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CN-121995757-A - High-precision automatic docking control method and system for airport scene boarding bridge

CN121995757ACN 121995757 ACN121995757 ACN 121995757ACN-121995757-A

Abstract

The invention discloses a high-precision automatic docking control method and a system for an airport surface boarding bridge, and relates to the field of automatic control of airport surface equipment. Aiming at the interference problems of variable structure inertia characteristics, nonlinear dynamic coupling, flexible vibration caused by a long cantilever structure of a composite environment and the like in the docking process of a boarding bridge, the invention firstly establishes a three-degree-of-freedom Euler-Lagrange dynamic model considering a telescopic coupling effect, secondly constructs a data guiding learning control framework, designs a smooth soft projection operator to process physical parameter constraint, utilizes iterative domain process data to update a physical parameter estimated value, a repeated interference estimated value and a non-repeated interference boundary on line, and finally combines an instruction filtering back-stepping method to design a control law and introduces a robust damping item to inhibit random interference.

Inventors

  • MENG DEYUAN
  • LU CHANGXIN

Assignees

  • 北京航空航天大学

Dates

Publication Date
20260508
Application Date
20260203

Claims (10)

  1. 1. The high-precision automatic docking control method for the airport scene boarding bridge is characterized by comprising the following steps of: Step S1, establishing a variable structure dynamics model, which comprises the following steps: Establishing a three-degree-of-freedom dynamic model of the boarding bridge system, and defining generalized coordinate vectors Wherein For the yaw angle of the rotating upright post, For the length of the channel to be telescopic, Is a pitch angle of the machine connecting port, Represent the first Multiple butt joint iterations, the model comprises a length along with the expansion and contraction Non-linearly changing inertial matrix And coriolis Li Juzhen Is a dynamic equation of (2); step S2, designing a command filtering kinematics controller, which comprises the following steps: Defining position tracking errors Design of a kinematic virtual control law And introducing a first order instruction filter to obtain a filtered speed reference signal And its derivative Simultaneously building auxiliary compensation system state To compensate for the filtering error; step S3, constructing a linear parameterized regression matrix, comprising defining a velocity tracking error based on the dynamics model And compensated speed error Converting system dynamics equations into linear parameterized form And calculate regression matrix In which The force vector of the gravity is used to determine, Is a physical parameter vector and is calculated by analysis The method comprises the specific elements of a telescopic-rotary coupling effect; step S4, designing a data-guided parameter update law, including defining a smooth soft projection operator Respectively constructing a physical parameter self-adaptive law, a repetitive interference learning law and a non-repetitive interference boundary learning law by utilizing error information on an iteration axis, and updating a physical parameter estimated value in real time Repetitive interference estimation And non-repetitive interference upper bound ; Step S5, calculating and applying a control moment, including combining the compensated position error based on the estimated value updated in step S4 And speed error Calculating a control moment comprising a robust damping term The boarding bridge is driven to articulate.
  2. 2. The method according to claim 1, wherein the kinetic model in step S1 is specifically: ; Wherein, the For symmetrical positive definite inertia matrix, its diagonal line element includes the length of extension Variable items ; Is a matrix of coriolis and centrifugal forces; A gravity vector; To control input torque; To total disturbance, including periodic flexible vibration components caused by the boarding bridge cantilever structure, decomposed into repetitive disturbances And non-repetitive interference 。
  3. 3. The method according to claim 1, wherein the regression matrix in step S3 The element calculation formula of (2) is as follows: , wherein, For reference acceleration vector Components of (2); is a reference velocity vector Components of (2); the coupling term elements are specifically as follows: ; ; ; Wherein, the , , , Gravitational acceleration.
  4. 4. The method according to claim 1, wherein the smooth soft projection operator in step S4 The definition is as follows: ; wherein, the The operator has a continuous first derivative at the boundary.
  5. 5. The method according to claim 4, wherein the update law in step S4 is specifically: Update law of physical parameters: ; Repetitive interference update law: ; non-repetitive interference boundary update law: ; Wherein, the Is the positive learning gain of the device, To compensate for the velocity tracking error.
  6. 6. The method according to claim 1, wherein the control torque of step S5 The calculation formula of (2) is as follows: ; Wherein, the In order to feed back the gain matrix, In order to compensate for the post-position error, The product of the Hadamard is represented, Is a robust control parameter that decays with iteration number.
  7. 7. The high-precision automatic docking control system for the boarding bridge is characterized by comprising the following components: The sensing module comprises high-precision rotary encoders arranged on all joints of the boarding bridge and a laser range finder or a stay wire type displacement sensor arranged in the channel and used for collecting yaw angles in real time Telescoping length l and pitch angle Status data of (2); the storage module is used for storing a preset reference track And historical data generated during the course of the past iteration including the physical parameter estimates generated Repetitive interference estimation And boundary estimation value ; A calculation module configured to execute the control method according to any one of claims 1 to 6, read the data of the sensing module and the history data of the storage module, and calculate the control moment at the current time by using the data-guided learning law Writing the updated parameters back to the storage module; the driving module comprises a servo motor driver and is used for receiving the moment instruction output by the calculating module and controlling the motors of the boarding bridge slewing mechanism, the telescopic mechanism and the lifting mechanism to act.
  8. 8. An electronic device comprising one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-6.
  9. 9. A computer readable storage medium having stored thereon executable instructions which when executed by a processor cause the processor to implement the method of any of claims 1 to 6.
  10. 10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the method of any of claims 1 to 6.

Description

High-precision automatic docking control method and system for airport scene boarding bridge Technical Field The invention belongs to the technical field of automatic control of airport ground service equipment, and particularly relates to a high-precision automatic docking control method and system for airport scene boarding bridges. Background The passenger boarding bridge (PASSENGER BOARDING BRIDGE, PBB) is a key hub connecting the terminal building to the aircraft, and its docking efficiency directly determines the transit turnaround time of the aircraft. At present, the boarding bridge docking mainly depends on manual operation, and has the problems of low efficiency and high risk. Realizing full-automatic docking is a core requirement for intelligent airport construction. However, achieving high precision automatic docking faces the following core challenges: 1. The aerobridge is a huge cantilever structure. As the Tunnel (Tunnel) expands and contracts, its moment of inertia (in particular yaw axis inertia) relative to the center of rotation varies strongly and non-linearly. For example, the inertia may be several times greater when extended than when retracted. 2. The strong nonlinear coupling is that the telescoping motion can generate huge Coriolis force (Coriolis force) due to the configuration of R-P-R (rotation-motion-rotation), which strongly affects the stability of yaw and pitch motions. 3. The saturation risk of the actuator, namely, when the system approaches a safety boundary, the existing optimal control method (such as a control barrier function CBF) often requires extremely large control moment, and the motor is easy to saturate and lose efficacy. 4. The complex environment interference is that the airport parking apron has normal wind (repeated interference) and gust wind (random non-repeated interference), the traditional PID control is difficult to be compatible with low-speed stability and high-speed disturbance resistance, and as a long cantilever structure, the boarding bridge is easy to excite flexible mode vibration with low frequency (0.1-0.5 Hz) in motion, and the terminal precision is affected. In the existing control scheme, the traditional feedback control cannot adapt to the wide variation of parameters, and the simple adaptive control ignores the characteristic of high repetition of airport operation (same machine type and same stand). Therefore, the invention provides a data-guided learning control method. Disclosure of Invention The invention aims to solve the problems of insufficient precision and actuator saturation caused by parameter time-varying and interference in automatic docking of a boarding bridge, and provides a control method and a control system combining physical model priori and iterative data. The technical scheme of the invention is as follows: The high-precision automatic butt joint control method of airport scene boarding bridge is based on Euler-Lagrange modeling and instruction filtering back-step method framework, and is characterized by introducing a data guiding mechanism, namely constructing a smooth projection operator by using priori physical constraint (Prior Constraints), and performing parameter learning by using iterative process data (ITERATIVE DATA), and comprises the following steps: Step S1, establishing a variable structure dynamics model, which comprises the following steps: Establishing a three-degree-of-freedom dynamic model of the boarding bridge system, and defining generalized coordinate vectors WhereinFor the yaw angle of the rotating upright post,For the length of the channel to be telescopic,Is a pitch angle of the machine connecting port,Represent the firstMultiple butt joint iterations, the model comprises a length along with the expansion and contractionNon-linearly changing inertial matrixAnd coriolis Li JuzhenIs a dynamic equation of (2); step S2, designing a command filtering kinematics controller, which comprises the following steps: Defining position tracking errors Design of a kinematic virtual control lawAnd introducing a first order instruction filter to obtain a filtered speed reference signalAnd its derivativeSimultaneously building auxiliary compensation system stateTo compensate for the filtering error; step S3, constructing a linear parameterized regression matrix, comprising defining a velocity tracking error based on the dynamics model And compensated speed errorConverting system dynamics equations into linear parameterized formAnd calculate regression matrixIn whichThe force vector of the gravity is used to determine,Is a physical parameter vector and is calculated by analysisThe method comprises the specific elements of a telescopic-rotary coupling effect; step S4, designing a data-guided parameter update law, including defining a smooth soft projection operator Respectively constructing a physical parameter self-adaptive law, a repetitive interference learning law and a non-repetitive interference bounda