A speed adjustment method for unmanned vehicle based on servo-driven throttle

Abstract

This invention belongs to the field of electronic control technology, specifically to a method for regulating the speed of an unmanned vehicle using a servo-actuated throttle. The method comprises the following steps: a chassis computer transmits a throttle control command to a throttle control unit via a CAN network; the throttle control unit determines the throttle change based on the throttle control command issued by the chassis computer and sends a corresponding control command to the servo-actuated throttle to control the servo-actuated throttle. This technical solution improves the control accuracy of the system through engine speed feedback, exhibiting excellent dynamic adaptability and meeting the speed tracking requirements of unmanned vehicles.

Description

Technical Field

[0001] The present invention belongs to the technical field of electronic control, and in particular relates to a speed regulation method for an unmanned vehicle based on a servo-actuator driven throttle. Background Art

[0002] The wire-controlled chassis is an important carrier for realizing unmanned driving technology. It replaces traditional mechanical connections with wire-controlled technology and transmits instructions to actuators and electronic controllers through the network via electrical signals. It has the characteristics of high control accuracy and flexible signal processing, and is widely used in the field of contemporary unmanned vehicles.

[0003] The dynamic performance matching of engine speed, throttle opening and gear position plays an important role in the speed control of unmanned vehicles, so its real-time control is the key to research. Summary of the Invention

[0004] (1) Technical issues to be resolved

[0005] The technical problem to be solved by the present invention is: how to provide a speed adjustment method for an unmanned vehicle based on an electric servo steering gear driving a throttle, so as to solve the problems existing in the prior art.

[0006] (2) Technical solution

[0007] To solve the problems of the prior art, the present invention provides a method for adjusting the speed of an unmanned vehicle based on a servo-actuated throttle. The method is implemented based on an unmanned vehicle system, which includes: a vehicle chassis integrated control system, a throttle control unit, and a throttle opening control mechanism; the throttle opening control mechanism includes a servo-actuated throttle, a steering wheel, and a connecting rod;

[0008] Wherein, the vehicle chassis integrated control system and the throttle control unit are connected via a cable;

[0009] The throttle control unit and the throttle opening control mechanism are connected via a cable;

[0010] The throttle opening control mechanism and the accelerator pedal are mechanically connected;

[0011] The method comprises the following steps:

[0012] Step S1: the vehicle chassis integrated control system reads the real-time vehicle speed and the target vehicle speed;

[0013] Step S2: The vehicle chassis integrated control system determines the target engine speed based on the real-time vehicle speed and the target vehicle speed, generates a throttle opening control instruction, and sends it to the throttle control unit;

[0014] Step S3: The throttle control unit reads the throttle opening control command, calculates the target angle of the servo steering gear, converts it into a specific control command and sends it to the servo steering gear to achieve speed regulation;

[0015] Wherein, the step S2 further includes the following sub-steps:

[0016] Step S201: The vehicle chassis integrated control system determines the target engine speed based on the real-time vehicle speed and the target vehicle speed;

[0017] Step S202: the vehicle chassis integrated control system reads the real-time engine speed, and obtains the current speed difference and speed difference change rate based on the target engine speed and the real-time engine speed;

[0018] Step S203: The vehicle chassis integrated control system optimizes the control parameters of the controller inside the vehicle chassis integrated control system by using a particle swarm optimization algorithm;

[0019] Specifically, the engine target speed is adjusted using a fractional-order PID controller, and the model form of the fractional-order PID controller is:

[0020]

[0021] The fractional-order PID controller parameter k is obtained using the particle swarm optimization algorithm. p , k i , k d , λ, μ initial values, complete the optimization of the control parameters in the controller, as shown in the following formula (2) and formula (3);

[0022]

[0023]

[0024] Where, is the inertia weight; k is the current iteration number; V id is the velocity of the particle; x id is the position of the particle, corresponding to the above fractional-order PID controller parameter k p , k i , k d ,λ,μ;P id is the individual extreme value; P gd is the extreme value of the group; c1 and c2 are non-negative constants, which are acceleration factors; r1 and r2 are random numbers distributed in the interval [0,1];

[0025] In order to prevent the blind search of particles, the k p , k i , k dThe range of the three variables is selected as [0,3000], and the range of the fractional order λ and μ is [-1,1];

[0026] Step S204: The vehicle chassis integrated control system dynamically adjusts the control parameters through a fractional-order PID controller to obtain a change in the controller parameters;

[0027] Step S205: Based on the initial value of the controller parameter in step S203 and the change in the controller parameter in step S204, the real-time controller parameter is obtained, and the controller outputs the throttle opening control instruction under the real-time controller parameter;

[0028] Step S206: The vehicle chassis integrated control system sends the throttle opening control instruction to the throttle control unit.

[0029] In step S204, a fuzzy neural network fractional-order PID controller is used for control; the neural network uses a BP learning algorithm to automatically adjust the network weights; the network is divided into four layers:

[0030] The first layer is the input variable, which is the two control inputs of the controller. That is, step 202 obtains the current speed difference and the speed difference change rate. The output function is:

[0031]

[0032] The second layer divides the distribution of the input signal and uses the Gaussian function as the membership function to divide the distribution of the input signal. The fuzzy language is represented by five variables, representing negative large, negative small, zero, positive small, and positive large. The output function of each node is given by the following formula:

[0033]

[0034] Among them, σ im and b im are the center value and width of the Gaussian function respectively;

[0035] The nodes in the third layer represent fuzzy rules. Since both input variables are divided into 5 fuzzy spaces, there are 25 fuzzy rules in total. The output function of each rule is:

[0036]

[0037] The fourth layer completes the defuzzification, and the output function of the node is:

[0038]

[0039] where w nj is the output layer connection weight, and the controller parameter k is obtained p , k i, k d The amount of change.

[0040] Wherein, the step S3 includes the following sub-steps:

[0041] Step S301: The throttle control unit reads the throttle opening control instruction;

[0042] Step S302: The throttle control unit determines the servo steering target angle according to the throttle opening control command;

[0043] Step S303: The throttle control unit converts the servo steering gear target angle into a specific control instruction and transmits it to the servo steering gear.

[0044] Wherein, the step S3 further includes:

[0045] Step S304: the servo steering engine converts the electrical signal into a mechanical signal, drives the steering wheel to rotate, and drives the connecting rod to drive the accelerator pedal to move.

[0046] Among them, in step S304, when the accelerator pedal is activated, the longitudinal pull rod will change accordingly, and together with the return spring, it will drive the refueling pull rod to rotate the refueling pull arm, and manipulate the engine's high-pressure diesel pump refueling gear rod to move to change the oil supply, thereby changing the engine speed, cooperating with the vehicle's clutch and gearbox, and thus realizing the adjustment of the vehicle's longitudinal speed.

[0047] The vehicle chassis integrated control system is used to send target throttle opening control instructions and receive real-time vehicle speed and engine speed signals.

[0048] The vehicle chassis integrated control system reads the real-time vehicle speed and the target vehicle speed through the CAN network.

[0049] Among them, the servo steering gear is DH03X steering gear.

[0050] The power of the servo steering gear is provided by the power distribution system on the vehicle.

[0051] Among them, according to the response speed of the servo steering engine to the specific control command, the sending frequency of the throttle opening control command is set to 2Hz.

[0052] (3) Beneficial effects

[0053] The present invention provides a speed regulation method for an unmanned vehicle. By judging the vehicle speed through the chassis integrated control system, the engine speed is adjusted based on the throttle opening adjustment, so that the speed tracking system of the unmanned vehicle has good dynamic response characteristics and can meet the needs of the unmanned vehicle's longitudinal speed tracking.

[0054] Other features and advantages of the present invention will be produced in the subsequent description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the structure of the particular points in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a schematic diagram of the working system principle involved in the technical solution of the present invention.

[0056] Figure 2 and Figure 4 This is a schematic diagram of the working process principle of the technical solution of the present invention.

[0057] Figure 3 This is a schematic diagram of the algorithm for running the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, content, and advantages of the present invention more clear, the specific implementation methods of the present invention are further described in detail below with reference to the accompanying drawings and examples.

[0059] In order to solve the existing technical problems, the present invention provides a method for adjusting the speed of an unmanned vehicle based on a servo-driven throttle. Figure 1 As shown, the method is implemented based on an unmanned vehicle system, which includes: a vehicle chassis integrated control system, a throttle control unit and a throttle opening control mechanism; the throttle opening control mechanism includes a servo steering gear, a steering wheel, and a connecting rod;

[0060] Wherein, the vehicle chassis integrated control system and the throttle control unit are connected via a cable;

[0061] The throttle control unit and the throttle opening control mechanism are connected via a cable;

[0062] The throttle opening control mechanism and the accelerator pedal are mechanically connected;

[0063] Among them, Figure 2 and Figure 4 As shown, the method includes the following steps:

[0064] Step S1: the vehicle chassis integrated control system reads the real-time vehicle speed and the target vehicle speed;

[0065] Step S2: The vehicle chassis integrated control system determines the target engine speed based on the real-time vehicle speed and the target vehicle speed, generates a throttle opening control instruction, and sends it to the throttle control unit;

[0066] Step S3: The throttle control unit reads the throttle opening control command, calculates the target angle of the servo steering gear, converts it into a specific control command and sends it to the servo steering gear to achieve speed regulation;

[0067] Wherein, the step S2 further includes the following sub-steps:

[0068] Step S201: The vehicle chassis integrated control system determines the target engine speed based on the real-time vehicle speed and the target vehicle speed;

[0069] Step S202: the vehicle chassis integrated control system reads the real-time engine speed, and obtains the current speed difference and speed difference change rate based on the target engine speed and the real-time engine speed;

[0070] Step S203: The vehicle chassis integrated control system optimizes the control parameters of the controller inside the vehicle chassis integrated control system by using a particle swarm optimization (PSO) algorithm;

[0071] Specifically, the engine target speed is adjusted using a fractional-order PID controller, and the model form of the fractional-order PID controller is:

[0072]

[0073] The particle swarm optimization (PSO) algorithm is used to obtain the fractional-order PID controller parameter k p , k i , k d , λ, μ initial values, complete the optimization of the control parameters in the controller, as shown in the following formula (2) and formula (3);

[0074]

[0075]

[0076] Where, is the inertia weight; k is the current iteration number; V id is the velocity of the particle; x id is the position of the particle, corresponding to the above fractional-order PID controller parameter k p , k i , k d ,λ,μ;P id is the individual extreme value; P gd is the extreme value of the group; c1 and c2 are non-negative constants, which are acceleration factors; r1 and r2 are random numbers distributed in the interval [0,1];

[0077] In order to prevent the blind search of particles, the k p , k i , kd The range of the three variables is selected as [0,3000], and the range of the fractional order λ and μ is [-1,1];

[0078] Step S204: The vehicle chassis integrated control system dynamically adjusts the control parameters through a fractional-order PID controller to obtain a change in the controller parameters;

[0079] Step S205: Based on the initial value of the controller parameter in step S203 and the change in the controller parameter in step S204, the real-time controller parameter is obtained, and the controller outputs the throttle opening control instruction under the real-time controller parameter;

[0080] Step S206: The vehicle chassis integrated control system sends the throttle opening control instruction to the throttle control unit.

[0081] Among them, in the step S204, a fuzzy neural network fractional order PID controller is specifically used for control, and its block diagram is as follows Figure 3 As shown; the neural network uses BP learning algorithm to automatically adjust the network weights; the network is divided into four layers:

[0082] The first layer is the input variable, which is the two control inputs of the controller. That is, step 202 obtains the current speed difference and the speed difference change rate. The output function is:

[0083]

[0084] The second layer divides the distribution of the input signal and uses the Gaussian function as the membership function to divide the distribution of the input signal. The fuzzy language is represented by five variables, representing negative large, negative small, zero, positive small, and positive large. The output function of each node is given by the following formula:

[0085]

[0086] Among them, σ im and b im are the center value and width of the Gaussian function respectively;

[0087] The nodes in the third layer represent fuzzy rules. Since both input variables are divided into 5 fuzzy spaces, there are 25 fuzzy rules in total. The output function of each rule is:

[0088]

[0089] The fourth layer completes the defuzzification, and the output function of the node is:

[0090]

[0091] where w njis the output layer connection weight, and the controller parameter k is obtained p , k i , k d The amount of change;

[0092] Wherein, the step S3 includes the following sub-steps:

[0093] Step S301: The throttle control unit reads the throttle opening control instruction;

[0094] Step S302: The throttle control unit determines the servo steering target angle according to the throttle opening control command;

[0095] Step S303: The throttle control unit converts the servo steering gear target angle into a specific control instruction and transmits it to the servo steering gear.

[0096] Wherein, the step S3 further includes:

[0097] Step S304: the servo steering engine converts the electrical signal into a mechanical signal, drives the steering wheel to rotate, and drives the connecting rod to drive the accelerator pedal to move.

[0098] Among them, in step S304, when the accelerator pedal is activated, the longitudinal pull rod will change accordingly, and together with the return spring, it will drive the refueling pull rod to rotate the refueling pull arm, and manipulate the engine's high-pressure diesel pump refueling gear rod to move to change the oil supply, thereby changing the engine speed, cooperating with the vehicle's clutch and gearbox, and thus realizing the adjustment of the vehicle's longitudinal speed.

[0099] The vehicle chassis integrated control system is used to send target throttle opening control instructions and receive real-time vehicle speed and engine speed signals.

[0100] The vehicle chassis integrated control system reads the real-time vehicle speed and the target vehicle speed through the CAN network.

[0101] Among them, the servo steering gear is DH03X steering gear.

[0102] The power of the servo steering gear is provided by the power distribution system on the vehicle.

[0103] Among them, according to the response speed of the servo steering engine to the specific control command, the sending frequency of the throttle opening control command is set to 2Hz.

[0104] Example 1

[0105] According to one embodiment of the present invention, a method for adjusting the speed of an unmanned vehicle is provided, comprising the following steps:

[0106] Step S1: The vehicle chassis integrated control system reads the real-time vehicle speed and target vehicle speed through the CAN network;

[0107] Specifically, the chassis integrated control system includes an input processing circuit, a microprocessor, an output processing circuit, a system communication circuit and a power supply circuit. It is the core component of the unmanned tracked vehicle chassis control, and is used to complete the reception of sensor signals, perform calculations internally and complete various control processes, and send drive signals to each subsystem through cables to drive each actuator.

[0108] Step S2: The vehicle chassis integrated control system determines the target engine speed based on the real-time vehicle speed and the target vehicle speed, generates a throttle opening control instruction, and sends it to the throttle control unit;

[0109] Furthermore, step S2 further includes the following sub-steps:

[0110] Step S201: The vehicle chassis integrated control system determines whether there is a gear shift operation based on the real-time vehicle speed and the target vehicle speed;

[0111] Step S202: The vehicle chassis integrated control system determines the target engine speed;

[0112] Step S203: The vehicle chassis integrated control system determines the target engine speed based on the real-time vehicle speed and the target vehicle speed;

[0113] Step S204: The vehicle chassis integrated control system reads the real-time engine speed, and obtains the current speed difference and speed difference change rate based on the target engine speed and the real-time engine speed;

[0114] Step S205: The vehicle chassis integrated control system optimizes the control parameters in the controller through the particle swarm optimization (PSO) algorithm.

[0115] Specifically, the engine target speed is adjusted using a fractional-order PID controller, and the model form of the fractional-order PID controller is:

[0116]

[0117] The particle swarm optimization (PSO) algorithm is used to obtain the fractional order controller parameter k p , k i , k d , the initial values ​​of λ and μ;

[0118]

[0119]

[0120] Where, is the inertia weight; k is the current iteration number; V id is the velocity of the particle; x idis the position of the particle, corresponding to the above fractional-order PID controller parameter k p , k i , k d ,λ,μ;P id is the individual extreme value; P gd is the extreme value of the group; c1 and c2 are non-negative constants, which are acceleration factors; r1 and r2 are random numbers distributed in the interval [0,1];

[0121] In order to prevent the blind search of particles, the k in the PID controller is p , k i , k d The range of the three variables is selected as [0,3000], and the range of the fractional order is [-1,1].

[0122] Step S206: The vehicle chassis integrated control system dynamically adjusts the control parameters through a fractional-order PID controller to obtain a change in the controller parameters;

[0123] Specifically, a fuzzy neural network fractional-order PID controller is used for control, and its block diagram is as follows: Figure 1 As shown; the neural network uses BP learning algorithm to automatically adjust the network weights; the network is divided into four layers:

[0124] The first layer is the input variables, which are the two control inputs of the controller. The output function can be written as:

[0125]

[0126] The second layer divides the distribution of the input signal and uses the Gaussian function as the membership function to divide the distribution of the input signal. The fuzzy language is represented by five variables, representing negative large, negative small, zero, positive small, and positive large. The output function of each node is given by the following formula:

[0127]

[0128] Among them, σ im and b im are the center value and width of the Gaussian function respectively;

[0129] The nodes in the third layer represent fuzzy rules. Since both input variables are divided into 5 fuzzy spaces, there are 25 fuzzy rules in total. The output function of each rule is:

[0130]

[0131] The fourth layer completes the defuzzification, and the output function of the node is:

[0132]

[0133] where w nj is the output layer connection weight, and the controller parameter k is obtained p , k i , k d The amount of change;

[0134] Step S207: obtaining real-time controller parameters based on the initial values ​​of the controller parameters and the changes in the controller parameters in step S204, and the controller outputs a throttle opening control instruction under the real-time controller parameters;

[0135] Step S208: The vehicle chassis integrated control system sends the throttle opening control instruction to the throttle control unit;

[0136] Furthermore, step S3 further includes the following sub-steps:

[0137] Step S301: The throttle control unit reads the throttle opening control instruction;

[0138] Step S302: The throttle control unit determines the servo steering target angle according to the throttle opening control command;

[0139] Specifically, the throttle control unit calculates the angle of the servo steering gear signal corresponding to the pre-read throttle initial position and final position, uses the initial position as an offset, and calculates the angle corresponding to the servo steering gear according to the read throttle opening percentage;

[0140] Step S303: The throttle control unit converts the servo steering gear target angle into a specific control instruction and transmits it to the servo steering gear;

[0141] Preferably, the DH03X servo is selected, and its main technical parameters and performance parameters are as follows:

[0142] The maximum angle is 270°, the control signal is 0-5V analog voltage, and the maximum torque is 380kg·cm.

[0143] The load on the drone's servo is the resistance encountered when the accelerator pedal is moved. First, adjust the servo angle to 0° and install it in the appropriate position. Then, install the steering wheel and connecting rod. Adjust the servo angle until the accelerator pedal reaches the bottom. Record the command corresponding to the servo angle at this point as 0x45 and add a limiter at the current position.

[0144] The power of the servo steering gear is provided by the power distribution system on the vehicle;

[0145] First, the chassis integrated control system reads the current target vehicle speed vref, current vehicle speed v, current gear position i, and current engine speed r from the CAN bus. vref is read by the control system, vehicle speed v is read by the vehicle speed sensor, current gear position is read by the drive-by-wire chassis control system, and current engine speed is read by the engine speed sensor. The default initial value of the throttle amount x is 0.

[0146] Then, according to the current engine speed, current gear position and target vehicle speed, the engine speed-vehicle speed shifting curve (taking Table 1 as an example) is used to determine whether the vehicle needs to shift gears to achieve the target vehicle speed: if shifting gears is required, the target engine speed is determined according to the parameters in Table 1; if shifting gears is not required, the target engine speed rref is determined according to formula (1); then, according to these two situations, the target engine speed rref is determined;

[0147] Table 1 Schematic diagram of the relationship between vehicle speed, engine speed and gear shifting of a certain unmanned vehicle

[0148]

[0149] Note: The vehicle speed unit in the table is km / h, and the engine speed unit is rpm. Except for the gear position, the parameters in the table are not accurate values ​​and there are fluctuations around the values ​​in the table.

[0150] The corresponding relationship between engine speed and vehicle speed in the same gear can be obtained using the following formula:

[0151]

[0152] Where i m is the main reduction ratio, i x is the current gear ratio, r e is the wheel radius;

[0153] Then, by comparing the current engine speed with the target speed, a control command is sent to the servo actuator.

[0154] Considering the response speed of the servo to the control command, the sending frequency of the throttle control command is set to 2Hz;

[0155] Then, the vehicle chassis integrated control system sends the throttle opening control command to the throttle control unit. The throttle amount varies in the range of 0-100 (%), and the range of the servo steering engine command is 0x00-0xFF (hexadecimal). Therefore, the specific correspondence between the throttle amount and the throttle command can be obtained as follows:

[0156]

[0157] Finally, the throttle control unit reads the throttle control command sent by the chassis integrated control system and converts it into specific servo control commands, as follows:

[0158]

[0159] The servo control command offset is related to the servo installation position, and the servo stroke represents the full stroke of the servo.

[0160] In summary, this embodiment of the present invention provides a method for regulating the speed of an unmanned vehicle. This method uses engine speed as the research object and designs an engine speed regulation control method to achieve vehicle speed regulation. To achieve comprehensive speed regulation across multiple gears, the method also incorporates engine speed regulation during gear shifting, providing a basis for implementing speed regulation for unmanned vehicles.

[0161] Those skilled in the art will appreciate that all or part of the process steps of the above-described example method can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0162] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for adjusting the speed of an unmanned vehicle based on a servo-driven throttle, characterized in that: The method is implemented based on an unmanned vehicle system, which includes: a vehicle chassis integrated control system, a throttle control unit, and a throttle opening control mechanism; the throttle opening control mechanism includes a servo steering gear, a steering wheel, and a connecting rod; Wherein, the vehicle chassis integrated control system and the throttle control unit are connected via a cable; The throttle control unit and the throttle opening control mechanism are connected via a cable; The throttle opening control mechanism and the accelerator pedal are mechanically connected; The method comprises the following steps: Step S1: the vehicle chassis integrated control system reads the real-time vehicle speed and the target vehicle speed; Step S2: The vehicle chassis integrated control system determines the target engine speed based on the real-time vehicle speed and the target vehicle speed, generates a throttle opening control instruction, and sends it to the throttle control unit; Step S3: The throttle control unit reads the throttle opening control command, calculates the target angle of the servo steering gear, converts it into a specific control command and sends it to the servo steering gear to achieve speed regulation; Wherein, the step S2 further includes the following sub-steps: Step S201: The vehicle chassis integrated control system determines the target engine speed based on the real-time vehicle speed and the target vehicle speed; Step S202: the vehicle chassis integrated control system reads the real-time engine speed, and obtains the current speed difference and speed difference change rate based on the target engine speed and the real-time engine speed; Step S203: The vehicle chassis integrated control system optimizes the control parameters of the controller inside the vehicle chassis integrated control system by using a particle swarm optimization algorithm; Specifically, the engine target speed is adjusted using a fractional-order PID controller, and the model form of the fractional-order PID controller is: The fractional-order PID controller parameter k is obtained using the particle swarm optimization algorithm. p , k i , k d , λ, μ initial values, complete the optimization of the control parameters in the controller, as shown in the following formula (2) and formula (3); Where, is the inertia weight; k is the current iteration number; V id is the velocity of the particle; x id is the position of the particle, corresponding to the above fractional-order PID controller parameter k p , k i , k d ,λ,μ;P id is the individual extreme value; P gd is the extreme value of the group; c1 and c2 are non-negative constants, which are acceleration factors; r1 and r2 are random numbers distributed in the interval [0,1]; In order to prevent the blind search of particles, the k p , k i , k d The range of the three variables is selected as [0,3000], and the range of the fractional order λ and μ is [-1,1]; Step S204: The vehicle chassis integrated control system dynamically adjusts the control parameters through a fractional-order PID controller to obtain a change in the controller parameters; Step S205: Based on the initial value of the controller parameter in step S203 and the change in the controller parameter in step S204, the real-time controller parameter is obtained, and the controller outputs the throttle opening control instruction under the real-time controller parameter; Step S206: The vehicle chassis integrated control system sends the throttle opening control instruction to the throttle control unit; In step S204, a fuzzy neural network fractional-order PID controller is used for control; the neural network uses a BP learning algorithm to automatically adjust the network weights; the network is divided into four layers: The first layer is the input variable, which is the two control inputs of the controller. That is, step 202 obtains the current speed difference and the speed difference change rate. The output function is: The second layer divides the distribution of the input signal and uses the Gaussian function as the membership function to divide the distribution of the input signal. The fuzzy language is represented by five variables, representing negative large, negative small, zero, positive small, and positive large. The output function of each node is given by the following formula: Among them, σ im and b im are the center value and width of the Gaussian function respectively; The nodes in the third layer represent fuzzy rules. Since both input variables are divided into 5 fuzzy spaces, there are 25 fuzzy rules in total. The output function of each rule is: The fourth layer completes the defuzzification, and the output function of the node is: where w nj is the output layer connection weight, and the controller parameter k is obtained p , k i , k d The amount of change; Wherein, the step S3 includes the following sub-steps: Step S301: The throttle control unit reads the throttle opening control instruction; Step S302: The throttle control unit determines the servo steering target angle according to the throttle opening control command; Step S303: The throttle control unit converts the servo steering gear target angle into a specific control instruction and transmits it to the servo steering gear; Step S304: the servo steering engine converts the electrical signal into a mechanical signal, drives the steering wheel to rotate, and drives the connecting rod to drive the accelerator pedal; Among them, in step S304, when the accelerator pedal is activated, the longitudinal pull rod will change accordingly, and together with the return spring, it will drive the refueling pull rod to rotate the refueling pull arm, and manipulate the engine's high-pressure diesel pump refueling gear rod to move to change the oil supply, thereby changing the engine speed, cooperating with the vehicle's clutch and gearbox, and thus realizing the adjustment of the vehicle's longitudinal speed.

2. The unmanned vehicle speed control method based on servo-actuated throttle as claimed in claim 1, characterized in that: The vehicle chassis integrated control system is used to send a target throttle opening control instruction and receive real-time vehicle speed and engine speed signals.

3. The unmanned vehicle speed adjustment method based on servo-actuated throttle as claimed in claim 1, characterized in that: The vehicle chassis integrated control system reads the real-time vehicle speed and the target vehicle speed through the CAN network.

4. The method for controlling the speed of an unmanned vehicle based on a servo-actuated throttle according to claim 1, wherein: The servo steering gear is selected from DH03X steering gear.

5. The unmanned vehicle speed adjustment method based on servo-actuated throttle as claimed in claim 1, characterized in that: The power of the servo steering engine is provided by the power distribution system on the vehicle.

6. The unmanned vehicle speed adjustment method based on servo-actuated throttle as claimed in claim 1, characterized in that: According to the response speed of the servo to the specific control command, the sending frequency of the throttle opening control command is set to 2Hz.

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