CN114355770A - Hypersonic aircraft reentry segment controller for optimizing invasive weed population distribution - Google Patents
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Abstract
The invention discloses a hypersonic aircraft reentry segment controller for optimizing invasive weed population distribution, which consists of an aircraft altitude sensor, an aircraft flight speed sensor, an aircraft flight channel inclination angle sensor, an aircraft horizontal flight distance sensor, an aircraft MCU and an aircraft attack angle controller. And after the hypersonic aircraft MCU reaches a reentry section airspace, starting each sensor to obtain the information of the altitude, the speed, the flight channel inclination angle and the flight horizontal distance of the aircraft, automatically executing an internal invasive weed population distribution optimization algorithm by the MCU according to the set altitude, speed and flight channel inclination angle requirements to obtain a track optimization control strategy which enables the hypersonic aircraft to have the longest horizontal flight distance, and sending the track optimization control strategy to an aircraft attack angle controller for execution by replacing a control command to enable the hypersonic aircraft to obtain the longer horizontal flight distance.
Description
Technical Field
The invention relates to the field of trajectory optimization of reentry sections of hypersonic flight vehicles, in particular to a hypersonic flight vehicle reentry trajectory optimization controller for optimizing invasive weed population distribution.
Background
The hypersonic aircraft is an aircraft with cruise speed higher than Mach 5, and has the characteristics of high-speed flight, quick response, remote attack, high-efficiency penetration and the like. Due to the characteristics of high nonlinearity and multiple constraints of the glide trajectory of the hypersonic flight vehicle, the optimization of the glide trajectory design of the hypersonic flight vehicle is a difficult point and a hot point in the trajectory design.
Because the atmosphere enters the atmosphere from the outer edge, the change range of the altitude and the speed is large, the hypersonic flight vehicle faces various severe reentry environments, and the reentry section track optimization technology is the key for ensuring the hypersonic flight vehicle to complete the flight task and has more important practical value for improving the hitting range and the landing precision. Therefore, it is very important to research an efficient hypersonic aircraft reentry section trajectory optimization method.
Disclosure of Invention
In order to enable the hypersonic aerocraft to obtain longer horizontal flight distance and improve the hitting range of the hypersonic aerocraft, the invention provides a hypersonic aerocraft reentry segment controller for optimizing the invasive weed population distribution.
The hypersonic aircraft reentry section trajectory optimization problem can be described as:
wherein t is time, x (t) represents the state variable of the hypersonic aerocraft, x1(t) denotes aircraft altitude, x2(t) denotes aircraft flight speed, x3(t) denotes the aircraft flight path inclination, x4(t) represents the horizontal flight distance of the aircraft, and u (t) represents the angle of attack control quantity of the aircraft, which is the control variable of the problem;
representing the first derivative of the state variable x (t), F (x (t), u (t), t) is a mathematical model of a differential equation set established according to a three-dimensional space motion equation of a reentry stage of the hypersonic aerocraft; t is t0Point in time, h, indicating the start of the reentry segment trajectory optimization0Representing the initial altitude, v, of the aircraft at the moment of the start of the optimization0Representing the initial speed, gamma, of the aircraft at the moment of the start of the optimization0Representing the initial flight path angle, r, of the aircraft at the start of the optimization0Indicating the initial level of the aircraft at the start of the optimizationDistance of flight, tfRepresents the optimized ending time point, h, of the reentry segment trajectoryfIndicating the altitude, v, of the aircraft at the end of the optimizationfIndicating the speed of the aircraft at the end of the optimization, gammafRepresenting the flight path angle of the aircraft at the optimization end time; j [ u (t)]An objective function representing the optimization of the hypersonic speed aircraft track, namely the horizontal flight distance of the aircraft at the optimization ending time G [ u (t), x (t), t]Is a constraint condition of the reentry section process of the hypersonic flight vehicle uminAnd umaxThe lower limit value and the upper limit value of the angle of attack control range are shown.
The technical scheme adopted by the invention for solving the technical problem is as follows: an invasive weed population distribution optimization algorithm is integrated in the hypersonic aircraft MCU, and a control instruction of an attack angle of the hypersonic aircraft can be given after the hypersonic aircraft reaches a reentry section, so that the hypersonic aircraft can obtain a longer horizontal flight distance.
The hypersonic aircraft MCU can be regarded as an automatic control signal generator, the controller is shown in figure 1 and comprises a pneumatic coefficient model, an aircraft performance constraint condition, a specified optimization target setting module, a hypersonic aircraft MCU module, an aircraft altitude sensor, an aircraft flight speed sensor, an aircraft flight channel inclination angle sensor, an aircraft horizontal flight distance sensor, an aircraft altitude, speed and flight channel inclination angle setting module and an aircraft attack angle control, and all components in the system are connected through a data bus in the controller.
The operation process of the controller is as follows:
step 1): the controller is installed on a certain hypersonic aircraft, and a pneumatic coefficient model, an aircraft performance constraint condition and specified optimization target parameter information 1 corresponding to the aircraft are input into an aircraft MCU;
step 2): after the hypersonic aircraft reaches the reentry section, acquiring current state information of the hypersonic aircraft, such as altitude, speed, flight channel inclination angle and flight horizontal distance, by an aircraft altitude sensor, an aircraft speed sensor, an aircraft flight channel inclination angle sensor and an aircraft horizontal flight distance sensor;
step 3): the aircraft MCU2 acquires control target information according to the aircraft altitude, speed and flight channel inclination angle setting module, and the MCU executes an internal invasive weed population distribution optimization algorithm to obtain a trajectory control strategy for enabling the aircraft to have the farthest horizontal flight distance;
step 4): the aircraft MCU converts the obtained control strategy into an attack angle control command and outputs the attack angle control command to the aircraft attack angle controller module 8;
the core of the invention is a hypersonic aerocraft MCU integrated with an invasive weed population distribution optimization algorithm, as shown in FIG. 2, the hypersonic aerocraft MCU internally comprises an information acquisition module, an initialization module, a system state solving module, an invasive weed population distribution optimization module, a convergence judgment module and a control instruction output module. The information acquisition module comprises five submodules, namely current aircraft altitude and speed acquisition, current aircraft flight channel inclination angle and flight horizontal distance acquisition, aircraft altitude and speed setting acquisition, aircraft flight channel inclination angle setting acquisition, an aerodynamic coefficient model and performance constraint conditions of the aircraft and specified optimization target parameter acquisition.
The invasive weed population distribution optimization module is realized by adopting the following steps:
step 1): defining an augmented performance indicator function after adding a penalty term
The form is as follows:
where t denotes the integration time, tfIndicating the end time, X (t) indicating the addition of xn+1New state vector after (t), Φ0[X(tf)]Representing the final value function of the new state vector at the final value instant,
represents nuThe dimensions of the control vector are such that,
represents nxThe dimension state vector, ρ is a penalty factor, and l represents the smoothing function parameter. H (t, x (t), u (t), rho, l, epsilon) is a penalty item related to constraint, and the specific form is as follows:
wherein Q isi(t, x (t), u (t), ρ, l, ε) is a function that characterizes the amount of constraint violation. Theta (Q)i(. -) is a multi-stage configuration function, m1Number of constraints representing standard equality, m2Representing the number of standard inequality constraints. Epsilon is a smoothing factor, which takes the value of a small integer.
γ(Qi(. -) is a penalty function index, updated as follows:
θ(Qi(. -) is a multi-stage configuration function defined as follows:
based on the above, the value of the penalty term factor varies adaptively with the magnitude of the constraint violation. When the constraint violation amount is larger, adopting a larger penalty factor to push more candidate solutions into a feasible region; when the constraint is that the violation quantity is gradually reduced, the penalty factor is also reduced, the search of the evolutionary algorithm around the feasible optimal solution is ensured, and the discovery of the global optimal value around the feasible domain boundary is facilitated.
Step 2): the number of seeds diffusible by the individual weeds obtained according to the formula (13) is:
wherein n issmaxAnd nsminThe maximum and minimum seed numbers f of the individual fertile weedsi (k)Is the fitness value of the ith individual weed,
and
the floor (·) function represents rounding down for maximum and minimum fitness values, respectively, for the current iteration.
Step 3): the standard deviation corresponding to the ith individual weed in the population is obtained according to equation (14):
wherein, deltamaxAnd deltaminMaximum and minimum values, respectively, of a preset standard deviation, fi (k)Is the fitness value of the ith individual weed,
and
the maximum and minimum fitness values for the current iteration are provided.
Step 4): according to a normal distribution
Weed seeds corresponding to the ith weed individual are randomly generated, and the new seeds are added into the weed population.
Step 5): and executing a system state solving module to obtain an objective function value corresponding to the new seed, and sequencing the new population according to the objective function value.
Step 6): judging whether the number of the current population individuals exceeds pmax. If the value exceeds the preset value, the front p with smaller adaptability value is preferentially selectedmaxIndividual subjects were kept and others were discarded. Turning to the step 2;
the invention has the following beneficial effects: and a track optimization attack angle control instruction for enabling the horizontal flight distance of the hypersonic aerocraft to be longer is obtained, and the hitting range of the hypersonic aerocraft is enlarged.
Drawings
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a block diagram of the internal modules of the MCU of the hypersonic aerocraft of the invention;
fig. 3 is an attack angle trajectory diagram of the hypersonic aircraft of example 1.
Detailed Description
Example 1
Assuming that the hypersonic flight vehicle reaches the reentry section airspace, the altitude sensor 3, the speed sensor 4, the flight channel tilt angle sensor 5, the horizontal flight distance sensor 6 and the MCU 7 of the hypersonic flight vehicle are all started. The information acquisition module 9 immediately acquires the initial altitude, speed, flight channel inclination angle and horizontal flight distance when the aircraft enters the reentry section, and sets the current initial time t00s, the altitude of the altitude sensor transmitted into the MCU 7 is h080,000m, the speed v of the speed sensor 4 transmitted into the MCU 706400m/s, the flight path inclination angle gamma transmitted from the flight path inclination angle sensor 5 to the MCU0The horizontal flying distance of the sensor 6 into the MCU is r, which is-0.052 rad00 m; end time tfThe hypersonic flight vehicle needs to meet the condition that the altitude is set as hf24000m, speed set vf760m/s, the flight path inclination angle is set to gammaf-0.08 rad; combining a three-dimensional space motion equation, a pneumatic coefficient model, an aircraft performance constraint condition and a specified optimization target of the aircraft to obtain a mathematical model of the problem as follows:
wherein L represents lift, D represents drag, CLDenotes the coefficient of lift, CDDenotes drag coefficient, h denotes flight altitude, v denotes flight speed, γ denotes flight path inclination, u denotes horizontal flight distance of the aircraft, x1Representing dimensionless ground center-to-center distance, x2Representing dimensionless speed, x3Representing track angle, x4Indicating longitude.
For convenience of description, F (x (t), u (t), and t) are used to represent a mathematical model of a differential equation set established by a hypersonic aircraft reentry section three-dimensional space motion equation, that is:
where L represents lift, D represents drag, h represents flight altitude, v represents flight speed, and γ represents flight path inclination.
In addition, J [ u (t) ] represents the objective function of the hypersonic flight vehicle trajectory optimization, namely the horizontal flight distance of the flight vehicle at the optimization ending moment.
An invasive weed population distribution optimization algorithm for automatically generating an attack angle control command by a hypersonic flight vehicle MCU 7 is shown in FIG. 2, and the operation steps are as follows:
step 1): inputting a pneumatic coefficient model, an aircraft performance constraint condition and a specified optimization target corresponding to the aircraft into a hypersonic aircraft MCU 7;
step 2): after the hypersonic aircraft reaches the reentry section, starting an aircraft altitude sensor 3, an aircraft flight speed sensor 4, an aircraft flight channel inclination angle sensor 5 and an aircraft horizontal flight distance sensor 6 to obtain the current state information of the hypersonic aircraft on altitude, speed, flight channel inclination angle and flight horizontal distance;
step 3): the hypersonic aircraft MCU 7 automatically executes an internal invasive weed population distribution optimization algorithm according to the set requirements of altitude, speed and flight channel inclination angle to obtain a track optimization control strategy for enabling the hypersonic aircraft to have the longest horizontal flight distance;
step 4): the hypersonic aircraft MCU 7 sends the obtained track optimization control strategy to the control strategy output module 14, converts the control strategy into a control command and sends the control command to the aircraft attack angle controller 8 for execution.
The hypersonic aerocraft MCU 7 is shown in FIG. 2 and comprises an information acquisition module 9, an initialization module 10, a system state solving module 11, a convergence judging module 12, an invasive weed population distribution optimizing module 13 and a control instruction output module 14. The information acquisition module comprises five sub-modules of acquiring the altitude and the speed of the aircraft, acquiring the inclination angle and the horizontal flight distance of a flight channel of the aircraft, setting and acquiring the altitude and the speed of the aircraft, setting and acquiring the inclination angle of the flight channel of the aircraft, acquiring a pneumatic coefficient model and a performance constraint condition of the aircraft and acquiring a specified optimization target parameter.
The operation steps of the invasive weed population distribution optimization algorithm for automatically generating the attack angle control command by the hypersonic flight vehicle MCU 7 are as follows:
step 1): after the hypersonic aircraft MCU 7 reaches the reentry section, the aircraft altitude sensor 3, the aircraft flight speed sensor 4, the aircraft flight channel inclination angle sensor 5 and the aircraft horizontal flight distance sensor 6 are started, and the information acquisition module 9 acquires the initial time t0Altitude h of hypersonic aerocraft at 0s080,000m, flight speed v06400m/s, the flight path inclination angle is gamma0The horizontal flying distance of the sensor is set as r00 m; end time tfThe altitude requirement of the hypersonic flight vehicle is set as hf24000m, the flight speed requirement is set to vf760m/s, the flight channel inclination angle requirement is setIs defined as gammaf＝-0.008rad；
Step 2): the initialization module 10 starts to operate, initializes each parameter, sets the number of segments in a time period as NE, and sets the interpolation fitting mode of parameterization of a control variable as a segment constant; parameters of the invasive weed population distribution optimization method are initialized as follows: setting the maximum size p of the populationmaxInitial size p of population 60040 initial position vector of ith individual in population
Initial historical optimal position of population
Where J (-) represents the objective function to be maximized. Setting the maximum iteration times max _ iters of individual optimization as 3000, the maximum and minimum seed numbers n of weed individual reproductionsmax＝6、n smin1, the maximum value delta of the standard deviation is presetmaxPreset standard deviation minimum δ 4minThe smoothing function parameter l is 0.0005, the smoothing factor e is 0.0001, and the current iteration number k is 0.
Step 3): obtaining an objective function value of a k iteration weed individual i through a system state solving module 11
Updating the optimal historical position g of the kth iteration population(k),bestRecording the maximum objective function value
And minimum objective function value
Step 4): executing a convergence judging module 12, if the current iteration number k reaches the maximum iteration number max _ iters, the convergence condition is satisfied, and the current population history optimal position g is(k),bestThe discrete solution serving as the optimal control strategy is output through a control instruction output module;if the convergence condition is not satisfied, the number of iterations k is increased by 1.
Step 5): and executing an invasive weed population distribution optimization module 13, dynamically generating corresponding numbers of filial generations according to population distribution conditions, adding the filial generations into a new population, and preferentially selecting a certain number of individuals with smaller objective function values to form the new population. After the step is finished, the step jumps to the step 3 until the convergence condition is met.
The invasive weed population distribution optimization module 13 is realized by adopting the following steps:
step 1): defining an augmented performance indicator function after adding a penalty term
The form is as follows:
where t denotes the integration time, tfIndicating the end time, X (t) indicating the addition of xn+1New state vector after (t), Φ0[X(tf)]Representing the final value function of the new state vector at the final value instant,
represents nuThe dimensions of the control vector are such that,
represents nxThe dimension state vector, ρ is a penalty factor, and l represents the smoothing function parameter. H (t, x (t), u (t), rho, l, epsilon) is a penalty item related to constraint, and the specific form is as follows:
wherein Q isi(t, x (t), u (t), ρ, l, ε) is a function that characterizes the amount of constraint violation. Theta (Q)i(. -) is a multi-stage configuration function, m1Represents the standard equality constraintNumber, m2Representing the number of standard inequality constraints. Epsilon is a smoothing factor, which takes the value of a small integer.
γ(Qi(. -) is a penalty function index, updated as follows:
θ(Qi(. -) is a multi-stage configuration function defined as follows:
based on the above, the value of the penalty term factor varies adaptively with the magnitude of the constraint violation. When the constraint violation amount is larger, adopting a larger penalty factor to push more candidate solutions into a feasible region; when the constraint is that the violation quantity is gradually reduced, the penalty factor is also reduced, the search of the evolutionary algorithm around the feasible optimal solution is ensured, and the discovery of the global optimal value around the feasible domain boundary is facilitated.
Step 2): the number of diffusible seeds of the individual weeds i is obtained according to the formula (22):
wherein n issmaxAnd nsminThe maximum and minimum seed numbers f of the individual fertile weedsi (k)Is the fitness value of the ith individual weed,
and
the floor (·) function represents rounding down for maximum and minimum fitness values, respectively, for the current iteration.
Step 3): obtaining the standard deviation corresponding to the ith weed individual in the population according to the formula (23):
wherein, deltamaxAnd deltaminMaximum and minimum values, respectively, of a preset standard deviation, fi (k)Is the fitness value of the ith individual weed,
and
the maximum and minimum fitness values for the current iteration are provided.
Step 4): according to a normal distribution N (0, δ)i 2) Weed seeds corresponding to the ith weed individual are randomly generated, and the new seeds are added into the weed population.
Step 5): and executing the system state solving module 11 to obtain the objective function value corresponding to the new seed, and sequencing the new population according to the objective function value.
Step 6): judging whether the number of the current population individuals exceeds pmax. If the value exceeds the preset value, the front p with smaller adaptability value is preferentially selectedmaxIndividual subjects were kept and others were discarded. Turning to the step 2;
and finally, the aircraft MCU 7 outputs the obtained optimized track to the control strategy output module 14 as an instruction, converts the optimized track into a control instruction and sends the control instruction to the aircraft attack angle controller 8, thereby completing the execution of track optimization. Fig. 3 is an attack angle trajectory diagram of the hypersonic aircraft of example 1.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and is not intended to limit the practice of the invention to these embodiments. For those skilled in the art to which the invention pertains, several simple deductions or substitutions may be made without departing from the inventive concept, which should be construed as falling within the scope of the present invention.
Claims (2)
1. A hypersonic aircraft reentry section controller for invasive weed population distribution optimization is characterized in that: the controller consists of an aircraft MCU, an aircraft altitude sensor, an aircraft flight speed sensor, an aircraft flight channel inclination angle sensor, an aircraft horizontal flight distance sensor, an aircraft MCU and an aircraft attack angle controller which are connected with the aircraft MCU through a data bus. The operation process of the device comprises the following steps:
step 1): inputting a pneumatic coefficient model, an aircraft performance constraint condition and a specified optimization target corresponding to the aircraft into a hypersonic aircraft MCU;
step 2): after the hypersonic aircraft reaches the reentry section, starting an aircraft altitude sensor, an aircraft flight speed sensor, an aircraft flight channel inclination angle sensor and an aircraft horizontal flight distance sensor to obtain the current state information of the hypersonic aircraft on altitude, speed, flight channel inclination angle and flight horizontal distance;
step 3): the aircraft MCU automatically executes an internal invasive weed population distribution optimization algorithm according to the set requirements of altitude, speed and flight channel inclination angle to obtain a track optimization control strategy for enabling the hypersonic aircraft to have the longest horizontal flight distance;
step 4): and the hypersonic aircraft MCU sends the obtained track optimization control strategy to the control strategy output module, converts the control strategy into a control command and sends the control command to the aircraft attack angle controller for execution.
The hypersonic aircraft MCU comprises an information acquisition module, an initialization module, a system state solving module, an invasive weed population distribution optimizing module, a convergence judging module and a control instruction output module. The information acquisition module comprises five sub-modules of acquiring the altitude and the speed of the aircraft, acquiring the inclination angle and the horizontal flight distance of a flight channel of the aircraft, setting and acquiring the altitude and the flight speed of the aircraft, setting and acquiring the inclination angle of the flight channel of the aircraft, setting and acquiring a pneumatic coefficient model and a performance constraint condition of the aircraft and acquiring a specified optimization target parameter.
The operation steps of the invasive weed population distribution optimization algorithm for automatically generating the attack angle control command by the hypersonic aircraft MCU are as follows:
step 1): after the hypersonic aircraft MCU reaches the reentry section, an aircraft altitude sensor, an aircraft flight speed sensor, an aircraft flight channel inclination angle sensor and an aircraft horizontal flight distance sensor are started, and an information acquisition module 9 acquires the current state information of the altitude, speed, flight channel inclination angle and flight horizontal distance of the hypersonic aircraft;
step 2): and the initialization module starts to operate and initializes various parameters including control variable parameterization parameters and invasive weed population distribution optimization method parameters. The control variable parameterization parameters are initialized as follows: setting the number of segments of the time period as NE, and setting an interpolation fitting mode of parameterization of a control variable: piecewise constant or piecewise linear; parameters of the invasive weed population distribution optimization method are initialized as follows: setting the maximum size p of the populationmaxPopulation initial size p0Initial position vector of ith individual in population
Initial historical optimal position of population
Where J (-) represents the objective function to be maximized. Setting the maximum iteration times max _ iters of individual optimization, the maximum seed number and the minimum seed number n of the reproductive weed individualsmax、nsminPresetting the maximum value delta of standard deviationmaxThe minimum value delta of the standard deviation is presetminThe smoothing function parameter l, the smoothing factor epsilon, and the current iteration number k is 0.
Step 3): obtaining an objective function value of a kth iteration weed individual i through a system state solving module
Updating the optimal historical position g of the kth iteration population(k),bestRecording the maximum objective function value
And minimum objective function value
Step 4): executing a convergence judgment module, if the current iteration number k reaches the maximum iteration number max _ iters, meeting the convergence condition, and obtaining the historical optimal position g of the current population(k),bestAs a discrete solution of the optimal control strategy, the discrete solution is output through a control instruction output module 8; if the convergence condition is not satisfied, the number of iterations k is increased by 1.
Step 5): and executing an invasive weed population distribution optimization module, dynamically generating corresponding numbers of filial generations according to population distribution conditions, adding the filial generations into a new population, and preferentially selecting a certain number of individuals with smaller objective function values to form the new population. After the step is executed, the step is skipped to until the convergence condition is satisfied.
The system state solving module adopts a four-step four-order Runge-Kutta method, and the calculation formula is as follows:
wherein, tmRepresents the integration time t selected by the four-step fourth-order Runge-Kutta methodm+1Indicating that it is at time tmThe latter integration time, and tm+1＝tm+ h, h is the integration step, F (-) is a function describing the differential equation of state, K1, K2, K3, K4 represent the function values of 4 nodes in the integration process of Runge-Kutta method, respectively, x (-) is a function describing the system state variables, and x (t)m) Represents the time tmX (t) ofm+1) Represents the time tm+1The system state variable of (1).
2. The hypersonic aircraft reentry segment controller for optimizing population distribution of invasive weeds of claim 1, wherein: the invasive weed population distribution optimization module is realized by adopting the following steps:
step 1): defining an augmented performance indicator function after adding a penalty term
The form is as follows:
where t denotes the integration time, tfIndicating the end time, X (t) indicating the addition of xn+1New state vector after (t), Φ0[X(tf)]Representing the final value function of the new state vector at the final value instant,
represents nuThe dimensions of the control vector are such that,
represents nxThe dimension state vector, ρ is a penalty factor, and l represents the smoothing function parameter. H (t, x (t), u (t), p and l) are penalty items related to constraint, and the specific form is as follows:
wherein Q isi(t, x (t), u (t), p, l) is a function characterizing the constraint violations, θ (Q)i(. -) is a multi-stage configuration function, m1Number of constraints representing standard equality, m2Representing the number of standard inequality constraints. Epsilon is a smoothing factor, which takes the value of a small integer.
γ(Qi(. -) is a penalty function index, updated as follows:
θ(Qi(. -) is a multi-stage configuration function defined as follows:
based on the above, the value of the penalty term factor varies adaptively with the magnitude of the constraint violation. When the constraint violation amount is larger, adopting a larger penalty factor to push more candidate solutions into a feasible region; when the constraint is that the violation quantity is gradually reduced, the penalty factor is also reduced, the search of the evolutionary algorithm around the feasible optimal solution is ensured, and the discovery of the global optimal value around the feasible domain boundary is facilitated.
Step 2): obtaining the number n of diffusible seeds of the ith weed individual according to the formula (6)siComprises the following steps:
wherein n issmaxAnd nsminThe maximum and minimum seed numbers of the individual which can be bred,
is the fitness value of the ith weed,
and
the floor (·) function represents rounding down for maximum and minimum fitness values, respectively, for the current iteration.
Step 3): obtaining the standard deviation delta corresponding to the ith weed individual in the population according to the formula (7)iComprises the following steps:
wherein, deltamaxAnd deltaminRespectively the maximum and minimum values of the preset standard deviation,
is the fitness value of the ith individual weed,
and
the maximum and minimum fitness values for the current iteration are provided.
Step 4): according to a normal distribution N (0, δ)i 2) Weed seeds corresponding to the ith weed individual are randomly generated, and the new seeds are added into the weed population.
Step 5): and executing a system state solving module to obtain an objective function value corresponding to the new seed, and sequencing the new population according to the objective function value.
Step 6): judging whether the number of the current population individuals exceeds pmax. If the value exceeds the preset value, the front p with smaller adaptability value is preferentially selectedmaxIndividual subjects were kept and others were discarded. Go to step 2.
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| 洪网君;汤新民;陈平;顾俊伟;: "面向控制到达时间的巡航飞行剖面实时优化", 中国民航大学学报, no. 04, 15 August 2018 (2018-08-15) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114488788A (en) * | 2021-11-30 | 2022-05-13 | 浙江大学 | Probability self-adaptive pollination optimized hypersonic aircraft reentry segment controller |
| CN114488788B (en) * | 2021-11-30 | 2024-07-19 | 浙江大学 | Hypersonic aircraft reentry section controller with probability self-adaptive pollination optimization function |
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