A Design Method for the Control Scheme of the Pan-Tilt on a Surface Unmanned Boat

Abstract

The present invention discloses a design method for the control scheme of the pan-tilt of an unmanned surface vessel. In order to enable the three-axis stable pan-tilt to adapt to the stability tasks under different sea conditions, two different control methods are designed under different sea conditions. Under high-level sea conditions, the angle values predicted by the AR prediction model for the sea wave environment are used for closed-loop angle compensation. And in the drive control method of the brushless DC motor, on the basis of the FOC three-closed-loop control, position-loop fuzzy PID adaptive control is added to make the servo control smoother and more accurate.

Description

Technical Field

[0001] The present invention belongs to the technical field of pan-tilt control, and particularly relates to a design method for a pan-tilt control scheme of an unmanned surface vessel. Background Art

[0002] A pan-tilt is a support device for installing and fixing devices such as cameras. Its English name is Pan-Tilt (abbreviated as PT). It can control the Euler rotation angle of the device. According to the degree of freedom, it can be divided into: 1-DOF pan-tilt, 2-DOF pan-tilt, 3-DOF pan-tilt. The three-axis stabilized pan-tilt was originally a three-dimensional mechanical structure carried under an unmanned aerial vehicle for aerial photography, and is applied to an unmanned surface vessel for target tracking and marine exploration. A highly stable pan-tilt needs to isolate the interference of the carrier attitude and the coupling between the frames, reduce mechanical vibration and the error interference of the back electromotive force of the motor, and ensure the stability of the camera optical axis. During the actual pan-tilt shooting process, the carrier will be affected by wave interference and its own mechanical vibration on the sea surface, especially for small carriers, this situation will be more serious. The camera optical axis will jitter with the sudden change of the carrier attitude, thus reducing the quality of the camera shooting image. To ensure the stability of the camera optical axis, it is necessary to isolate the attitude disturbance of the body, and a highly stable inertial platform must be carried to mount the camera for shooting. At the same time, in order to shoot at a specified angle, the platform frame carrying the camera needs to rotate the camera optical axis to the specified channel angle, and the platform frame needs to rotate according to the received instruction, and the frame motor needs to achieve stable speed control.

[0003] Traditional pan-tilts usually adopt a two-degree-of-freedom design, with a small monitoring range and effective sensing range, and the control method is relatively single. Position control is mostly achieved by means of preset point positioning, which cannot meet the requirements of high precision and high stability. With the continuous development of turntable technology, stabilized pan-tilt technology has received more and more extensive attention and research. In the foreign research field, stabilized pan-tilts have long played a great role in the military field, and their performance is excellent, and all performance indicators have reached a quite high application level. For example, the optoelectronic reconnaissance pan-tilt carried under the ESP-600C type unmanned aerial vehicle developed by Israel can achieve a stable accuracy of the optical axis of 15 μrad, the pitch angle can move in the range of +10° to -110°, the azimuth rotation can reach 360°×N, the maximum angular velocity can reach 50° / s, and the large angular acceleration can reach 60° / s. Another optoelectronic reconnaissance pan-tilt carried under the COMPASS type unmanned aerial vehicle developed by Israel can achieve a stable accuracy of the optical axis of 25 μrad, the pitch angle can move in the range of +35° to -85°, the azimuth rotation can reach 360°×N, the maximum angular velocity can reach 60° / s.

[0004] In recent years, the application of various modern control technologies in the stabilized pan-tilt system has also received extensive attention. For example, optimal control, variable structure control, neural networks, etc. have gradually been applied to the control of airborne stabilized pan-tilts. Most of the existing technical solutions are based on unmanned aerial vehicles, and very few studies focus on the design of stabilized pan-tilts in the sea wave environment. Among the few design solutions for pan-tilts in the marine environment, the pan-tilt motors used are brushless DC motors or stepper motors. For the control scheme of brushless DC motors, it is based on the SPWM control method, which is favored due to its simple control. However, this control method has significant drawbacks, including low position control accuracy and the superposition of position feedback errors. Secondly, the current design control method of marine pan-tilts is too single and cannot meet the stability requirements for complex sea conditions. Summary of the Invention

[0005] To overcome the deficiencies of the prior art, the present invention provides a design method for the control scheme of a pan-tilt on a surface unmanned vessel. In order to enable the three-axis stabilized pan-tilt to adapt to stability tasks under different sea conditions, two different control methods are designed under different sea conditions. Under high-level sea condition conditions, the AR prediction model is used to perform closed-loop angle compensation on the angle values obtained from the prediction of the sea wave environment. And for the drive control method of the brushless DC motor, on the basis of the FOC three-loop control, position-loop fuzzy PID adaptive control is added to make the servo control smoother and more accurate.

[0006] The technical solution adopted by the present invention to solve its technical problems includes the following steps:

[0007] Step 1: Set the pan-tilt on the surface unmanned vessel as a three-axis stabilized pan-tilt, and its brushless DC motor control method is the vector control FOC algorithm;

[0008] Step 2: Add fuzzy PID control to the three loops of the FOC algorithm;

[0009] Step 2-1: Use the Laplace domain to calculate the PI gain parameters. The open-loop gain of the current regulator is:

[0010]

[0011] In the formula, K p-ACR-d / q represents the current loop proportional coefficient, K I-ACR represents the current loop integral coefficient, L d / q represents the transformed stator flux linkage, ω B-ACR represents the motor angular velocity, R s represents the stator resistance of the brushless DC motor;

[0012] Let K P / K I =R s / L s, then according to the open-loop amplitude-frequency characteristic, the PI parameters are obtained:

[0013]

[0014] In the formula, L s represents the stator flux linkage of the motor, K P represents the proportionality coefficient to be obtained, K I represents the integral coefficient to be obtained, K P-ACR represents the proportionality coefficient of the current loop to be obtained, and T represents the control period;

[0015] The setting method of the PI parameters in the Laplace domain of the speed loop is:

[0016]

[0017] where F represents the drag coefficient, J represents the moment of inertia, and ω B-ASR represents the passband width of the speed regulator, and the unit is rad / s;

[0018] Step 2-2: The fuzzy PID takes the error between the target value and the output value and the change rate of the error as the inputs of the fuzzy controller. The fuzzy controller first performs fuzzy processing on the inputs, then conducts fuzzy inference, and finally defuzzifies the results of the fuzzy inference to output the three parameters K P 、K I 、K D of the PID controller, thereby realizing the adaptive tuning of the PID controller parameters;

[0019] Step 3: Predict the attitude of the water surface unmanned ship's pan-tilt body to achieve synchronous compensation with the waves;

[0020] The motion of the pan-tilt carrier, i.e., the water surface unmanned ship, in the waves is described by the six-degree-of-freedom motion of a rigid body in space; the longitudinal motion of the carrier includes surge, heave, and pitch, and the lateral motion of the carrier includes roll, sway, and yaw; the influence of roll and pitch on the carrier is much greater than that of sway, surge, yaw, and heave motions. Therefore, sway, surge, yaw, and heave motions are ignored;

[0021] Step 3-1: In the roll motion, the motion of the carrier is a linear motion system, whose input is the wave tilt angle α(t), and the output is the carrier roll angle θ(t); there are the roll angle θ and its angular velocity and angular acceleration According to Newton's second law, the differential equation of the carrier roll motion is obtained as:

[0022]

[0023] In the formula, J θ represents the moment of inertia of the pan-tilt carrier about the Gx axis, ΔJθ It represents the rolling added moment of inertia of the fluid mass caused by the angular velocity of the carrier's motion about the Gx axis. D is the displacement of the carrier, h is the height of the transverse stability center of the pan-tilt carrier, and N θ is the rolling damping coefficient, and M θ is the rolling disturbing moment, which is related to the motion of random waves;

[0024] By analogy with the rolling motion of a ship on waves, it is estimated that:

[0025]

[0026] where ρ θ is the radius of gyration of rolling, and its value is proportional to the size of the carrier;

[0027] Since the carrier's rolling moment is composed of the restoring disturbing moment and the damping disturbing moment generated by waves and the inertial disturbing moment generated by the carrier's added mass, it is expressed as:

[0028]

[0029] In the formula, α represents the wave inclination angle in the rolling motion;

[0030] Also, let ω θ be the natural angular frequency of the carrier's roll, be the rolling damping factor, and its value is

[0031]

[0032] Then

[0033]

[0034] Taking the Laplace transform of Equation (8), the transfer function of the carrier's rolling motion is obtained:

[0035]

[0036] Setting the correction parameter of the wave inclination angle as σ, σ < 1, the wave inclination angle of the wave on the carrier is expressed as:

[0037] α0 = σα (10)

[0038] The mathematical model of the wave inclination angle of a fixed point in space is expressed as:

[0039]

[0040] In the formula: ω n is the frequency of the wave angle, ε n is the initial phase randomly distributed between 0 and 2π, and S α (ω) is the angular spectrum of the wave frequency. Then the wave inclination angle of the wave on the carrier is:

[0041]

[0042] For pitching, the analysis results are obtained by the same method as the above-mentioned rolling.

[0043] Step 3-2: Predict the inclination angle of the pan-tilt carrier through the transfer relationship between the carrier and the sea wave inclination angle, and realize the pre-stabilization control of the pan-tilt.

[0044] The prediction model established is a stationary time prediction model, that is, an AR prediction model. First, predict the roll angle. For n sample sea wave roll angles X1, X2,... X n data, establish an autoregressive model:

[0045]

[0046] In the formula, m represents the order of the autoregressive model, a i represents the autoregressive coefficient, and ε t represents the model correction value;

[0047] If n data are to be used for fitting and statistical prediction of the AR model, first determine the order of the model; the covariance matrix of the known sample X is:

[0048]

[0049] The relationship between the covariance matrix and the autoregressive model parameters is:

[0050]

[0051] Determine the order of the model through the K-L information amount and its AIC criterion, where the K-L information amount is defined as:

[0052]

[0053] Among them, the AIC criterion is:

[0054]

[0055] When the K-L information amount and the AIC criterion are the smallest, the order of the prediction model is determined;

[0056] After that, calculate the model variance for the estimated autoregressive coefficients. After the variance meets the preset conditions, add the prediction model to the pan-tilt controller.

[0057] Preferably, in the fuzzy PID control, the set inputs are the error E and the error change rate EC, and the outputs are ΔK P , ΔK I , ΔK D; It is set that the universes of discourse of the input parameters and output parameters are both (-200, 200), the output is divided into seven levels, and the fuzzy subsets corresponding to the universes of discourse of the variables are {NB, NM, NS, Z, PS, PM, PB} respectively. The subset elements represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large respectively. Among them, the membership function of the input variable is selected as Gaussian type, and the membership function of the output variable is selected as triangular type.

[0058] The beneficial effects of the present invention are as follows:

[0059] 1. Based on the three closed-loop control of the brushless DC motor, the present invention adds a position-loop fuzzy PID controller to complete the BLDCM servo adaptive control, which can improve the position tracking and speed tracking accuracy of the servo control system to adapt to the complex marine environment.

[0060] 2. The present invention applies the three-axis stabilized pan-tilt to the unmanned ship, expanding the functions of the unmanned ship and providing stronger technical support for marine exploration and target tracking.

[0061] 3. The present invention establishes an AR prediction model for sea waves and establishes the inclination transfer relationship between sea waves and the carrier (unmanned ship), which can provide accurate predicted angles under complex sea conditions to realize the control of the three-axis stabilized pan-tilt. Brief Description of the Drawings

[0062] Figure 1 is the SVPWM control module established by the present invention.

[0063] Figure 2 is the fuzzy controller ΔK established by the present invention P , ΔK I , ΔK D variation schematic diagram.

[0064] Figure 3 is the simulation model of the position fuzzy control PID brushless DC motor of the present invention.

[0065] Figure 4 is the schematic diagram of the comparison between the traditional PID and the fuzzy PID results.

[0066] Figure 5 is the BLDCM motor control system diagram of the embodiment of the present invention.

[0067] Figure 6 is the speed variation schematic diagram of the motor trapezoidal speed method in the embodiment of the present invention.

[0068] Figure 7 is the schematic diagram of the motion definition of six degrees of freedom of the pan-tilt carrier in the embodiment of the present invention.

[0069] Figure 8It is the force analysis diagram of the roll motion of the unmanned ship in the embodiment of the present invention.

[0070] Figure 9 It is the simulation schematic diagram of the wave roll angle and the roll angle of the unmanned ship in the embodiment of the present invention.

[0071] Figure 10 It is the prediction effect diagram of the longitudinal inclination angle of the unmanned ship and its carrier in the embodiment of the present invention.

[0072] Figure 11 It is the structure diagram of the pan-tilt controller of the present invention.

[0073] Figure 12 It is the software design flow chart of the pan-tilt of the present invention. Specific embodiments

[0074] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0075] At present, the research on the pan-tilt applied to unmanned ships is relatively less. In the complex and uncertain sea condition environment, the stability performance requirements for the three-axis pan-tilt are higher. Since the currently designed pan-tilts all achieve stable control through angle compensation, when the sea condition level is relatively high, the actual control system cannot achieve the ideal stable effect. In practice, it is always hoped that the system can have a faster response speed and a wider bandwidth. However, various factors always interfere with the response speed of the system during the operation of the system.

[0076] In view of the above problems, the present invention designs a new control scheme for a three-axis stable pan-tilt based on the changes of sea waves. In order to make the three-axis stable pan-tilt adapt to the stability tasks under different sea conditions, the present invention designs two different control methods under different sea conditions. Under high-level sea condition conditions, the AR prediction model is used to perform closed-loop angle compensation on the angle values obtained by predicting the sea wave environment, and in the drive control method of the brushless DC motor, position-loop fuzzy PID adaptive control is added on the basis of the FOC three-closed-loop control to make the servo control smoother and more accurate.

[0077] 1. In the control of brushless DC motors, many algorithms have been developed for the control of current loops, speed loops, and position loops. The most common one is the classical PID control. Due to its simplicity, few control parameters, and ease of implementation, it has always been a favorite in engineering applications. However, with the development of modern science and technology, the PID algorithm can no longer meet the stability and market requirements for complex controlled objects. Therefore, the present invention proposes to use a fuzzy PID control method to control the brushless DC pan-tilt motor. The addition of fuzzy adaptive PID enables the motor control of the three-axis stable pan-tilt to operate accurately in more complex sea conditions, further improving the stability of pan-tilt control. For the controllers of the inner current loop and speed loop, the present invention proposes to use Laplace domain settings to calculate the PI gain parameters. The open-loop gain of the current regulator is:

[0078]

[0079] In the formula, K p-ACR-d / q represents the current loop proportional coefficient, K I-ACR represents the current loop integral coefficient, R s represents the stator resistance of the brushless DC motor. Let K P / K I =R s / L s , then according to the open-loop amplitude-frequency characteristic, the PI parameters can be approximately obtained:

[0080]

[0081] The setting method of the speed loop Laplace domain PI parameters is:

[0082]

[0083] where F represents the resistance coefficient, ω B-ASR represents the passband width of the speed regulator, and the unit is rad / s.

[0084] Fuzzy PID takes the error between the target value and the output value and the change rate of the error as the inputs of the fuzzy controller. The fuzzy controller first performs fuzzy processing on the inputs, then conducts fuzzy reasoning, and finally defuzzifies the results of the fuzzy reasoning to output the three parameters Kp, Ki, and Kd of the PID controller, thereby achieving the effect of self-adaptive tuning of the PID controller parameters.

[0085] The present invention sets the inputs as the error E and the error change rate EC, and the outputs as ΔK P , ΔK I , ΔK D, as the system dynamically changes, these three parameters also change accordingly, causing the PID parameters to keep changing. The universes of discourse for the set input parameter and the output parameter are both (-200, 200). The output is divided into seven levels, and the fuzzy subsets corresponding to the universes of discourse of the variables are {NB, NM, NS, Z, PS, PM, PB} respectively. The subset elements represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large respectively. Among them, the membership function of the input variable is selected as Gaussian type, and the membership function of the output variable is selected as triangular type.

[0086] 2. Due to the complex sea wave environment, the simple traditional pan-tilt control method has poor control effect on the stability of the pan-tilt in the sea wave environment. In the state of stable sea surface environment, the pan-tilt can rely on the attitude information obtained by the attitude sensor to perform attitude compensation, so as to reach a stable state. However, when the sea surface environment is extreme and the sea state level is relatively high, the compensation speed of the pan-tilt cannot meet the stability requirements. Therefore, the present invention proposes to perform attitude prediction on the body of the marine pan-tilt, so as to meet the requirement of synchronous compensation with the sea waves.

[0087] Predictive control is a control theory that has gradually developed in the process of industrial practice. Because of the uniqueness of its own control method, this control method has relatively low requirements for the control model, good control effect and great advantages in online calculation. When the three-axis pan-tilt cannot achieve self-stabilization under complex sea conditions, predictive control can preferentially predict the sway state of the pan-tilt body and maintain the stability of the camera to the greatest extent.

[0088] The motion of the pan-tilt carrier in the waves can be described by the motion of a rigid body in six degrees of freedom in space. Since the sea waves are irregular random motions, the motion of the pan-tilt carrier on the sea surface is also complex and irregular. For the motion of the carrier in six degrees of freedom, it can be divided into two groups during research. One group is the longitudinal motion of the carrier, including surge, heave and pitch. The other group is the lateral motion, including roll, sway and yaw. During the motion of the carrier, there are strong coupling relationships among various motions, while there is no coupling relationship between the oscillatory motions of different groups. When studying the influence of sea waves on the motion of the carrier, the influence of sway, surge, yaw and heave on the resulting inclination is the smallest, while the influence of roll and pitch on the stability of the carrier is the greatest. Since there is no coupling relationship between roll and pitch, this article will study the roll motion, and the analysis method of pitch motion is similar to that of roll motion. At this time, it can be considered that the motion of the carrier is a linear motion system, with its input quantity being the wave tilt angle α(t) and the output quantity being the carrier roll angle θ(t). The carrier has a roll angle θ and its angular velocity and angular acceleration According to Newton's second law, the differential equation of the carrier roll motion can be obtained as:

[0089]

[0090] In the formula, J θ represents the moment of inertia of the pan-tilt carrier about the Gx axis, and ΔJ θ represents the additional rolling moment of inertia of the fluid mass caused by the carrier's motion angular velocity about the Gx axis. D is the displacement of the carrier, h is the height of the transverse stability center of the pan-tilt carrier, and N θ is the rolling damping coefficient, and M θ is the rolling disturbing moment, which is related to the motion of random waves. By analogy with the rolling motion of a ship on waves, it is estimated that

[0091]

[0092] where ρ θ is the radius of gyration of rolling, and its value is generally proportional to the size of the carrier. Also, since the rolling moment of the carrier is composed of the restoring disturbing moment and the damping disturbing moment generated by the waves and the inertial disturbing moment generated by the additional mass of the carrier, it can be expressed as

[0093]

[0094] Let ω θ be the natural angular frequency of the carrier's roll, be the rolling damping factor, and its value is

[0095]

[0096] Then

[0097]

[0098] Taking the Laplace transform of the above formula, the transfer function of the carrier's rolling motion is obtained:

[0099]

[0100] Since the draft depth of the pan-tilt carrier on the sea surface will affect the main disturbing moment of the rolling waves, that is, it affects the actual rolling wave angle, so the correction parameter of the wave angle is set as σ, σ < 1, so the rolling wave angle of the waves on the carrier can be expressed as:

[0101] α0 = σα (10)

[0102] Since the mathematical model of the wave angle at a fixed point in space can be expressed as:

[0103]

[0104] In the formula: ω n is the frequency of the wave angle, ε n is the initial phase randomly distributed between 0 and 2π, and S α (ω) is the angular spectrum of the wave frequency.

[0105] Then the wave inclination angle of the wave to the carrier is

[0106]

[0107] Next, the AR prediction model is introduced. After the prediction of sea waves is realized, it is possible to predict the inclination angle of the pan-tilt carrier through the transfer relationship between the carrier and the sea wave inclination angle, and realize the pre-stabilization control of the pan-tilt.

[0108] The established prediction model is a stationary time prediction model, that is, an AR prediction model. First, the roll angle is predicted. For n sample sea wave roll angles X1, X2,... X n data, an autoregressive model is established:

[0109]

[0110] If n data are to be used for fitting and statistical prediction of the AR model, the order of the model must be determined first. The covariance matrix of the known sample X is:

[0111]

[0112] The relationship between it and the autoregressive model parameters is:

[0113]

[0114] Subsequently, the order of the model is determined through the K-L information quantity and its AIC criterion. The K-L information quantity is defined as:

[0115]

[0116] The AIC criterion is:

[0117]

[0118] When the K-L information quantity and the AIC criterion are the smallest, the order of the prediction model is determined. Then, the model variance is calculated for the estimated autoregressive coefficients. After the variance meets the conditions, the prediction model can be added to the pan-tilt controller. Specific embodiment:

[0120] 1. First, determine that the brushless DC motor control method is the vector control FOC algorithm, and establish an SVPWM module as Figure 1 shown.

[0121] 2. In the control of brushless DC motors, many algorithms have emerged for the control of the current loop, speed loop, and position loop. The most common one is the classical PID control. Due to the characteristics of simple algorithm, few control parameters, and easy implementation, it has always been a favored object in engineering applications. However, with the development of modern science and technology, the PID algorithm can no longer meet the stability and market requirements for complex controlled objects. Therefore, the present invention proposes to use the fuzzy PID control method to control the brushless DC pan-tilt motor. The addition of fuzzy adaptive PID enables the motor control of the three-axis stable pan-tilt to operate accurately in a more complex sea condition environment, further improving the stability of pan-tilt control.

[0122] Fuzzy PID takes the error between the target value and the output value and the change rate of the error as the inputs of the fuzzy controller. The fuzzy controller first performs fuzzy processing on the inputs, then conducts fuzzy reasoning, and finally defuzzifies the result of the fuzzy reasoning to output the three parameters Kp, Ki, and Kd of the PID controller, thereby achieving the effect of self-adaptive tuning of the PID controller parameters.

[0123] The present invention sets the inputs as the error E and the error change rate EC, and the output as ΔK used to correct the PID parameters P 、ΔK I 、ΔK D , and these three parameters change along with the dynamic changes of the system, causing the PID parameters to keep changing. The universes of discourse of the input parameters and output parameters are both set as (-200, 200). The output is divided into seven levels, and the fuzzy subsets corresponding to the universes of discourse of the variables are respectively {NB, NM, NS, Z, PS, PM, PB}. Among them, the membership function of the input variable is selected as Gaussian type, and the membership function of the output variable is selected as triangular type. The parameter change diagram of the fuzzy controller designed in this example is as shown in Figure 2 . And a simulation model of the position fuzzy control PID brushless DC motor is established as shown in Figure 3 . And the results of the traditional PID and fuzzy PID are compared, and the comparison diagram is as shown in Figure 4 . The fuzzy position control system of the brushless DC motor that can be built relying on theoretical knowledge is as shown in Figure 5 , and the trapezoidal speed method is adopted for motor position control, and the speed change diagram is as shown in Figure 6 .

[0124] 3. Due to the complex sea wave environment, the simple traditional pan-tilt control method has poor control effect on the stability of the pan-tilt in the sea wave environment. In the state of stable sea surface environment, the pan-tilt can rely on the attitude information obtained by the attitude sensor to perform attitude compensation, so as to reach a stable state. However, when the sea surface environment is extreme and the sea state level is relatively high, the compensation speed of the pan-tilt cannot meet the stability requirements. Therefore, the present invention proposes to perform attitude prediction on the body of the marine pan-tilt, so as to meet the requirement of synchronous compensation with the sea waves. In this example, the motion of the six degrees of freedom of the pan-tilt carrier is established, and the definition is as Figure 7 。

[0125] Predictive control is a control theory that has gradually developed in the process of industrial practice. Due to the uniqueness of its own control method, this control method has relatively low requirements for the control model, good control effect and great advantages in online calculation. When the three-axis pan-tilt cannot achieve self-stabilization under complex sea conditions, predictive control can give priority to predicting the sway state of the pan-tilt body and maintain the stability of the camera to the greatest extent.

[0126] The motion of the pan-tilt carrier in the waves can be described by the motion of a rigid body with six degrees of freedom in space. Since the sea waves are irregular random motions, the motion of the pan-tilt carrier on the sea surface is also complex and irregular. For the motion of the carrier with six degrees of freedom, it can be divided into two groups in the study. One group is the longitudinal motion of the carrier, including surge, heave and pitch, and the other group is the lateral motion, including roll, sway and yaw. During the motion of the carrier, there are strong coupling relationships among various motions, while there is no coupling relationship between the oscillatory motions of different groups. When studying the influence of sea waves on the motion of the carrier, the influence of sway, surge, yaw and heave on the resulting inclination is the smallest, while the influence of roll and pitch on the stability of the carrier is the greatest. Since there is no coupling relationship between roll and pitch, this article will study the roll motion, and the analysis method of pitch motion is similar to that of roll motion. At this time, it can be considered that the motion of the carrier is a linear motion system, with its input quantity being the wave inclination angle α(t) and the output quantity being the roll angle θ(t) of the carrier.

[0127] Analyze the roll motion of the carrier from a mechanical perspective, and analyze the force condition with a certain instantaneous position of the carrier on the sea surface, as Figure 8 shown. Assume that the center of gravity G is on the horizontal plane to establish a mathematical model. At this time, the carrier has a roll angle θ, its angular velocity and angular acceleration According to Newton's second law, the differential equation of the roll motion of the carrier can be obtained as:

[0128]

[0129] Simulate the wave roll angle and the roll angle of the unmanned ship. The simulation results are as Figure 9 。

[0130] Next, the AR prediction model is introduced. After the prediction of sea waves is realized, it is possible to predict the inclination angle of the pan-tilt carrier through the transmission relationship between the carrier and the sea wave inclination angle, and realize the pre-stabilization control of the pan-tilt.

[0131] After determining the model prediction order in this example, prediction simulation comparison is carried out. The longitudinal inclination angle comparison of the unmanned ship is as Figure 10 . Specifically, the pan-tilt controller is designed as Figure 11 , and for the software design process, as Figure 12 .

Claims

1. A design method for the control scheme of the pan-tilt on an unmanned surface vessel, characterized in that, It includes the following steps: Step 1: Set the gimbal of the unmanned surface vessel as a three-axis stable gimbal, and its brushless DC motor control method is the vector control FOC algorithm; Step 2: Add fuzzy PID control to the three closed loops of the FOC algorithm; Step 2-1: Calculate the PI gain parameters using the Laplace domain. The open-loop gain of the current regulator is: Where K p-ACR-d / q represents the current loop proportionality coefficient, and K I-ACR represents the current loop integral coefficient, L d / q represents the transformed stator flux linkage, ω B-ACR represents the motor angular velocity, and R s represents the stator resistance of the brushless DC motor; Let K P / K I = R s / L s , then according to the open-loop amplitude-frequency characteristic, the PI parameters are obtained as follows: where L s represents the stator flux linkage of the motor, K P represents the required proportionality coefficient, K I represents the required integral coefficient, K P-ACR represents the required proportionality coefficient of the current loop, and T represents the control period; The setting method of the PI parameters in the Laplace domain of the speed loop is: where F represents the drag coefficient, J represents the moment of inertia, and ω B-ASR represents the passband width of the speed regulator, with the unit of rad / s; Step 2-2: The fuzzy PID takes the error between the target value and the output value and the change rate of the error as the inputs of the fuzzy controller. The fuzzy controller first performs fuzzy processing on the inputs, then conducts fuzzy inference, and finally defuzzifies the result of the fuzzy inference to output the three parameters K P , K I , K D of the PID controller, thereby realizing the adaptive tuning of the parameters of the PID controller; Step 3: Predict the attitude of the gimbal body of the unmanned surface vessel to achieve synchronous compensation with the waves; The motion of the gimbal carrier, i.e., the unmanned surface vessel, in the waves is described by the motion of a rigid body in six degrees of freedom in space; the longitudinal motion of the carrier includes surge, heave, and pitch, and the lateral motion of the carrier includes roll, sway, and yaw; the influence of roll and pitch on the carrier is much greater than that of sway, surge, yaw, and heave motions. Therefore, sway, surge, yaw, and heave motions are ignored; Step 3-1: In the rolling motion, the motion of the vehicle is a linear motion system, with the input being the wave tilt angle α(t) and the output being the vehicle roll angle θ(t); there exist the roll angle θ of the vehicle and its angular velocity and angular acceleration According to Newton's second law, the differential equation of the vehicle rolling motion is obtained as follows: Where, J θ represents the moment of inertia of the pan-tilt carrier about the Gx axis, and ΔJ θ represents the additional rolling moment of inertia of the fluid mass caused by the carrier's motion angular velocity about the Gx axis. D is the displacement of the carrier, h is the height of the transverse stability center of the pan-tilt carrier, N θ is the rolling damping coefficient, and M θ is the rolling disturbing moment, which is related to the motion of random waves; By analogy with the roll motion of a ship on the waves, estimate: where ρ θ is the radius of gyration about roll, and its value is proportional to the size of the carrier; Since the roll moment of the carrier is composed of the restoring disturbing moment and damping disturbing moment generated by the waves and the inertial disturbing moment generated by the added mass of the carrier, it is expressed as: In the formula, α represents the wave inclination angle in the roll motion; Let ω θ be the natural angular frequency of the carrier roll, be the roll damping factor, and its value is Then Perform Laplace transform on Equation (8) to obtain the transfer function of the carrier's roll motion: Set the correction parameter of the wave inclination angle as σ, σ < 1, and the roll wave inclination angle of the waves on the carrier is expressed as: α0 = σα (10) The mathematical model of the wave inclination angle at a fixed point in space is expressed as: where: ω n is the frequency of the wave angle, ε n is the initial phase randomly distributed between 0 and 2π, S α (ω) is the angular spectrum of the wave frequency; then the wave tilt angle of the wave on the carrier is: For pitch, the analysis result is obtained using the same method as the above roll; Step 3-2: Achieve the prediction of the gimbal carrier inclination angle through the transfer relationship between the carrier and the wave inclination angle, and achieve the pre-stabilization control of the gimbal; The established prediction model is a stationary time prediction model, namely the AR prediction model. First, predict the roll angle. For n sample sea wave roll angles X1, X2, … X n data, establish an autoregressive model: where m represents the order of the autoregressive model, a i represents the autoregressive coefficient, and ε t represents the model correction value; If an AR model is to be used for fitting statistical prediction with n data, first determine the order of the model; the covariance matrix of the known sample X is: The relationship between the covariance matrix and the autoregressive model parameters is: Determine the order of the model through the K-L information amount and its AIC criterion, where the K-L information amount is defined as: The AIC criterion is: When the K-L information amount and the AIC criterion are the smallest, the order of the prediction model is determined; After that, calculate the model variance for the estimated autoregressive coefficients, and add the prediction model to the gimbal controller after the variance meets the preset conditions.

2. A design method for the pan-tilt control scheme of a surface unmanned ship, characterized in that, In the fuzzy PID control, the set inputs are the error E and the error change rate EC, and the output is ΔK used to correct the PID parameters P , ΔK I , ΔK D ; The universes of discourse of the set input parameters and output parameters are both (-200, 200). The output is divided into seven levels. The fuzzy subsets corresponding to the universes of discourse of the variables are {NB, NM, NS, Z, PS, PM, PB} respectively. The subset elements represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large respectively. The membership function of the input variable is selected as Gaussian type, and the membership function of the output variable is selected as triangular type.

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