Solar-powered unmanned aerial vehicle energy management strategy and track tracking control method

Abstract

The present application relates to a kind of solar unmanned aerial vehicle energy management strategy and track tracking control method, first establish solar unmanned aerial vehicle three-dimensional particle motion model, energy storage battery model and solar acquisition model;According to the aerodynamic parameters of solar unmanned aerial vehicle and the typical flight, climbing, descending of solar unmanned aerial vehicle process, such as motion, the optimal motion parameters of solar unmanned aerial vehicle under different motion conditions are determined. Through energy management strategy, longitudinal control of solar unmanned aerial vehicle under different illumination levels and energy state is realized. The lateral movement of aircraft depends on the task requirement of solar unmanned aerial vehicle, by decoupling the particle dynamics equation of solar unmanned aerial vehicle, a track tracking controller based on feedback linearization method is proposed, and the track control of solar unmanned aerial vehicle in lateral is realized. 24 hours time closed simulation shows that the method can meet the minimum energy remaining requirement of energy storage battery and track tracking control requirement.

Description

A solar-powered unmanned aerial vehicle (UAV) energy management strategy and trajectory tracking control method Technical Field

[0001] This invention pertains to unmanned aerial vehicle (UAV) tracking and control methods, specifically a solar-powered UAV energy management strategy and trajectory tracking and control method. Background Technology

[0002] As a type of aircraft using green energy, solar-powered drones are receiving increasing attention from researchers due to technological advancements and the growing awareness of sustainable development. Furthermore, solar-powered drones possess the capability for long-endurance high-altitude operations, with continuous operation times reaching several months – a significant advantage unmatched by other types of drones. Therefore, they have broad application potential in fields such as communication relay, agriculture, environmental monitoring, and military reconnaissance. (See references: Goraj Z, Frydrychiewic A, Winiecki J. Design concept of a high altitude long-enduranceunmanned aerial vehicle[J]. Aircraft Design,1999,2:19–44; Alvior. Development of solar powered aircraft for multi-purpose application:AIAA-2010-3061[R]. Reston:AIAA,2010.)

[0003] The flight time of solar-powered drones has always been a focus of attention for experts from various countries. In theory, solar-powered drones can stay airborne for a long time, but the payload of solar-powered drones is often limited, which hinders their application and promotion. To improve the performance of solar-powered UAVs, current research directions include: Literature on structural aspects: Wilson, C., et al. Aerodynamic and Structural Design of a Solar-Powered Micro Unmanned AirVehicle[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2000, 214(2):97–106; Wang Guan. Structural Design and Optimization of a Solar-Powered UAV[D]. Shenyang: Shenyang Aerospace University, 2018; Literature on control aspects: Ma Zhenyu, Zhu Xiaoping, Zhou Zhou. A lateral control method for an all-wing solar-powered UAV using rudder-propeller combination[J]. Acta Aeronautica Sinica, 2018, 39(03):215-225; Xiao Wei, Zhou Zhou, Zhu Xiaoping, Xu Mingxing. Research on flight dynamics and control simulation of flexible solar-powered UAV[J]. Journal of System Simulation, 2014, 26(03):704-709; Literature on energy aspects: Gao XZ, Hou ZX, Guo Z, Liu JX, Chen X Q. Energy management strategy for solar-powered high-altitude long-endurance aircraft[J]. Energy Conversion and Management, 2013, 70: 20-30; Sun KW., Ni M. Feasibility analysis of hybrid power system used on solar-powered aircraft[J]. Advanced Materials Research, 2014, 860-863: 118-123; Literature on trajectory direction: Wang Shaoqi, Ma Dongli, Yang Muqing, et al. Optimization of three-dimensional trajectory of high-altitude solar-powered UAV[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(5): 936-94; In terms of accuracy of solar-powered UAV system model, accuracy of energy storage battery status, and feasibility of flight strategy, the accuracy of solar-powered UAV system model, accuracy of energy storage battery status, and feasibility of flight strategy have a great impact on the energy acquisition efficiency and flight efficiency of solar-powered UAV.

[0004] Research on flight strategy planning for solar-powered unmanned aerial vehicles (UAVs) is discussed in the literature Colozza A J. Effect of date and location on maximum achievable altitude for a solar-powered aircraft [M]. Ohio: NASA Contractor Report 202326. NYMA, Inc. Brook Park, 1997. The literature focuses on the feasible takeoff time and location of solar-powered UAVs without energy storage, and determines the maximum altitude that solar-powered UAVs can reach based on the takeoff time and location. The literature Gao XZ, Hou ZX, Guo Z, et al. The equivalence of gravitational potential and rechargeable battery for high-altitude long-endurance solar-powered aircraft on energy storage[J]. Energy Conversion and Management, 2013, 76: 986-995 and Ma Dongli, Bao Wenzhuo, Qiao Yuhang. Study on flight trajectory of solar-powered aircraft based on gravity energy storage[J]. Acta Aeronautica Sinica, 2014, 35(2): 408-416 proposes the idea of ​​using gravity energy storage, that is, under sufficient sunlight, in addition to maintaining the daily flight needs of solar-powered UAVs and charging the batteries, a portion of solar energy is converted into the gravitational potential energy of solar-powered UAVs, and this portion of gravitational potential energy is released at night in the absence of sunlight through unpowered gliding and other methods. Compared with constant altitude continuous cruise, the variable altitude cruise strategy requires less battery power for solar-powered UAVs and requires a smaller wing area. Currently, many solar-powered UAVs adopt this cruise strategy. The research conducted by G. Sachs et al. in Sachs G, Lenz J, Holzapfel F. Unlimited endurance performance of solar UAVs with minimal or zero electric energy storage[C]. In AIAA Guidance, Navigation and Control Conference. Chicago: AIAA, 2009: 1-13 on the minimum energy storage flight strategy of solar-powered UAVs shows that UAVs can theoretically achieve continuous cruise flight without batteries by storing gravitational potential energy through climbing under sunlight and gliding without power in the absence of sunlight.

[0005] For the trajectory optimization problem of solar-powered UAVs, the literature Klesh AT, Kabamba P T. Energy-optimal path planning for solar-powered aircraft in level flight[C] / / In AIAA Guidance, Navigation and Control Conference and Exhibit. Hilton Head SC,2007:20-23 and Klesh AT, Kabamba P T. Solar-powered aircraft: energy-optimal pathplanning and perpetual endurance[J]. Journal of guidance, control, and dynamics,2009,32(4):1320-1329 considers the path planning problem of solar-powered UAVs given the starting and ending positions. A motion model and a solar radiation model of the solar-powered UAV are established. The relationship between energy consumption and the lateral attitude of the UAV is discussed. With the maximum remaining electric energy at the end of the cruise as the optimization objective, the trajectory optimization design and simulation of the solar-powered UAV under the horizontal constant altitude hovering condition are carried out. It is shown that the optimized horizontal constant altitude flight trajectory is significantly better than the horizontal straight flight trajectory. References Spangelo SC, Gilbert E., Klesh A, et al. Periodic energy-optimal pathplanning for solar-powered aircraft[C] / / In Proceedings of the AIAA Guidance, Navigation, and Control Conference. Chicago: AIAA, 2009 and Spangelo SC, Gilbert E. Power optimization of solar-powered aircraft with specified closed groundtracks[J]. Journal of Aircraft, 2012, 50(1):232-238 optimize the trajectory design of solar-powered UAVs flying within a three-dimensional cylindrical surface. References Dai R, Lee U, Hosseini S, et al. Optimal pathplanning for solar-powered UAVs based on unit quaternions[C] / / In the 51st IEEE Conference on Decision and Control. Hawaii: IEEE, 2012: 3104-3109. This paper presents trajectory optimization for solar-powered unmanned aerial vehicles (UAVs) based on the unit quaternion method. The paper Dai R. Path planning of solar-powered unmanned aerial vehicles at low altitude [C] / / In, IEEE 56th International Midwest Symposium on Circuits and Systems. Columbos: IEEE, 2013: 693-696. This paper studies the impact of weather changes on trajectory planning for low-altitude solar-powered UAVs. The paper Hosseini S, Dai R, Mesbahi M. Optimal path planning and power allocation for a long endurance solar-powered UAV [C] / / In American Control Conference. Washington: IEEE, 2013: 2588-259. This paper divides the flight process of a solar-powered UAV into three segments: takeoff, level flight, and descent, and performs optimization analysis on each segment separately, comparing the results with those from direct optimization.

[0006] In practical missions, the trajectories of solar-powered UAVs are often manually controlled or predetermined, making offline trajectory optimization methods unsuitable and inadequate for real-time requirements. Therefore, an energy management strategy for solar-powered UAVs was designed, defining the allocation mechanism for energy acquisition, storage, and consumption. Excess solar energy is stored using gravitational potential energy for altitude adjustment. Given the lateral projection of the UAV's trajectory onto the ground, a controller was designed based on the UAV's particle dynamics equations to achieve trajectory tracking. Summary of the Invention

[0007] Technical problems to be solved

[0008] To overcome the shortcomings of existing technologies, this invention proposes an energy management strategy and trajectory tracking control method for solar-powered unmanned aerial vehicles (UAVs). Reconciling the conflict between the high-altitude operational tasks and long-term energy efficiency of high-altitude, long-endurance solar-powered UAVs is one of the core issues in UAV control. Based on the close coupling relationship between energy acquisition, storage, and consumption of solar-powered UAVs, a comprehensive energy management strategy and trajectory tracking control method are proposed.

[0009] Technical solution

[0010] A solar-powered unmanned aerial vehicle (UAV) energy management strategy and trajectory tracking control method, characterized by the following steps:

[0011] Step 1: Establish a 3D mass motion model, energy storage battery model, and solar energy acquisition model for the solar-powered UAV:

[0012] (1) Three-dimensional particle dynamics model: For a solar-powered UAV flying in calm wind, its three-dimensional particle kinematic equations are:

[0013]

[0014] Where x, y, and z represent the three-dimensional coordinates in the ground reference coordinate system, ψ is the azimuth angle, γ is the inclination angle, V is the velocity, T is the thrust, D is the drag, and n h n v These are lateral overload and normal overload, respectively.

[0015] The n h n v Calculated using the following formula

[0016]

[0017] Where L is the lift of the solar-powered drone, φ is the roll angle, and m is the mass;

[0018] The lift and drag of a solar-powered drone can be calculated using aerodynamic methods:

[0019]

[0020] Where ρ is the air density, S w For the wing area, C L C is the lift coefficient. D drag coefficient

[0021] 2. Power Consumption Model: The relationship between the propulsion power consumption of a solar-powered UAV and its thrust and speed is as follows:

[0022]

[0023] Where, η prop Let be the overall energy conversion efficiency of the entire propulsion system, which is a constant.

[0024] 3. Solar radiation energy calculation model:

[0025] P sc =η MPPT η sc P sdS sc cosθ

[0026] Where, η MPPT For the maximum power point tracking efficiency (MPPT), η sc For the energy conversion efficiency of solar panels, P sd S is the solar constant, representing the solar radiation energy received per unit area perpendicular to the direction of sunlight at the average distance between the Earth and the Sun. sc Let θ be the area of ​​the solar panel, and θ be the angle of incidence of sunlight relative to the solar panel.

[0027] 4. Battery model:

[0028]

[0029] Among them, V OC P is the battery open-circuit voltage, SOC is the battery state of charge, representing the ratio of current charge to rated capacity, and R is the battery equivalent circuit resistance. When the battery is charging, P... batt When P is negative, it discharges. batt It is positive;

[0030] Step 2: Track tracking control:

[0031] Requirements: 1. Track the two-dimensional trajectory on the ground; 2. Only the ground projection of the solar-powered UAV during flight must match the required trajectory; it is not real-time trajectory tracking, and there is no time requirement.

[0032] Principle: The speed V and trajectory tilt angle of the solar-powered UAV are determined according to the energy management strategy. The trajectory tracking consists of outer loop control of the x and y coordinates and inner loop control of the ψ, γ, and V variables.

[0033] Adaptive control law for inner-loop control:

[0034]

[0035] Wherein: the control law for outer loop control: u x =-k x e x u y =-k y e y

[0036] in:

[0037] The energy management strategy described above involves the solar-powered drone utilizing gravitational potential energy for energy storage, and its motion state is mainly divided into three stages.

[0038] Flight Status 1: The solar-powered drone is in level flight, γ≡0, φ=0, and the solar power distribution is as follows:

[0039] P sol =P prop -P bat

[0040] At this time, P sol >P prop ,

[0041] in:

[0042] Best angle of attack

[0043] Battery power design is

[0044] Among them, P limit This refers to the maximum charging and discharging power of the battery.

[0045] The propulsion power of the solar-powered drone is P prop =P sol +P bat ;

[0046] Using an angle of attack α * The flight speed V and track tilt angle γ corresponding to the current power are calculated based on the three-dimensional particle dynamics model.

[0047] Flight state 2: Gravitational potential energy gliding state, the solar-powered drone begins to glide down from the highest point using gravitational potential energy;

[0048] When solar energy is not zero, the vehicle slides with power; when solar energy is zero, the vehicle slides without power, i.e., P... prop =P sol

[0049] The angle of attack used is also α. * ,according to The gliding velocity V and the trajectory inclination angle γ can be obtained by solving the equation.

[0050] Flight Status 3: Minimum Altitude Level Flight, the solar-powered drone flies at its lowest altitude.

[0051] Battery power is

[0052] Similar to the level flight phase of Phase 1, the solar-powered UAV selects α as its angle of attack. * And find the favorable speed V for level flight.

[0053] The incident angle of the incident light is: Where: the coordinate transformation matrix from the ground coordinate system to the body coordinate system is L gbThe incident ray in the body coordinate system is represented as The unit normal vector of the solar panel in the body coordinate system is

[0054] Beneficial effects

[0055] This invention proposes an energy management strategy and trajectory tracking control method for a solar-powered unmanned aerial vehicle (UAV). First, a three-dimensional particle motion model, an energy storage battery model, and a solar energy acquisition model for the solar-powered UAV are established. Based on the aerodynamic parameters of the solar-powered UAV and its typical flight, climb, and descent motions, the optimal motion parameters for the solar-powered UAV under different motion conditions are determined. The energy management strategy enables longitudinal control of the solar-powered UAV under different light levels and energy states. The lateral motion of the aircraft depends on the mission requirements of the solar-powered UAV. By decoupling the particle dynamics equations of the solar-powered UAV, a trajectory tracking controller based on a feedback linearization method is proposed, realizing lateral trajectory control of the solar-powered UAV. A 24-hour time-closed-loop simulation shows that this method can meet the minimum remaining energy requirements of the energy storage battery and the trajectory tracking control requirements. Attached Figure Description

[0056] Figure 1: Schematic diagram of the solar-powered UAV trajectory tracking problem

[0057] Figure 2: Relationship between lift coefficient, drag coefficient and angle of attack

[0058] Figure 3: Relationship with angle of attack

[0059] Figure 4: Flight trajectory of solar-powered drone

[0060] Figure 5: Power Relationships of Various Systems in a Solar-Powered UAV

[0061] Figure 6: Speed ​​and altitude of solar-powered drone

[0062] Figure 7: Battery power status

[0063] Figure 8: Thrust, angle of attack, and roll angle of solar-powered UAV Detailed Implementation

[0064] The present invention will now be further described in conjunction with the embodiments and accompanying drawings:

[0065] Modeling the solar-powered drone trajectory tracking problem:

[0066] Solar-powered drones obtain solar energy through solar panels installed on their wings, which power their onboard electronics and provide flight propulsion. Currently, the design level of solar-powered drones, as well as the performance of key components such as solar cells, batteries, and motors, enables them to fly day and night. However, to maintain nighttime flight capability, it is necessary to comprehensively design both battery energy storage and gravitational potential energy storage methods to meet the drone's loitering and load power requirements. Therefore, designing an energy management strategy for solar-powered drones and planning energy storage and utilization methods is essential for their normal operation throughout their entire flight cycle.

[0067] One important application scenario for solar-powered drones is communication and environmental monitoring, requiring continuous patrolling of ground targets or provision of relay communication services within a designated area. Therefore, trajectory tracking and control is a crucial issue in the application of solar-powered drones. Unlike typical trajectory tracking, the trajectory in this scenario is a two-dimensional trajectory projected onto the ground. Furthermore, to ensure that solar-powered drones do not interfere with the flight paths of normally operating civil aircraft and to guarantee service coverage, solar-powered drones have a minimum flight altitude requirement. The trajectory tracking problem of solar-powered drones is illustrated in Figure 1.

[0068] Considering that a core issue in the long-duration flight of solar-powered drones is the collection, storage, and utilization of solar energy, the mathematical models involved in establishing the solar-powered drone trajectory tracking problem include the three-dimensional particle dynamics equations of the solar-powered drone, the power consumption model, the solar radiation energy calculation model, and the battery model.

[0069] Considering that a core issue in the long-duration flight of solar-powered drones is the collection, storage, and utilization of solar energy, the mathematical models involved in establishing the solar-powered drone trajectory tracking problem include the three-dimensional particle dynamics equations of the solar-powered drone, the power consumption model, the solar radiation energy calculation model, and the battery model.

[0070] 1.2 Three-dimensional particle dynamics model

[0071] For a solar-powered drone flying in calm winds, its three-dimensional kinematic equations can be written as follows:

[0072]

[0073] Where x, y, and z represent the three-dimensional coordinates in the ground reference coordinate system, ψ is the azimuth angle, γ is the inclination angle, V is the velocity, T is the thrust, D is the drag, and n h n v These are lateral overload and normal overload, respectively. Since solar-powered unmanned aircraft have a small angle of attack during flight, one assumption of the above model is that the thrust and drag directions are parallel to the velocity direction, while the lift is perpendicular to the velocity direction.

[0074] n h n v Calculated using the following formula

[0075]

[0076] Where L is the lift of the solar-powered drone, φ is the roll angle, and m is the mass.

[0077] The lift and drag of a solar-powered drone can be calculated using aerodynamic methods and can be expressed as follows:

[0078]

[0079] Where ρ is the air density, S w For the wing area, C L C is the lift coefficient. D This is the drag coefficient.

[0080] The lift and drag coefficients are related to the airfoil design of solar-powered UAVs and are affected by the angle of attack and Mach number. Since solar-powered UAVs have relatively low flight speeds, their lift and drag coefficients are not sensitive to low Mach numbers; therefore, this paper considers the angle of attack to be the sole determinant of their lift and drag coefficients.

[0081] 1.3 Power Consumption Model

[0082] The power consumption of the solar-powered drone is primarily supplied to the propulsion system. Since the power consumption from other onboard systems is relatively small, it is not considered in this paper. The propulsion system consists of a motor and a propeller, and its extracted electrical power is expressed as follows:

[0083]

[0084] Where, η prop This refers to the overall energy conversion efficiency of the entire propulsion system.

[0085] The propeller's propulsion efficiency is related to the Mach number. Solar-powered UAVs fly at relatively low and unchanging Mach numbers, and their apparent η has little impact on the analysis presented in this paper. prop It is a constant. The propulsion power consumption of the solar-powered UAV is related to thrust and speed by equation (4).

[0086] 1.4 Solar Radiation Energy Calculation Model

[0087] Long-endurance solar-powered UAVs fly at high altitudes, with a design altitude of 12,000-30,000 meters. At these altitudes, the atmosphere is relatively thin, and there is no strong tropospheric disturbance. The influence of factors such as temperature, humidity, atmospheric reflection, and atmospheric scattering on the solar energy received by the UAV is negligible. Furthermore, due to computational complexity, the influence of wing airfoil curvature when the solar panels are mounted on the wings is not considered; the solar panels are assumed to be flat plates fixed in the aircraft's coordinate system. Under these conditions, the angle of incidence of sunlight relative to the solar panels is the primary influencing factor.

[0088] The formula for calculating the output power of a solar panel is:

[0089] P sc =η MPPT η sc P sd S sc cosθ (5)

[0090] Where, η MPPT For the maximum power point tracking efficiency (MPPT), η sc For the energy conversion efficiency of solar panels, P sd S is the solar constant, representing the solar radiation energy received per unit area perpendicular to the direction of sunlight at the average distance between the Earth and the Sun. sc Let θ be the area of ​​the solar panel, and θ be the angle of incidence of sunlight relative to the solar panel.

[0091] As can be seen from equation (5), the output power of the solar panel mainly depends on the incident angle θ. The incident angle is related to the solar altitude angle and azimuth angle on the one hand, and the attitude of the solar-powered UAV on the other hand. The incident angle of sunlight in the UAV coordinate system can be obtained by the coordinate transformation method between the ground coordinate system and the body coordinate system.

[0092] Solar altitude angle α s With azimuth ψ s It can be calculated using the following formula:

[0093]

[0094] Where, φ L The latitude is δ, the declination of the sun is ω. t d is the solar hour angle. n t represents the accumulated days of a year, and t represents true solar time.

[0095] The unit vector of the incident ray in the ground coordinate system is represented as:

[0096]

[0097] The coordinate transformation matrix from the ground coordinate system to the body coordinate system is L. gb The incident ray in the body coordinate system is represented as The unit normal vector of the solar panel in the body coordinate system is Then the angle of incidence of the incident ray is

[0098]

[0099] 1.5 Battery Model

[0100] Based on the simple equivalent circuit model of the battery, its energy output can be calculated using the following formula:

[0101]

[0102] Among them, V OC P is the battery open-circuit voltage, SOC is the battery state of charge, representing the ratio of current charge to rated capacity, and R is the battery equivalent circuit resistance. When the battery is charging, P... batt When P is negative, it discharges. batt It is positive.

[0103] To reduce battery wear and extend battery life, overcharging and over-discharging must be avoided. The State of Charge (SOC) must meet the following constraints:

[0104] 0.25≤SOC≤0.99 (10)

[0105] From equation (9), we can obtain the charging rate. for

[0106]

[0107] 2. Energy Management Strategy Design

[0108] Solar-powered drones have two energy sources: the initial charge from the battery at takeoff and solar energy collected by solar panels during daytime flight. The initial battery charge is primarily used for takeoff and the initial flight phase; during long-duration flights, the main energy source is solar energy. The solar-battery system powers the motor-propeller system to sustain flight and provides energy to the electronic load. The battery's role is energy storage and power matching. Since solar energy fluctuates throughout the day (0-low-high-low-0), when solar power is low, the battery provides additional energy; when solar power is sufficient, the extra power is stored in the battery. However, battery capacity is limited, and the load from a large battery can reduce the performance of the solar-powered drone. Converting excess solar energy into gravitational potential energy is a feasible approach. Furthermore, flight parameters should be optimized to minimize power loss. Therefore, coordinating solar energy, battery capacity, and flight status to improve solar energy utilization efficiency is the ultimate goal of energy management strategies.

[0109] The movement of solar-powered drones that utilize gravitational potential energy for energy storage can be mainly divided into three stages:

[0110] Phase 1: Battery charging and climbing phase

[0111] This stage primarily involves the solar-powered drone using solar energy to climb to altitude. Initially, solar energy sustains the aircraft in level flight. As solar energy increases, excess energy charges the battery. Once the battery reaches its maximum charging capacity, the remaining solar energy is used for the propulsion system, and the aircraft begins to climb, converting energy into gravitational potential energy.

[0112] Phase 2: Gravitational potential energy gliding phase

[0113] When the absorbed solar energy is insufficient to support the solar-powered drone's level flight power at its highest point, the aircraft begins to descend. During this phase, all the propulsion energy of the solar-powered drone comes from solar energy; when the solar energy diminishes to zero, the solar-powered drone glides without power.

[0114] Phase 3: Minimum Altitude Level Flight

[0115] When the solar-powered drone reaches the predetermined minimum altitude, it transitions to the third stage of flight, where it maintains flight at that altitude by discharging its lithium battery. This stage ends in two ways: either the battery runs out of power and reaches its minimum charge level, in which case it will re-enter the unpowered gliding phase (this generally doesn't occur if the solar-powered drone is well-designed and used correctly); or the solar-powered drone successfully survives the night on battery power until the solar energy can provide the necessary power for level flight at the current altitude, thus meeting the conditions for the first stage.

[0116] Energy management is an integral part of the operation of a solar-powered drone at each stage. Therefore, based on factors such as flight speed, thrust, and energy distribution at each stage, and aiming for optimal energy efficiency, the following energy management strategy is designed:

[0117] Phase 1: The initial phase of this phase is level flight, with solar power allocation as follows:

[0118] P sol =P prop -P bat (12)

[0119] At this time, P sol >P prop In order to obtain the smallest possible P batt (P batt <0 indicates battery charging), so the level flight propulsion power should be reduced. The solar-powered UAV is in a stable flight state, γ≡0, φ=0. Subsequent analyses assume the solar-powered UAV is in an instantaneous equilibrium state, and the attitude angle is approximated as 0 for small angles. According to equations (1)-(4), the required level flight propulsion power is...

[0120]

[0121] According to equation (13), when When the minimum is reached, the minimum value can be obtained.

[0122] Level flight propulsion power. As mentioned above, C D With C L All of these are related only to the angle of attack, therefore the optimal angle of attack for level flight can be obtained.

[0123]

[0124] Once the battery charging power reaches its maximum, excess solar energy begins to propel the drone upwards. A portion of the propulsion system's power is converted into gravitational potential energy, and the remainder is used to counteract drag. For a given flight path angle, the power consumed by drag is...

[0125]

[0126] It is evident that in order to make P csm The minimum angle of attack is chosen in the same way as in equation (14), that is... For the smallest angle of attack α * .

[0127] Battery power design is

[0128]

[0129] Among them, P limit This refers to the maximum charging and discharging power of the battery.

[0130] Whether in level flight or climbing, the propulsion power of the solar-powered drone is P. prop =P sol +P bat Based on the propulsion power and instantaneous balance assumptions, an angle of attack of α is adopted. * The flight speed V and track tilt angle γ corresponding to the current power can be calculated from equations (1)-(4).

[0131] Phase 2: In this phase, the acquired solar energy is insufficient to support the aircraft's climb and level flight. The solar-powered drone begins to glide down from its highest point using gravitational potential energy. When solar energy is not zero, it performs a powered descent; when solar energy is zero, it performs a powerless descent, i.e., P... prop =P sol .

[0132] At this point, the battery should be fully charged; it should neither be charging nor discharging.

[0133] P batt =0 (17)

[0134] During the descent, the drone mainly relies on the conversion of gravitational potential energy into kinetic energy. In order to make the drone stay in the air for as long as possible, the descent altitude per unit time needs to be minimized.

[0135] Solar-powered drones meet the requirements for stable gliding.

[0136]

[0137] The sinking speed of the solar-powered drone is

[0138]

[0139] when At its minimum, the sinking speed is also minimum. Therefore, consistent with the conclusion of stage 1, the solar-powered drone adopts an angle of attack of α in stage 2. * As can be seen from equation (19), the power P provided by solar energy... sol It can further reduce the sinking rate.

[0140] Similarly, when α * It is determined that the gliding speed V and the track inclination angle γ can be obtained by solving equation (18).

[0141] Phase 3: In this phase, the solar-powered drone flies at the lowest possible altitude. This is because the flight mission has a minimum altitude requirement, and the lower the altitude, the less power is consumed during level flight.

[0142] When solar energy is zero, the solar-powered drone's flight is entirely powered by the battery; when solar energy is greater than zero, it is powered by a combination of the battery and solar power. Therefore, the battery power is...

[0143]

[0144] Similar to the level flight phase of Phase 1, the solar-powered UAV selects α as its angle of attack. * And the favorable speed V for level flight can be obtained.

[0145] 3. Tracking and Control

[0146] There are two special features to the trajectory control of solar-powered drones. One is that it tracks a two-dimensional trajectory on the ground, and the other is that it only requires that the ground projection of the solar-powered drone during flight be consistent with the required trajectory. It is not real-time trajectory tracking and there is no time requirement.

[0147] Therefore, controlling the trajectory tilt angle ψ is sufficient to make the x and y coordinates of the solar-powered UAV track the trajectory. Furthermore, as discussed in the previous section, the speed V and trajectory tilt angle of the solar-powered UAV are determined by the energy management strategy. Therefore, the trajectory tracking problem can be decoupled into outer-loop control of the x and y coordinates and inner-loop control of the variables ψ, γ, and V.

[0148] First, for inner-loop control, ψ, γ, and V are required to track the signal ψ. r (t), γ r (t), V r (t), where the control variables are thrust T, angle of attack α, and roll angle φ.

[0149] First, the state differential equation of the inner loop control is linearized using feedback.

[0150] make

[0151]

[0152] Among them, C L -1 (·) represents the inverse function of the lift coefficient with respect to the angle of attack.

[0153] achievable

[0154]

[0155] Therefore, the state equation after feedback linearization is very simple. Let the tracking error be e1 = ψ - ψ r (t), e2=γ-γ r (t), e3=VV r (t), then the state equation of the error is

[0156]

[0157] like If these terms are precisely known, they can easily be eliminated through compensation methods, achieving exponential convergence. However, in reality, it is difficult to obtain accurate variable derivatives. Therefore, these terms are treated as disturbance terms, and adaptive control methods are used to predict their upper bounds.

[0158] Taking the first equation in equation (23) as an example, let Bounded, And d is an unknown quantity, design the adaptive control law as follows:

[0159]

[0160] It can be proven that the error e1 is uniformly bounded.

[0161] make Choose Lyapunov candidate functions as The derivative of V along the system trajectory is

[0162]

[0163] According to Young's inequality

[0164]

[0165] therefore

[0166]

[0167] When parameter ε satisfies

[0168] ε≤min{2k1,κσ} (27)

[0169] achievable

[0170]

[0171] According to the comparison lemma, the following equation holds:

[0172] V(t)<(V(0)-Δ)e -εt+Δ (29)

[0173] Where Δ=σ+σd 2 / 2.

[0174] As can be seen from equation (29), the tracking error is uniformly bounded and converges to a bounded set. Furthermore, the upper bound of the error can be made arbitrarily small by adjusting the parameter σ, but this will slow down the convergence speed of the system.

[0175] The control and controller design for states γ and V are similar to those in equation (24).

[0176] For outer-loop control, since it only requires tracking a time-independent two-dimensional reference track, the ground reference track can be considered as a series of connected track points. When the solar-powered UAV reaches one track point, it begins flying to the next. Therefore, after discretizing the reference track, the control objective for the solar-powered UAV's coordinates (x, y) in each time period is (x...). r ,y r ), x r y r Let e ​​be the coordinates of the waypoint, and it is a constant. x =xx r e y =yy r For tracking error, then we have

[0177]

[0178] make

[0179]

[0180] You can talk about e x e y Linearization of the state equations, i.e.

[0181]

[0182] Take u x =-k x e x u y =-k y e y (k x >0, k y With a value greater than 0, it is easy to see that the tracking error converges.

[0183] In equation (31), the value of Vcosγ is given, which is the projection of the solar-powered UAV's velocity onto the ground. Since the target values ​​of velocity V and track tilt angle γ have been determined in the energy management strategy and tracked through the inner loop control, the control output of Vcosγ is discarded in the outer loop control, and only the track tilt angle ψ is given and provided to the inner loop controller as a reference input.

[0184] 4. Simulation Verification

[0185] The parameters of the solar-powered drone are shown in the table below. The simulation location is at latitude 40°N, the flight date corresponds to the year 182 (June 21st in a common year), the initial time is 6:00 AM local time, and the initial battery energy is SOC0 = 0.8. The initial altitude is 0m, and the initial speed is 8m / s.

[0186] During the takeoff phase, if the solar energy level is insufficient to meet the climb criteria of Phase 1, a takeoff procedure for the solar-powered UAV was designed to achieve the optimal angle of attack α. * =2°, track inclination angle γ=2.9° for climbing.

[0187] Table 1 Basic Parameters of Solar-Powered UAVs

[0188] Table 1.Basic parameters of the solar-powered UAV

[0189]

[0190] Figure 2 shows the relationship between the lift coefficient, drag coefficient, and angle of attack of the solar-powered UAV. Both the lift coefficient and drag coefficient are represented by piecewise functions, and they are considered to remain unchanged when the angle of attack is less than -4° or greater than 4°.

[0191] Based on the lift coefficient versus drag coefficient curve The curve showing the variation of attack angle α is shown in Figure 3. When the attack angle is 2°... Maximum, that is Minimum. To ensure optimal efficiency, the angle of attack for a solar-powered drone should be kept below 2° during flight.

[0192] The solar-powered drone was simulated 24 hours a day according to the designed energy management strategy and trajectory control method. The flight trajectory of the solar-powered drone is shown in Figure 4.

[0193] As can be seen, for the preset flight path, the solar-powered drone takes off from its initial state and heads towards the shortest path point, then follows the path. Within 24 hours, the solar-powered drone circled the path twice. The flight path can be clearly divided into takeoff, climb, descent, and minimum altitude level flight phases, consistent with the energy management strategy. The solar-powered drone reached a maximum altitude of 34,152 meters and flew along the given path for nearly a full circle at the fastest speed throughout the entire flight.

[0194] The changes in energy parameters of the solar-powered UAV during flight are shown in the figure below. Figure 5 shows the changes in solar power P. sol Propulsion system power P prop Battery power P batt The interrelationship between them.

[0195] In Figure 5, 0h on the timeline corresponds to takeoff time 8:00. 2.9 hours after takeoff, the battery SOC reaches 0.9 and ceases charging; all solar power is supplied to the propulsion system. The power of each system satisfies P... sol =P prop -P bat The power distribution is clearly divided into three phases based on the energy management strategy. Phase 1 is the initial part of the takeoff phase, Phase 2 is divided into powered descent and unpowered descent, and Phase 3 maintains the minimum altitude level flight.

[0196] Figure 6 shows the speed and altitude changes of the solar-powered drone. The trends are consistent: the higher the altitude, the higher the speed. Figure 7 shows the changes in the State of Charge (SOC) of the battery. In stage 1, the battery energy level begins to rise; in stage 2, it remains constant; and in stage 3, it continues to discharge. Figure 7 shows that after 24 hours of flight, the battery SOC is 0.85, higher than the initial 0.8, and the lowest value is 0.72, indicating that the battery has some dead weight. This suggests that there is room for improvement in the design of the solar-powered drone, and its performance can be further enhanced.

[0197] The control variables for the inner loop control of the solar-powered UAV's trajectory tracking are thrust, angle of attack, and roll angle. The changes in these three control variables are shown in Figure 8. According to the time axis, thrust is maximum in stage 1, minimum in stage 2, and remains constant in stage 3. The angle of attack remains stable for most of the time, stabilizing at the optimal efficiency of 2°, which is pre-designed in the energy management strategy. The roll angle changes more drastically compared to the angle of attack, mainly when the solar-powered UAV is at its highest altitude and fastest speed, resulting in significant changes in trajectory yaw angle. Therefore, the overshoot is large when controlling the roll angle; in other stages, the roll angle is essentially maintained at 0°.

[0198] Therefore, this invention, based on the establishment of system models of each component of the solar-powered UAV, designs an energy management strategy for the solar-powered UAV and forms a trajectory tracking control method based on three-dimensional kinematic equations.

[0199] (1) In view of the characteristics of solar-powered UAVs with high altitude and long flight time and mission characteristics, the problem of two-dimensional ground track tracking of solar-powered UAVs is proposed.

[0200] (2) An energy management strategy for solar-powered UAVs with optimal flight parameters was designed. Based on tracking ground tracks, excess solar energy was stored using gravitational potential energy to achieve maximum utilization efficiency of solar energy.

[0201] (3) The dynamic equations of the solar-powered UAV were decoupled, and the inner-loop control law and outer-loop control law of the solar-powered UAV were designed by adopting the feedback linearization method. The inner-loop control takes into account the uncertainty caused by the change of the tracking signal and adopts an adaptive control method to estimate the upper bound of the first-order differential of the tracking signal. The Lyapunov theory was used to prove that the inner-loop control law can make the tracking error bounded.

[0202] (4) The simulation results verified the effectiveness of the energy management strategy and the trajectory tracking controller. The solar-powered UAV can achieve 24-hour uninterrupted flight and maintain an altitude of over 12,000m. At the same time, by analyzing the changes in various parameters of the solar-powered UAV, a useful reference can be provided for the design and performance optimization of solar energy.

Claims

1. A solar-powered unmanned aerial vehicle (UAV) energy management strategy and trajectory tracking control method, characterized in that... The steps are as follows: Step 1, establish a three-dimensional particle motion model, energy storage battery model and solar energy acquisition model for the solar-powered UAV: ​​(1) Three-dimensional particle dynamics model: For a solar-powered UAV flying in calm wind, its three-dimensional particle kinematic equation is: Where x, y, and z represent the three-dimensional coordinates in the ground reference coordinate system, ψ is the azimuth angle, γ is the inclination angle, V is the velocity, T is the thrust, D is the drag, and n h n v These are lateral overload and normal overload, respectively; the n h n v Calculated using the following formula Where L is the lift of the solar-powered drone, φ is the roll angle, and m is the mass; the lift and drag of the solar-powered drone can be calculated using aerodynamic methods. Where ρ is the air density, S w For the wing area, C L C is the lift coefficient. D For drag coefficient 2, power consumption model: The relationship between propulsion power consumption, thrust, and speed of solar-powered UAVs is as follows: Where, η prop The overall energy conversion efficiency of the entire propulsion system is a constant; 3. Solar radiation energy calculation model: P sc =η MPPT η sc P sd S sc cosθ where η MPPT For the maximum power point tracking efficiency (MPPT), η sc For the energy conversion efficiency of solar panels, P sd S is the solar constant, representing the solar radiation energy received per unit area perpendicular to the direction of sunlight at the average distance between the Earth and the Sun. sc 4. Battery Model: (The area of ​​the solar panel is θ, and the angle of incidence of sunlight relative to the solar panel is θ.) Among them, V OC P is the battery open-circuit voltage, SOC is the battery state of charge, representing the ratio of current charge to rated capacity, and R is the battery equivalent circuit resistance. When the battery is charging, P... batt When P is negative, it is discharged. batt Positive; Step 2: Tracking Control: Requirements:

1. Track the two-dimensional track on the ground; 2. Only the ground projection of the solar-powered UAV during flight must match the required track; it is not real-time tracking, and there is no time requirement; Principle: Determine the speed V and track tilt angle of the solar-powered UAV according to the energy management strategy. Tracking consists of outer-loop control of the x and y coordinates and inner-loop control of the ψ, γ, and V variables; Adaptive control law for inner-loop control: Wherein: the control law for outer loop control: u x =-k x e x u y =-k y e y in: The energy management strategy utilizes gravitational potential energy for energy storage. The motion state of the solar-powered drone is mainly divided into three stages: Flight State 1: The solar-powered drone is in level flight, γ≡0, φ=0, and the solar power allocation is P. sol =P prop -P bat At this time, P sol >P prop Among them: best angle of attack Battery power design is Among them, P limit This represents the maximum charge / discharge power of the battery; the propulsion power of the solar-powered drone is P. prop =P sol +P bat ; Angle of attack α is used * Based on the three-dimensional particle dynamics model, the flight speed V and trajectory tilt angle γ corresponding to the current power are calculated; Flight state 2: gravitational potential energy gliding state, the solar-powered drone begins to glide down from the highest point using gravitational potential energy; when the solar energy is not 0, it performs powered gliding; when the solar energy is 0, it performs unpowered gliding, i.e., P prop =P sol The angle of attack used is also α. * ,according to The gliding velocity V and the trajectory tilt angle γ are obtained by solving; Flight state 3: Level flight at the lowest altitude, the battery power of the solar-powered UAV in level flight at the lowest altitude is... Similar to the level flight phase of Phase 1, the solar-powered UAV selects α as its angle of attack. * And find the favorable speed V for level flight.

2. The solar-powered UAV energy management strategy and trajectory tracking control method according to claim 1, characterized in that: The incident angle of the incident light is: Where: the coordinate transformation matrix from the ground coordinate system to the body coordinate system is L gb The incident ray in the body coordinate system is represented as The unit normal vector of the solar panel in the body coordinate system is

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