A Complex Nonlinear Load Variable Control Method Based on UAV Electric Braking

Abstract

A complex nonlinear load variable control method based on UAV electric braking is proposed. When the UAV aborts takeoff or during landing roll, the electric braking control system first iterates the mathematical model of the control system by querying parameters such as ground conditions and weather conditions from the flight control system, updating the control objective and threshold values ​​of various output parameters. Then, the electric braking control system incorporates parameters such as the UAV's current mass, airspeed, ground speed, and wheel speed into the closed-loop control logic of the braking process, and introduces differential control and ABS anti-skid control strategies to achieve rapid response and adjustment in abnormal situations during braking. Finally, closed-loop control of the actuator braking force is achieved primarily using current and secondarily using position. This invention can replace hydraulic braking control, enabling rapid short-distance braking of UAVs under complex nonlinear load variables and solving problems such as wheel lock-up during braking and single-sided wheel lock-up in crosswinds.

Description

Technical Field

[0001] This invention relates to a complex nonlinear load variable control method based on UAV electric braking, belonging to the field of UAV electric braking control. Background Technology

[0002] In modern warfare, unmanned aerial vehicles (UAVs) are playing an increasingly important role. Various types of UAVs are frequently appearing in modern warfare, becoming a key factor influencing the course of battle. The UAV braking system, as an onboard device, also plays a crucial role, determining the UAV's ability to land safely under various runway conditions and weather environments. The UAV braking system is a relatively independent onboard device, primarily responsible for controlling the UAV's stopping load, dynamic impacts during landing, and braking during takeoff aborts, landing rolls, and turns. Its performance directly affects the UAV's ability to land safely and brake quickly during takeoff aborts. Therefore, effectively improving the adaptability of the UAV braking control system to various runways, loads, and weather conditions, maximizing braking efficiency, and resolving situations such as lateral deviation or tire blowouts under abnormal conditions are the main research directions for UAV braking systems.

[0003] Achieving maximum braking efficiency and avoiding yaw and wheel lock-up is a huge challenge. During the entire braking process, ground conditions and weather conditions can change suddenly, and airspeed and ground speed are also dynamic parameters. Therefore, it is very difficult to make the electric braking control system adaptable to various runways, loads and weather conditions. Summary of the Invention

[0004] The technical problem solved by this invention is to overcome the shortcomings of the prior art and provide a complex nonlinear load variable control method based on UAV electric braking, which solves the problems of abnormal situations such as lateral deviation and lock-up during UAV taxiing and can achieve the UAV to a stop with optimal braking efficiency.

[0005] The technical solution of this invention is:

[0006] A complex nonlinear load variable control method based on UAV electric braking includes:

[0007] The flight control system provides the current runway type τ0, ground conditions δ0, and UAV speed v. f Drone weight (m) f This is incorporated into the mathematical model of the control system to achieve preliminary planning of the target control threshold; the target control threshold includes the optimal slip ratio σ. t Maximum braking force F t ;

[0008] The wheel speeds are calculated and the results are filtered using a moving sliding window mean filter; during the pure rolling process of the UAV, the ground speed v of the UAV is used as the basis. di Wheel speed v ji Compensation is performed to obtain the compensation coefficient.

[0009] According to the planned time t p Apply braking force F s Given a step size, calculate the slip ratio σ and the binding coefficient μ at the corresponding time points. This results in a dataset P1 consisting of {time, given braking force, slip ratio, binding coefficient}, formed according to time intervals ΔT. Simultaneously, fit the binding coefficient curve and find the optimal binding coefficient μ. zy and the optimal slip ratio σ zj Exit;

[0010] The optimal binding coefficient μ zy Optimal slip ratio σ zj And the corresponding mathematical model of ground speed, wheel speed, and iterative control system, and the slip ratio σ td Maximum braking force F td Perform calculation updates;

[0011] With slip ratio σ td Calculate the corresponding velocity v for the control target y The expected braking force F y and with F y Set braking force F based on base value s The adjustment range is [(1-ε)*F y (1+ε)*F y ];

[0012] The system monitors the speed difference between the left and right wheels in real time. When the speed difference between the left and right wheels exceeds the set threshold, the left and right wheel differential control unit is triggered. When wheel slippage occurs between the left and right wheels, the ABS anti-slip control unit is triggered.

[0013] Establish an initial matrix P2 for motor current-position-braking force, and perform closed-loop control of braking force according to three working conditions: no-travel, braking tightening, and braking loosening.

[0014] Furthermore, the preliminary planning of the target control threshold specifically includes:

[0015] Step 1-1: Input the runway type τ0, weather conditions δ0, and UAV speed v provided by the flight control system. f Drone weight (m) f Introduced into the mathematical model of the control system:

[0016]

[0017] Where, μt m is the binding coefficient. fl For single-wheel load bearing at the rear, C ρ Let the drag coefficient be 0.08, ρ be the air density, and S be... j Where I is the frontal area, and I is the moment of inertia of the rotor wheels. Let r be the angular acceleration of the wheel. l Let μ be the radius of the wheel. p F is the coefficient of friction between the brake disc and brake pads. t For braking force, L p The braking torque radius; σ m For slip ratio, It is the differential of the aircraft's ground speed and the aircraft's deceleration; v j Let {μ} be the wheel speed. max C1, C2} are the factors affecting the combination coefficient and slip ratio; n is the number of brake wheels, and g is the gravitational acceleration.

[0018] Steps 1-2: First, the influence factors {μ} of the combination coefficient and slip ratio on the runway type τ0 and ground conditions δ0 are calculated. max Preliminary estimated values ​​for C1 and C2:

[0019] When the runway type τ0 is a concrete runway and the ground condition δ0 is a dry ground:

[0020] {μ max , C1, C2}={0.4, 2.0, 8.0}

[0021] When the runway type τ0 is an asphalt runway and the ground condition δ0 is a dry ground:

[0022] {μ max , C1, C2}={0.8, 1.5, 14.0}

[0023] When the runway type τ0 is a concrete runway and the ground condition δ0 is a wet ground:

[0024] {μ max , C1, C2}={0.3, 2.0, 7.5}

[0025] When the runway type τ0 is a concrete runway and the ground condition δ0 is a wet ground:

[0026] {μ max , C1, C2}={0.4, 2.0, 8.0}

[0027] Steps 1-3, when {μ max After assigning values ​​to C1, C2, etc., the maximum binding coefficient μ can be set. t :

[0028] μ t=μ max

[0029] Optimal slip ratio σ t It can be calculated as follows:

[0030] σ t =tan(asin(μ) t / μ max ) / C1) / C2

[0031] Steps 1-4, in μ p v f L p C ρ ,ρ,S j m fl Given the given information, calculate the speed of the drone as v. f Corresponding optimal slip ratio σ t Braking force F t :

[0032] F t =0.5*m f *μ t *r l +I / r l *(1-σ t )*(μ t *m f +0.5*C*ρ*S j *v f 2 ) / m f / (L p *μ p ).

[0033] Further, a moving sliding window mean filter is performed, and the compensation coefficient is calculated, specifically as follows:

[0034] Step 2-1: Use the system timer for timing and counting, with a timer frequency of F. timer The wheel speed sensor is a gear-type magnetic induction sensor with n teeth. ls It adopts a dual-edge triggering method with both rising and falling edges, meaning the single-cycle trigger count is 2*n. ls The wheel speed is calculated using the M / T combined speed measurement method.

[0035] Step 2-2: Use a moving sliding window mean filtering algorithm with a depth of 50 to analyze the wheel speed v. l Perform filtering;

[0036] Steps 2-3: During the un-braked, pure rolling process of the drone, record n. jljc The ground speed of the drone is v f and v at the corresponding time j Generate dataset The compensation coefficient can then be obtained.

[0037]

[0038] Furthermore, step 2-1, which uses the M / T combined speed measurement method to calculate the wheel speed, specifically involves:

[0039] (a) With fixed M l The trigger count value n of the wheel speed sensor is recorded in a timed cycle. ml Perform wheel speed calculation:

[0040] v ml =n ml / (2*n ls )*2*π*r l *F timer / M l

[0041] (b) The timer count value n between two triggers of the wheel speed sensor tl Perform wheel speed calculation:

[0042] v tl =1 / (2*n) ls )*2*π*r l *F timer / M l

[0043] (c) with v ml To determine the condition, wheel speed v l calculate:

[0044]

[0045] Furthermore, step 2-2 employs a moving sliding window mean filtering algorithm with a depth of 50 to evaluate the wheel speed v. l Filtering is performed as follows:

[0046] (a) Define an array list with a depth of 51. hc And initially assign all values ​​to 0, new data position p new The initial value is 50, and the new data position is p (where data is discarded). old The initial value is 0, and the sum of the array values ​​is D. sum Initially assigned a value of 0;

[0047] (b) When new data to be filtered arrives, it is placed in array p. new Location:

[0048] D sum =D sum +list hc[p new ]-list hc [p old ]

[0049] That is, update D sum The value is D sum +list hc [p new ]-list hc [p old ];

[0050] (c)p new and p old All values ​​are incremented by 1; if the value is greater than 50, the value is reset to 0.

[0051] (d) Return wheel speed v l :

[0052] v l =D sum / 50.

[0053] Furthermore, the determination of the optimal binding coefficient μ zy and the optimal slip ratio σ zj Specifically:

[0054] Step 3-1, at the start of braking control, during the planned time t p According to Δt p Apply braking force F sp Given that Δt is the interval time, i.e., the corresponding time t pi Braking force F spi :

[0055] F spi =t pi / t p *F t

[0056] Calculate the corresponding time t pi Real-time binding coefficient μ pi :

[0057]

[0058] Calculate the corresponding time t pi Real-time slip ratio σ pi :

[0059]

[0060] Among them, v fi v li It corresponds to time t pi The aircraft's ground speed and wheel speed at any given moment;

[0061] This will give us dataset P1:

[0062] P1={{t p0 ,F sp0 ,σ p0 ,μ p0},...,{t pn ,F spn ,σ pn ,μ pn}}

[0063] Step 3-2, for the associativity μ in dataset P1 pi The least squares method is used to perform curve fitting to obtain the assemblage dataset P. μ With dataset P μ The maximum value corresponds to the optimal binding coefficient μ at time k. zy ,Right now:

[0064] μ zy =max(P μ )

[0065] And obtain the slip ratio σ at time k. pi That is, the optimal slip ratio σ zj :

[0066] σzj=σpk.

[0067] Furthermore, the optimal binding coefficient μ zy Optimal slip ratio σ zj And the corresponding mathematical model of ground speed, wheel speed, and iterative control system, and the slip ratio σ td Maximum braking force F td Perform the calculation update, specifically:

[0068] Step 4-1, set the optimal binding coefficient μ zy Optimal slip ratio σ zj The mathematical model of the control system can be updated by iterating to the following formula:

[0069] F td =0.5*m f *μ zy *r l +I / r l *(1-σ zj )*(μ zy *m f +0.5*C*ρ*S j *v f 2 ) / m f / (L p *μ p )

[0070] Step 4-2, target slip ratio σ td Set to:

[0071] σ td =σ zj .

[0072] Furthermore, with slip ratio σ td Calculate the corresponding velocity v for the control target y The expected braking force F y and with F y Set braking force F based on base value s The adjustment range is as follows:

[0073] Step 5-1, with slip ratio σ td To control the target, determine the drone's velocity v′ at the next moment. f Estimated value:

[0074]

[0075] Where, v′ l It is the speed of the drone's wheels at the next moment;

[0076] And the slip ratio σ td v′ f μ zy Substituting into the following formula yields the braking force F. y Estimated value:

[0077] F y =0.5*m f *μ zy *r l +I / r l *(1-σ td )*(μ zy *m f +0.5*C*ρ*S j *v′ f 2 ) / m f / (L p *μ p )

[0078] Step 5-2, with F y Based on the baseline, set the target braking force F. s The adjustment range is:

[0079] F s ∈[(1-ε)*F y (1+ε)*F y ]

[0080] ε is the adjustment factor.

[0081] Furthermore, the system monitors the speed difference between the left and right wheels in real time. If the speed difference exceeds a set threshold, the left and right wheel differential control unit is triggered. If wheel slippage occurs, the ABS anti-slip control unit is triggered. Specifically:

[0082] Step 6-1, Real-time detection of the speed difference v between the left and right wheels ε :

[0083] v ε =v rl -v rr

[0084] v rl v rr It refers to the speed of the left wheel and the speed of the right wheel;

[0085] When |v ε |Exceeding the set threshold for wheel speed difference ε v Then, the left and right wheel differential control units are triggered and the braking force compensation value F of the left and right wheels is obtained. l_bc and F r_bc :

[0086] F i_bc =F y *(1+v ε *v rl / (v rl +v rr ))

[0087] F r_bc =F y *(1-v ε *v rr / (v rl +v rr ))

[0088] Step 6-2: Real-time detection of the slip ratio σ of the left and right wheels. l and σ r When σ is within 3 consecutive control cycles l or σ r Less than the set slip ratio threshold σ y This triggers the ABS anti-slip control unit:

[0089]

[0090]

[0091] Where v jl v jr v f The speeds of the left and right wheels and the ground speed of the UAV at the same moment;

[0092] Braking force compensation value F of left and right wheelsσl_bc and F σr_bc The calculation method is as follows:

[0093] F σl_bc =(σ td -σ l ) / σ td *F y σ l <σ y

[0094] F σr_bc =(σ td -σ r ) / σ td *Fy σ r <σ y .

[0095] Furthermore, the initial matrix P2 for motor current-position-braking force is established, and closed-loop control of braking force is performed according to three operating conditions: idle travel, braking engagement, and braking disengagement. Specifically:

[0096] Step 7-1: Set the braking force F for the left and right wheels. sl F sr calculate:

[0097] F sl =F y +F l_bc -F σl_bc

[0098] F sr =F y +F r_bc -F σr_bc

[0099] Step 7-2, establish the initial matrix P2 of motor current-position-braking force:

[0100] (a) During the no-travel motion, the motor current is fixed at I. k The range of the motor's movement position is [0, p]. k The braking force is 0.

[0101] (b) During the braking process, the motor current I sj :

[0102] I si =I k +k sj *I max *(F s *F s ) / (F max *F max )

[0103] Where Imax k is the maximum motor current. sj F is the compression coefficient. max Set the maximum braking force;

[0104] The range of motor movement positions is [p k ,p max ], p max This represents the maximum stroke of the motor drive.

[0105] (c) During the brake release process, the motor current I ss :

[0106] I ss =I k +k ss *I max *(F s *F s ) / (F max *F max )

[0107] Where k ss This is the brake release coefficient;

[0108] Step 7-3: Perform closed-loop control of braking force according to three operating conditions: no-travel, brake tightening, and brake loosening, using the motor current I... k As the closed-loop control target, the motor's moving position p motor Motor control is performed as an auxiliary judgment condition.

[0109] 4) Idle travel:

[0110]

[0111] 5) Braking process:

[0112]

[0113] 6) Brake release process:

[0114]

[0115] The advantages of this invention compared to the prior art are:

[0116] (1) The complex nonlinear load variable control method based on UAV electric braking designed in this invention is suitable for adaptive matching of control parameters of UAV in complex nonlinear load working environment and realizes rapid short-distance braking.

[0117] (2) The control mathematical model designed in this invention iterates according to the braking environment of skidding or landing and automatically updates the control target and each output threshold.

[0118] (3) The present invention adopts multi-parameter combination closed-loop control and introduces differential control and ABS anti-slip control, which improves the control system’s rapid response and adjustment to abnormal situations. Attached Figure Description

[0119] Figure 1 This is a system framework diagram of the complex nonlinear load variable control method based on UAV electric braking of the present invention;

[0120] Figure 2 This is a flowchart of the complex nonlinear load variable control method based on UAV electric braking according to the present invention. Detailed Implementation

[0121] The specific embodiments of the present invention will now be described in further detail with reference to the accompanying drawings.

[0122] A complex nonlinear load variable control method based on UAV electric braking can realize rapid short-distance braking of small and medium-sized UAVs under complex nonlinear load variables, and solve problems such as wheel lock-up during braking, single-sided wheel lock-up or whole aircraft rollover when there is crosswind.

[0123] The main idea of ​​this method is as follows: When a UAV aborts takeoff or lands during its takeoff roll, the electric braking control system first iterates the mathematical model of the control system by querying parameters such as ground conditions and weather conditions from the flight control system, and updates the control target and threshold values ​​of various output parameters. Then, the electric braking control system incorporates parameters such as the UAV's current mass, airspeed, ground speed, and wheel speed into the closed-loop control logic during the braking process, and introduces differential control and ABS anti-skid control strategies to achieve rapid response and adjustment in abnormal situations during braking. Finally, closed-loop control of the actuator's braking force is achieved primarily using current and secondarily using position. This invention designs a complex nonlinear load variable control method based on UAV electric braking, which can be used to replace hydraulic braking control and also has the advantages of rapid response, device protection, and system status recognition.

[0124] When the electric braking control system incorporates parameters such as ground conditions, weather conditions, current UAV mass, airspeed, and ground speed obtained from the flight control system into the braking closed-loop control algorithm, the electric braking control system performs normal braking logic control according to the control output. If crosswinds or varying ground conditions (dry or wet) cause abnormalities in the braking process, the system can quickly adjust to these abnormalities through the introduction of differential control and ABS anti-skid control strategies, thus preventing the UAV from yawing or locking up and bursting. Therefore, proposing a complex nonlinear load variable control method based on UAV electric braking is particularly important. However, achieving maximum braking efficiency and avoiding yaw and wheel lockup is a significant challenge. During the entire braking process, ground conditions and weather conditions can change suddenly, and airspeed and ground speed are also dynamically changing parameters. Therefore, achieving adaptability of the electric braking control system to various runways, loads, and weather conditions is extremely difficult.

[0125] The method of this invention can incorporate parameters such as current ground conditions, weather environment, airspeed, and UAV weight into the mathematical model of the control system to achieve preliminary planning of target control thresholds such as slip ratio and maximum braking force. Then, according to the planned time, a given braking force is planned and the optimal slip ratio is obtained simultaneously. The mathematical model of the control system is iterated to update the target control thresholds. Next, the target braking thrust is calculated using parameters such as the optimal slip ratio, and a closed-loop control of the actuator's braking force is achieved by using current as the primary factor and position as the secondary factor. Finally, when the speed difference between the left and right wheels exceeds the threshold and the wheels brake to a stop, differential control and ABS anti-skid control strategies are introduced to achieve rapid response and adjustment in abnormal situations during braking.

[0126] The method of this invention can identify abnormal situations such as drone yaw and lock-up, and achieve the shortest braking distance for drones with optimal braking efficiency.

[0127] Figure 1 and Figure 2 This is a framework diagram and flowchart of a complex nonlinear load variable control method and device based on UAV electric braking according to the present invention. The method includes the following steps:

[0128] Step 1: Input the current runway type τ0, ground conditions δ0, and UAV speed v provided by the flight control system. f Drone weight (m) f By incorporating parameters such as σ into the mathematical model of the control system, the optimal slip ratio σ can be determined. t Maximum braking force F t Preliminary planning of target control thresholds;

[0129] Step 2: The wheel speed is calculated using the M / T combined speed measurement method, and the calculation results are filtered by a moving sliding window with a depth of 50. During the pure rolling process of the UAV, the ground speed v of the UAV is used as the basis for the calculation. di Wheel speed v ji Compensation is performed to obtain the compensation coefficient.

[0130] Step 3, according to the planned time t p Apply a given braking force F s Given a step size, calculate the slip ratio σ and the binding coefficient μ at the corresponding time points. This results in a dataset P1 consisting of {time, given braking force, slip ratio, binding coefficient}, formed according to time intervals ΔT. Simultaneously, fit the binding coefficient curve and find the optimal binding coefficient μ. zy and the optimal slip ratio σ zj Exit;

[0131] Step 4, set the optimal binding coefficient μ zy Optimal slip ratio σ zj And the corresponding ground speed, wheel speed, etc., are iterated into the mathematical model of the control system, and the slip ratio σ is... td Maximum braking force F td Perform calculation updates;

[0132] Step 5, with slip ratio σ td Calculate the corresponding velocity v for the control target y The expected braking force F y and with F y Set braking force F based on base value s The adjustment range is [(1-ε)*F y (1+ε)*F y ];

[0133] Step 6: Real-time detection of the speed difference between the left and right wheels. When the speed difference between the left and right wheels exceeds the set threshold, the left and right wheel differential control unit is triggered; when wheel slippage occurs between the left and right wheels, the ABS anti-slip control unit is triggered.

[0134] Step 7: Establish the initial matrix P2 of motor current-position-braking force, and perform closed-loop control of braking force according to three working conditions: no-travel, braking tightening and braking loosening.

[0135] Step 1 includes:

[0136] Step 1-1: Input the runway type τ0, weather conditions δ0, and UAV speed v provided by the flight control system. f Drone weight (m) f These parameters are introduced into the mathematical model of the control system:

[0137]

[0138] Where μ t m is the binding coefficient. fl For single-wheel load bearing at the rear, C ρ Let the drag coefficient be 0.08, ρ be the air density, and S be... j The frontal area is given by I, and the moment of inertia of the rotor is taken as 4050 kg / mm². 2 , Let r be the angular acceleration of the wheel. l Let μ be the radius of the wheel. p F is the coefficient of friction between the brake disc and brake pads. t For braking force, L p The braking torque radius; σ m v is the slip ratio. f For the ground speed of the drone, v j Let {μ} be the wheel speed. max C1, C2} are the factors influencing the binding coefficient and slip ratio.

[0139] Steps 1-2: First, the influence factors {μ} of the combination coefficient and slip ratio on the runway type τ0 and ground conditions δ0 are calculated. max Preliminary estimated values ​​for C1 and C2:

[0140] When the runway type τ0 is a concrete runway and the ground condition δ0 is a dry ground:

[0141] {μ max , C1, C2}={0.4, 2.0, 8.0}

[0142] When the runway type τ0 is an asphalt runway and the ground condition δ0 is a dry ground:

[0143] {μ max , C1, C2}={0.8, 1.5, 14.0}

[0144] When the runway type τ0 is a concrete runway and the ground condition δ0 is a wet ground:

[0145] {μ max , C1, C2}={0.3, 2.0, 7.5}

[0146] When the runway type τ0 is a concrete runway and the ground condition δ0 is a wet ground:

[0147] {μ max , C1, C2}={0.4, 2.0, 8.0}

[0148] Steps 1-3, when {μ maxAfter assigning values ​​to C1, C2, etc., the maximum binding coefficient μ can be set. t :

[0149] μ t =μ max

[0150] Optimal slip ratio σ t It can be calculated as follows:

[0151] σ t =tan(asin(μ) t / μ max ) / C1) / C2

[0152] And in μ p v f L p C ρ ,ρ,S j m fl Given the given information, calculate the speed of the drone as v. f Corresponding optimal slip ratio σ t Braking force F t :

[0153] F t =0.5*m f *μ t *r l +I / r l *(1-σ t )*(μ t *m f +0.5*C*ρ*S j *v f 2 ) / m f / (L p *μ p )

[0154] Step 2 includes:

[0155] Step 2-1: Use the system timer for timing and counting, with a timer frequency of F. timer The wheel speed sensor is a gear-type magnetic induction sensor with n teeth. ls The software uses a dual-edge triggering method with both rising and falling edges, meaning the single-cycle trigger count is 2*n. ls The M / T combined speed measurement method is as follows:

[0156] M / T combined speed measurement method: with fixed M l The trigger count value n of the wheel speed sensor is recorded in a timed cycle. ml Perform wheel speed calculation:

[0157] vml =n ml / (2*n ls )*2*π*r l *F timer / M l

[0158] T-speed measurement method: using the timer count value n between two triggers of the wheel speed sensor. tl Perform wheel speed calculation:

[0159] v tl =1 / (2*n) ls )*2*π*r l *F timer / M l

[0160] M / T combined speed measurement method: using v ml To determine the condition, wheel speed v l calculate:

[0161]

[0162] Step 2-2: Use a moving sliding window mean filtering algorithm with a depth of 50 to analyze the wheel speed v. l To significantly conserve software computing resources, the following algorithm was designed for filtering:

[0163] Define an array list with a depth of 51. hc And initially assign all values ​​to 0, new data position p new The initial value is 50, and the new data position is p (where data is discarded). old The initial value is 0, and the sum of the array values ​​is D. sum Initially assigned a value of 0;

[0164] When new data to be filtered enters, it is placed in array p. new Location:

[0165] D sum =D sum +list hc [p new ]-list hc [p old ]

[0166] p new and p old All values ​​are incremented by 1; if the value is greater than 50, the value is reset to 0.

[0167] Return wheel speed v l :

[0168] v l =D sum / 50

[0169] Steps 2-3: During the un-braked, pure rolling process of the drone, record n. jljc The ground speed of the drone is v f and v at the corresponding time j Generate dataset The compensation coefficient can then be obtained.

[0170]

[0171] Step 3 includes:

[0172] Step 3-1, at the start of braking control, during the planned time t p According to Δt p Apply braking force F sp Given, i.e., corresponding time t pi Braking force F spi :

[0173] F spi =t pi / t p *F t

[0174] Calculate the corresponding time t pi Real-time binding coefficient μ pi :

[0175]

[0176] Calculate the corresponding time t pi Real-time slip ratio σ pi :

[0177]

[0178] This will give us dataset P1:

[0179] P1={{t p0 ,F sp0 ,σ p0 ,μ p0},...,{t pn ,F spn ,σ pn ,μ pn}}

[0180] Step 3-2, for the associativity μ in dataset P1 piThe least squares method is used to perform curve fitting to obtain the assemblage dataset P. μ With dataset P μ The maximum value corresponds to the optimal binding coefficient μ at time k. zy ,Right now:

[0181] μ zy =max(P μ )

[0182] And obtain the slip ratio σ at time k. pi That is, the optimal slip ratio σ zj :

[0183] σ zj =σ pk

[0184] Step 4 includes:

[0185] Step 4-1, set the optimal binding coefficient μ zy Optimal slip ratio σ zj The mathematical model of the control system can be updated by iterating to the following formula:

[0186] F td =0.5*m f *μ zy *r l +I / r l *(1-σ zj )*(μ zy *m f +0.5*C*ρ*S j *v f 2 ) / m f / (L p *μ p )

[0187] Step 4-2, target slip ratio σ td Set to:

[0188] σ td =σ zj

[0189] Step 5 includes:

[0190] Step 5-1, with slip ratio σ td To control the target, determine the drone's speed v at the next moment. f Estimated value:

[0191]

[0192] And the slip ratio σ td v f '、μzy Substituting into the following formula yields the braking force F. y Estimated value:

[0193] F y =0.5*m f *μ zy *r l +I / r l *(1-σ td )*(μ zy *m f +0.5*C*ρ*S j *v f ') / m f / (L p *μ p )

[0194] Step 5-2, with F y Based on the baseline, set the target braking force F. s The adjustment range is:

[0195] F s ∈[(1-ε)*F y (1+ε)*F y ]

[0196] Step 6 includes:

[0197] Step 6-1, Real-time detection of the speed difference v between the left and right wheels ε :

[0198] v ε =v rl -v rr

[0199] When |v ε |Exceeding the set threshold for wheel speed difference ε v Then, the left and right wheel differential control units are triggered and the braking force compensation value F of the left and right wheels is obtained. l_bc and F r_bc :

[0200] F i_bc =F y *(1+v ε *v rl / (v rl +v rr ))

[0201] F r_bc =F y *(1-v ε *v rr / (v rl +v rr ))

[0202] Step 6-2: Real-time detection of the slip ratio σ of the left and right wheels. l and σ r When σ is within 3 consecutive control cycles l or σ r Less than the set slip ratio threshold σ y This triggers the ABS anti-slip control unit:

[0203]

[0204]

[0205] Where v jl v jr v f The speeds of the left and right wheels, as well as the ground speed of the UAV, are measured at the same time.

[0206] Braking force compensation value F of left and right wheels σl_bc and F σr_bc The calculation method is as follows:

[0207] F σl_bc =(σ td -σ l ) / σ td *F y σ l <σ y

[0208] F σr_bc =(σ td -σ r ) / σ td *F y σ r <σ y

[0209] Step 7 includes:

[0210] Step 7-1, combining steps 5-1, 6-1, and 6-2, sets the braking force F for the left and right wheels. sl F sr calculate:

[0211] F sl =F y +F l_bc -F σl_bc

[0212] F sr =F y +F r_bc -F σr_bc

[0213] Step 7-2, establish the initial matrix P2 of motor current-position-braking force:

[0214] During the idle stroke, the motor current is fixed at I. k The range of the motor's movement position is [0, p]. k The braking force is 0.

[0215] During the braking process, the motor current I sj :

[0216] I sj =I k +k sj *I max *(F s *F s ) / (F max *F max )

[0217] Where I max k is the maximum motor current. sj F is the compression coefficient. max Set the maximum braking force.

[0218] The range of motor movement positions is [p k ,p max ], p max This represents the maximum stroke of the motor drive.

[0219] During the brake release process, the motor current I ss :

[0220] I ss =I k +k ss *I max *(F s *F s ) / (F max *F max )

[0221] Where k ss This is the brake release coefficient.

[0222] Step 7-3: Perform closed-loop control of braking force according to three operating conditions: no-travel, brake tightening, and brake loosening, using the motor current I... k As the closed-loop control target, the motor's moving position p motor Motor control is performed as an auxiliary judgment condition.

[0223] 7) Empty journey:

[0224]

[0225] 8) Braking process:

[0226]

[0227] 9) Brake release process:

[0228]

[0229] The computing device used in this invention is a common computing module (such as a CPU) to perform various calculations in the method.

[0230] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The embodiments of the present invention can be implemented using various computer languages.

[0231] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0232] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0233] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0234] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0235] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A complex nonlinear load variable control method based on UAV electric braking, characterized in that... include: The flight control system provides the current runway type τ0, ground conditions δ0, and UAV speed v. f Drone weight (m) f This is incorporated into the mathematical model of the control system to achieve preliminary planning of the target control threshold; the target control threshold includes the optimal slip ratio σ. t Maximum braking force F t ; The wheel speeds are calculated and the results are filtered using a moving sliding window mean filter; during the pure rolling process of the UAV, the ground speed v of the UAV is used as the basis. di Wheel speed v ji Compensation is performed to obtain the compensation coefficient. According to the planned time t p Apply braking force F s Given a step size, calculate the slip ratio σ and the binding coefficient μ at the corresponding time points. This results in a dataset P1 consisting of {time, given braking force, slip ratio, binding coefficient}, formed according to time intervals ΔT. Simultaneously, fit the binding coefficient curve and find the optimal binding coefficient μ. zy and the optimal slip ratio σ zj Exit; The optimal binding coefficient μ zy Optimal slip ratio σ zj And the corresponding mathematical model of ground speed, wheel speed, and iterative control system, and the slip ratio σ td Maximum braking force F td Perform calculation updates; With slip ratio σ td Calculate the corresponding velocity v for the control target y The expected braking force F y and with F y Set braking force F based on base value s The adjustment range is [(1-ε)*F y (1+ε)*F y ]; The system monitors the speed difference between the left and right wheels in real time. When the speed difference between the left and right wheels exceeds the set threshold, the left and right wheel differential control unit is triggered. When wheel slippage occurs between the left and right wheels, the ABS anti-slip control unit is triggered. Establish an initial matrix P2 for motor current-position-braking force, and perform closed-loop control of braking force according to three working conditions: no-travel, braking tightening, and braking loosening.

2. The complex nonlinear load variable control method based on UAV electric braking according to claim 1, characterized in that: The preliminary planning of the target control threshold specifically includes: Step 1-1: Input the runway type τ0, weather conditions δ0, and UAV speed v provided by the flight control system. f Drone weight (m) f Introduced into the mathematical model of the control system: Where, μ t m is the binding coefficient. fl For single-wheel load bearing at the rear, C ρ Let the drag coefficient be 0.08, ρ be the air density, and S be... j Where I is the frontal area, and I is the moment of inertia of the rotor wheels. Let r be the angular acceleration of the wheel. l Let μ be the radius of the wheel. p F is the coefficient of friction between the brake disc and brake pads. t For braking force, L p The braking torque radius; σ m For slip ratio, It is the differential of the aircraft's ground speed and the aircraft's deceleration; v j Let {μ} be the wheel speed. max C1, C2} are the factors affecting the combination coefficient and slip ratio; n is the number of brake wheels, and g is the gravitational acceleration. Steps 1-2: First, the influence factors {μ} of the combination coefficient and slip ratio on the runway type τ0 and ground conditions δ0 are calculated. max Preliminary estimated values ​​for C1 and C2: When the runway type τ0 is a concrete runway and the ground condition δ0 is a dry ground: {μ max ,C1,C2}={0.4,2.0,8.0} When the runway type τ0 is an asphalt runway and the ground condition δ0 is a dry ground: {μ max ,C1,C2}={0.8,1.5,14.0} When the runway type τ0 is a concrete runway and the ground condition δ0 is a wet ground: {μ max ,C1,C2}={0.3,2.0,7.5} When the runway type τ0 is a concrete runway and the ground condition δ0 is a wet ground: {μ max ,C1,C2}={0.4,2.0,8.0} Steps 1-3, when {μ max After assigning values ​​to C1, C2, etc., the maximum binding coefficient μ can be set. t : m t =μ max Optimal slip ratio σ t It can be calculated as follows: s t =tan(asin(μ t / m max ) / C1) / C2 Steps 1-4, in μ p v f L p C ρ ,ρ,S j m fl Given the given information, calculate the speed of the drone as v. f Corresponding optimal slip ratio σ t Braking force F t : F t =0.5*m f *m t *r l +I / r l *(1-s t )*(m t *m f +0.5*C*p*S j *v f 2 ) / m f / (L p *m p )。 3. The complex nonlinear load variable control method based on UAV electric braking according to claim 2, characterized in that: Perform moving window mean filtering and calculate the compensation coefficient, specifically as follows: Step 2-1: Use the system timer for timing and counting, with a timer frequency of F. timer The wheel speed sensor is a gear-type magnetic induction sensor with n teeth. ls It adopts a dual-edge triggering method with both rising and falling edges, meaning the single-cycle trigger count is 2*h. ls The wheel speed is calculated using the M / T combined speed measurement method. Step 2-2: Use a moving sliding window mean filtering algorithm with a depth of 50 to analyze the wheel speed v. l Perform filtering; Steps 2-3: During the un-braked, pure rolling process of the drone, record n. jljc The ground speed of the drone is v f and v at the corresponding time j Generate dataset The compensation coefficient can then be obtained.

4. The complex nonlinear load variable control method based on UAV electric braking according to claim 3, characterized in that: Step 2-1, which uses the M / T combined speed measurement method to calculate the wheel speed, specifically involves: (a) With fixed M l The trigger count value n of the wheel speed sensor is recorded in a timed cycle. ml Perform wheel speed calculation: v ml =n ml / (2*n ls )*2*π*r l *F timer / M l (b) The timer count value n between two triggers of the wheel speed sensor tl Perform wheel speed calculation: v tl =1 / (2*n ls )*2*π*r l *F timer / M l (c) with v ml To determine the condition, wheel speed v l calculate:

5. A complex nonlinear load variable control method based on UAV electric braking according to claim 3, characterized in that: Step 2-2 uses a moving sliding window mean filtering algorithm with a depth of 50 to evaluate the wheel speed v. l Filtering is performed as follows: (a) Define an array list with a depth of 51. hc And initially assign all values ​​to 0, new data position p new The initial value is 50, and the new data position is p (where data is discarded). old The initial value is 0, and the sum of the array values ​​is D. sum Initially assigned a value of 0; (b) When new data to be filtered arrives, it is placed in array p. new Location: D sum =D sum +list hc [p new ]-list hc [p old ] That is, update D sum The value is D sum +list hc [p new ]-list hc [p old ]; (c)p new and p old All values ​​are incremented by 1; if the value is greater than 50, the value is reset to 0. (d) Return wheel speed v l : v l =D sum / 50。 6. A complex nonlinear load variable control method based on UAV electric braking according to claim 3, characterized in that: Determine the optimal binding coefficient μ zy and the optimal slip ratio σ zj Specifically: Step 3-1, at the start of braking control, during the planned time t p According to Δt p Apply braking force F sp Given Δt p It is the interval time, that is, the corresponding time t. pi Braking force F spi : F spi =t pi / t p *F t Calculate the corresponding time t pi Real-time binding coefficient μ pi : Calculate the corresponding time t pi Real-time slip ratio σ pi : Among them, v fi v li It corresponds to time t pi The aircraft's ground speed and wheel speed at any given moment; This will give us dataset P1: P1={{t p0 ,F sp0 ,s p0 ,m p0 }},...,{t pn ,F spn ,s pn ,m pn }} Step 3-2, for the associativity μ in dataset P1 pi The least squares method is used to perform curve fitting to obtain the assemblage dataset P. μ With dataset P μ The maximum value corresponds to the optimal binding coefficient μ at time k. zy ,Right now: m zy =max(P μ ) And obtain the slip ratio σ at time k. pi That is, the optimal slip ratio σ zj : s zj =s pk 。 7. A complex nonlinear load variable control method based on UAV electric braking according to claim 3, characterized in that: The optimal binding coefficient μ zy Optimal slip ratio σ zj And the corresponding mathematical model of ground speed, wheel speed, and iterative control system, and the slip ratio σ td Maximum braking force F td Perform the calculation update, specifically: Step 4-1, set the optimal binding coefficient μ zy Optimal slip ratio σ zj The mathematical model of the control system can be updated by iterating to the following formula: F td =0.5*m f *m zy *r l +I / r l *(1-s zj )*(m zy *m f +0.5*C*p*S j *v f 2 ) / m f / (L p *m p ) Step 4-2, target slip ratio σ td Set to: s td =s zj 。 8. A complex nonlinear load variable control method based on UAV electric braking according to claim 7, characterized in that: With slip ratio σ td Calculate the corresponding velocity v for the control target y The expected braking force F y and with F y Set braking force F based on base value s The adjustment range is as follows: Step 5-1, with slip ratio σ td To control the target, determine the drone's velocity v′ at the next moment. f Estimated value: Where, v′ l It is the speed of the drone's wheels at the next moment; And the slip ratio σ td v′ f μ zy Substituting into the following formula yields the braking force F. y Estimated value: F y =0.5*m f *m zy *r l +I / r l *(1-s td )*(m zy *m f +0.5*C*p*S j *v′ f 2 ) / m f / (L p *m p ) Step 5-2, with F y Based on the baseline, set the target braking force F. s The adjustment range is: F s ∈[(1-ε)*F y ,(1+ε)*F y ] ε is the adjustment factor.

9. A complex nonlinear load variable control method based on UAV electric braking according to claim 8, characterized in that: The system monitors the speed difference between the left and right wheels in real time. When the speed difference exceeds a set threshold, the left and right wheel differential control unit is activated. If wheel slippage occurs, the ABS anti-slip control unit is activated. Specifically: Step 6-1, Real-time detection of the speed difference v between the left and right wheels ε : v ε =v rl -v rr v rl v rr It refers to the speed of the left wheel and the speed of the right wheel; When |v ε |Exceeding the set threshold for wheel speed difference ε v Then, the left and right wheel differential control units are triggered and the braking force compensation value F of the left and right wheels is obtained. l_bc and F r_bc : F l_bc =F y *(1+v ε *v rl / (v rl +v rr )) F r_bc =F y *(1-v ε *v rr / (v rl +v rr )) Step 6-2: Real-time detection of the slip ratio σ of the left and right wheels. l and σ r When σ is within 3 consecutive control cycles l or σ r Less than the set slip ratio threshold σ y This triggers the ABS anti-slip control unit: Where v jl v jr v f The speeds of the left and right wheels and the ground speed of the UAV at the same moment; Braking force compensation value F of left and right wheels σl_bc and F σr_bc The calculation method is as follows: F σl_bc =(s td -s l ) / s td *F y s l <s y F σr_bc =(s td -s r ) / s td *F y s r <s y 。 10. A complex nonlinear load variable control method based on UAV electric braking according to claim 9, characterized in that: The initial matrix P2 for motor current-position-braking force is established, and closed-loop control of braking force is performed according to three operating conditions: no-travel, braking tightening, and braking loosening. Specifically: Step 7-1: Set the braking force F for the left and right wheels. sl F sr calculate: F sl =F y +F l_bc -F σl_bc F sr =F y +F r_bc -F σr_bc Step 7-2, establish the initial matrix P2 of motor current-position-braking force: (a) During the no-travel motion, the motor current is fixed at I. k The range of the motor's movement position is [0, p]. k The braking force is 0. (b) During the braking process, the motor current I sj : I sj =I k +k sj *I max *(F s *F s ) / (F max *F max ) Where I max k is the maximum motor current. sj F is the compression coefficient. max Set the maximum braking force; The range of motor movement positions is [p k p max ], p max This represents the maximum stroke of the motor drive. (c) During the brake release process, the motor current I ss : I ss =I k +k ss *I max *(F s *F s ) / (F max *F max ) Where k ss This is the brake release coefficient; Step 7-3: Perform closed-loop control of braking force according to three operating conditions: no-travel, brake tightening, and brake loosening, using the motor current I... k As the closed-loop control target, the motor's moving position p motor Motor control is performed as an auxiliary judgment condition. 1) Empty journey: 2) Braking process: 3) Brake release process: 。

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