Solar-powered unmanned aerial vehicle power gliding power control method based on particle swarm algorithm

Abstract

The application provides a solar unmanned aerial vehicle power gliding power control method based on a particle swarm algorithm, which comprises the following steps: initializing setting of aircraft parameters and flight parameters; establishing a target function of a flight stage; setting iteration parameters of the particle swarm algorithm; and using the particle swarm algorithm to solve an optimization result scheme.The application has the beneficial effect that by reasonably distributing power of a power propulsion system in a gliding process, the solar unmanned aerial vehicle can fly for a long time at night, slowly releases gravitational potential energy, and maintains low energy consumption; the maximum speed constraint of the particle ensures that the search is neither too conservative nor too aggressive, balances the global search and local search capabilities, helps to avoid unnecessary long-distance jumping of the particle in the search space, and improves the adaptability and efficiency of the algorithm.

Description

Technical Field

[0001] This invention belongs to the field of solar-powered unmanned aerial vehicle (UAV) flight power control, and in particular relates to a power control method for powered gliding of solar-powered UAVs based on particle swarm optimization algorithm. Background Technology

[0002] High-altitude solar-powered drones, nicknamed "pseudo-satellites" due to their ability to fly for extended periods at high altitudes and their cost-effectiveness and flexibility compared to traditional satellites, can utilize solar energy to sustain long-duration flights at stratospheric altitudes, performing various tasks such as communication relay, surveillance and reconnaissance, and environmental monitoring. These characteristics have made high-altitude solar-powered drones a new frontier in global technological competition.

[0003] The energy harvesting efficiency of solar-powered drones varies depending on the geographical location and the intensity of solar radiation at different times. A key characteristic of their energy harvesting is the time-varying power output; they can generate electricity during the day but not at night. To ensure drones can continuously perform high-altitude, long-endurance missions, especially at night or under insufficient sunlight conditions, an effective energy management strategy is essential to guarantee a stable energy supply. Currently, the mainstream approach is to utilize gravitational potential energy to store excess solar energy. Drones can store energy by climbing in sunny conditions, and release this stored energy by gliding down in the absence of sunlight or with insufficient sunlight, thus reducing reliance on battery power—essentially climbing during the day and gliding at night. This method not only improves energy utilization but also extends the drone's operating time and range.

[0004] This invention proposes a power control method for powered gliding based on particle swarm optimization for the gliding phase of high-altitude solar-powered unmanned aerial vehicles (UAVs) that allows for a more gradual release of the UAV's gravitational potential energy, enabling it to glide for longer periods at night. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a power control method for powered gliding of solar-powered UAVs based on particle swarm optimization, which is particularly suitable for extending the gliding time of solar-powered UAVs during nighttime gliding.

[0006] The technical solution adopted in this invention is: to provide a power control method for powered gliding of a solar-powered unmanned aerial vehicle based on particle swarm optimization algorithm, comprising the following steps:

[0007] Initialize and set aircraft and flight parameters;

[0008] Establish the objective function for the flight phase;

[0009] Set the iteration parameters for the particle swarm optimization algorithm;

[0010] The optimization scheme is solved using a particle swarm optimization algorithm.

[0011] Furthermore, the flight phases include daytime climb, daytime maximum altitude cruise, nighttime gliding, and nighttime minimum altitude cruise.

[0012] Furthermore, when the flight phase is nighttime gliding, the objective function formula is:

[0013]

[0014] In the formula, P glide =[P glide_1 ,P glide_2 ,…,P glide_n ] T

[0015] T glide For gliding time, E glide The energy consumed by the propulsion system during the gliding phase is represented by α and β, which are weighting coefficients and α + β = 1.

[0016] Furthermore, the iteration parameters include: number of particles, particle dimension, maximum number of iterations, maximum inertia weight, minimum inertia weight, maximum learning factor, and minimum learning factor.

[0017] Furthermore, the particle swarm optimization algorithm includes the following steps:

[0018] Initialize particle swarm parameters;

[0019] Initialize the particle swarm;

[0020] Assess particle fitness;

[0021] Update the individual optimal solution and the global optimal solution;

[0022] Update the particle's velocity and position;

[0023] Iterative search and termination condition determination;

[0024] Output the optimal gliding power distribution result.

[0025] Furthermore, the maximum velocity of the particles in the initial particle swarm is constrained to be 10% to 20% of the maximum gliding power.

[0026] Furthermore, based on the optimized scheme, the flight trajectory diagram can be simulated and the flight duration and energy consumption can be calculated, thereby verifying the effectiveness of the method.

[0027] The advantages and positive effects of this invention are as follows: By adopting the above technical solution and rationally allocating the power of the propulsion system during gliding, the solar-powered UAV can glide for a longer period of time at night, slowly releasing gravitational potential energy and maintaining low energy consumption; the maximum velocity constraint of the particles ensures that the search is neither too conservative nor too aggressive, balancing the ability of global search and local search, which helps to avoid unnecessary long-distance jumps of particles in the search space and improves the adaptability and efficiency of the algorithm. Attached Figure Description

[0028] Figure 1 This is a flowchart of the method steps according to an embodiment of the present invention.

[0029] Figure 2 This is a schematic diagram of the flight phase distribution according to an embodiment of the present invention.

[0030] Figure 3 This is a schematic diagram of the gliding force analysis of an aircraft according to an embodiment of the present invention.

[0031] Figure 4 This is a flowchart of the particle swarm optimization algorithm according to an embodiment of the present invention.

[0032] Figure 5 This is a fitness curve diagram of a particle swarm optimization algorithm according to an embodiment of the present invention.

[0033] Figure 6 This is a power distribution sequence diagram of a powered gliding according to an embodiment of the present invention.

[0034] Figure 7 This is a longitudinal flight path diagram of a powered gliding drone according to an embodiment of the present invention. Detailed Implementation

[0035] In the following description, those skilled in the art will recognize that the embodiments of the invention described below can be implemented in various ways (e.g., processes, apparatus, systems, devices, or methods) on a tangible computer-readable medium. Throughout this discussion, components may be described as individual functional units (which may include subunits); however, those skilled in the art will recognize that various components or portions thereof may be divided into individual components or may be integrated together (including within a single system or component). It should be noted that the functions or operations discussed herein may be implemented as components. Components may be implemented in software, hardware, or a combination thereof.

[0036] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0037] like Figure 1 As shown, the present invention provides a power control method for powered gliding of a solar-powered UAV based on particle swarm optimization algorithm, including the following steps: initializing and setting aircraft parameters and flight parameters.

[0038] Setting aircraft parameters and flight parameters allows the particle swarm optimization algorithm to be effectively applied to the power control of the aircraft in the corresponding flight phases, including UAV mass, wing area, gravitational acceleration, maximum gliding power, number of power distribution segments, gliding start altitude, gliding end altitude, drag coefficient, lift coefficient, lift-to-drag ratio, maximum track angle, minimum track angle, maximum gliding speed, and minimum gliding speed.

[0039] Establish the objective function for the flight phase;

[0040] The objective function is derived based on the desired optimization objectives and is used to solve various problems that need to be satisfied at each stage of flight.

[0041] Set the iteration parameters for the particle swarm optimization algorithm;

[0042] The performance and convergence speed of the particle swarm optimization (PSO) algorithm can be affected by setting its iteration parameters. The choice of these parameters depends on the nature of the specific problem and the characteristics of the search space. Preferred weighting coefficients are α = 0.2 and β = 0.8.

[0043] The optimization scheme is solved using a particle swarm optimization algorithm.

[0044] The particle swarm optimization algorithm is used to solve this objective function optimization problem. The main steps of the algorithm include: initializing a set of random particles, evaluating the fitness of each particle according to the objective optimization function, identifying the local and global best particles, updating the particle velocity and position according to cognitive and social factors and randomness, ensuring that the particles are within the constraints, repeating the process until the termination condition is reached, and then selecting the particle with the best fitness as the optimal solution.

[0045] In one embodiment, such as Figure 2 As shown, the flight phases include daytime climb, daytime maximum altitude cruise, nighttime gliding, and nighttime minimum altitude cruise. When the flight phase is nighttime gliding, the objective function formula is:

[0046] In the formula, P glide =[P glide_1 P glide_2 , ..., P glide_n ] T T glide For gliding time, E glide The energy consumed by the propulsion system during the gliding phase is represented by α and β, which are weighting coefficients, and α + β = 1.

[0047] Under a gravity-based energy storage management strategy, the high-altitude solar-powered UAV will climb from its lowest to its highest flight altitude during the day, and glide from its highest to its lowest flight altitude at night, before conducting nighttime cruising. During the nighttime gliding phase, the solar-powered UAV releases gravitational potential energy by descending, resulting in low power consumption in the propulsion system. During the nighttime cruising phase, the propulsion system needs to output significant power to maintain the lowest flight altitude. Assume the total nighttime duration is T. night Nighttime gliding time is T glide Then the nighttime cruise time is: T cruise =T night -T glide During the gliding phase, the solar-powered drone experiences forces such as... Figure 3 As shown, based on force analysis, the solar-powered drone satisfies the following dynamic equations: Where γ is the thrust of the drone, W is gravity, and γ is the trajectory during gliding.

[0048] Let D be the drag angle, m be the mass of the UAV, a be the acceleration of the UAV during gliding, and L be the lift. The formulas for lift and drag are as follows: Where v is the flight speed of the UAV, S is the wing area, and C... D C is the drag coefficient. L Let p(h) be the lift coefficient, and p(h) be the atmospheric density at flight altitude h. The ratio of the lift coefficient to the drag coefficient is called the lift-to-drag ratio, denoted as k = C. L / C D Assuming that a large solar-powered drone experiences zero acceleration for a short period, the power required by the propulsion system during gliding is: With the propulsion system providing a certain thrust, the differential equation between the flight speed and the flight path angle of the solar-powered UAV is as follows:

[0049]

[0050] Where g is the acceleration due to gravity, typically taken as 9.8 m / s². 2 Solar-powered drones fly from their highest altitude of H... max Glide to the lowest flight altitude H min The flight time is: The greater the power of the drone during the gliding phase, the slower the descent speed, the longer the time it takes to glide from its highest to its lowest altitude, and the slower the release of the potential energy stored in gravity. The energy consumption of the power system during the gliding phase of a solar-powered drone is calculated using the following formula: Clearly, considering the above analysis of the two optimization objectives, there is a contradiction between them. For such multi-objective optimization problems, the two objective optimization functions can be weighted and summed to form a multi-objective optimization function:

[0051] Where α and β are weighting coefficients, and α + β = 1, the optimization function must satisfy the following constraints:

[0052]

[0053] During the powered gliding phase, solar-powered drones continuously optimize the gliding power of their propulsion system to achieve optimal gliding speed and flight path angle, thereby obtaining a longer gliding time while constraining power consumption during gliding. This optimization problem is clearly a continuous optimization problem, essentially involving allocating the drone's propulsion system gliding power at different times to achieve the multi-objective optimization function. In practice, we can divide the power allocation into segments, transforming this continuous optimization problem into a discrete optimization problem. Assuming the number of power segments is n, the larger n is, the closer the propulsion system power allocation during gliding is to continuous allocation. During the powered gliding phase, under the discrete optimization approach of power segmentation, the maximum flight altitude H is... max and minimum height H min The system is divided into equally spaced segments. It is assumed that within each power segment, the solar-powered drone is in uniform flight with a constant trajectory angle. In the first power segment, the initial values ​​of the flight speed and trajectory angle are v0 and γ0, respectively. It is assumed that the gliding power allocated to the i-th segment is P. glide_i Then the gliding motion state of the i-th segment is updated by the following formula:

[0054]

[0055] Gliding duration T within the corresponding power segment glide_i With energy consumption E glide_i Calculation method:

[0056]

[0057] Therefore, it can be obtained that the solar-powered drone can fly from its highest altitude H. max To the lowest height H min Powered gliding flight time T glide With energy consumption E glide for:

[0058]

[0059] The continuous multi-objective optimization function is transformed into the following discrete objective optimization function:

[0060]

[0061] Among them, P glide =[P glide_1 P glide_2 , ..., P glide_n ] T .

[0062] In one embodiment, the iteration parameters include the number of particles, particle dimension, maximum number of iterations, maximum inertia weight, minimum inertia weight, maximum learning factor, and minimum learning factor.

[0063] In one embodiment, such as Figure 4 As shown, the particle swarm optimization algorithm includes the following steps: initializing the particle swarm parameters.

[0064] The relevant parameters include the number of particles N, the particle swarm dimension D, and the maximum number of iterations of the algorithm (iter). max Learning factors c1 and c2, inertial weight ω, and the gliding altitude range of the solar-powered UAV [H] max H min [Power segment number n and maximum gliding power constraint P] max The particle swarm size typically ranges from 20 to 1000, with 20 to 50 suitable for simple problems and 100 to 200 for complex problems. The particle dimension D in the particle swarm optimization algorithm is determined by the number of power allocation segments, n. The maximum iteration parameter is iter. max The range is 50–100. The learning factor c1 reflects the individual particle's cognitive parameters, representing the optimal value of the current search result. c2 reflects the particle's social cognitive parameters, representing the optimal value of the global search. Both factors jointly influence the particle's optimization direction. To enhance the search optimization process and make the learning factor adaptively change with iteration, the following learning factor update formula is used:

[0065]

[0066] Where iter is the current iteration number, C max and c min These represent the maximum and minimum values ​​of the learning factor, respectively. The choice of the inertia weight ω in the particle swarm optimization algorithm greatly affects the algorithm's convergence. To ensure that the optimal solution is found within the set maximum number of iterations, the general update rule is to select a larger inertia weight in the early stages of iteration to ensure global convergence, and a smaller inertia weight in the later stages to ensure local convergence. The update formula can be used for calculation in the following form: Where, ω max and ω max These are the maximum and minimum values ​​of the inertia weight, respectively.

[0067] By rationally allocating the power of the propulsion system during gliding, solar-powered drones can glide for extended periods at night, slowly releasing gravitational potential energy and maintaining low energy consumption.

[0068] Initialize the particle swarm: Randomly initialize N particles in the search space. Each particle has two attributes: position and velocity. The position vector represents the initial power allocation scheme, and its dimension is equal to the number of power allocation segments. The value in each dimension represents the propulsion system power of the corresponding gliding segment. Note that the initial propulsion system power must meet the maximum power constraint of powered gliding. The velocity attribute represents the distance and direction of the particle's next iteration search. The particle swarm generally has a maximum velocity constraint. The reasonable selection of the maximum velocity can balance the exploration and development capabilities of the search for the optimal solution. When the maximum velocity is selected, the particle search range is large and the exploration capability is strong, but it is easy to miss the optimal solution. When the maximum velocity is selected, the particle search range is more refined and the development capability is strong, but it is easy to get trapped in the optimal solution.

[0069] Evaluate particle fitness: For each particle, i.e., each power allocation scheme, calculate the powered gliding time and energy consumption of the solar-powered UAV under that power allocation scheme. Then, determine the particle's fitness value P based on the objective optimization function.

[0070] Update the individual optimal solution and the global optimal solution: In each iteration, compare the current fitness of each particle with its historical best fitness, i.e., the individual optimal solution P. pbest This process updates the individual optimal solution. Simultaneously, it identifies the particle with the best fitness among all particles, representing the global optimal solution P. gbest .

[0071] Update particle velocity and position: Based on the velocity and position update formulas of the particle swarm optimization algorithm, and combining the individual optimal solution and the global optimal solution of the particle swarm, calculate the velocity V and position X of each particle in the current iteration, as follows:

[0072]

[0073] r1 and r2 are random numbers in the interval [0, 1] to enhance the randomness of the search direction.

[0074] Iterative search and termination condition judgment: The process involves repeatedly evaluating the particle's fitness and updating its speed and position until the maximum number of iterations is reached.

[0075] like Figure 5-6 As shown, the optimal gliding power allocation result is output: After the iterative search ends, the global optimal solution is output, which is the power allocation scheme for the segmented powered gliding of the solar-powered UAV. Under the condition of setting relevant parameters, the fitness change curve with the number of iterations during the iteration process, as well as the power allocation result of the powered gliding of the solar-powered UAV, are obtained with the help of simulation calculation tools.

[0076] In the initialization of the particle swarm, the maximum velocity of the particles is constrained to be 10%–20% of the maximum gliding power. Generally, the maximum velocity of the particle swarm is within the range of 10%–20% of the variable variation; specifically, for the optimization variable in the objective problem, the maximum velocity of the particle swarm is 10%–20% of the maximum gliding power.

[0077] The maximum velocity constraint on particles ensures that the search is neither too conservative nor too aggressive, balancing the capabilities of global and local search. This helps avoid unnecessary long-distance jumps by particles in the search space, improving the adaptability and efficiency of the algorithm.

[0078] In one embodiment, such as Figure 7 As shown, the flight trajectory diagram can be obtained by simulation based on the optimized scheme, and the flight time and energy consumption can be calculated to verify the effectiveness of the method.

[0079] Based on the calculated power distribution scheme for the powered gliding phase, combined with the kinematic equations of the solar-powered UAV during gliding described above, and with the help of simulation calculation tools, the longitudinal trajectory of the solar-powered UAV during powered gliding under the given power distribution scheme is obtained. Furthermore, the total flight time of the gliding phase is calculated to be 6 hours, and the energy consumption is 2.43 kWh.

[0080] The embodiments of the present invention have been described in detail above, but the content described is only a preferred embodiment of the present invention and should not be considered as limiting the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the patent coverage of the present invention.

[0081] The present invention will now be described with reference to the accompanying drawings. Experimental methods not specifically described in terms of operation steps are performed in accordance with the corresponding product manuals. Unless otherwise specified, the instruments, reagents, and consumables used in the embodiments can be purchased from commercial companies.

Claims

1. A solar-powered unmanned aerial vehicle power gliding power control method based on a particle swarm algorithm, characterized by, The method comprises the following steps: initializing setting of aircraft parameters and flight parameters; establishing a target function of a flight phase; setting iteration parameters of a particle swarm algorithm; solving an optimization result scheme based on the particle swarm algorithm; the flight phase comprises daytime climbing, daytime highest altitude cruising, nighttime gliding and nighttime lowest altitude cruising; when the flight phase is nighttime gliding, the power is distributed to the unmanned aerial vehicle in segments, the number of power segments is n, and the gliding motion state of the i-th segment power is updated by the following formula, wherein, is the flight speed of the i-th power segment, is the atmospheric density of the i-th power segment, S is the wing area of the UAV, is the drag coefficient, g is the gravitational acceleration, is the flight path angle of the i-th power segment, is the lift coefficient, m is the mass of the UAV; the gliding time length in the corresponding power segment and the energy consumption are calculated by the following formulas, wherein, is the maximum height of the drone, is the minimum height of the drone; the objective function formula is, wherein, , is the gliding power, is the gliding power of the nth power segment, is the gliding time, is the power system energy consumption in the gliding phase, and α and β are weighting factors, with α + β = 1.

2. The control method according to claim 1, characterized by, the iteration parameters comprise the number of particles, the dimension of particles, the maximum number of iterations, the maximum inertia weight, the minimum inertia weight, the maximum learning factor and the minimum learning factor.

3. The control method according to claim 1, characterized by, The particle swarm algorithm solving comprises the following steps: initializing particle swarm parameters; initializing the particle swarm; evaluating particle fitness; updating individual optimal solution and global optimal solution; updating the speed and position of the particle; iterative search and termination condition judgment; outputting the optimal gliding power distribution result.

4. The control method according to claim 3, characterized in that: The maximum speed of the particle in the initialized particle swarm is constrained, and the maximum speed of the particle swarm is 10% to 20% of the maximum gliding power.

5. The control method according to claim 1, characterized by, According to the optimization result scheme, a flight trajectory graph is simulated and the flight time and flight energy consumption are calculated, so as to verify the effectiveness of the method.

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