Multi-constraint-condition automatic solving method based on error transfer function
By constructing a simulation model of the error transfer function, calculating the phase margin and gain margin, and combining it with MATLAB solution, the problem of optoelectronic tracking device tracking accuracy relying on empirical design is solved, and high tracking accuracy and design efficiency are achieved.
Patent Information
- Application Number
- CN202411958463.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-30
- Publication Date
- 2025-09-12
AI Technical Summary
The tracking accuracy design of existing optoelectronic search and tracking devices relies on empirical methods, which makes design iteration difficult and lacks high-precision automatic solution methods for multiple constraints.
By constructing a simulation model based on the error transfer function, the phase margin and gain margin are calculated using the pattern sampling time, delay time and velocity loop bandwidth, and the solution is performed in combination with MATLAB to solve the constraints while meeting the tracking accuracy requirements.
It achieves rapid iterative design, improves the tracking accuracy of the optoelectronic search and tracking device, avoids invalid design, and improves design efficiency.
Smart Images
Figure CN120633124A_ABST
Abstract
Description
Technical field:
[0001] The present invention relates to the field of tracking control technology for photoelectric tracking devices, and in particular to a method for automatically solving multiple constraints based on an error transfer function. Background technology:
[0002] Currently, tracking accuracy is a key technical specification for optoelectronic search and tracking systems, impacting their range and angle measurement, as well as the user experience. Currently, achieving tracking accuracy relies primarily on empirical design. This involves empirically determining the requirements for optoelectronic search and tracking subsystems, and then performing simulation verification based on the subsystem design. This is a verification-based design approach. If accuracy fails to meet requirements, the subsystem design must be completely redesigned, hindering rapid design iteration.
[0003] There is an urgent need for an automatic solution method for multiple constraints based on an error transfer function, which can help solve the technical problem in the existing technology of lacking an automatic solution method for multiple constraints with high accuracy and application of simulation. Summary of the invention:
[0004] In one embodiment, the present invention provides a method for automatically solving multiple constraints based on an error transfer function. A simulation model is constructed using pattern sampling time, delay time, and velocity loop bandwidth. Tracking accuracy is calculated using phase margin and gain margin. Constraints are ultimately solved while meeting tracking accuracy requirements.
[0005] The method for automatically solving multiple constraints based on the error transfer function includes:
[0006] The simulation model is constructed based on the pattern sampling time and delay time, as well as the speed loop bandwidth, where t s is the image sampling time, t d is the delay time, ω b is the speed band, and the data expression of the simulation model is
[0007]
[0008] H(S)=1 (4)
[0009] Among them, Kp is the controller proportional coefficient, Ki is the controller integral coefficient, and ξ is the speed loop damping;
[0010] According to G c (s)*G o (s) Calculate phase margin and gain margin;
[0011] Determine whether the phase margin and the gain margin meet the predetermined threshold range. If so, calculate the tracking accuracy based on the known ω and α. The tracking accuracy calculation formula is:
[0012]
[0013] Among them, err trk For tracking accuracy;
[0014] If the tracking accuracy is less than the maximum tracking error, the above constraints are used as solution conditions.
[0015] In one embodiment, the predetermined threshold range is that the phase margin should be greater than 50 degrees and the gain margin should be greater than 10 db.
[0016] In one embodiment, before the step of constructing a simulation model based on the pattern sampling time and delay time, and the speed loop bandwidth, the method further includes:
[0017] The pattern sampling time, the delay time, and the speed loop bandwidth are calculated in sequence by setting the initial value and the step size, and each step size is used for calculation in subsequent steps.
[0018] In one embodiment, the pattern sampling time, the delay time, and the speed loop bandwidth are step-by-step calculated one by one.
[0019] In one embodiment, the controller proportional coefficient and the controller integral coefficient are solved sequentially in a step-by-step manner.
[0020] In one embodiment, the simulation model is equivalent to a second-order link connected in series with an integral link.
[0021] In one embodiment, the tracking closed loop uses a deflection measurement as its control input. The deflection measurement is generated by the deviation between the target and the current direction of the optical axis. Since the target angle is difficult to obtain directly, a visual sensor and an image tracking algorithm are used to directly obtain the deflection measurement.
[0022] In one embodiment, the acquisition of the deviation measurement value requires collection and transmission by the visual sensor and calculation by the image control board, which causes a delay.
[0023] In one embodiment, the controller is a PID controller.
[0024] In one embodiment, MATLAB is used to solve the stability margin. Description of the drawings:
[0025] Figure 1 Schematic diagram of a second-order link connected in series with an integral link in one embodiment of the present invention;
[0026] Figure 2 The figure is a flow chart of a method for automatically solving multiple constraints based on an error transfer function in one embodiment of the present invention. Specific embodiment:
[0027] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.
[0028] Various aspects and features of the present application are described herein with reference to the accompanying drawings.
[0029] These and other characteristics of the present application will become apparent from the following description of a preferred form of embodiment given as a non-limiting example with reference to the accompanying drawings.
[0030] It should also be understood that although the present application has been described with reference to certain specific examples, those skilled in the art will be able to implement many other equivalent forms of the present application that have the features described in the claims and are therefore within the scope of protection defined thereby.
[0031] The above and other aspects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.
[0032] Specific embodiments of the present application will be described below with reference to the accompanying drawings; however, it should be understood that the embodiments described are merely examples of the present application and may be implemented in a variety of ways. Familiar and / or repetitive functions and structures are not described in detail to clarify the true intent based on the user's historical operations and to avoid obscuring the present application with unnecessary or redundant details. Therefore, the specific structural and functional details described herein are not intended to be limiting, but rather serve merely as a basis and representative basis for the claims to teach those skilled in the art to use the present application in a variety of ways with substantially any appropriate detailed structure.
[0033] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," which may all refer to one or more of the same or different embodiments according to the present application.
[0034] In order to avoid ineffective design and improve design efficiency, it is necessary to impose necessary constraints on the design or selection of subsystems, that is, to carry out forward design.
[0035] The present invention clarifies the constraints and tracking control architecture that affect tracking accuracy, and designs a tracking control forward design method that automatically solves multiple constraints. The constraints of the search and tracking instrument subsystem can be calculated according to the tracking accuracy requirements.
[0036] Figure 1 Schematic diagram of a second-order link connected in series with an integral link in one embodiment of the present invention; Figure 2 The figure is a flow chart of a method for automatically solving multiple constraints based on an error transfer function in one embodiment of the present invention.
[0037] like Figure 1 and Figure 2 As shown, in one embodiment, the present invention provides a method for automatically solving multiple constraints based on an error transfer function, the method comprising:
[0038] The simulation model is constructed based on the pattern sampling time and delay time, as well as the speed loop bandwidth, where t s is the image sampling time, t d is the delay time, ω b is the speed band, and the data expression of the simulation model is
[0039]
[0040] H(S)=1 (4)
[0041] Among them, Kp is the controller proportional coefficient, Ki is the controller integral coefficient, and ξ is the speed loop damping;
[0042] According to G c (s)*G o (s) Calculate phase margin and gain margin;
[0043] Determine whether the phase margin and the gain margin meet the predetermined threshold range. If so, calculate the tracking accuracy based on the known ω and α. Use MATLAB to solve according to the following formula. The tracking accuracy calculation formula is:
[0044]
[0045] Among them, err trk For tracking accuracy;
[0046] If the tracking accuracy is less than the maximum tracking error, the above constraints are used as solution conditions.
[0047] The calculation principle of tracking error. According to the simulation model, the relationship between tracking error and given input is:
[0048] According to the above formula:
[0049]
[0050] The indicator generally limits the target to ω (rad / s) and acceleration to α (rad / s 2 ), and the corresponding maximum tracking error err max , the target input can be converted into a sinusoidal input according to the velocity and acceleration:
[0051]
[0052] When ω and α become known quantities, s is α / ω, and the specific value can be calculated. The above simulation model is a function with only s as the independent variable, in which the maximum and minimum values of the phase margin and the gain margin can be calculated according to the formula G c (s)*G o (s) itself can be calculated, similar to the maximum value calculation of a quadratic function. The remaining parameters are thus known and can be calculated using the above method. Finally, parameters such as the pattern sampling time, delay time, and speed loop bandwidth are added as constraints to the system to complete the solution and be used for subsequent calculations.
[0053] In one embodiment, the predetermined threshold range is that the phase margin should be greater than 50 degrees and the gain margin should be greater than 10 db.
[0054] In one embodiment, before the step of constructing a simulation model based on the pattern sampling time and delay time, and the speed loop bandwidth, the method further includes:
[0055] The pattern sampling time, the delay time, and the speed loop bandwidth are calculated in sequence by setting the initial value and the step size, and each step size is used for calculation in subsequent steps.
[0056] The pattern sampling time and the like are formed into an array according to the step size for subsequent calculation.
[0057] In one embodiment, the pattern sampling time, the delay time, and the speed loop bandwidth are step-by-step calculated one by one.
[0058] Solving one by one will result in different delay times at each pattern sampling time and the speed loop bandwidth at different delay times.
[0059] In one embodiment, the controller proportional coefficient and the controller integral coefficient are solved in sequence in a step-by-step manner. Each time a pattern sampling time and a delay time are formed, as well as a speed loop bandwidth, the calculations in the embodiment are performed.
[0060] In one embodiment, the simulation model is equivalent to a second-order link connected in series with an integral link.
[0061] In one embodiment, the tracking closed loop uses a deflection measurement as its control input. The deflection measurement is generated by the deviation between the target and the current direction of the optical axis. Since the target angle is difficult to obtain directly, a visual sensor and an image tracking algorithm are used to directly obtain the deflection measurement.
[0062] In one embodiment, the acquisition of the deviation measurement value requires collection and transmission by the visual sensor and calculation by the image control board, which causes a delay.
[0063] In one embodiment, the controller adopts a PID controller.
[0064] According to the tracking control principle, a tracking control error calculation model is established. The tracking closed-loop uses the deviation measurement as its input, and the output is the desired speed. The deviation measurement is generated by the deviation between the target and the current pointing of the optical axis. Since the target angle is difficult to obtain directly, a vision sensor and an image tracking algorithm are used to directly obtain the deviation measurement.
[0065] The acquisition of the deviation measurement needs to go through the acquisition and transmission of the vision sensor and the calculation of the image control board, resulting in a delay; the process from the given speed to the pointing of the optical axis can be equivalent to a second-order link in series with an integral link; the controller adopts a PID controller.
[0066] In one embodiment, multi-constraint automatic solution is carried out to calculate the constraint conditions that meet the index requirements. For each constraint, the maximum range that can be achieved by the current technical level is taken, and matlab is used to solve the stability margin.
[0067] Specifically, in step 1, the angular velocity of the tracking target is set as ω, and the angular acceleration is set as α; the tracking target input is r max = ω 2 / α.
[0068] In step 2, constraint conditions are set. The constraint conditions include the pattern sampling time t s , the delay time t d , the speed loop bandwidth ω b . The initial values of the pattern sampling time, the delay time, and the speed loop bandwidth are set as t s0 , td0, ωb0 respectively. The maximum values of the initial values of the pattern sampling time, the delay time, and the speed loop bandwidth are tsmax, tdmax, ωbmax respectively. The constraint growth steps of the pattern sampling time, the delay time, and the speed loop bandwidth are Δt s , Δt d , Δω b respectively; the initial value of the controller proportional coefficient is Kp, the maximum value is Kpmax, the initial value of the integral coefficient is Ki, and the maximum value is Kimax.
[0069] In step 3, if the pattern sampling time ts < tsmax, the pattern sampling time ts is incremented by Δts, otherwise, the automatic solution ends.
[0070] In step 4, if the delay time td < tdmax, the delay time td is incremented by Δtd; otherwise, steps 3-4 are repeated; the sampling time ts and the delay time td are independent variables and have no relation. Repeating step 3 has no impact on step 4, and it can be considered that step 3 is executed again from the beginning.
[0071] Step 5: If the speed loop bandwidth ωb < ωbmax, solve and increase ωb by Δωb0; otherwise repeat steps 4-5; there is no relationship between the speed loop bandwidth ωb and the speed loop damping ξ, and there is no relationship between the speed loop bandwidth ωb and the delay time td, both of which are independent variables. Step 4 has no effect on step 5, and it can be considered that the execution starts from step 4 again. In addition, Formula 2 needs to be supplemented with the tracking open-loop transfer function, s represents the frequency variable, has no specific value, represents the natural frequency, and s and wn are not known, but are calculated based on wb, which is one of the set constraints.
[0072] Step 6: If the controller proportional coefficient Kp is less than Kpmax, Kp is incremented by 1; otherwise, repeat steps 5-6. Setting Kpmax was not mentioned previously, but this has been explained in step 2. In addition, there is no relationship between the controller proportional coefficient and step 5.
[0073] Step 7: If the controller integral coefficient Ki is less than Kimax, Ki is incremented by 1; otherwise, repeat steps 6-7. The setting of Kimax was not mentioned previously, but has been explained in step 2. In addition, there is no relationship between the controller integral coefficient Ki and step 6.
[0074] Step 8: Calculate the phase margin and gain margin, including calculating the error transfer coefficient at the corresponding frequency.
[0075] The specific calculation formula is:
[0076] Where H(s) represents the feedback transfer function H(s) = 1, represents the controller transfer function X, G C (s) = K P +K I / s
[0077] Step 9, if G c (s)*G o The phase margin of (s) should be greater than 50 degrees and the gain margin>10db, then calculate the tracking error err trk =; otherwise repeat steps 6-9.
[0078] Step 10: If the tracking error err trk <err max , record the constraints and repeat steps 6-10.
[0079] Step 11: Output all the constraints that meet the requirements and select the constraints with the largest range as the design input of the optoelectronic tracking system subsystem.
[0080] Beneficial effects:
[0081] The present invention can calculate the limit requirements for image sampling time, delay time, and speed loop bandwidth according to the tracking accuracy of the photoelectric tracking device, limit the design input of the subsystem, avoid invalid design, and improve design efficiency.
[0082] Based on the above embodiment description:
[0083] Step 1: Index conversion, for speed ω (rad / s) and acceleration α (rad / s 2 ) target, the input amplitude is: r max =ω 2 / α.
[0084] Step 2: Set the initial value of the constraint condition t s0 , t d0 、ω b0 , maximum value t smax , t dmax 、ω bmax , constrained growth step Δt s , Δt d , Δω b .
[0085] Step 3: If t s <t smax , t s Self-increment Δt s ; Otherwise, the solution ends.
[0086] Step 4: If t d <t dmax , t d Self-increment Δt d ; Otherwise, repeat steps 3-4.
[0087] Step 5: If ω b <ω bmax , calculation formula (2), ω b Self-increasing Δω b0 ; Otherwise, repeat steps 4-5.
[0088] Step 6: If Kp < Kpmax, Kp is incremented by 1; otherwise, repeat steps 5-6.
[0089] Step 7: If Ki < Kimax, Ki is incremented by 1; otherwise, repeat steps 6-7.
[0090] Step 8. Calculate the stability margin and formula (5) to obtain the error transfer coefficient of the corresponding frequency
[0091] Step 9: If the stability margin meets the requirements, calculate the tracking error Otherwise repeat steps 6-9.
[0092] Step 10: If the tracking error err trk <err max , record the constraints and repeat steps 6-10.
[0093] Step 11: Output the constraints that meet the requirements.
[0094] Beneficial effects:
[0095] The present invention can calculate the limit requirements for image sampling time, delay time, and speed loop bandwidth according to the tracking accuracy of the photoelectric tracking device, limit the design input of the subsystem, avoid invalid design, and improve design efficiency.
[0096] The above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention. The scope of protection of the present invention is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present invention within the spirit and scope of protection of the present invention, and such modifications or equivalent substitutions shall also be deemed to fall within the scope of protection of the present invention.
Claims
1. A method for automatically solving multiple constraints based on error transfer function, characterized in that: The method for automatically solving multiple constraints based on the error transfer function includes: The simulation model is constructed based on the pattern sampling time and delay time, as well as the speed loop bandwidth, where t s is the image sampling time, t d is the delay time, ω b is the speed band, and the data expression of the simulation model is Among them, Kp is the controller proportional coefficient, Ki is the controller integral coefficient, and ξ is the speed loop damping; According to G c (s)*G o (s) Calculate phase margin and gain margin; Determine whether the phase margin and the gain margin meet the predetermined threshold range. If so, calculate the tracking accuracy based on the known ω and α. The tracking accuracy calculation formula is: Among them, err trk For tracking accuracy; If the tracking accuracy is less than the maximum tracking error, the above constraints are used as solution conditions.
2. The automatic solution method for multiple constraints based on error transfer function according to claim 1 is characterized in that: The predetermined threshold range is that the phase margin should be greater than 50 degrees and the gain margin should be greater than 10 db.
3. The automatic solution method for multiple constraints based on error transfer function according to claim 2, characterized in that: Before the step of constructing a simulation model according to the pattern sampling time and delay time, and the speed loop bandwidth, the method further includes: The pattern sampling time, the delay time, and the speed loop bandwidth are calculated in sequence by setting the initial value and the step size, and each step size is used for calculation in subsequent steps.
4. The automatic solution method for multiple constraints based on error transfer function according to claim 3 is characterized in that: The pattern sampling time, the delay time, and the speed loop bandwidth are step-by-step calculated one by one.
5. The automatic solution method for multiple constraints based on error transfer function according to claim 4 is characterized in that: The controller proportional coefficient and the controller integral coefficient are solved sequentially in a step-by-step manner.
6. The automatic solution method for multiple constraints based on error transfer function according to claim 5, characterized in that: The simulation model is equivalent to a second-order link connected in series with an integral link.
7. The automatic solution method for multiple constraints based on error transfer function according to claim 6, characterized in that: The tracking closed loop uses the deviation measurement as its control input. The deviation measurement is generated by the deviation between the target and the current direction of the optical axis. Since the target angle is difficult to obtain directly, the deviation measurement is directly obtained using a visual sensor and image tracking algorithm.
8. The automatic solution method for multiple constraints based on error transfer function according to claim 7, characterized in that: The acquisition of the deviation measurement requires the acquisition, transmission and calculation of the visual sensor and the image control board, which causes a delay.
9. The automatic solution method for multiple constraints based on error transfer function according to claim 8, characterized in that: The controller adopts PID controller.
10. The automatic solution method for multiple constraints based on error transfer function according to claim 9, characterized in that: Matlab is used to solve the stability margin.