Control parameter adaptive method based on hybrid inertial navigation system
By combining PID control and fuzzy control and adaptively adjusting the control parameters, the problem of poor adaptability of traditional PID control in hybrid inertial navigation systems is solved, and the rotation modulation effect and control accuracy are improved, especially in complex environments.
Patent Information
- Application Number
- CN202411394712.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-08
- Publication Date
- 2024-12-27
Smart Images
Figure CN119200409A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the field of navigation, guidance and control, and in particular relates to a control parameter self-adaptation method based on a hybrid inertial navigation system. Background Art
[0002] As an inertial navigation system, the hybrid inertial navigation system adopts rotation modulation technology, uses gyro output information as feedback signal, drives the rotation mechanism, and makes the inertial measurement unit body rotate stably relative to the geographic coordinate system, thereby effectively isolating external angular motion interference and greatly improving the navigation accuracy of the system.
[0003] In order to prevent the accumulation of errors such as gyro scale factor, a combined forward and reverse rotation scheme is generally adopted. In order to ensure the reliability of the system, PID control is often selected in actual projects. However, this control method has low control accuracy at the moment of forward and reverse switching. At the same time, when faced with complex environments such as high dynamics, high and low temperatures, the control effect is often unsatisfactory, which greatly reduces the rotation modulation effect.
[0004] The traditional PID control method has the problem of poor adaptability. The three established PID parameters are often adjusted based on a specific environment. When environmental parameters such as temperature, external vibration amplitude, and vibration frequency change, the control stability and speed will be greatly reduced, which will seriously affect the modulation effect. The traditional PID control method will have the disadvantages of slow adjustment speed and large overshoot during the forward and reverse switching process, which will also seriously affect the overall rotation modulation effect. Summary of the invention
[0005] In order to solve the above technical problems, the present invention provides a control parameter adaptation method based on a hybrid inertial navigation system, which can effectively improve the control accuracy of the entire rotation process and improve the rotation modulation effect in complex environments.
[0006] In order to achieve the above object, the present invention adopts the following technical solution: A control parameter adaptive method based on a hybrid inertial navigation system comprises the following steps: Step 1. Collect the internal temperature sensor data of the hybrid inertial navigation system and give the initial control value; Step 2. Collect data from the inertial measurement unit and the grating, perform attitude calculation, and calculate the position deviation and position deviation change rate; Step 3. Position deviation and position deviation change rate Perform linear fuzzification; Step 4. Fuzzy position deviation and position deviation change rate Perform fuzzy segmentation; Step 5. Formulate the control parameters P, I, D for one-way continuous rotation and the control parameters for the U-turn stage The setting rules of Step 6. When the rotation is continuous in one direction, the weighted average method is used for defuzzification judgment; when the rotation is U-turn, the center of gravity method is used for defuzzification judgment; Step 7. Calculate the PWM value and assign it to the motor for control.
[0007] Beneficial effects: The present invention is based on PID control and fuzzy control. It adopts PID control with adaptive function in unidirectional uniform rotation. When the external environment changes, the control method can adjust the control parameters in time to keep the control effect in a relatively stable state. At the same time, fuzzy control is used in the forward and reverse switching process to avoid the overshoot problem and long adjustment time of PID control, and at the same time avoid the problems of loss of control and excessive control force that may exist in open-loop control. BRIEF DESCRIPTION OF THE DRAWINGS
[0008] Figure 1 A flow chart of a control parameter adaptive method based on a hybrid inertial navigation system of the present invention; Figure 2 is the position deviation Membership function diagram of . DETAILED DESCRIPTION
[0009] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.
[0010] In order to meet the stability required by the motor in continuous forward and reverse control and ensure the high-precision requirements of the motor angular position at the moment of turning, the controller is based on PID control and fuzzy control. When the motor rotates at a constant speed in one direction, in order to ensure stability, PID control with adaptive function is adopted, that is, the three parameters P, I, and D are adjusted in real time and the control voltage is calculated, and then converted into PWM value for motor control. When the rotation direction is about to switch, in order to increase the adjustment speed, fuzzy control is adopted, that is, the control voltage value is directly assigned within a certain number of control cycles and converted into the corresponding PWM value, so as to directly control the motor, and then change back to PID control with adaptive function. Based on prior engineering experience, different initial values of P, I, and D parameters and initial values of voltage under fuzzy control are set at different temperatures. In the two different processes of forward rotation to reverse rotation and reverse rotation to forward rotation, the initial values of voltage under fuzzy control are also different.
[0011] In summary, the full closed-loop control can sense the current external maneuvering situation and avoid loss of control. At the same time, the introduction of the adaptive link enables the control parameters to be automatically adjusted in time when facing complex environments to adapt to different temperatures, different maneuvers, etc.
[0012] like Figure 1 As shown, a control parameter adaptive method based on a hybrid inertial navigation system of the present invention comprises the following steps: Step 1. Collect the internal temperature sensor data of the hybrid inertial navigation system and give the initial control value, including: the built-in temperature sensor of the hybrid inertial navigation system is used to collect the real-time temperature inside the shell of the hybrid inertial navigation system. According to prior engineering experience, it can be divided into three conditions: low temperature, medium temperature and high temperature. The initial control value is given according to different conditions.
[0013] ; in, are the three parameters P, I, and D in adaptive PID control and the initial value of voltage U in fuzzy control. Indicates the internal temperature of the shell collected by the sensor. is the initial value set at low temperature based on prior engineering experience, where: Indicates the total The specific number of the initial voltage values of each control period can be selected according to the control frequency and the actual motor characteristics. The value of the embodiment of the present invention is 4. and Similarly, initial values are set for medium and high temperatures based on prior experience.
[0014] Step 2. Collect data from the inertial measurement unit and grating, perform attitude calculation, and calculate deviations, including: According to the target position, actual position and motor control cycle, the current position deviation and position deviation change rate are calculated.
[0015] ; in, is the target position of the inertial measurement unit body in the current control cycle calculated by the hybrid inertial navigation system, Indicates the actual position of the inertial measurement unit during the current control cycle obtained by grating measurement. are the position deviations of the current control cycle and the previous control cycle respectively, is the motor control cycle, It is the position deviation change rate of the current control cycle.
[0016] Step 3. Position deviation and position deviation change rate Linear fuzzification is performed to obtain the position deviation For example, let the actual range of variation be , the fuzzified domain is , then the quantization factor k is: ; in, is the quantization factor of the position deviation, and the value in the embodiment of the present invention is The linear fuzzification process can be expressed as: ; in, is the position deviation after fuzzification. Similarly, the change rate of the position deviation after fuzzification is: ; in, is the rate of change of position deviation after fuzzification, They respectively represent the actual range of change of the position deviation change rate and the maximum value of the domain after fuzzification.
[0017] Step 4. Perform fuzzy segmentation, including: According to prior engineering experience, the number of fuzzy subsets is 7, and the symbol is expressed as , respectively representing negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. Using linear non-uniform fuzzy segmentation, taking position deviation as an example, the membership function is as follows Figure 2 shown.
[0018] Step 5. Formulate tuning rules, including: Based on prior engineering experience, the effect of each parameter on control regulation in adaptive PID control and fuzzy control can be obtained. Based on this, the tuning rules of each parameter are determined, where parameter tuning refers to adjusting the parameter size to achieve the best control effect of the control loop.
[0019] When the value of whether the rotation direction is switched is No, it indicates that the current rotation is unidirectional and continuous, and adaptive PID control is adopted, such as Figure 2 As shown, taking parameter P as an example, the setting rules are shown in Table 1. In Table 1, Indicates position deviation, Indicates the rate of change of position deviation. It represents the 7 levels after fuzzy segmentation of the two, namely negative large, negative medium, negative small, zero, positive small, positive medium, and positive large.
[0020] Table 1
[0021] When the judgment of whether the rotation direction is switched to yes, it means that a U-turn is about to be made, and fuzzy control is adopted, and the voltage parameter The setting rules are shown in Table 2: Table 2
[0022] Step 6. Perform defuzzification judgment, including: when judging whether the rotation direction is switched to yes, it means that a U-turn is about to be made, fuzzy control is adopted, fuzzy reasoning adopts the Mamdani method, and the defuzzification operation is the center of gravity method. When judging whether the rotation direction is switched to no, it means that the current rotation is unidirectional and continuous, adaptive PID control is adopted, fuzzy reasoning adopts the zero-order TS type, and the defuzzification operation is the weighted average method. Taking parameter P as an example, its output is: ; in, represents the final output under P parameter adaptation, Indicates The output of the rule, is the weight of the rule in the total output, and n is the total number of rules.
[0023] The remaining parameters are output as follows: ; The definition is similar to above.
[0024] Step 7. Calculate the PWM value, including: When judging whether the rotation direction is switched to yes, it means that a U-turn is about to be made, and fuzzy control is adopted. The final real-time control parameters are: ; in, Indicates the final output voltage value, Indicates the initial value assigned according to the real-time temperature voltage during the fuzzy control process. Indicates the voltage change value calculated by fuzzy control. At this time, the voltage value can be directly converted into PWM to control the motor.
[0025] When the value of whether the rotation direction is switched is “No”, it means that the current rotation is one-way continuous. Adaptive PID control is adopted, and the final real-time control parameters are: ; According to the PID control method, the corresponding control voltage is calculated based on the real-time position deviation, converted into a PWM value, and assigned to the motor for control.
[0026] In order to verify the superiority of the present invention, the following simulation experiment is carried out using the Simulink toolbox.
[0027] In the simulation experiment, a sinusoidal maneuver is selected, which is expressed as follows: ; in, represents the maneuvering angular velocity at time t, A is the maneuvering amplitude, T is the maneuvering period, For maneuvering noise, zero-mean Gaussian white noise is selected here to make the simulation situation closer to reality. Adjust the setting parameters to make multiple groups of simulation comparisons, and the results are shown in Table 3 (Comparison table of characteristics of different control methods).
[0028] Each row of the table is an independent experiment. The first three columns show the specific parameters of the external sinusoidal maneuvering disturbance of each group of experiments. Columns 4 and 5 are the control error peaks during unidirectional continuous rotation under the application of the traditional PID control method and the control method of the present invention. Columns 6 and 7 are the control error peaks during the rotation U-turn under the application of the traditional PID control method and the control method of the present invention. Columns 8 and 9 are the adjustment time during the rotation U-turn under the application of the traditional PID control method and the control method of the present invention.
[0029] Table 3
[0030] It can be seen from Table 3 that the present invention has obvious advantages in both the smooth rotation section and the U-turn section. Compared with the traditional control method, it has a smaller overshoot and a faster adjustment speed, and the parameter self-adjustment function has a significant effect.
Claims
1. A control parameter adaptive method based on a hybrid inertial navigation system, characterized in that: The steps include: Step 1. Collect the internal temperature sensor data of the hybrid inertial navigation system and give the initial control value; Step 2. Collect data from the inertial measurement unit and the grating, perform attitude calculation, and calculate the position deviation and position deviation change rate; Step 3. Position deviation and position deviation change rate Perform linear fuzzification; Step 4. Fuzzy position deviation and position deviation change rate Perform fuzzy segmentation; Step 5. Formulate the control parameters P, I, D for one-way continuous rotation and the control parameters for the U-turn stage The setting rules of Step 6. When the rotation is continuous in one direction, the weighted average method is used for defuzzification judgment; when the rotation is U-turn, the center of gravity method is used for defuzzification judgment; Step 7. Calculate the PWM value and assign it to the motor for control.
2. The control parameter adaptive method based on a hybrid inertial navigation system according to claim 1, characterized in that: The step 1 includes: using the built-in temperature sensor of the hybrid inertial navigation system to collect the real-time temperature inside the housing of the hybrid inertial navigation system, and according to prior engineering experience, the temperature is divided into three conditions: low temperature, medium temperature, and high temperature, and the initial control value is given according to different conditions: ； in, are the three parameters P, I, and D in adaptive PID control and the initial value of voltage U in fuzzy control. Indicates the internal temperature of the shell collected by the sensor; is the initial value set at low temperature based on prior engineering experience, Indicates the total The specific number of initial voltage values for each control cycle can be selected based on the control frequency and actual motor characteristics. and are the initial values set at medium and high temperatures based on prior experience.
3. The control parameter adaptive method based on a hybrid inertial navigation system according to claim 1, characterized in that: The step 2 comprises: According to the target position, actual position and motor control cycle, calculate the current position deviation and position deviation change rate: ； in, is the target position of the inertial measurement unit body in the current control cycle calculated by the hybrid inertial navigation system, Indicates the actual position of the inertial measurement unit during the current control cycle obtained by grating measurement. are the position deviations of the current control cycle and the previous control cycle respectively, is the motor control cycle, It is the position deviation change rate of the current control cycle.
4. The control parameter adaptive method based on a hybrid inertial navigation system according to claim 1, characterized in that: The step 3 comprises: for the position deviation , assuming that its actual range of variation is , the fuzzified domain is , then the quantization factor k is: ； in, is the quantization factor of the position deviation; The linear fuzzification process is expressed as: ； in, is the position deviation after fuzzification, and the change rate of the position deviation after fuzzification is: ； in, is the rate of change of position deviation after fuzzification, They respectively represent the actual range of change of the position deviation change rate and the maximum value of the domain after fuzzification.
5. The control parameter adaptive method based on a hybrid inertial navigation system according to claim 1, characterized in that: The step 4 comprises: According to prior engineering experience, the number of fuzzy subsets is 7, and the symbol is expressed as , respectively representing negative large, negative medium, negative small, zero, positive small, positive medium, and positive large; linear non-uniform fuzzy segmentation is adopted.
6. The control parameter adaptive method based on a hybrid inertial navigation system according to claim 1, characterized in that: The step 5 includes: obtaining the effect of each parameter on the control regulation in the adaptive PID control and fuzzy control according to prior engineering experience; based on this, determining the setting rules of each parameter, wherein the setting of the parameter refers to adjusting the size of the parameter so that the control loop achieves the best control effect.
7. The control parameter adaptive method based on a hybrid inertial navigation system according to claim 6, characterized in that: When it is judged whether the rotation direction is switched to no, it means that the current rotation is unidirectional continuous, and adaptive PID control is used to obtain the setting rules of parameters P, I, and D; when it is judged whether the rotation direction is switched to yes, it means that a U-turn is about to be made, and fuzzy control is used to obtain the voltage parameters The setting rules.
8. The control parameter adaptive method based on a hybrid inertial navigation system according to claim 7, characterized in that: The step 6 includes: when judging whether the rotation direction is switched to yes, it means that a U-turn is about to be made, fuzzy control is adopted, fuzzy reasoning adopts Mamdani method, and defuzzification operation is centroid method; when judging whether the rotation direction is switched to no, it means that the current rotation is unidirectional continuous, adaptive PID control is adopted, fuzzy reasoning adopts zero-order TS type, and defuzzification operation is weighted average method; the output of parameter P is: ； in, represents the final output under P parameter adaptation, Indicates The output of the rule, is the weight of its proportion in the total output, and n is the total number of rules; The remaining parameters are output as follows: 。 9. The control parameter adaptive method based on a hybrid inertial navigation system according to claim 1, characterized in that: The step 7 includes: when judging whether the rotation direction is switched to yes, it means that a U-turn is about to be made, fuzzy control is adopted, and the final real-time control parameters are: ； in, Indicates the final output voltage value, Indicates the initial value assigned according to the real-time temperature and voltage during the fuzzy control process. Indicates the voltage change value calculated by fuzzy control; at this time, the voltage value is directly converted into PWM for motor control; When the value of whether the rotation direction is switched is “No”, it means that the current rotation is one-way continuous. Adaptive PID control is adopted, and the final real-time control parameters are: ； According to the PID control method, the corresponding control voltage is calculated based on the real-time position deviation, converted into a PWM value, and assigned to the motor for control.