Fault-tolerant control method for rotating speed of driving motor of deep-sea robot

Abstract

The invention discloses a fault-tolerant control method for the rotating speed of a driving motor of a deep-sea robot. The fault-tolerant control method comprises the following steps: S1, establishing a discrete state space model based on electrical and mechanical equations of the motor; s2, determining a fault characteristic parameter to describe the loss probability of the control signal when the control signal is attacked and interfered; s3, calculating a feedback control matrix according to the fault characteristic parameters; s4, calculating a feedforward control matrix according to the fault characteristic parameters; s5, constructing a controller based on the feedback control matrix and the feedforward control matrix, calculating a control signal online and transmitting the control signal to an execution mechanism; and S6, the execution mechanism receives the control signal, and if the signal is lost, the stored control signal is adopted to replace the control signal, and control is executed in real time. According to the invention, fault-tolerant control of the motor rotating speed can be realized under the condition that the control signal is randomly lost, and the motor rotating speed can stably track a predetermined trajectory.

Description

Technical Field

[0001] The present invention relates to the technical field of deep-sea motor control, and more specifically, to a speed fault-tolerant control method for a deep-sea robot driving motor. Background Art

[0002] As an important driving and control component of underwater equipment, deep-sea DC motors usually have the characteristics of small size and high power, and the motor housing is generally sealed to prevent seawater intrusion. Therefore, the heat conduction and dissipation of the motor are key issues that must be paid attention to. To this end, the heat dissipation system can be optimized through a multi-modular stator structure design, that is, using multiple armatures to drive the same rotor, and adding impellers between the armatures to improve the heat dissipation efficiency inside the motor. In addition, when the motor is operating in a deep-sea environment, it faces a variety of potential attacks and interferences such as cyber attacks, seawater corrosion, and physical damage. Therefore, these potential risks should be considered when applying deep-sea motors and corresponding countermeasures should be taken to ensure the stability and reliability of underwater equipment.

[0003] At present, there are some methods that can be used for fault-tolerant control of motors. For example, Chinese invention patent publication number CN113844267A discloses a four-wheel hub motor fault-tolerant control system based on the SQP algorithm, Chinese invention patent publication number CN115149883A discloses a built-in five-phase permanent magnet fault-tolerant motor fault-tolerant control method, and Chinese invention patent publication number CN113162517A discloses a magnetic levitation motor fault-tolerant control system and method based on self-sensing technology; however, fault-tolerant control methods specifically for deep-sea motors are relatively scarce, especially for deep-sea robot drive motor speed fault-tolerant control methods with multi-module stators have not yet appeared.

[0004] Therefore, it is necessary to propose a new fault-tolerant control method for deep-sea robot drive motors with multi-module stators. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method for fault-tolerant control of the speed of a deep-sea robot driving motor.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A deep-sea robot driving motor speed fault-tolerant control method comprises the following steps:

[0008] Step S1, establishing a discrete state space model of the motor based on its electrical and mechanical equations;

[0009] Step S2, determining fault characteristic parameters to describe the probability of loss of the control signal when it is attacked and interfered;

[0010] Step S3, calculating a feedback control matrix according to the fault characteristic parameters;

[0011] Step S4, calculating a feedforward control matrix according to the fault characteristic parameters;

[0012] Step S5, constructing a controller based on the feedback control matrix and the feedforward control matrix, calculating the control signal online and transmitting it to the actuator;

[0013] Step S6, the actuator receives the control signal. If the signal is lost, the stored control signal is used instead and the control is performed in real time.

[0014] Further, in step S1, the process of establishing the electrical and mechanical equations of each armature is as follows:

[0015] For a motor with n armatures driving the same rotor, write the electrical equation for the jth (j=1,2,…,n) armature:

[0016] (1)

[0017] In formula (1), is the armature voltage, is the armature resistance, is the armature current, is the armature inductance, is the time variable, is the armature emf and

[0018] (2)

[0019] In formula (2), is the electromotive force constant of the jth (j=1,2,…,n) armature, is the rotor speed;

[0020] The mechanical equation for the motor rotor includes the total torque produced by all armatures:

[0021] (3)

[0022] In formula (3), is the torque constant of the jth (j=1,2,…,n) armature;

[0023] The mechanical equation of the motor rotor can be written as:

[0024] (4)

[0025] In formula (4), is the moment of inertia, is the load torque, is the friction coefficient.

[0026] Furthermore, a state space model of the motor under no-load condition is established based on the armature electrical and mechanical equations, including the following steps:

[0027] Based on formula (1) and formula (2), we can get:

[0028] (5)

[0029] Arrange equation (5) into a matrix form, and let the motor load torque , the state space model of the motor under no-load condition can be obtained:

[0030] (6)

[0031] In formula (6), , , ,

[0032] , ,

[0033] is the state vector, is the input vector, For the controlled output.

[0034] Furthermore, a motor discrete state space model is established based on the state space model of the motor in the no-load state, including the following steps:

[0035] Discretizing equation (6) yields the discrete state space model of the motor:

[0036] (7)

[0037] In formula (7), is a discrete time variable, , , is the sampling period, is the base of natural logarithms.

[0038] Furthermore, in step S2, the process of determining the fault characteristic parameters is as follows:

[0039] Control signals based on the discrete state space model of the motor Each component in ,set up The probability of loss is .

[0040] Furthermore, in step S3, the process of calculating the feedback control matrix is ​​as follows:

[0041] Step S301, initialization, given by the matrix in formula (7) Identity matrix of the same dimensions and zero matrix , given the termination iteration error constant ;

[0042] Step S302, based on the matrix in equation (7) and , update according to formula (8) The value of

[0043] (8)

[0044] in, , is an n-order square matrix with all ones.

[0045] The symmetric matrix Q is the state vector in equation (7) The weighted matrix represents the importance of different state components, and the symmetric matrix R is the control input in equation (7) The weighted matrix represents the importance of the cost of the control input. By adjusting the values ​​of Q and R, the trade-off between the state and the control input can be achieved. The symbol represents the Hadamard product operation of the matrix, the superscript Indicates the transpose operation of the matrix, the superscript Represents the inverse operation of a matrix;

[0046] Step S303, update The value of ;

[0047] Step S304, if , then go to step S305, otherwise go to step S302;

[0048] Step S305, calculate and output the feedback control matrix:

[0049] (9).

[0050] Furthermore, in step S4, the process of calculating the feedforward control matrix is ​​as follows:

[0051] Step S401, establish an autonomous system model in the form of formula (10) to describe the preset speed signal that the motor needs to track in real time,

[0052] (10)

[0053] In formula (10), is a discrete time variable, is the state vector of the autonomous system, The matrix is ​​the preset speed signal that the motor needs to track in real time. and Determined according to the specific form of the speed signal;

[0054] Step S402, calculate the matrix variable based on formula (11) and The value of

[0055] (11);

[0056] Step S403, based on the matrix variable obtained by formula (11) and The value of the feedback control matrix is ​​used to calculate the feedforward control matrix,

[0057] (12)

[0058] In formula (12), is the feedback control matrix, is the feedforward control matrix.

[0059] Furthermore, in step S5, the process of online calculating the control signal is as follows:

[0060] Based on the feedback control matrix and the feedforward control matrix Construct a controller that calculates the real-time control signal online according to formula (13) , and send the obtained control signal to the motor's actuator in real time.

[0061] (13)

[0062] In formula (13), and are the state vectors of equation (7) and equation (11) respectively.

[0063] Furthermore, in step S6, the process of executing the control signal in real time is as follows:

[0064] Step S601: The driver in the motor actuator receives the control signal sent by the controller in real time. and store ;

[0065] Step S602, if At this moment, the motor driver does not receive the control signal due to attack or interference. The amount in , the control signal stored at the previous moment is used instead, and the following control signal is used:

[0066] (14)

[0067] and store ;

[0068] Step S603: the motor driver uses a control signal Drive motor.

[0069] The beneficial effects of the present invention are as follows: the present invention can realize fault-tolerant control of the motor speed when the control signal is randomly lost, and ensure that the motor speed can stably track a predetermined trajectory. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a control structure block diagram of the fault-tolerant control method for the speed of the deep-sea robot driving motor in this embodiment;

[0071] Figure 2 This is an effect diagram of the fault-tolerant control of the speed of the deep-sea robot driving motor in this embodiment;

[0072] Figure 3 This is an error diagram for tracking the controlled speed of the deep-sea robot driving motor in this embodiment. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] Embodiment: In order to solve the fault-tolerant control problem of the deep-sea robot drive motor and improve the safety and reliability of its operation, this embodiment proposes a deep-sea robot drive motor speed fault-tolerant control method, such as Figure 1 As shown, the following steps are included:

[0075] Step S1, establishing a discrete state space model of the motor based on its electrical and mechanical equations;

[0076] Step S2, determining fault characteristic parameters to describe the probability of loss of the control signal when it is attacked and interfered;

[0077] Step S3, calculating a feedback control matrix according to the fault characteristic parameters;

[0078] Step S4, calculating a feedforward control matrix according to the fault characteristic parameters;

[0079] Step S5, constructing a controller based on the feedback control matrix and the feedforward control matrix, calculating the control signal online and transmitting it to the actuator;

[0080] Step S6, the actuator receives the control signal. If the signal is lost, the stored control signal is used instead and the control is performed in real time.

[0081] Furthermore, in step S1, the process of establishing the motor discrete model is as follows:

[0082] Without loss of generality, consider a motor with n armatures driving the same rotor. For the jth (j=1,2,…,n) armature, write its electrical equation:

[0083] (1)

[0084] In formula (1), is the armature voltage, is the armature resistance, is the armature current, is the armature inductance, is the time variable, is the armature emf and

[0085] (2)

[0086] In formula (2), is the electromotive force constant of the jth (j=1,2,…,n) armature, is the rotor speed;

[0087] The mechanical equation for the motor rotor includes the total torque produced by all armatures:

[0088] (3)

[0089] In formula (3), is the torque constant of the jth (j=1,2,…,n) armature;

[0090] The mechanical equation of the motor rotor can be written as:

[0091] (4)

[0092] In formula (4), is the moment of inertia, is the load torque, is the friction coefficient;

[0093] Based on formula (1) and formula (2), we can get:

[0094] (5)

[0095] Arrange equation (5) into a matrix form, and let the motor load torque , the state space model of the motor under no-load condition can be obtained:

[0096] (6)

[0097] In formula (6), , , ,

[0098] , ,

[0099] is the state vector, is the input vector, is the controlled output;

[0100] Discretizing equation (6) yields the discrete state space model of the motor:

[0101] (7)

[0102] In formula (7), is a discrete time variable, , , is the sampling period, is the base of natural logarithms.

[0103] Furthermore, in step S2, the process of determining the fault characteristic parameters is as follows:

[0104] Considering potential attacks and interferences such as network attacks and corrosion damage, the control signal in the motor discrete state model (7) is Each component in You may face random loss.

[0105] set up The probability of loss is .

[0106] Furthermore, in step S3, the process of calculating the feedback control matrix is ​​as follows:

[0107] Step S301, initialization is performed, given by the matrix in formula (7) Identity matrix of the same dimensions and zero matrix , given the termination iteration error constant ;

[0108] Step S302, based on the matrix in equation (7) and , update according to formula (8) The value of

[0109] (8)

[0110] in, , is an n-order square matrix with all ones.

[0111] The symmetric matrix Q is the state vector in equation (7) The weighted matrix represents the importance of different state components, and the symmetric matrix R is the control input in equation (7) The weighted matrix represents the importance of the cost of the control input. By adjusting the values ​​of Q and R, the trade-off between the state and the control input can be achieved. The symbol represents the Hadamard product operation of the matrix, the superscript Indicates the transpose operation of the matrix, the superscript Represents the inverse operation of a matrix;

[0112] Step S303, update The value of ;

[0113] Step S304, if , then go to step S305, otherwise go to step S302;

[0114] Step S305, calculate and output the feedback control matrix:

[0115] (9).

[0116] Furthermore, in step S4, the process of calculating the feedforward control matrix is ​​as follows:

[0117] Step S401, establish an autonomous system model in the form of formula (10) to describe the preset speed signal that the motor needs to track in real time,

[0118] (10)

[0119] In formula (10), is a discrete time variable, is the state vector of the autonomous system, The matrix is ​​the preset speed signal that the motor needs to track in real time. and Determined according to the specific form of the speed signal;

[0120] Step S402, calculate the matrix variable based on formula (11) and The value of

[0121] (11);

[0122] Step S403, based on the matrix variable obtained by formula (11) and The value of and the feedback control matrix obtained based on formula (9) Calculate the feedforward control matrix,

[0123] (12)

[0124] In formula (12), is the feedback control matrix, is the feedforward control matrix.

[0125] Furthermore, in step S5, the process of online calculating the control signal is as follows:

[0126] The feedback control matrix obtained based on formula (11) The feedforward control matrix obtained by equation (12) Construct a controller that calculates the real-time control signal online according to formula (13) , and send the obtained control signal to the motor's actuator in real time.

[0127] (13)

[0128] In formula (13), and are the state vectors of equation (7) and equation (11) respectively.

[0129] Furthermore, in step S6, the process of executing the control signal in real time is as follows:

[0130] Step S601: The driver in the motor actuator receives the control signal sent by the controller in real time. and store ;

[0131] Step S602, if At this moment, the motor driver does not receive the control signal due to attack or interference. The amount in , the control signal stored at the previous moment is used instead, that is, the following control signal is used:

[0132] (14)

[0133] and store ;

[0134] Step S603: the motor driver uses a control signal Drive motor.

[0135] In this embodiment, a motor with three armatures driving the same rotor is considered, that is, , the characteristic parameters in the electrical equation (1) and the mechanical equation (4) are set as: , , , , , , set the sampling period , then the specific matrix parameters in the discrete state space model of the motor (7) are as follows:

[0136] ,

[0137] In this embodiment, the control signal The three components in , , The probability of random loss is , , ;

[0138] Assume the symmetric weight matrix in equation (8):

[0139] , ;

[0140] Get the matrix The values ​​are as follows:

[0141] ,

[0142] The feedback control matrix is ​​constructed according to formula (9):

[0143] ;

[0144] Set the speed that the motor needs to track to: , To generate a signal;

[0145] Assume the matrix in equation (10) and The values ​​are as follows:

[0146] , ,

[0147] Solve equation (11) and construct the feedforward control matrix according to equation (12) as follows:

[0148] .

[0149] According to formula (13), the control input is obtained , where the three components , , According to probability , , Random loss occurs. If loss occurs, it is processed according to formula (14) to obtain the control effect of the motor as follows Figure 2 As shown in Figure 2, the tracking error of the motor speed is as follows: Figure 3 As shown. Figure 1 and Figure 2 As shown, the speed of the motor can better track the predetermined sinusoidal trajectory when the control is randomly lost, indicating that the fault-tolerant control method of this embodiment can achieve a better fault-tolerant control effect.

[0150] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A fault-tolerant control method for the speed of a deep-sea robot driving motor, characterized in that: The steps include: Step S1, establishing a discrete state space model of the motor based on its electrical and mechanical equations; Step S2, determining fault characteristic parameters to describe the probability of loss of the control signal when it is attacked and interfered; Step S3, calculating a feedback control matrix according to the fault characteristic parameters; Step S4, calculating a feedforward control matrix according to the fault characteristic parameters; Step S5, constructing a controller based on the feedback control matrix and the feedforward control matrix, calculating the control signal online and transmitting it to the actuator; Step S6, the actuator receives the control signal. If the signal is lost, the stored control signal is used instead and the control is performed in real time.

2. According to claim 1, a deep-sea robot driving motor speed fault-tolerant control method is characterized in that: In step S1, the process of establishing the electrical and mechanical equations for each armature is as follows: For a motor with n armatures driving the same rotor, write the electrical equation for the jth (j=1,2,…,n) armature: (1) In formula (1), is the armature voltage, is the armature resistance, is the armature current, is the armature inductance, is the time variable, is the armature emf and (2) In formula (2), is the electromotive force constant of the jth (j=1,2,…,n) armature, is the rotor speed; The mechanical equation for the motor rotor includes the total torque produced by all armatures: (3) In formula (3), is the torque constant of the jth (j=1,2,…,n) armature; The mechanical equation of the motor rotor can be written as: (4) In formula (4), is the moment of inertia, is the load torque, is the friction coefficient.

3. According to claim 2, a deep-sea robot driving motor speed fault-tolerant control method is characterized in that: The state space model of the motor under no-load condition is established based on the armature electrical and mechanical equations, including the following steps: Based on formula (1) and formula (2), we can get: (5) Arrange equation (5) into a matrix form, and let the motor load torque , the state space model of the motor under no-load condition can be obtained: (6) In formula (6), , , , ， ， is the state vector, is the input vector, For the controlled output.

4. According to claim 3, a deep-sea robot driving motor speed fault-tolerant control method is characterized in that: The motor discrete state space model is established based on the state space model of the motor in the no-load state, including the following steps: Discretizing equation (6) yields the discrete state space model of the motor: (7) In formula (7), is a discrete time variable, , , is the sampling period, is the base of natural logarithms.

5. According to claim 1, a deep-sea robot driving motor speed fault-tolerant control method is characterized in that: In step S2, the process of determining the fault characteristic parameters is as follows: Control signals based on the discrete state space model of the motor Each component in ,set up The probability of loss is .

6. A method for fault-tolerant control of the speed of a deep-sea robot driving motor according to claim 4, characterized in that: In step S3, the process of calculating the feedback control matrix is ​​as follows: Step S301, initialization, given by the matrix in formula (7) Identity matrix of the same dimensions and zero matrix , given the termination iteration error constant ; Step S302, based on the matrix in equation (7) and , update according to formula (8) The value of (8) in, , is an n-order square matrix with all ones. The symmetric matrix Q is the state vector in equation (7) The weighted matrix represents the importance of different state components, and the symmetric matrix R is the control input in equation (7) The weighted matrix represents the importance of the cost of the control input. By adjusting the values ​​of Q and R, the trade-off between the state and the control input can be achieved. The symbol represents the Hadamard product operation of the matrix, the superscript Indicates the transpose operation of the matrix, the superscript Represents the inverse operation of a matrix; Step S303, update The value of ; Step S304, if , then go to step S305, otherwise go to step S302; Step S305, calculate and output the feedback control matrix: (9)。 7. A method for fault-tolerant control of the speed of a deep-sea robot driving motor according to claim 6, characterized in that: In step S4, the process of calculating the feedforward control matrix is ​​as follows: Step S401, establish an autonomous system model in the form of formula (10) to describe the preset speed signal that the motor needs to track in real time, (10) In formula (10), is a discrete time variable, is the state vector of the autonomous system, The matrix is ​​the preset speed signal that the motor needs to track in real time. and Determined according to the specific form of the speed signal; Step S402, calculate the matrix variable based on formula (11) and The value of (11)； Step S403, based on the matrix variable obtained by formula (11) and The value of the feedback control matrix is ​​used to calculate the feedforward control matrix, (12) In formula (12), is the feedback control matrix, is the feedforward control matrix.

8. A method for fault-tolerant control of the speed of a deep-sea robot driving motor according to claim 7, characterized in that: In step S5, the process of online calculation of the control signal is as follows: Based on the feedback control matrix and the feedforward control matrix Construct a controller that calculates the real-time control signal online according to formula (13) , and send the obtained control signal to the motor's actuator in real time. (13) In formula (13), and are the state vectors of equation (7) and equation (11) respectively.

9. A method for fault-tolerant control of the speed of a deep-sea robot driving motor according to claim 8, characterized in that: In step S6, the process of executing the control signal in real time is as follows: Step S601: The driver in the motor actuator receives the control signal sent by the controller in real time. and store ; Step S602, if At this moment, the motor driver does not receive the control signal due to attack or interference. The amount in , the control signal stored at the previous moment is used instead, and the following control signal is used: (14) and store ; Step S603: the motor driver uses a control signal Drive motor.

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