A method and device for controlling two motors of a top cover of a drone nest

Abstract

This invention discloses a dual-motor control method and device for the top cover of an unmanned aerial vehicle (UAV) nest, relating to the field of UAV technology. The method includes: setting system state variables and control input vectors based on nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data; constructing motor rotor dynamic equations based on the corresponding data and shaft torsional effects; constructing nest top cover angle dynamic equations and electrodynamic equations based on the corresponding data; constructing a state-space model based on the system state variables, control input vector, and the above dynamic equations to generate LQR optimal control, integral control, and coupling suppression control by combining current difference; and determining a comprehensive control model based on gravity feedforward compensation combined with LQR optimal control, integral control, and coupling suppression control for dual-motor control. This invention ensures global optimality and control stability, achieves multi-objective control, and significantly improves the control accuracy of the dual motors.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a dual-motor control method and device for the top cover of a UAV's nest. Background Technology

[0002] With the rapid development of drone technology, drone nests, as a crucial infrastructure for autonomous drone operation, face increasingly stringent requirements for reliability and accuracy. The nest cover, a key component protecting drones from severe weather, needs high-precision opening and closing control. Traditional nest covers often employ a single-motor drive, but for large or heavy-duty nests, a single motor often cannot provide sufficient driving torque and carries the risk of single-point failure. Therefore, dual-motor coaxial drive has become a development trend. Existing dual-motor control methods are mainly based on traditional proportional-integral-derivative control or simple master-slave control strategies. However, these methods cannot handle multi-variable coupling characteristics, lack effective feedforward compensation for gravity disturbances, and do not adequately consider shaft torsion issues, with a singular optimization objective, making it difficult to simultaneously meet the requirements of high precision, strong robustness, and motor coordination. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a dual-motor control method and device for the top cover of an unmanned aerial vehicle (UAV) nest, which ensures global optimality and control stability, realizes multi-objective control, and significantly improves the control accuracy of the dual motors.

[0004] To address the aforementioned technical problems, this invention provides a dual-motor control method for the top cover of a drone's nest, the method comprising:

[0005] Acquire nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data, and set system state variables and control input vectors based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data;

[0006] Based on the first motor drive data, the second motor drive data, and the coaxial transmission data, combined with the shaft torsion effect, the motor rotor dynamics equation is constructed;

[0007] Based on the data of the nest top cover, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data, construct the nest top cover angle dynamics equation and the electrodynamics equation;

[0008] A state-space model is constructed based on the system state variables, control input vector, nest top cover angle dynamics equation, motor rotor dynamics equation, and electrical dynamics equation. Based on the state-space model and the current difference, optimal control, integral control, and coupling suppression control of the linear quadratic regulator (LQR) are generated.

[0009] Gravity feedforward compensation is determined, and a comprehensive control model is determined based on the gravity feedforward compensation combined with LQR optimal control, integral control and coupling suppression control. The dual motors of the UAV nest top cover are then controlled based on the comprehensive control model.

[0010] Optionally, the expression for the system state variable is:

[0011] ,

[0012] Where X is the system state variable. For the angle of the top cover of the machine nest, The angular velocity of the top cover of the nest. For the rotor angle of the first motor, The rotor angle of the second motor. Let be the rotor angular velocity of the first motor. The rotor angular velocity of the second motor is... This is the drive current of the first motor. The current is the drive current of the second motor, and T is the transpose.

[0013] The expression for the control input vector is:

[0014] ,

[0015] Where U is the control input vector. This is the driving voltage of the first motor. is the driving voltage of the second motor, and T is the transpose.

[0016] Optionally, the step of constructing the motor rotor dynamics equation based on the first motor drive data, the second motor drive data, and the coaxial transmission data, combined with the shaft torsional effect, includes:

[0017] Based on the first motor drive data, the second motor drive data, and the coaxial transmission data, a set of equations for shaft torsional effect is constructed;

[0018] Based on the first motor drive data, the second motor drive data, and the coaxial transmission data, combined with the shaft torsion effect equations, the motor rotor dynamics equations are constructed.

[0019] Optionally, the expression for the torsional effect equations of the shaft is:

[0020] ,

[0021] ,

[0022] ,

[0023] in, For the axial torsional moment, For shaft stiffness, For the rotor angle of the first motor, The rotor angle of the second motor. Let be the rotor angular velocity of the first motor. The rotor angular velocity of the second motor is... This is the shaft damping coefficient. The torsional torque at one end of the connecting shaft. This refers to the torsional torque at the other end of the connecting shaft;

[0024] The expression for the motor rotor dynamics equation is as follows:

[0025] ,

[0026] ,

[0027] in, Let the moment of inertia of the first motor rotor be _____. The first motor rotor speed angular acceleration, This is the driving torque of the first motor. The torsional torque at one end of the connecting shaft. The damping coefficient of the first motor is... Let be the rotor angular velocity of the first motor. The moment of inertia of the second motor rotor. The angular acceleration of the second motor rotor speed. This is the driving torque of the second motor. For the torsional torque at the other end of the connecting shaft, This is the damping coefficient of the second motor. ω is the rotor angular velocity of the second motor.

[0028] Optionally, the construction of the nest top cover angle dynamics equation and electrodynamics equation based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data includes:

[0029] Based on the data of the top cover of the machine nest, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data, the rotational inertia of the top cover, the average driving torque of the two motors, and the gravitational torque are determined.

[0030] The dynamic equation of the nest top cover angle is constructed based on the rotational inertia of the top cover, the average driving torque of the dual motors, and the gravitational torque combined with the load damping coefficient.

[0031] Based on the data of the machine nest top cover, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data, the motor current dynamics equation and the motor torque equation are constructed, and the electrodynamics equation is determined based on the motor current dynamics equation and the motor torque equation.

[0032] Optionally, the expression for the dynamic equation of the nest top cover angle is:

[0033] ,

[0034] in, The moment of inertia of the top cover. The angular acceleration of the top cover of the nest, This represents the average driving torque of the two motors. For gravitational torque, This is the load damping coefficient. The angular velocity of the top cover of the nest;

[0035] The expression for the motor current dynamic equation is:

[0036]

[0037]

[0038] in, This is the drive current of the first motor. This is the driving voltage of the first motor. The driving resistor for the first motor is... Let be the torque constant of the first motor. Let be the rotor angular velocity of the first motor. Let be the electrical time constant of the first motor. The rotor angular velocity of the second motor is... This is the drive voltage for the second motor. This is the drive current for the second motor. This is the drive resistor for the second motor. Let be the torque constant of the second motor. The electrical time constant of the second motor;

[0039] The expression for the motor torque equation is as follows:

[0040]

[0041]

[0042] in, This is the driving torque of the first motor. Let be the torque constant of the first motor. This is the drive current of the first motor. This is the driving torque of the second motor. Let be the torque constant of the second motor. This is the drive current for the second motor.

[0043] Optionally, the step of constructing a state-space model based on the system state variables, control input vector, nest top cover angle dynamics equation, motor rotor dynamics equation, and electrical dynamics equation, and generating optimal control, integral control, and coupling suppression control of a linear quadratic regulator (LQR) based on the state-space model and the current difference, includes:

[0044] Based on the dynamic equations of the nest top cover angle, the dynamic equations of the motor rotor, and the electrical dynamic equations, the state matrix and the input matrix are determined, and a state space model is constructed based on the state matrix and the input matrix in combination with the system state variables and the control input vector.

[0045] The current difference is calculated based on the first motor drive data and the second motor drive data, and the extended state space variables and extended state matrix are set based on the current difference in combination with the nest top cover data, the first motor drive data, the second motor drive data, the coaxial transmission data and the top cover angle data.

[0046] Obtain the angle tracking weight, angular velocity weight, motor angle weight, motor current weight, and current difference weight, and determine the weight matrix based on the angle tracking weight, angular velocity weight, motor angle weight, motor current weight, and current difference weight;

[0047] Based on the extended state-space variables and the weight matrix, an LQR objective function is constructed using the control weight matrix;

[0048] Based on the state-space model, combined with the LQR objective function and the extended state matrix, LQR optimal control, integral control, and coupling suppression control are generated.

[0049] Optionally, the expression for the extended state-space variable is:

[0050]

[0051] in, To expand the state-space variables, For the angle of the top cover of the machine nest, The angular velocity of the top cover of the nest. For the rotor angle of the first motor, The rotor angle of the second motor. Let be the rotor angular velocity of the first motor. The rotor angular velocity of the second motor is... This is the drive current of the first motor. This is the drive current for the second motor. The difference in current is T, and T is the transpose.

[0052] The expression for the weight matrix is:

[0053]

[0054] in, This is the weight matrix. For angle tracking weights, As the weight for angular velocity, The first motor angle weight. The rotor angular velocity weight of the first motor. For the angle weight of the second motor, The rotor angular velocity weight of the second motor. As the weight of the first motor current, As the weight of the second motor current, Weighted by current difference;

[0055] The expression for the LQR objective function is:

[0056]

[0057] Where J is the LQR objective function, To expand the state-space variables, T is the transpose. This is the weight matrix. R is the control input vector, and R is the control weight matrix.

[0058] Optionally, determining the gravity feedforward compensation and determining the integrated control model based on the gravity feedforward compensation combined with the LQR optimal control, integral control, and coupling suppression control includes:

[0059] The gravity torque model of the nest top cover is determined, and the expression of the gravity torque model is:

[0060]

[0061] in, Let M be the gravitational torque, M be the mass of the nest top cover, g be the gravitational acceleration, and L be the length of the nest top cover. The target angle for the top cover of the aviary. This is the angle of centroid offset;

[0062] A feedforward current model is established based on the aforementioned gravity torque model, and the expression of the feedforward current model is as follows:

[0063]

[0064] in, For feedforward current, For gravitational torque, It is the average torque constant;

[0065] A feedforward voltage model is constructed based on the feedforward current model, and the expression of the feedforward voltage model is as follows:

[0066]

[0067] in, For feedforward voltage, For feedforward current, Average resistance;

[0068] Gravity feedforward compensation is determined based on the aforementioned feedforward voltage model;

[0069] Based on the gravity feedforward compensation combined with LQR optimal control, integral control, and coupling suppression control, a comprehensive control model is determined. The expression of the comprehensive control model is as follows:

[0070]

[0071] in, For the integrated control model, For gravity feedforward compensation, For LQR optimal control, For integral control, This is for coupling suppression control.

[0072] In addition, the present invention also provides a dual-motor control device for the top cover of a drone nest, characterized in that the device comprises:

[0073] Variable vector setting module: used to acquire nest top cover data, first motor drive data, second motor drive data, coaxial transmission data and top cover angle data, and set system state variables and control input vectors based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data and top cover angle data;

[0074] First equation construction module: used to construct the motor rotor dynamics equation based on the first motor drive data, the second motor drive data, and the coaxial transmission data, combined with the shaft torsion effect;

[0075] The second equation construction module is used to construct the nest top cover angle dynamics equation and the electrodynamics equation based on the nest top cover data, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data.

[0076] Control generation module: used to construct a state space model based on the system state variables, control input vector, nest top cover angle dynamics equation, motor rotor dynamics equation and electrical dynamics equation, and to generate optimal control, integral control and coupling suppression control of the linear quadratic regulator based on the state space model and the current difference;

[0077] Integrated control module: used to determine gravity feedforward compensation, and based on the gravity feedforward compensation combined with LQR optimal control, integral control and coupling suppression control to determine the integrated control model, and based on the integrated control model to control the dual motors of the UAV nest top cover.

[0078] In this invention, motor rotor dynamics equations are constructed based on data from the UAV nest top cover, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data, combined with shaft torsional effects. Incorporating shaft torsional effects into system modeling and considering the elastic characteristics of the connecting shaft improves modeling accuracy. A state-space model is constructed based on system state variables, control input vectors, nest top cover angle dynamics equations, motor rotor dynamics equations, and electrical dynamics equations. Based on this state-space model and current difference, LQR optimal control, integral control, and coupling suppression control are generated. LQR optimal control achieves multi-variable coordinated optimization, and incorporating current difference into the LQR optimization objective minimizes motor torque difference, improving motor coordination. Gravity feedforward compensation is determined, effectively eliminating steady-state error. Based on gravity feedforward compensation combined with LQR optimal control, integral control, and coupling suppression control, a comprehensive control model is determined for controlling the dual motors of the UAV nest top cover. This effectively improves the robustness of the comprehensive control model, ensures global optimality and control stability, achieves multi-objective control, and significantly improves the control accuracy of the dual motors. Attached Figure Description

[0079] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0080] Figure 1 This is a flowchart illustrating the dual-motor control method for the top cover of a drone's nest in an embodiment of the present invention.

[0081] Figure 2 This is a flowchart illustrating a dual-motor control method for the top cover of a drone's nest, according to another embodiment of the present invention.

[0082] Figure 3This is a schematic diagram of the structural composition of the dual-motor control device for the top cover of the drone's nest in an embodiment of the present invention. Detailed Implementation

[0083] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0084] Example 1

[0085] Please see Figure 1 , Figure 1 This is a flowchart illustrating the dual-motor control method for the top cover of a drone's nest according to an embodiment of the present invention. The method includes:

[0086] S11: Acquire the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data, and set system state variables and control input vectors based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data;

[0087] In the specific implementation of this invention, corresponding data are extracted from the corresponding sensor nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data to set system state variables and control input vectors, providing data support for subsequent model construction.

[0088] S12: Construct the motor rotor dynamics equation based on the first motor drive data, the second motor drive data, and the coaxial transmission data, combined with the shaft torsion effect;

[0089] In the specific implementation of this invention, a set of shaft torsion effect equations is constructed based on the data of the nest top cover, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data. The motor rotor dynamics equations are constructed by combining the data of the nest top cover, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data with the set of shaft torsion effect equations. The set of shaft torsion effect equations can take into account the elastic characteristics of the connecting shaft, improve the modeling accuracy, and incorporate shaft torsion into the modeling process, which can suppress shaft torsion and improve the modeling stability of the dual-motor system.

[0090] S13: Based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data, construct the nest top cover angle dynamics equation and electrodynamics equation;

[0091] In the specific implementation of this invention, the rotational inertia of the top cover, the average driving torque of the two motors, and the gravitational torque are determined by using data from the top cover of the nest, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data. Based on the rotational inertia of the top cover, the average driving torque of the two motors, and the gravitational torque, combined with the load damping coefficient, the dynamic equation of the nest top cover angle is constructed. Based on the data from the top cover of the nest, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data, the dynamic equation of the motor current and the motor torque are constructed. Based on the dynamic equation of the motor current and the motor torque, the electrical dynamic equation is determined. By incorporating the electrical dynamics and the dynamics of the nest top cover angle into the state space modeling, motor coordination optimization can be achieved.

[0092] S14: Construct a state-space model based on the system state variables, control input vector, nest top cover angle dynamic equation, motor rotor dynamic equation and electrical dynamic equation, and generate LQR optimal control, integral control and coupling suppression control based on the state-space model and current difference;

[0093] In the specific implementation of this invention, the state matrix and input matrix are determined based on the dynamic equations of the nest top cover angle, the motor rotor, and the electrodynamic equations. A state space model is then constructed based on the state matrix and input matrix, combined with system state variables and control input vectors. The current difference is calculated based on the first and second motor drive data. Extended state space variables and an extended state matrix are set based on the current difference, combined with nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data. Incorporating the current difference into the optimization objective minimizes the motor torque difference, resulting in more uniform motor wear and extended motor lifespan. A weight matrix is ​​determined using angle tracking weights, angular velocity weights, motor angle weights, motor current weights, and current difference weights. A linear quadratic regulator is constructed based on the extended state space variables and the weight matrix, utilizing the control weight matrix. The LQR (Low-QR) objective function is used to generate LQR optimal control, integral control, and coupling suppression control based on the state-space model, combined with the LQR objective function and the extended state matrix. The LQR objective function simultaneously considers angle tracking accuracy and current difference minimization. Through LQR optimal control, multivariate coordinated optimization can be achieved, which improves the motor control accuracy compared with traditional proportional-integral-derivative (PID) control.

[0094] S15: Determine gravity feedforward compensation, and based on the gravity feedforward compensation combined with the LQR optimal control, integral control and coupling suppression control, determine the integrated control model, and control the dual motors of the UAV nest top cover based on the integrated control model.

[0095] In the specific implementation of this invention, a gravity torque model of the UAV nest top cover is determined, a feedforward current model is established based on the gravity torque model, a feedforward voltage model is constructed based on the feedforward current model, and gravity feedforward compensation is determined based on the feedforward voltage model. Gravity feedforward compensation can effectively eliminate steady-state error. Based on gravity feedforward compensation, combined with the LQR optimal control, integral control, and coupling suppression control, a comprehensive control model is determined, which improves the overall performance. The dual motors of the UAV nest top cover are controlled through the comprehensive control model, ensuring the global optimality and system stability of the control process.

[0096] In this embodiment of the invention, the motor rotor dynamics equation is constructed based on the data of the nest top cover, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data, combined with the shaft torsional effect. The shaft torsional effect is incorporated into the system modeling, and considering the elastic characteristics of the connecting shaft, the modeling accuracy is improved. A state-space model is constructed based on the system state variables, control input vector, nest top cover angle dynamics equation, motor rotor dynamics equation, and electrical dynamics equation. Based on the state-space model and current difference, LQR optimal control, integral control, and coupling suppression control are generated. Multi-variable coordinated optimization is achieved through LQR optimal control, and incorporating the current difference into the LQR optimization objective minimizes the motor torque difference, improving motor coordination. Gravity feedforward compensation is determined, which effectively eliminates steady-state error. Based on gravity feedforward compensation combined with LQR optimal control, integral control, and coupling suppression control, a comprehensive control model is determined for controlling the dual motors of the UAV nest top cover. This effectively improves the robustness of the comprehensive control model, ensures global optimality and control stability, achieves multi-objective control, and significantly improves the control accuracy of the dual motors.

[0097] Example 2

[0098] Please see Figure 2 , Figure 2 This is a flowchart illustrating a dual-motor control method for the top cover of a drone's nest, according to another embodiment of the present invention. The method includes:

[0099] S201: Acquire the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data, and set system state variables and control input vectors based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data;

[0100] In the specific implementation of this invention, data such as the nest top cover, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data are acquired. The nest top cover data includes the nest top cover's mass, length, angular velocity, and center of gravity offset angle. The first motor drive data includes the first motor's drive current, drive voltage, drive resistance, drive torque, rotor angle, and rotor angular velocity. The second motor drive data includes the second motor's drive current, drive torque, drive voltage, drive resistance, rotor angle, and rotor angular velocity. The coaxial transmission data includes shaft stiffness and shaft damping. Based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data, system state variables and control input vectors are set. The system state variables can be defined as 8-dimensional variables, and the expressions for the system state variables are as follows:

[0101] ,

[0102] Where X is the system state variable. For the angle of the top cover of the machine nest, The angular velocity of the top cover of the nest. For the rotor angle of the first motor, The rotor angle of the second motor. Let be the rotor angular velocity of the first motor. The rotor angular velocity of the second motor is... This is the drive current of the first motor. The current is the drive current of the second motor, and T is the transpose.

[0103] The expression for the control input vector is:

[0104] ,

[0105] Where U is the control input vector. This is the driving voltage of the first motor. is the driving voltage of the second motor, and T is the transpose.

[0106] S202: Construct the motor rotor dynamics equation based on the first motor drive data, the second motor drive data, and the coaxial transmission data, combined with the shaft torsion effect;

[0107] In a specific implementation of the present invention, the step of constructing the motor rotor dynamics equation based on the first motor drive data, the second motor drive data, and the coaxial transmission data combined with the shaft torsion effect includes: constructing a set of shaft torsion effect equations based on the first motor drive data, the second motor drive data, and the coaxial transmission data; and constructing the motor rotor dynamics equation based on the first motor drive data, the second motor drive data, and the coaxial transmission data combined with the set of shaft torsion effect equations.

[0108] Furthermore, the expression for the equations governing the shaft torsion effect is as follows:

[0109] ,

[0110] ,

[0111] ,

[0112] in, For the axial torsional moment, For shaft stiffness, For the rotor angle of the first motor, The rotor angle of the second motor. Let be the rotor angular velocity of the first motor. The rotor angular velocity of the second motor is... This is the shaft damping coefficient. The torsional torque at one end of the connecting shaft. This refers to the torsional torque at the other end of the connecting shaft;

[0113] The expression for the motor rotor dynamics equation is as follows:

[0114] ,

[0115] ,

[0116] in, Let the moment of inertia of the first motor rotor be _____. The first motor rotor speed angular acceleration, This is the driving torque of the first motor. The torsional torque at one end of the connecting shaft. The damping coefficient of the first motor is... Let be the rotor angular velocity of the first motor. The moment of inertia of the second motor rotor. The angular acceleration of the second motor rotor speed. This is the driving torque of the second motor. For the torsional torque at the other end of the connecting shaft, This is the damping coefficient of the second motor. ω is the rotor angular velocity of the second motor.

[0117] Specifically, a set of equations for the shaft torsion effect is constructed based on the first motor drive data, the second motor drive data, and the coaxial transmission data. The expression for the set of equations for the shaft torsion effect is as follows:

[0118] ,

[0119] ,

[0120] ,

[0121] in, For the axial torsional moment, For shaft stiffness, For the rotor angle of the first motor, The rotor angle of the second motor. Let be the rotor angular velocity of the first motor. The rotor angular velocity of the second motor is... This is the shaft damping coefficient. The torsional torque at one end of the connecting shaft. The torsional moment at the other end of the connecting shaft; shaft stiffness represents the connecting shaft's resistance to the difference in torsional angle; shaft damping coefficient represents the magnitude of the damping effect of the connecting shaft on the difference in torsional angular velocity; shaft torsional moment represents the reaction moment generated at both ends of the connecting shaft due to the combined effect of the angle difference and velocity difference at both ends.

[0122] Based on the first motor drive data, the second motor drive data, and the coaxial transmission data, combined with the shaft torsional effect equations, the motor rotor dynamics equations are constructed. The expression of the motor rotor dynamics equations is as follows:

[0123] ,

[0124] ,

[0125] in, Let the moment of inertia of the first motor rotor be _____. The first motor rotor speed angular acceleration, This is the driving torque of the first motor. The torsional torque at one end of the connecting shaft. The damping coefficient of the first motor is... Let be the rotor angular velocity of the first motor. The moment of inertia of the second motor rotor. The angular acceleration of the second motor rotor speed. This is the driving torque of the second motor. For the torsional torque at the other end of the connecting shaft, This is the damping coefficient of the second motor. The rotor angular velocity of the second motor is used to incorporate the shaft torsion effect into the modeling of the UAV nest control system, enabling the subsequent control model to directly suppress shaft torsion through state feedback and improve system stability.

[0126] S203: Based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data, construct the nest top cover angle dynamics equation and electrodynamics equation;

[0127] In the specific implementation of this invention, the construction of the nest top cover angle dynamic equation and electrodynamic equation based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data includes: determining the top cover rotational inertia, the average driving torque of the two motors, and the gravitational torque based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data; constructing the nest top cover angle dynamic equation based on the top cover rotational inertia, the average driving torque of the two motors, and the gravitational torque combined with the load damping coefficient; constructing the motor current dynamic equation and motor torque equation based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data, and determining the electrodynamic equation based on the motor current dynamic equation and motor torque equation.

[0128] Furthermore, the expression for the dynamic equation of the nest top cover angle is as follows:

[0129] ,

[0130] in, The moment of inertia of the top cover. The angular acceleration of the top cover of the nest, This represents the average driving torque of the two motors. For gravitational torque, This is the load damping coefficient. The angular velocity of the top cover of the nest;

[0131] The expression for the motor current dynamic equation is:

[0132]

[0133]

[0134] in, This is the drive current of the first motor. This is the driving voltage of the first motor. The driving resistor for the first motor is... Let be the torque constant of the first motor. Let be the rotor angular velocity of the first motor. Let be the electrical time constant of the first motor. The rotor angular velocity of the second motor is... This is the drive voltage for the second motor. This is the drive current for the second motor. This is the drive resistor for the second motor. Let be the torque constant of the second motor. The electrical time constant of the second motor;

[0135] The expression for the motor torque equation is as follows:

[0136]

[0137]

[0138] in, This is the driving torque of the first motor. Let be the torque constant of the first motor. This is the drive current of the first motor. This is the driving torque of the second motor. Let be the torque constant of the second motor. This is the drive current for the second motor.

[0139] Specifically, based on the data of the machine nest top cover, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data, the rotational inertia of the top cover, the average driving torque of the two motors, and the gravitational torque are determined. The expression for the rotational inertia of the top cover is:

[0140]

[0141] in, Let M be the rotational inertia of the top cover, M be the mass of the top cover of the nest, and L be the length of the top cover of the nest. The average driving torque of the dual motors is calculated by averaging the driving torques of the first motor and the second motor. The gravitational torque is calculated using the gravitational torque formula.

[0142] Based on the rotational inertia of the top cover, the average driving torque of the dual motors, and the gravitational torque combined with the load damping coefficient, a dynamic equation for the angle of the nest top cover is constructed. The expression for the dynamic equation for the angle of the nest top cover is as follows:

[0143] ,

[0144] in, The moment of inertia of the top cover. The angular acceleration of the top cover of the nest, This represents the average driving torque of the two motors. For gravitational torque, This is the load damping coefficient. ω represents the angular velocity of the top cover of the nest.

[0145] Based on the data of the machine nest top cover, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data, the motor current dynamics equation and the motor torque equation are constructed. Based on the motor current dynamics equation and the motor torque equation, the electrodynamics equation is determined. The expression for the motor current dynamics equation is as follows:

[0146]

[0147]

[0148] in, This is the drive current of the first motor. This is the driving voltage of the first motor. The driving resistor for the first motor is... Let be the torque constant of the first motor. Let be the rotor angular velocity of the first motor. Let be the electrical time constant of the first motor. The rotor angular velocity of the second motor is... This is the drive voltage for the second motor. This is the drive current for the second motor. This is the drive resistor for the second motor. Let be the torque constant of the second motor. The electrical time constant of the second motor;

[0149] The expression for the motor torque equation is as follows:

[0150]

[0151]

[0152] in, This is the driving torque of the first motor. Let be the torque constant of the first motor. This is the drive current of the first motor. This is the driving torque of the second motor. Let be the torque constant of the second motor. The torque constant represents the amount of electromagnetic torque generated by the motor for every ampere of current input.

[0153] S204: Determine the state matrix and input matrix based on the dynamic equation of the nest top cover angle, the dynamic equation of the motor rotor, and the electrical dynamic equation, and construct a state space model based on the state matrix and input matrix combined with the system state variables and control input vector;

[0154] In the specific implementation of this invention, the gravity restoration coefficient, load damping coefficient, current-angle coupling coefficient, shaft stiffness coefficient, damping coefficient, motor torque coefficient, and back electromotive force coefficient are determined based on the dynamic equations of the machine top cover angle, the motor rotor dynamic equation, and the electrodynamic equation. A state matrix is ​​constructed using a preset state matrix template based on these coefficients. The electrical time constant, driving voltage, and resistance of the first and second motors are determined based on the electrodynamic equation. An input matrix is ​​constructed using a preset input matrix template based on these coefficients. A state space model is constructed based on the state matrix and input matrix, combined with the system state variables and control input vector. The expression of the state space model is as follows:

[0155]

[0156] in, Let X be the derivative of the system state variable X with respect to time, U be the control input vector, A be the state matrix, and B be the input matrix.

[0157] S205: Calculate the current difference based on the first motor drive data and the second motor drive data, and set extended state space variables and extended state matrix based on the current difference combined with the nest top cover data, the first motor drive data, the second motor drive data, the coaxial transmission data and the top cover angle data;

[0158] In a specific implementation of this invention, the drive currents of the first motor and the second motor are extracted from the first motor drive data and the second motor drive data to calculate the current difference. Based on the current difference, combined with the nest top cover data, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data, extended state space variables and an extended state matrix are set. The expression for the extended state space variables is:

[0159]

[0160] in, To expand the state-space variables, For the angle of the top cover of the machine nest, The angular velocity of the top cover of the nest. For the rotor angle of the first motor, The rotor angle of the second motor. Let be the rotor angular velocity of the first motor. The rotor angular velocity of the second motor is... This is the drive current of the first motor. This is the drive current for the second motor. Let T be the current difference and T be the transpose. Incorporating the current difference into the LQR optimization objective can directly minimize the motor torque difference.

[0161] S206: Obtain the angle tracking weight, angular velocity weight, motor angle weight, motor current weight, and current difference weight, and determine the weight matrix based on the angle tracking weight, angular velocity weight, motor angle weight, motor current weight, and current difference weight;

[0162] In the specific implementation of this invention, angle tracking weight, angular velocity weight, motor angle weight, motor current weight, and current difference weight are obtained. Each weight can be set according to actual conditions. For example, the angle tracking weight can be set to 100, the angular velocity weight to 20, the motor angle weight to 10, the motor current weight to 5, and the current difference weight to 50. The weight matrix is ​​determined based on the angle tracking weight, angular velocity weight, motor angle weight, motor current weight, and current difference weight. The expression for the weight matrix is ​​as follows:

[0163]

[0164] in, This is the weight matrix. For angle tracking weights, As the weight for angular velocity, The first motor angle weight. The rotor angular velocity weight of the first motor. For the angle weight of the second motor, The rotor angular velocity weight of the second motor. As the weight of the first motor current, As the weight of the second motor current, The current difference weights are used to simultaneously optimize angle tracking accuracy and motor coordination through the weight matrix.

[0165] S207: Construct an LQR objective function based on the extended state-space variables and the weight matrix using the control weight matrix;

[0166] In a specific implementation of this invention, an LQR objective function is constructed based on the extended state-space variables and the weight matrix using a control weight matrix. The control weight matrix R can be set as diag([10.0, 10.0]), and the expression of the LQR objective function is:

[0167]

[0168] Where J is the LQR objective function, To expand the state-space variables, T is the transpose. This is the weight matrix. R is the control input vector, and R is the control weight matrix.

[0169] S208: Based on the state-space model, combined with the LQR objective function and the extended state matrix, generate LQR optimal control, integral control and coupling suppression control;

[0170] In the specific implementation of this invention, the algebraic Riccati equation is solved based on the state-space model, combined with the LQR objective function and the extended state matrix to obtain the optimal control law, which is the LQR optimal control. The integral control and the coupling suppression control are output by combining the state-space model with the integrator and the coupling suppression controller. The integral control provides additional robustness guarantee for the model, and the coupling suppression control can suppress shaft torsion and improve system stability.

[0171] S209: Determine gravity feedforward compensation, and based on the gravity feedforward compensation combined with the LQR optimal control, integral control and coupling suppression control, determine the integrated control model, and control the dual motors of the UAV nest top cover based on the integrated control model.

[0172] In the specific implementation of this invention, determining the gravity feedforward compensation and determining the integrated control model based on the gravity feedforward compensation combined with the LQR optimal control, integral control, and coupling suppression control includes: determining the gravity torque model of the nest top cover, wherein the expression of the gravity torque model is:

[0173]

[0174] in, Let M be the gravitational torque, M be the mass of the nest top cover, g be the gravitational acceleration, and L be the length of the nest top cover. The target angle for the top cover of the aviary. This is the angle of centroid offset;

[0175] A feedforward current model is established based on the aforementioned gravity torque model, and the expression of the feedforward current model is as follows:

[0176]

[0177] in, For feedforward current, For gravitational torque, It is the average torque constant;

[0178] A feedforward voltage model is constructed based on the feedforward current model, and the expression of the feedforward voltage model is as follows:

[0179]

[0180] in, For feedforward voltage, For feedforward current, Average resistance;

[0181] Gravity feedforward compensation is determined based on the aforementioned feedforward voltage model;

[0182] Based on the gravity feedforward compensation combined with LQR optimal control, integral control, and coupling suppression control, a comprehensive control model is determined. The expression of the comprehensive control model is as follows:

[0183]

[0184] in, For the integrated control model, For gravity feedforward compensation, For LQR optimal control, For integral control, This is for coupling suppression control.

[0185] Specifically, the gravity torque model of the nest top cover is determined, and the expression of the gravity torque model is as follows:

[0186]

[0187] in, Let M be the gravitational torque, M be the mass of the nest top cover, g be the gravitational acceleration, and L be the length of the nest top cover. The target angle for the top cover of the aviary. Let be the center of gravity offset angle. Based on the aforementioned gravity torque model, a feedforward current model is established, and the expression for the feedforward current model is:

[0188]

[0189] in, For feedforward current, For gravitational torque, Let be the average torque constant. Based on the feedforward current model, a feedforward voltage model is constructed. The expression for the feedforward voltage model is:

[0190]

[0191] in, For feedforward voltage, For feedforward current, The average resistance is given. Gravity feedforward compensation is determined based on the aforementioned feedforward voltage model, and steady-state error is effectively eliminated through gravity feedforward compensation.

[0192] Based on the gravity feedforward compensation combined with LQR optimal control, integral control, and coupling suppression control, a comprehensive control model is determined. The expression of the comprehensive control model is as follows:

[0193]

[0194] in, For the integrated control model, For gravity feedforward compensation, For LQR optimal control, For integral control, For coupling suppression control, the dual motors of the UAV nest top cover are controlled based on the comprehensive control model. The comprehensive control model yields a control law that integrates multiple control strategies. Control commands for the motor servo system are generated based on this control law, and the dual motors of the UAV nest top cover are controlled according to these commands. This achieves coordinated operation and parameter optimization of different control strategies, resulting in a high-precision and robust comprehensive control effect.

[0195] In this embodiment of the invention, the motor rotor dynamics equation is constructed based on the data of the nest top cover, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data, combined with the shaft torsional effect. The shaft torsional effect is incorporated into the system modeling, and considering the elastic characteristics of the connecting shaft, the modeling accuracy is improved. A state-space model is constructed based on the system state variables, control input vector, nest top cover angle dynamics equation, motor rotor dynamics equation, and electrical dynamics equation. Based on the state-space model and current difference, LQR optimal control, integral control, and coupling suppression control are generated. Multi-variable coordinated optimization is achieved through LQR optimal control, and incorporating the current difference into the LQR optimization objective minimizes the motor torque difference, improving motor coordination. Gravity feedforward compensation is determined, which effectively eliminates steady-state error. Based on gravity feedforward compensation combined with LQR optimal control, integral control, and coupling suppression control, a comprehensive control model is determined for controlling the dual motors of the UAV nest top cover. This effectively improves the robustness of the comprehensive control model, ensures global optimality and control stability, achieves multi-objective control, and significantly improves the control accuracy of the dual motors.

[0196] Example 3

[0197] Please see Figure 3 , Figure 3 This is a schematic diagram of the structural composition of the dual-motor control device for the top cover of a drone's nest, as described in this embodiment of the invention. The device includes:

[0198] Variable vector setting module 31: used to acquire nest top cover data, first motor drive data, second motor drive data, coaxial transmission data and top cover angle data, and set system state variables and control input vectors based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data and top cover angle data;

[0199] First equation construction module 32: used to construct the motor rotor dynamics equation based on the first motor drive data, the second motor drive data and the coaxial transmission data combined with the shaft torsion effect;

[0200] Second equation construction module 33: used to construct the nest top cover angle dynamics equation and the electrodynamics equation based on the nest top cover data, the first motor drive data, the second motor drive data, the coaxial transmission data and the top cover angle data;

[0201] Control generation module 34: is used to construct a state space model based on the system state variables, control input vector, nest top cover angle dynamic equation, motor rotor dynamic equation and electrical dynamic equation, and generate linear quadratic regulator LQR optimal control, integral control and coupling suppression control based on the state space model and current difference;

[0202] Integrated control module 35: used to determine gravity feedforward compensation, and based on the gravity feedforward compensation combined with LQR optimal control, integral control and coupling suppression control, determine an integrated control model, and based on the integrated control model, control the dual motors of the UAV nest top cover.

[0203] In the specific implementation of this invention, the specific implementation of the device item can be referred to the implementation of the method item above, and will not be repeated here.

[0204] In this embodiment of the invention, the motor rotor dynamics equation is constructed based on the data of the nest top cover, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data, combined with the shaft torsional effect. The shaft torsional effect is incorporated into the system modeling, and considering the elastic characteristics of the connecting shaft, the modeling accuracy is improved. A state-space model is constructed based on the system state variables, control input vector, nest top cover angle dynamics equation, motor rotor dynamics equation, and electrical dynamics equation. Based on the state-space model and current difference, LQR optimal control, integral control, and coupling suppression control are generated. Multi-variable coordinated optimization is achieved through LQR optimal control, and incorporating the current difference into the LQR optimization objective minimizes the motor torque difference, improving motor coordination. Gravity feedforward compensation is determined, which effectively eliminates steady-state error. Based on gravity feedforward compensation combined with LQR optimal control, integral control, and coupling suppression control, a comprehensive control model is determined for controlling the dual motors of the UAV nest top cover. This effectively improves the robustness of the comprehensive control model, ensures global optimality and control stability, achieves multi-objective control, and significantly improves the control accuracy of the dual motors.

[0205] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0206] Furthermore, the above provides a detailed description of the dual-motor control method and device for the top cover of a drone's nest provided by the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A dual-motor control method for the top cover of an unmanned aerial vehicle (UAV) nest, characterized in that, The method includes: Acquire nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data, and set system state variables and control input vectors based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data; Based on the first motor drive data, the second motor drive data, and the coaxial transmission data, combined with the shaft torsion effect, the motor rotor dynamics equation is constructed; Based on the data of the nest top cover, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data, construct the nest top cover angle dynamics equation and the electrodynamics equation; A state-space model is constructed based on the system state variables, control input vector, nest top cover angle dynamics equation, motor rotor dynamics equation, and electrical dynamics equation. Based on the state-space model and the current difference, optimal control, integral control, and coupling suppression control of the linear quadratic regulator (LQR) are generated. Gravity feedforward compensation is determined, and a comprehensive control model is determined based on the gravity feedforward compensation combined with LQR optimal control, integral control and coupling suppression control. The dual motors of the UAV nest top cover are then controlled based on the comprehensive control model.

2. The dual-motor control method for the top cover of an unmanned aerial vehicle (UAV) nest according to claim 1, characterized in that, The expression for the system state variable is: ， Where X is the system state variable. For the angle of the top cover of the machine nest, The angular velocity of the top cover of the nest. For the rotor angle of the first motor, The rotor angle of the second motor. Let be the rotor angular velocity of the first motor. The rotor angular velocity of the second motor is... This is the drive current of the first motor. The current is the drive current of the second motor, and T is the transpose. The expression for the control input vector is: ， Where U is the control input vector. This is the driving voltage of the first motor. is the driving voltage of the second motor, and T is the transpose.

3. The dual-motor control method for the top cover of an unmanned aerial vehicle (UAV) nest according to claim 1, characterized in that, The process of constructing the motor rotor dynamics equation based on the first motor drive data, the second motor drive data, and the coaxial transmission data, combined with the shaft torsional effect, includes: Based on the first motor drive data, the second motor drive data, and the coaxial transmission data, a set of equations for shaft torsional effect is constructed; Based on the first motor drive data, the second motor drive data, and the coaxial transmission data, combined with the shaft torsion effect equations, the motor rotor dynamics equations are constructed.

4. The dual-motor control method for the top cover of an unmanned aerial vehicle (UAV) nest according to claim 3, characterized in that, The expression for the equations governing the shaft torsion effect is: ， ， ， in, For the axial torsional moment, For shaft stiffness, For the rotor angle of the first motor, The rotor angle of the second motor. Let be the rotor angular velocity of the first motor. The rotor angular velocity of the second motor is... This is the shaft damping coefficient. The torsional torque at one end of the connecting shaft. This refers to the torsional torque at the other end of the connecting shaft; The expression for the motor rotor dynamics equation is as follows: ， ， in, Let the moment of inertia of the first motor rotor be _____. The first motor rotor speed angular acceleration, This is the driving torque of the first motor. The torsional torque at one end of the connecting shaft. The damping coefficient of the first motor is... Let be the rotor angular velocity of the first motor. The moment of inertia of the second motor rotor. The angular acceleration of the second motor rotor speed. This is the driving torque of the second motor. For the torsional torque at the other end of the connecting shaft, The damping coefficient of the second motor. ω is the rotor angular velocity of the second motor.

5. The dual-motor control method for the top cover of an unmanned aerial vehicle (UAV) nest according to claim 1, characterized in that, The construction of the nest top cover angle dynamics equation and electrodynamics equation based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data, and top cover angle data includes: Based on the data of the top cover of the machine nest, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data, the rotational inertia of the top cover, the average driving torque of the two motors, and the gravitational torque are determined. The dynamic equation of the nest top cover angle is constructed based on the rotational inertia of the top cover, the average driving torque of the dual motors, and the gravitational torque combined with the load damping coefficient. Based on the data of the machine nest top cover, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data, the motor current dynamics equation and the motor torque equation are constructed, and the electrodynamics equation is determined based on the motor current dynamics equation and the motor torque equation.

6. The dual-motor control method for the top cover of an unmanned aerial vehicle (UAV) nest according to claim 5, characterized in that, The expression for the dynamic equation of the nest top cover angle is: ， in, The moment of inertia of the top cover. The angular acceleration of the top cover of the nest, This represents the average driving torque of the two motors. For gravitational torque, This is the load damping coefficient. The angular velocity of the top cover of the nest; The expression for the motor current dynamic equation is: in, This is the drive current of the first motor. This is the driving voltage of the first motor. The driving resistor for the first motor is... Let be the torque constant of the first motor. Let be the rotor angular velocity of the first motor. Let be the electrical time constant of the first motor. The rotor angular velocity of the second motor is... This is the drive voltage for the second motor. This is the drive current for the second motor. This is the drive resistor for the second motor. Let be the torque constant of the second motor. The electrical time constant of the second motor; The expression for the motor torque equation is as follows: in, This is the driving torque of the first motor. Let be the torque constant of the first motor. This is the drive current of the first motor. This is the driving torque of the second motor. Let be the torque constant of the second motor. This is the drive current for the second motor.

7. The dual-motor control method for the top cover of an unmanned aerial vehicle (UAV) nest according to claim 1, characterized in that, The process involves constructing a state-space model based on the system state variables, control input vector, nest top cover angle dynamics equation, motor rotor dynamics equation, and electrical dynamics equation. Based on this state-space model and the current difference, optimal linear quadratic regulator (LQR) control, integral control, and coupling suppression control are generated, including: Based on the dynamic equations of the nest top cover angle, the dynamic equations of the motor rotor, and the electrical dynamic equations, the state matrix and the input matrix are determined, and a state space model is constructed based on the state matrix and the input matrix in combination with the system state variables and the control input vector. The current difference is calculated based on the first motor drive data and the second motor drive data, and the extended state space variables and extended state matrix are set based on the current difference in combination with the nest top cover data, the first motor drive data, the second motor drive data, the coaxial transmission data and the top cover angle data. Obtain the angle tracking weight, angular velocity weight, motor angle weight, motor current weight, and current difference weight, and determine the weight matrix based on the angle tracking weight, angular velocity weight, motor angle weight, motor current weight, and current difference weight; Based on the extended state-space variables and the weight matrix, an LQR objective function is constructed using the control weight matrix; Based on the state-space model, combined with the LQR objective function and the extended state matrix, LQR optimal control, integral control, and coupling suppression control are generated.

8. The dual-motor control method for the top cover of an unmanned aerial vehicle (UAV) nest according to claim 7, characterized in that, The expression for the extended state-space variable is: in, To expand the state-space variables, For the angle of the top cover of the machine nest, The angular velocity of the top cover of the nest. For the rotor angle of the first motor, The rotor angle of the second motor. Let be the rotor angular velocity of the first motor. The rotor angular velocity of the second motor is... This is the drive current of the first motor. This is the drive current for the second motor. The difference in current is T, and T is the transpose. The expression for the weight matrix is: in, This is the weight matrix. For angle tracking weights, As the weight for angular velocity, The first motor angle weight. The rotor angular velocity weight of the first motor. For the angle weight of the second motor, The rotor angular velocity weight of the second motor As the weight of the first motor current, As the weight of the second motor current, Weighted by current difference; The expression for the LQR objective function is: Where J is the LQR objective function, To expand the state-space variables, T is the transpose. This is the weight matrix. R is the control input vector, and R is the control weight matrix.

9. The dual-motor control method for the top cover of an unmanned aerial vehicle (UAV) nest according to claim 1, characterized in that, The determination of gravity feedforward compensation, and the determination of a comprehensive control model based on the gravity feedforward compensation combined with LQR optimal control, integral control, and coupling suppression control, includes: The gravity torque model of the nest top cover is determined, and the expression of the gravity torque model is: in, Let M be the gravitational torque, M be the mass of the nest top cover, g be the gravitational acceleration, and L be the length of the nest top cover. The target angle for the top cover of the aviary. This is the angle of centroid offset; A feedforward current model is established based on the aforementioned gravity torque model, and the expression of the feedforward current model is as follows: in, For feedforward current, For gravitational torque, It is the average torque constant; A feedforward voltage model is constructed based on the feedforward current model, and the expression of the feedforward voltage model is as follows: in, For feedforward voltage, For feedforward current, Average resistance; Gravity feedforward compensation is determined based on the aforementioned feedforward voltage model; Based on the gravity feedforward compensation combined with LQR optimal control, integral control, and coupling suppression control, a comprehensive control model is determined. The expression of the comprehensive control model is as follows: in, For the integrated control model, For gravity feedforward compensation, For LQR optimal control, For integral control, This is for coupling suppression control.

10. A dual-motor control device for the top cover of an unmanned aerial vehicle (UAV) nest, characterized in that, The device includes: Variable vector setting module: used to acquire nest top cover data, first motor drive data, second motor drive data, coaxial transmission data and top cover angle data, and set system state variables and control input vectors based on the nest top cover data, first motor drive data, second motor drive data, coaxial transmission data and top cover angle data; First equation construction module: used to construct the motor rotor dynamics equation based on the first motor drive data, the second motor drive data, and the coaxial transmission data, combined with the shaft torsion effect; The second equation construction module is used to construct the nest top cover angle dynamics equation and the electrodynamics equation based on the nest top cover data, the first motor drive data, the second motor drive data, the coaxial transmission data, and the top cover angle data. Control generation module: used to construct a state space model based on the system state variables, control input vector, nest top cover angle dynamics equation, motor rotor dynamics equation and electrical dynamics equation, and to generate optimal control, integral control and coupling suppression control of the linear quadratic regulator based on the state space model and the current difference; Integrated control module: used to determine gravity feedforward compensation, and based on the gravity feedforward compensation combined with LQR optimal control, integral control and coupling suppression control to determine the integrated control model, and based on the integrated control model to control the dual motors of the UAV nest top cover.

Images