Aircraft stability control system parameter optimization method based on value function

Abstract

A method for optimizing parameters of an aircraft stability control system based on a value function belongs to the technical field of flight control and comprises the following steps: acquiring basic control parameters according to dynamic coefficients of flight feature points of an aircraft; setting an expected stability control index of the feature point; calculating stable control performance under the current control parameters; constructing a value function about the stability control performance and the expected stability control index; designing an iteration mode of the control parameter group in the adjacent domain optimization process; and searching an optimal control parameter. Aiming at the problems that the flight feature point parameter design of the aircraft is limited by a plurality of stable control performance indexes, the indexes are difficult to meet in a balanced manner or the design margin of a single index is as large as possible, a value function about typical stable control performance and expected performance indexes is constructed; and searching a parameter combination which enables a value function to be larger in an adjacent domain of the current control parameter group, so as to obtain a better control parameter as a reference for next search, and determining an optimal control parameter group through multi-round iteration.

Description

Technical Field

[0001] The invention relates to a parameter optimization method of an aircraft stability control system based on a value function, and belongs to the technical field of aircraft flight control. Background Art

[0002] The design of the stability control system parameters of an aircraft refers to designing reasonable control structure parameters to make the aircraft respond to commands smoothly and quickly. The control structure parameters to be designed need to make the system time domain response meet the rapidity requirements and have sufficient stability margin in the frequency domain. However, under certain working conditions, it is difficult to take both rapidity requirements and stability requirements into account, which increases the difficulty of control parameter design.

[0003] In control engineering, PID controller is a commonly used control structure. There are usually empirical formula method, frequency domain analysis method and various optimization algorithms to design PID parameters. The empirical formula method manually adjusts the controller parameters through experience and trial and error. It requires a certain understanding of the system and requires multiple tests. The method is not very generalizable. The frequency domain analysis method performs frequency domain analysis on the system transfer function and uses tools such as root loci or bode diagrams to design parameters. However, this method is difficult to take into account rapidity indicators when designing. Various optimization algorithms such as the LQR parameter design method can optimize the performance of some control systems, but cannot accurately meet the expected performance indicators. Summary of the invention

[0004] The technical problem solved by the present invention is: to overcome the shortcomings of the prior art and provide a method for optimizing the parameters of an aircraft stability control system based on a value function. In the design process of an aircraft stability control system, a value function regarding typical stability control performance and expected indicators is constructed, and the control parameters are iteratively optimized by maximizing the value function, so that the control system can meet the expected indicators in a balanced manner or make the design margin of a single performance as large as possible, thereby allowing the aircraft to respond to instructions more smoothly and quickly.

[0005] The technical solution of the present invention is: a method for optimizing parameters of an aircraft stability control system based on a value function, comprising:

[0006] Obtain basic control parameters based on the missile body dynamic coefficients at the flight characteristic points of the aircraft;

[0007] Set the expected performance indicators according to the airspace and speed range of the flight feature points to be designed;

[0008] According to the missile body dynamic coefficient and basic control parameters at the flight characteristic point, fixed-point simulation is performed to calculate the control performance in the time domain and frequency domain;

[0009] Design a value function for stable control performance and expected performance indicators, and calculate the value function based on the set expected performance indicators and the control performance obtained by simulation;

[0010] Design the callback function and iteration method of the current control parameter in the process of optimizing its neighborhood domain, including setting the neighborhood domain range, traversal step length and iteration exit condition;

[0011] The value functions of other basic control parameter combinations in the neighboring domain are calculated according to the calculation method of the value function, the optimal value is taken to replace the current control parameter, and the iterative operation is performed until the iteration exit condition is met, and finally the optimal control parameter is obtained.

[0012] Furthermore, the method of obtaining basic control parameters based on the projectile dynamic coefficients of the aircraft flight characteristic points includes: obtaining a control parameter group that satisfies the control capability under different aircraft dynamic coefficients based on prior knowledge, making a table of the correspondence between the dynamic coefficients and the basic control parameters, and interpolating the table based on the dynamic coefficients of the characteristic points to be designed to obtain a group of basic control parameters.

[0013] Furthermore, the expected performance indicators include expected rapidity, expected overshoot, expected amplitude margin, and expected phase margin.

[0014] Furthermore, the control performance in the time domain and frequency domain includes rapidity, overshoot, amplitude margin, and phase margin.

[0015] Furthermore, the value function includes:

[0016] If the stability control performance meets the expected index, the value function V is

[0017] V=-v1*(Ts-Ts d )+v2*(Pm-Pm d )+v3*(Gm-Gm d )

[0018] If the stability control performance does not fully meet the expected indicators, the value function V is

[0019] V=-v1*(△Ts)-v2*(△Pm)-v3*(△Gm)-v4*(△Ct)

[0020] in, Ts is rapidity, Ts d is the expected rapidity, Pm is the phase margin, Pm d is the expected phase margin, Gm is the amplitude margin, Gm d is the expected amplitude margin, Ct is the overshoot, Ct d is the expected overshoot, and v1, v2, v3, and v4 are weights.

[0021] Furthermore, the callback function includes:

[0022] For a set of input basic control parameters K1, K2, K3, three callback functions are designed, namely:

[0023] The first callback function: input K1, K2, K3, keep K1 and K2 unchanged, traverse the K3 neighborhood, calculate the value function under the same K1, K2 and different K3 respectively, if the value function is maximum when it is not the K3 traversal boundary point, then return the K1, K2, K3 and value corresponding to the maximum value function; otherwise, call the first callback function with the basic control parameter corresponding to the neighborhood boundary point as the basic value, and return the return value of the first callback function;

[0024] Second callback function: input K1, K2, K3, keep K1, K3 unchanged, traverse in the neighborhood of K2, call the return value of the first callback function for different combinations of K1, K2, K3 to calculate the value function, solve the K1, K2, K3 with the largest value function and the corresponding value; if the maximum value is not the boundary point traversed by K2, return the current K1, K2, K3 and value; otherwise, repeatedly call the second callback function with K1, K2, K3 of the boundary point as the basic value, and return the return value of the second callback function;

[0025] The third callback function: input K1, K2, K3, traverse the neighborhood of K1, keep K2, K3 unchanged, call the return value of the second callback function for different K1, K2, K3 combinations to calculate the value function, solve the K1, K2, K3 with the largest value function and the corresponding value; if the maximum value is not the boundary point traversed by K1, return the current K1, K2, K3 and value; otherwise, repeatedly call the third callback function with K1, K2, K3 of the boundary point as the basic value, and return the return value of the third callback function;

[0026] According to the three callback functions, a set of basic control parameters K1 c ,K2 c ,K3 c Input, call the third callback function and nestedly call the first and second callback functions, iteratively find a set of optimal control parameters with the largest value.

[0027] Furthermore, the iterative search method includes:

[0028] Enter a set of basic control parameters K1 c ,K2 c ,K3 c , call the third callback function in K1 c traverses the neighborhood of K1, and calls the second callback function for different K1s to calculate the value function; when calling the second callback function, traverses K2 cIn the neighborhood, in order to calculate the maximum value function under the current K1 and K2, the first callback function is called for different K1 and K2 combinations; the first callback function traverses K3 c The domain calculates the corresponding value function, and then returns the maximum value and the corresponding K3; the first callback function returns the value function to the second callback function, and then returns the maximum value and the corresponding K2, K3; the second callback function returns the value function to the third callback function, and then returns the maximum value and the corresponding K1, K2, K3; the return value of the third callback function is the final control parameter group K1 after the iteration is completed f ,K2 f ,K3 f .

[0029] Furthermore, the neighborhood range includes the basic control parameter K1 c The minimum and maximum values ​​of the traversal, the basic control parameter K2 c The minimum and maximum values ​​of the traversal, basic control parameter K3 c The minimum and maximum values ​​to traverse.

[0030] Furthermore, the traversal step size includes the traversal basic control parameter K1 c , K2 c 、K3 c Minimum interval between different K1 in the neighborhood.

[0031] Furthermore, the iterative exit condition includes the completion of the execution of the third callback function or the calculation of the value function reaching a preset number of times.

[0032] The advantages of the present invention compared with the prior art are:

[0033] (1) The present invention fully considers the expected stability control index of the aircraft's flight characteristic points, constructs a value function about typical stability control performance and expected index, and designs control parameters by maximizing the value function, which can accurately meet the expected performance index or make the single performance design margin as large as possible.

[0034] (2) The parameter optimization method of the present invention is not limited to the control structure, and the purpose of optimizing control performance can be achieved by rationally constructing the value function.

[0035] (3) The present invention can be implemented in software and is easy to operate in engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0037] Figure 1 A schematic diagram of the flow chart of the parameter optimization method of the aircraft stability control system based on the value function provided by the present invention;

[0038] Figure 2 This is a flow chart of iterative optimization of control parameters provided by the present invention;

[0039] Figure 3 This is an example PID control structure block diagram in a specific implementation manner. DETAILED DESCRIPTION

[0040] In order to better understand the above technical scheme, the technical scheme of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical scheme of the present invention, rather than limitations on the technical scheme of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0041] The following is a further detailed description of a method for optimizing parameters of an aircraft stability control system based on a value function provided by an embodiment of the present invention in conjunction with the accompanying drawings of the specification. The specific implementation method may include:

[0042] Step 1: quickly obtain a set of basic control parameters according to the body dynamic coefficients of the aircraft's flight characteristic points;

[0043] Step 2: Set the expected performance index according to the airspace and speed domain of the flight feature point to be designed;

[0044] Step 3: Based on the dynamic coefficients and control parameters of the flight characteristic points in step 1, a fixed-point simulation is performed to calculate the control performance in the time domain and frequency domain.

[0045] Step 4: Design a value function for stable control performance and expected indicators, and calculate the value function based on the expected performance indicators set in step 2 and the control performance obtained by simulation in step 3.

[0046] Step 5: Design the callback function and iteration method of the current control parameter group in the optimization process of its neighborhood domain, including setting the neighborhood domain range, traversal step size and iteration exit condition.

[0047] Step 6: Calculate the value functions of other control parameter combinations in the neighboring domain according to the value function calculation method of step 4, take the optimal value to replace the current control parameter group, iterate until the exit condition of step 5 is met, and finally obtain the optimal control parameter group.

[0048] Furthermore, the method of quickly obtaining a set of basic control parameters in step 1 is as follows:

[0049] Based on prior knowledge, a control parameter group that basically meets the control capability under different aircraft power coefficients is obtained. The corresponding relationship between the power coefficient and the control parameter is made into a table. By interpolating the table according to the power coefficient of the feature point to be designed, a set of basic control parameters can be quickly obtained. The value of the basic control parameter group is recorded as K1 c ,K2 c ,K3 c .

[0050] Furthermore, the control index expected to be achieved in step 2 is:

[0051] Typical indicators commonly used in stable control systems are: rapidity, overshoot, amplitude margin, and phase margin. The above indicators that are expected to be achieved are recorded as: Ts d ,Ct d ,Gm d ,Pm d .

[0052] Furthermore, the performance of calculating the time domain and frequency domain in the fixed-point simulation in step 3 is:

[0053] According to the power coefficient of the flight characteristic point in step 1, substitute it into the control parameter group and calculate the typical stable control in time domain and frequency domain through fixed-point simulation: rapidity Ts, overshoot Ct, amplitude margin Gm, and phase margin Pm.

[0054] Furthermore, the value function V of the stable control performance and the expected index in step 4 is designed as:

[0055] If the stability control performance meets the expected indicators, the value function V is calculated as follows

[0056] V=-v1*(Ts-Ts d )+v2*(Pm-Pm d )+v3*(Gm-Gm d )

[0057] If the stability control performance does not fully meet the expected indicators, the value function V is calculated as follows

[0058] V=-v1*(△Ts)-v2*(△Pm)-v3*(△Gm)-v4*(△Ct)

[0059] in,

[0060]

[0061] The above v1, v2, v3, and v4 are the weights of various stability control system performances in the value function.

[0062] Furthermore, the callback function in the optimization process in step 5 is designed as follows:

[0063] The first callback function: input K1, K2, K3, keep K1 and K2 unchanged, traverse the K3 neighborhood, calculate the value function under the same K1, K2 and different K3 respectively, if the value function is maximum when it is not the K3 traversal boundary point, then return the K1, K2, K3 and value corresponding to the maximum value function; otherwise, call the first callback function based on the control parameter corresponding to the neighborhood boundary point, and return the return value of the first callback function;

[0064] Second callback function: input K1, K2, K3, keep K1, K3 unchanged, traverse in the neighborhood of K2, call the return value of the first callback function for different combinations of K1, K2, K3 to calculate the value function, solve the K1, K2, K3 with the largest value function and the corresponding value; if the maximum value is not the boundary point traversed by K2, return the current K1, K2, K3 and value; otherwise, repeatedly call the second callback function with K1, K2, K3 of the boundary point as the basic value, and return the return value of the second callback function;

[0065] The third callback function: input K1, K2, K3, traverse the neighborhood of K1, keep K2, K3 unchanged, call the return value of the second callback function for different combinations of K1, K2, K3 to calculate the value function, and solve the K1, K2, K3 with the largest value function and the corresponding value; if the maximum value is not the boundary point traversed by K1, return the current K1, K2, K3 and value; otherwise, repeatedly call the third callback function with K1, K2, K3 of the boundary point as the basic value, and return the return value of the third callback function; the above K1, K2, K3 are the control parameter group of the current callback function.

[0066] Furthermore, the iterative method in the optimization process in step 5 is as follows:

[0067] The iterative method refers to Figure 2 , input a set of basic control parameters K1 c ,K2 c ,K3 c , call the third callback function in K1 c traverses the neighborhood of K1, and calls the second callback function for different K1s to calculate the value function; when calling the second callback function, traverses K2 c In the neighborhood, in order to calculate the maximum value function under the current K1 and K2, the first callback function needs to be called for different K1 and K2 combinations; the first callback function traverses K3 cThe domain calculates the corresponding value function, and then returns the maximum value and the corresponding K3; the first callback function returns the value function to the second callback function, and then returns the maximum value and the corresponding K2, K3; the second callback function returns the value function to the third callback function, and then returns the maximum value and the corresponding K1, K2, K3; the return value of the third callback function is the final control parameter group K1 after the iteration is completed f ,K2 f ,K3 f .

[0068] Further, the optimal control parameter group in step six is:

[0069] The basic control parameter K1 quickly obtained in step 1 c ,K2 c ,K3 c Substitute it into the iterative method designed in step 5 and calculate the optimal control parameter group, which is recorded as K1 f ,K2 f ,K3 f .

[0070] In the solution provided in the embodiment of the present invention, reference is made to Figure 1 The control parameter optimization method flow gives the algorithm steps. Step 1: obtain a set of basic control parameters according to the dynamic coefficient of the aircraft's flight characteristic point; Step 2: set the expected performance index of the characteristic point; Step 3: calculate the typical stable control performance under the current control parameters; Step 4: construct a value function about the stable control performance and the expected performance index; Step 5: design the callback function and iterative method of the control parameter group in the optimization process of its neighboring domain; Step 6: substitute the characteristic point for iterative operation to find the optimal control parameters.

[0071] In one embodiment of the present invention, the control structure diagram is as follows Figure 3 As shown, the projectile dynamics model is:

[0072]

[0073] Among them, the missile input δ is the control rudder deflection; the missile output ω is the angular velocity, a acc is the acceleration. The intermediate state quantity α represents the angle of attack, θ represents the ballistic inclination, represents the derivative of the ballistic inclination angle, is the derivative of angular velocity, is the attitude angle, is the derivative of the attitude angle, and Vm is the velocity.

[0074] The dynamic coefficients of the aircraft's flight characteristic points are a2=-11.6, a3=29, a4=1.2, a5=0.12, and the speed Vm=145.

[0075] The specific workflow is described as follows:

[0076] 1. The control system quickly obtains a set of basic control parameters in the following ways:

[0077] Based on prior knowledge, a control parameter group that basically meets the control capability under different aircraft power coefficients is obtained. The corresponding relationship between the power coefficient and the control parameter is made into a table. By interpolating the table according to the power coefficient of the feature point to be designed, a set of basic control parameters can be quickly obtained. The value of the basic control parameter group is recorded as K1 c =0.85,K2 c =161,K3 c =0.06.

[0078] 2. Set the control performance indicators expected to be achieved as follows:

[0079] Typical indicators commonly used in stability control systems are: expected rapidity, expected overshoot, expected

[0080] Amplitude margin, expected phase margin. The above indicators expected to be achieved are recorded as:

[0081] Ts d =0.5s,Ct d =10%,Gm d =6db,Pm d =30°.

[0082] 3. The performance indicators of time domain and frequency domain in fixed-point simulation are:

[0083] According to the dynamic coefficient of the aircraft's flight characteristic point, the control parameter group is substituted and the typical stability control indicators in the time domain and frequency domain are obtained through fixed-point simulation calculation: rapidity Ts=0.3s, overshoot Ct=5%, amplitude margin Gm=8db, phase margin Pm=40°.

[0084] 4. Design of value function V for stable control index and expected index:

[0085] If the stability control performance indicators all meet expectations, the value function V is calculated as follows

[0086] V=-30*(Ts-Ts d )+(Pm-Pm d )+5*(Gm-Gm d )

[0087] If the stability control performance indicators do not all meet expectations, the value function V is calculated as follows

[0088] V=-30*(△Ts)-(△Pm)-5*(△Gm)-1.5*(△Ct)

[0089] in,

[0090]

[0091] Substituting the expected index and the index obtained by simulation calculation into the operation, the value function V can be obtained as follows:

[0093] V=-30*(0.3-0.5)+(40-30)+5*(8-6)=26

[0094] 5. The neighborhood range, traversal step size, iteration method and optimal control parameter group are as follows:

[0095] The range of the neighborhood domain is ±20% of the control parameter value, and the traversal step is 5% of the control parameter value.

[0096] The iteration method and the iteration exit condition in the optimization process are as described in step 5 of the invention content. According to the three designed callback functions, the basic control parameter K1 is c ,K2 c ,K3 c Input, call the third callback function to find a set of optimal control parameters with the greatest value

[0097] K1 f =0.69,K2 f =55.9,K3 f =0.089.

[0098] The present invention provides a model given point simulation system by an aircraft dynamics system and a controller. The simulation system calculates typical stability control indicators for iteratively optimizing controller parameters until the performance is optimal. The parameter group is the final design of the controller characteristic point.

[0099] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0100] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A method for optimizing parameters of an aircraft stability control system based on a value function, characterized in that: include: Obtain basic control parameters based on the missile body dynamic coefficients at the flight characteristic points of the aircraft; Set the expected performance indicators according to the airspace and speed range of the flight feature points to be designed; According to the missile body dynamic coefficient and basic control parameters at the flight characteristic point, fixed-point simulation is performed to calculate the control performance in the time domain and frequency domain; Design a value function for stable control performance and expected performance indicators, and calculate the value function based on the set expected performance indicators and the control performance obtained by simulation; Design the callback function and iteration method of the current control parameter in the process of optimizing its neighborhood domain, including setting the neighborhood domain range, traversal step length and iteration exit condition; The value functions of other basic control parameter combinations in the neighboring domain are calculated according to the calculation method of the value function, the optimal value is taken to replace the current control parameter, and the iterative operation is performed until the iteration exit condition is met, and finally the optimal control parameter is obtained.

2. The method for optimizing parameters of an aircraft stability control system based on a value function according to claim 1, characterized in that: The method of obtaining basic control parameters based on the projectile dynamic coefficient of the aircraft flight characteristic point includes: obtaining a control parameter group that satisfies the control capability under different aircraft dynamic coefficients based on prior knowledge, making a table of the corresponding relationship between the dynamic coefficient and the basic control parameters, and interpolating the table based on the dynamic coefficient of the characteristic point to be designed to obtain a group of basic control parameters.

3. The method for optimizing parameters of an aircraft stability control system based on a value function according to claim 1, characterized in that: The expected performance indicators include expected rapidity, expected overshoot, expected amplitude margin, and expected phase margin.

4. The method for optimizing parameters of an aircraft stability control system based on a value function according to claim 1, characterized in that: The control performance in the time domain and frequency domain includes rapidity, overshoot, amplitude margin, and phase margin.

5. The method for optimizing parameters of an aircraft stability control system based on a value function according to claim 1, characterized in that: The value function includes: If the stability control performance meets the expected index, the value function V is V=-v1*(Ts-Ts d )+v2*(Pm-Pm d )+v3*(Gm-Gm d ) If the stability control performance does not fully meet the expected indicators, the value function V is V=-v1*(△Ts)-v2*(△Pm)-v3*(△Gm)-v4*(△Ct) in, Ts is rapidity, Ts d is the expected rapidity, Pm is the phase margin, Pm d is the expected phase margin, Gm is the amplitude margin, Gm d is the expected amplitude margin, Ct is the overshoot, Ct d is the expected overshoot, and v1, v2, v3, and v4 are weights.

6. The method for optimizing parameters of an aircraft stability control system based on a value function according to claim 1, characterized in that: The callback function includes: For a set of input basic control parameters K1, K2, K3, three callback functions are designed, namely: The first callback function: input K1, K2, K3, keep K1 and K2 unchanged, traverse the K3 neighborhood, calculate the value function under the same K1, K2 and different K3 respectively, if the value function is maximum when it is not the K3 traversal boundary point, then return the K1, K2, K3 and value corresponding to the maximum value function; otherwise, call the first callback function with the basic control parameter corresponding to the neighborhood boundary point as the basic value, and return the return value of the first callback function; Second callback function: input K1, K2, K3, keep K1, K3 unchanged, traverse in the neighborhood of K2, call the return value of the first callback function for different combinations of K1, K2, K3 to calculate the value function, solve the K1, K2, K3 with the largest value function and the corresponding value; if the maximum value is not the boundary point traversed by K2, return the current K1, K2, K3 and value; otherwise, repeatedly call the second callback function with K1, K2, K3 of the boundary point as the basic value, and return the return value of the second callback function; The third callback function: input K1, K2, K3, traverse the neighborhood of K1, keep K2, K3 unchanged, call the return value of the second callback function for different K1, K2, K3 combinations to calculate the value function, solve the K1, K2, K3 with the largest value function and the corresponding value; if the maximum value is not the boundary point traversed by K1, return the current K1, K2, K3 and value; otherwise, repeatedly call the third callback function with K1, K2, K3 of the boundary point as the basic value, and return the return value of the third callback function; According to the three callback functions, a set of basic control parameters K1 c ,K2 c ,K3 c Input, call the third callback function and nestedly call the first and second callback functions, iteratively find a set of optimal control parameters with the largest value.

7. The method for optimizing parameters of an aircraft stability control system based on a value function according to claim 6, characterized in that: The iterative search method includes: Enter a set of basic control parameters K1 c ,K2 c ,K3 c , call the third callback function in K1 c traverses the neighborhood of K1, and calls the second callback function for different K1s to calculate the value function; when calling the second callback function, traverses K2 c In the neighborhood, in order to calculate the maximum value function under the current K1 and K2, the first callback function is called for different K1 and K2 combinations; the first callback function traverses K3 c The domain calculates the corresponding value function, and then returns the maximum value and the corresponding K3; the first callback function returns the value function to the second callback function, and then returns the maximum value and the corresponding K2, K3; the second callback function returns the value function to the third callback function, and then returns the maximum value and the corresponding K1, K2, K3; the return value of the third callback function is the final control parameter group K1 after the iteration is completed f ,K2 f ,K3 f .

8. The method for optimizing parameters of an aircraft stability control system based on a value function according to claim 6, characterized in that: The iterative exit condition includes the completion of the execution of the third callback function or the calculation of the value function reaching a preset number of times.

9. The method for optimizing parameters of an aircraft stability control system based on a value function according to claim 1, characterized in that: The neighborhood range includes the basic control parameter K1 c The minimum and maximum values ​​of the traversal, the basic control parameter K2 c The minimum and maximum values ​​of the traversal, basic control parameter K3 c The minimum and maximum values ​​to traverse.

10. The method for optimizing parameters of an aircraft stability control system based on a value function according to claim 1, characterized in that: The traversal step length includes the traversal basic control parameter K1 c , K2 c 、K3 c Minimum interval between different K1 in the neighborhood.

Images