

[0011] Based on the thrust vector of the motor at the next moment, determine the total lift of the motor at the next moment and the angular acceleration of the flying car at the next moment;

[0012] Determine the total torque of the motor at the next moment based on the angular acceleration of the flying car at the next moment and the angular acceleration of the flying car at the current moment;

[0013] Determine the speed vectors of the four motor rotors at the next moment based on the total lift of the motor at the next moment, the total torque of the motor at the next moment, and the speed vectors of the four motor rotors at the current moment;

[0014] The speed vectors of the four motor rotors at the next moment are sent to the motor controller of the flying car.

[0015] Furthermore, the pre-established aerodynamic model includes: a translation model and a rotation model;

[0016] Based on the state quantity of the flying car at the current moment and the speed vectors of the four motor rotors, the state quantity of the flying car at the next moment is obtained by using a pre-established aerodynamic model; including:

[0017] The translation model is:

[0018]

[0019] Among them, ξ k+1 For the next moment t k+1 The position of the flying car in the geocentric rectangular coordinate system, For k+1 The second-order differential of k is the current time t k The total lift of the flying car, m is the total mass of the flying car; g is the gravity vector; f a，k is the current time t k The air resistance of the flying car; f w，k is the current time t k The wind force on the flying car;

[0020] Current time t k The total lift of the flying car is T k Calculated by the following formula:

[0021]

[0022] Among them, u k represents the current time t k The four-dimensional thrust vector generated by the four motor rotors is:

[0023] u k =c t w k 2

[0024] c t is the thrust coefficient, w k is the current time t k The speed vectors of the four motor rotors; τ k，1 is the current time t k A first total torque vector of the motor rotor;

[0025] G1 is the first coefficient matrix:

[0026]

[0027] G2 is the second coefficient matrix:

[0028]

[0029] G3 is the third coefficient matrix:

[0030]

[0031] Among them, c q is the torque coefficient; β and l are the geometric parameters of the flying car; I P is the moment of inertia of the rotor around the Z axis; Ω y，k is the current time t k The y-direction angular velocity of the flying car;

[0032] Current time t k The air resistance f of the flying car a，k for:

[0033]

[0034] Among them, k d，x , k d，y , k d，z and k h are all known positive parameters; v x，k , v y，k and v z，k are the current time t k The speed of the flying car in three directions in the body coordinate system;

[0035] Current time tk The wind force f on the flying car w，k for:

[0036]

[0037] Among them, c represents the proportionality coefficient, ρ represents the air density, and S represents the wind-exposed area of ​​the flying car;

[0038] The rotation model is:

[0039]

[0040] Among them, I v represents the inertia matrix of the flying car; Ω k is the current time t k The three-dimensional angular velocity vector of the flying car: Ω k =[Ω x，k ,Ω y，k ,Ω z，k ];q k is the current time t k The quaternion converted from the Euler angle of the flying car; q k+1 is the current time t k+1 The quaternion converted from the Euler angle of the flying car;

[0041] Based on the current time t k The location of the flying car k ,speed Quaternion q k and the three-dimensional angular velocity vector Ω k , and the current time t k The speed vectors w of the four motor rotors k , using the translation model and rotation model, calculate the position of the flying car at the next moment speed Quaternions and the three-dimensional angular velocity vector

[0042] Furthermore, based on the state quantity and expected state quantity of the flying car at the next moment, the thrust vector of the motor at the next moment is calculated by using the NMPC controller; including:

[0043] The cost function f(u k+1 )for:

[0044] f(u k+1 )=||β k+1 -β k+1，r ||+||u k+1 ||+||MSI(a k+1 )-MSI(ak+1，r )||

[0045] Among them, β k+1 The state of the flying car at the next moment:

[0046]

[0047] β k+1，r is the expected state of the flying car at the next moment; u k+1 represents the thrust vector of the flying car motor at the next moment, which is the quantity to be determined; a k+1 is the Z-direction acceleration of the flying car at the next moment; a k+1，r The expected acceleration of the flying car in the Z direction at the next moment;

[0048] Among them, MSI (a k+1 ) is the acceleration in the Z direction a k+1 The incidence of motion sickness:

[0049]

[0050] in, represents the amplitude, H Δt Indicates vibration frequency;

[0051]

[0052] Where φ(·) represents the normal function; time Δt = t k -t k-1 ;

[0053]

[0054] Where f is the frequency of vertical oscillation of the flying car;

[0055] By solving the cost function f(u k+1 ) to obtain the minimum value of the four-dimensional thrust vector of the flying car motor at the next moment

[0056] Further, based on the thrust vector of the motor at the next moment, determining the total lift of the motor at the next moment and the angular acceleration of the flying car at the next moment; including:

[0057] According to the following formula

[0058]

[0059] Calculate the total lift of the flying car at the next moment and angular acceleration

[0060] Furthermore, the method further comprises: determining a second total torque vector τ of the motor at the current moment based on the speed vectors of the four motor rotors at the current moment and the speed vectors of the four motor rotors at the previous moment k，2 :

[0061] τ k，2 =G1w k 2 +Δt -1 G2(w k -w k-1 )

[0062] Among them, w k-1 is the previous moment t k-1 The speed vectors of the four motor rotors, time △t = t k -t k-1 .

[0063] Further, based on the angular acceleration of the flying car at the next moment and the angular acceleration of the flying car at the current moment, determining the total torque of the motor at the next moment; including:

[0064] The total torque of the motor at the next moment for:

[0065]

[0066] in, is the angular acceleration of the flying car at the current moment.

[0067] Further, based on the total lift of the motor at the next moment, the total torque of the motor at the next moment and the speed vectors of the four motor rotors at the current moment, the speed vectors of the four motor rotors at the next moment are determined, including:

[0068] According to the following formula:

[0069]

[0070] Calculate the speed vectors of the four motor rotors at the next moment

[0071] In a second aspect, an embodiment of the present application provides an active fault-tolerant control device for an intelligent flying car, which is applied to a flying car, wherein the flying car includes four motors, each of which includes a motor rotor; including:

[0072] An acquisition unit is used to acquire the state quantity of the flying car and the speed vectors of the four motor rotors at the current moment, wherein the state quantity includes: position, speed, Euler angle and angular speed;

[0073] A first calculation unit is used to obtain the state quantity of the flying car at the next moment by using a pre-established aerodynamic model based on the state quantity of the flying car at the current moment and the speed vectors of the four motor rotors;

[0074] A second calculation unit is used to calculate the thrust vector of the motor at the next moment by using the NMPC controller based on the state quantity and expected state quantity of the flying car at the next moment;

[0075] A first determining unit, configured to determine a total lift of the motor at a next moment and an angular acceleration of the flying car at a next moment based on a thrust vector of the motor at a next moment;

[0076] a second determining unit, configured to determine a total torque of the motor at a next moment based on the angular acceleration of the flying car at a next moment and the angular acceleration of the flying car at a current moment;

[0077] a third determining unit, configured to determine the speed vectors of the four motor rotors at the next moment based on the total lift of the motor at the next moment, the total torque of the motor at the next moment, and the speed vectors of the four motor rotors at the current moment;

[0078] The sending unit is used to send the speed vectors of the four motor rotors at the next moment to the motor controller of the flying car.

[0079] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of the embodiment of the present application when executing the computer program.

[0080] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions implement the method of the embodiment of the present application when executed by a processor.

[0081] This application improves the stability, accuracy and anti-interference ability of the flying car. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0083] Figure 1 A flow chart of an active fault-tolerant control method for an intelligent flying car provided in an embodiment of the present application;

[0084] Figure 2 A functional structure diagram of the active fault-tolerant control device for an intelligent flying car provided in an embodiment of the present application;

[0085] Figure 3 A functional structure diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0086] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0087] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0088] First, the design concept of the embodiments of the present application is briefly introduced.

[0089] The present application provides an active fault-tolerant control method for an intelligent flying car, and the control method for the intelligent flying car includes: receiving the power parameters of the intelligent flying car in a high-speed flight state, adding an air resistance model and a wind disturbance model to the coupled dynamic model to improve the tracking accuracy and anti-interference ability, while minimizing the computational overhead. In view of the problem of driver comfort, a nonlinear model predictive control (NMPC) controller based on the driver's motion sickness model is designed in the above-mentioned coupled dynamic model, and a cost function that can optimize motion sickness is formulated. The control method can maximize the comfort of passengers. In order to further improve the accuracy of the control method, an inner loop controller based on nonlinear incremental dynamic inversion (INDI) is also designed that considers additional torques including gyroscopic torque. The present application can improve the stability and flexible adaptability of the flying car's flight automatic driving system during mode switching.

[0090] The system corresponding to this application is deployed on a four-axis eight-propeller flying car. In terms of hardware, the avionics system and the ground-based wire control system are integrated. The CAN bus, encoder and driver are used to form a control system to provide higher accuracy, reliability and real-time performance, reduce costs, and improve the flexibility and maintainability of the system. It is convenient for highly precise control and data communication.

[0091] In summary, the present application provides a control method for a flying car, which solves the driving experience problems caused by the huge air resistance, attitude oscillation caused by wind disturbance and motion sickness faced by the flying car under high-speed movement, optimizes the control problems during mode switching, and improves the stability and safety of the flying car.

[0092] Compared with the prior art, this application has the following technical effects:

[0093] 1. This application adds an air resistance model and a wind disturbance model to improve the controller's tracking accuracy and anti-interference ability; the outer loop NMPC controller uses this model for rolling optimization and considers the additional torque to use the inner loop INDI controller for further feedback control, which greatly improves the stability, accuracy and anti-interference ability of the flying car.

[0094] 2. This application conducts a joint simulation of Simulink and Adams in a wind disturbance environment. Finally, the code is deployed for real machine demonstration. The results show that the flying car using the controller of this application has good tracking performance during high-speed flight.

[0095] After introducing the application scenarios and design concepts of the embodiments of the present application, the technical solutions provided by the embodiments of the present application are described below.

[0096] like Figure 1 As shown, an embodiment of the present application provides an active fault-tolerant control method for an intelligent flying car, which is applied to a flying car, wherein the flying car includes four motors, each of which includes a motor rotor; the method includes steps 101 to 107:

[0097] Step 101: Obtain the current state of the flying car and the speed vectors of the four motor rotors, wherein the state includes: position, speed, Euler angle and angular velocity;

[0098] Step 102: Based on the state quantity of the flying car at the current moment and the speed vectors of the four motor rotors, the state quantity of the flying car at the next moment is obtained by using a pre-established aerodynamic model;

[0099] Step 103: Based on the state quantity and expected state quantity of the flying car at the next moment, the thrust vector of the motor at the next moment is calculated by using the NMPC controller;

[0100] Step 104: determining the total lift of the motor at the next moment and the angular acceleration of the flying car at the next moment based on the thrust vector of the motor at the next moment;

[0101] Step 105: determining the total torque of the motor at the next moment based on the angular acceleration of the flying car at the next moment, the angular acceleration of the flying car at the current moment, and the total torque of the motor at the current moment;

[0102] Step 106: determining the speed vectors of the four motor rotors at the next moment based on the total lift of the motor at the next moment, the total torque of the motor at the next moment, and the speed vectors of the four motor rotors at the current moment;

[0103] Step 107: Send the speed vectors of the four motor rotors at the next moment to the motor controller of the flying car.

[0104] The above steps 104 to 107 are all processing steps of the inner loop INDI controller. As an outer loop controller, NMPC generates thrust of four motors according to the aerodynamic model and optimization theory of the flying car. The INDI inner loop controller converts the thrust input from the advanced controller into the speed command of the rotor through algebraic equations. It is transmitted to each motor electronic control system through the bus system to control the corresponding speed of the motor. It is suitable for vertical take-off and landing, mode switching and cruising of the flying car.

[0105] In some embodiments, the pre-established aerodynamic model includes: a translation model and a rotation model;

[0106] Based on the state quantity of the flying car at the current moment and the speed vectors of the four motor rotors, the state quantity of the flying car at the next moment is obtained by using a pre-established aerodynamic model; including:

[0107] The translation model is:

[0108]

[0109] Among them, ξ k+1 For the next moment t k+1 The position of the flying car in the geocentric rectangular coordinate system, For k+1 The second-order differential of k is the current time t k The total lift of the flying car, m is the total mass of the flying car; g is the gravity vector; f a，k is the current time t k The air resistance of the flying car; f w，k is the current time t k The wind force on the flying car;

[0110] Current time t k The total lift of the flying car is T k Calculated by the following formula:

[0111]

[0112] Among them, u k represents the current time tk The four-dimensional thrust vector generated by the four motor rotors is:

[0113] u k =c t w k 2

[0114] c t is the thrust coefficient, w k is the current time t k The speed vectors of the four motor rotors; τ k，1 is the current time t k A first total torque vector of the motor rotor;

[0115] G1 is the first coefficient matrix:

[0116]

[0117] G2 is the second coefficient matrix:

[0118]

[0119] G3 is the third coefficient matrix:

[0120]

[0121] Among them, c q is the torque coefficient; β and l are the geometric parameters of the flying car; I P is the moment of inertia of the rotor around the Z axis; Ω y，k is the current time t k The y-direction angular velocity of the flying car;

[0122] Current time t k The air resistance f of the flying car a，k for:

[0123]

[0124] Among them, k d，x , k d，y , k d，z and k h are all known positive parameters; v x，k , v y，k and v z，k are the current time t k The speed of the flying car in three directions in the body coordinate system;

[0125] Current time t k The wind force f on the flying car w，k for:

[0126]

[0127] Among them, c represents the proportionality coefficient, ρ represents the air density, and S represents the wind-exposed area of ​​the flying car;

[0128] The rotation model is:

[0129]

[0130] Among them, I v represents the inertia matrix of the flying car; Ω k is the current time t k The three-dimensional angular velocity vector of the flying car: Ω k =[Ω x，k ,Ω y，k ,Ω z，k ];q k is the current time t k The quaternion converted from the Euler angle of the flying car; q k+1 is the current time t k+1 The quaternion converted from the Euler angle of the flying car;

[0131] Based on the current time t k The location of the flying car k ,speed Quaternion q k and the three-dimensional angular velocity vector Ω k , and the current time t k The speed vectors w of the four motor rotors k , using the translation model and rotation model, calculate the position of the flying car at the next moment speed Quaternions and the three-dimensional angular velocity vector

[0132] In particular, the translation model and rotation model can also be used to calculate the acceleration of the flying car at the next moment.

[0133] In some embodiments, based on the state quantity and expected state quantity of the flying car at the next moment, the thrust vector of the motor at the next moment is calculated by using the NMPC controller; including:

[0134] The cost function f(u k+ 1 ) for:

[0135] f(u k+1 )=||β k+1 -β k+1，r ||+||u k+1 ||+||MSI(ak+1 )-MSI(a k+1，r )||

[0136] Among them, β k+1 The state of the flying car at the next moment:

[0137]

[0138] β k+1，r is the expected state of the flying car at the next moment; u k+1 represents the thrust vector of the flying car motor at the next moment, which is the quantity to be determined; a k+1 is the Z-direction acceleration of the flying car at the next moment; a k+1，r The expected acceleration of the flying car in the Z direction at the next moment;

[0139] Among them, MSI (a k+1 ) is the acceleration in the Z direction a k+1 The incidence of motion sickness:

[0140]

[0141] in, represents the amplitude, H Δt Indicates vibration frequency;

[0142]

[0143] Where φ(·) represents the normal function; time Δt = t k -t k-1 ;

[0144]

[0145] Where f is the frequency of vertical oscillation of the flying car;

[0146] By solving the cost function f(u k+1 ) to obtain the minimum value of the four-dimensional thrust vector of the flying car motor at the next moment

[0147] In the above cost function, according to the MSI model derived from the vertical conflict theory (SVC), vertical acceleration is an important factor affecting the degree of motion sickness. For this reason, the penalty term ||MSI(a k+1 )-MSI(a k+1 , r)||, where MSI(·) is the measurement model for the incidence of motion sickness.

[0148] In some embodiments, based on the thrust vector of the motor at the next moment, determining the total lift of the motor at the next moment and the angular acceleration of the flying car at the next moment includes:

[0149] According to the following formula

[0150]

[0151] Calculate the total lift of the flying car at the next moment and angular acceleration

[0152] In some embodiments, the method further includes: determining a second total torque vector τ of the motor at the current moment based on the speed vectors of the four motor rotors at the current moment and the speed vectors of the four motor rotors at the previous moment k，2 :

[0153] τ k，2 =G1w k 2 +Δt -1 G2(w k -w k-1 )

[0154] Among them, w k-1 is the previous moment t k-1 The speed vectors of the four motor rotors, time △t = t k -t k-1 .

[0155] In the NMPC controller, the first total torque vector ignores the unmodeled term dτ in the rotational dynamics, which has a significant impact on the control performance. Modeling dτ for real-world systems is very challenging. Incremental nonlinear dynamic inversion (INDI) is used here to address this problem. INDI uses instantaneous sensor measurements instead of explicit models to represent the system dynamics, making it robust to model uncertainties and external disturbances. In other words, the second total torque vector is more accurate than the first total torque vector.

[0156] In some embodiments, based on the angular acceleration of the flying car at the next moment and the angular acceleration of the flying car at the current moment, determining the total torque of the motor at the next moment includes:

[0157] The total torque of the motor at the next moment for:

[0158]

[0159] in, is the angular acceleration of the flying car at the current moment.

[0160] In some embodiments, based on the total lift of the motor at the next moment, the total torque of the motor at the next moment, and the speed vectors of the four motor rotors at the current moment, determining the speed vectors of the four motor rotors at the next moment includes:

[0161] According to the following formula:

[0162]

[0163] Calculate the speed vectors of the four motor rotors at the next moment

[0164] Based on the above embodiments, the present application provides an active fault-tolerant control device for an intelligent flying car. Figure 2 As shown, the active fault-tolerant control device 200 for an intelligent flying car provided in the embodiment of the present application at least includes:

[0165] An acquisition unit 201 is used to acquire the state quantity of the flying car and the speed vectors of the four motor rotors at the current moment, wherein the state quantity includes: position, speed, Euler angle and angular speed;

[0166] The first calculation unit 202 is used to obtain the state quantity of the flying car at the next moment by using a pre-established aerodynamic model based on the state quantity of the flying car at the current moment and the speed vectors of the four motor rotors;

[0167] The second calculation unit 203 is used to calculate the thrust vector of the motor at the next moment by using the NMPC controller based on the state quantity and the expected state quantity of the flying car at the next moment;

[0168] A first determining unit 204 is used to determine the total lift of the motor at the next moment and the angular acceleration of the flying car at the next moment based on the thrust vector of the motor at the next moment;

[0169] A second determining unit 205, configured to determine a total torque of the motor at a next moment based on the angular acceleration of the flying car at a next moment and the angular acceleration of the flying car at a current moment;

[0170] A third determining unit 206, configured to determine the speed vectors of the four motor rotors at the next moment based on the total lift of the motor at the next moment, the total torque of the motor at the next moment, and the speed vectors of the four motor rotors at the current moment;

[0171] The sending unit 207 is used to send the speed vectors of the four motor rotors at the next moment to the motor controller of the flying car.

[0172] It should be noted that the principle of solving the technical problem by the active fault-tolerant control device 200 for an intelligent flying car provided in the embodiment of the present application is similar to that of the active fault-tolerant control method for an intelligent flying car provided in the embodiment of the present application. Therefore, the implementation of the active fault-tolerant control device 200 for an intelligent flying car provided in the embodiment of the present application can refer to the implementation of the active fault-tolerant control method for an intelligent flying car provided in the embodiment of the present application, and the repeated parts will not be repeated.

[0173] Based on the above embodiments, the present application also provides an electronic device, referring to Figure 3 As shown, the electronic device 300 provided in the embodiment of the present application at least includes: a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, the active fault-tolerant control method for the intelligent flying car provided in the embodiment of the present application is implemented.

[0174] The electronic device 300 provided in the embodiment of the present application may further include a bus 303 connecting different components (including the processor 301 and the memory 302). The bus 303 represents one or more of several types of bus structures, including a memory bus, a peripheral bus, a local bus, and the like.

[0175] The memory 302 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 3021 and / or a cache memory 3022 , and may further include a read-only memory (ROM) 3023 .

[0176] The memory 302 may also include a program tool 3025 having a set (at least one) of program modules 3024, the program modules 3024 including but not limited to: an operating subsystem, one or more applications, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0177] The electronic device 300 may also communicate with one or more external devices 304 (e.g., keyboards, remote controls, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 300 (e.g., mobile phones, computers, etc.), and / or communicate with any device that enables the electronic device 300 to communicate with one or more other electronic devices 300 (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 305. Furthermore, the electronic device 300 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 306. Figure 3 As shown, the network adapter 306 communicates with other modules of the electronic device 300 via the bus 303. It should be understood that although Figure 3 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 300, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, disk arrays (Redundant Arrays of Independent Disks, RAID) subsystems, tape drives, and data backup storage subsystems.

[0178] It should be noted that Figure 3 The electronic device 300 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0179] The embodiment of the present application also provides a computer-readable storage medium, which stores computer instructions, and when the computer instructions are executed by the processor, the active fault-tolerant control method of the intelligent flying car provided in the embodiment of the present application is implemented. Specifically, the executable program can be built-in or installed in the electronic device 300, so that the electronic device 300 can implement the active fault-tolerant control method of the intelligent flying car provided in the embodiment of the present application by executing the built-in or installed executable program.

[0180] The active fault-tolerant control method for an intelligent flying car provided in the embodiment of the present application can also be implemented as a program product, which includes a program code. When the program product can be run on an electronic device 300, the program code is used to enable the electronic device 300 to execute the active fault-tolerant control method for an intelligent flying car provided in the embodiment of the present application.

[0181] The program product provided in the embodiments of the present application may adopt any combination of one or more readable media, wherein the readable medium may be a readable signal medium or a readable storage medium, and the readable storage medium may be but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination of the above. Specifically, more specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, RAM, ROM, Erasable Programmable Read Only Memory (EPROM), optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0182] The program product provided in the embodiment of the present application may adopt a CD-ROM and include program code, and may also be run on a computing device. However, the program product provided in the embodiment of the present application is not limited thereto. In the embodiment of the present application, the readable storage medium may be any tangible medium containing or storing a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.

[0183] It should be noted that, although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided into multiple units to be embodied.

[0184] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0185] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application is described in detail with reference to the embodiments, a person skilled in the art should understand that any modification or equivalent replacement of the technical solution of the present application does not depart from the spirit and scope of the technical solution of the present application and should be included in the scope of the claims of the present application.

Claims

1. An active fault-tolerant control method for an intelligent flying car, applied to a flying car, wherein the flying car comprises four motors, each of which comprises a motor rotor; characterized in that: include: Obtain the current state of the flying car and the speed vectors of the four motor rotors, wherein the state includes: position, speed, Euler angle and angular velocity; Based on the state quantity of the flying car at the current moment and the speed vectors of the four motor rotors, the state quantity of the flying car at the next moment is obtained by using a pre-established aerodynamic model; Based on the state quantity and expected state quantity of the flying car at the next moment, the thrust vector of the motor at the next moment is calculated using the NMPC controller; Based on the thrust vector of the motor at the next moment, determine the total lift of the motor at the next moment and the angular acceleration of the flying car at the next moment; Determine the total torque of the motor at the next moment based on the angular acceleration of the flying car at the next moment and the angular acceleration of the flying car at the current moment; Determine the speed vectors of the four motor rotors at the next moment based on the total lift of the motor at the next moment, the total torque of the motor at the next moment, and the speed vectors of the four motor rotors at the current moment; The speed vectors of the four motor rotors at the next moment are sent to the motor controller of the flying car.

2. The method according to claim 1, characterized in that The pre-established aerodynamic models include: translation model and rotation model; Based on the state quantity of the flying car at the current moment and the speed vectors of the four motor rotors, the state quantity of the flying car at the next moment is obtained by using a pre-established aerodynamic model; including: The translation model is: Among them, ξ k+1 For the next moment t k+1 The position of the flying car in the geocentric rectangular coordinate system, For k+1 The second-order differential of k is the current time t k The total lift of the flying car, m is the total mass of the flying car; g is the gravity vector; f a,k is the current time t k The air resistance of the flying car; f w,k is the current time t k The wind force on the flying car; Current time t k The total lift of the flying car is T k Calculated by the following formula: Among them, u k represents the current time t k The four-dimensional thrust vector generated by the four motor rotors is: u k =c t w k 2 c t is the thrust coefficient, w k is the current time t k The speed vectors of the four motor rotors; τ k,1 is the current time t k A first total torque vector of the motor rotor; G1 is the first coefficient matrix: G2 is the second coefficient matrix: G3 is the third coefficient matrix: Among them, c q is the torque coefficient; β and l are the geometric parameters of the flying car; I P is the moment of inertia of the rotor around the Z axis; Ω y,k is the current time t k The y-direction angular velocity of the flying car; Current time t k The air resistance f of the flying car a,k for: Among them, k d,x , k d,y , k d,z and k h are all known positive parameters; v x,k , v y,k and v z,k are the current time t k The speed of the flying car in three directions in the body coordinate system; Current time t k The wind force f on the flying car w,k for: Among them, c represents the proportionality coefficient, ρ represents the air density, and S represents the wind-exposed area of ​​the flying car; The rotation model is: Among them, I v represents the inertia matrix of the flying car; Ω k is the current time t k The three-dimensional angular velocity vector of the flying car: Ω k =[Ω x,k ,Ω y,k ,Ω z,k ];q k is the current time t k The quaternion converted from the Euler angle of the flying car; q k+1 is the current time t k+1 The quaternion converted from Euler angle of the flying car; Based on the current time t k The location of the flying car k ,speed Quaternion q k and the three-dimensional angular velocity vector Ω k , and the current time t k The speed vectors w of the four motor rotors k , using the translation model and rotation model, calculate the position of the flying car at the next moment speed Quaternions and the three-dimensional angular velocity vector 3. The method according to claim 2, characterized in that Thrust vector based on the flight at the next moment; including: The cost function f(u k+1 )for: f(u k+1 )=||β k+1 -β k+1,r ||+||u k+1 ||+||MSI(a k+1 )-MSI(a k+1,r )|| Among them, β k+1 is the state of the flying car at the next moment: β k+1,r is the expected state of the flying car at the next moment; u k+1 represents the thrust vector of the flying car motor at the next moment, which is the quantity to be determined; a k+1 is the Z-direction acceleration of the flying car at the next moment; a k+1,r The expected acceleration of the flying car in the Z direction at the next moment; Among them, MSI (a k+1 ) is the acceleration in the Z direction a k+1 The incidence of motion sickness: in, represents the amplitude, H Δt Indicates vibration frequency; Where φ(·) represents the normal function; time Δt = t k -t k-1 ; Where f is the frequency of vertical oscillation of the flying car; By solving the cost function f(u k+1 ) to obtain the minimum value of the four-dimensional thrust vector of the flying car motor at the next moment 4. The method according to claim 3, characterized in that Based on the thrust vector of the motor at the next moment, determining the total lift of the motor at the next moment and the angular acceleration of the flying car at the next moment; including: According to the following formula Calculate the total lift of the flying car at the next moment and angular acceleration 5. The method according to claim 4, characterized in that The method further includes: determining a second total torque vector τ of the motor at the current moment based on the speed vectors of the four motor rotors at the current moment and the speed vectors of the four motor rotors at the previous moment k，2 : t k，2 =G1w k 2 +Δt -1 G2(w k -w k-1 ) Among them, w k-1 is the previous moment t k-1 The speed vectors of the four motor rotors, time △t = t k -t k-1 .

6. The method according to claim 5, characterized in that Determining the total torque of the motor at the next moment based on the angular acceleration of the flying car at the next moment and the angular acceleration of the flying car at the current moment; including: The total torque of the motor at the next moment for: in, is the angular acceleration of the flying car at the current moment.

7. The method according to claim 6, characterized in that Based on the total lift of the motor at the next moment, the total torque of the motor at the next moment, and the speed vectors of the four motor rotors at the current moment, the speed vectors of the four motor rotors at the next moment are determined, including: According to the following formula: Calculate the speed vectors of the four motor rotors at the next moment 8. An active fault-tolerant control device for an intelligent flying car, applied to a flying car, wherein the flying car comprises four motors, each of which comprises a motor rotor; characterized in that: include: An acquisition unit is used to acquire the state quantity of the flying car and the speed vectors of the four motor rotors at the current moment, wherein the state quantity includes: position, speed, Euler angle and angular speed; A first calculation unit is used to obtain the state quantity of the flying car at the next moment by using a pre-established aerodynamic model based on the state quantity of the flying car at the current moment and the speed vectors of the four motor rotors; A second calculation unit is used to calculate the thrust vector of the motor at the next moment by using the NMPC controller based on the state quantity and expected state quantity of the flying car at the next moment; A first determining unit, configured to determine a total lift of the motor at a next moment and an angular acceleration of the flying car at a next moment based on a thrust vector of the motor at a next moment; a second determining unit, configured to determine a total torque of the motor at a next moment based on the angular acceleration of the flying car at a next moment and the angular acceleration of the flying car at a current moment; a third determining unit, configured to determine the speed vectors of the four motor rotors at the next moment based on the total lift of the motor at the next moment, the total torque of the motor at the next moment, and the speed vectors of the four motor rotors at the current moment; The sending unit is used to send the speed vectors of the four motor rotors at the next moment to the motor controller of the flying car.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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