Chapter One  Overview of Aircraft Traction Cascade System
  1.1  Introduction
  1.2  Transmission and control modes of aircraft tractors
    1.2.1  Traction modes
    1.2.2  Transmission control system
    1.2.3  Hydraulic transmission control system of aircraft towing tra
    1.2.4  Automatic control of aircraft tractors
  1.3  Aircraft automatic parking and anti-collision system
  1.4  Airport FOD intelligent detection
Chapter Two  Machine Learning Models and Algorithms
  2.1  The development of machine learning
    2.1.1  Budding period
    2.1.2  Enthusiastic period
    2.1.3  Calm period
    2.1.4  Revival period
    2.1.5  Diversified development period
  2.2  Feature engineering
    2.2.1  Data structure
    2.2.2  Data loading
    2.2.3  Data processing
    2.2.4  Feature processing
    2.2.5  Feature dimensionahty reduction
  2.3  Learning methods for dealing with forecasting problems
    2.3.1  Linear algebra and machine learning
    2.3.2  Univariate linear regression
    2.3.3  Multivariate linear regression
  2.4  Learning methods for dealing with classification problems
    2.4.1  Logistic regression
    2.4.2  Classification expression
    2.4.3  Logical distribution
    2.4.4  Logistic regression solution
  2.5  Neural network
    2.5.1  Machine learning models
    2.5.2  Loss function
    2.5.3  Convolutional neural network
  2.6  Intelligent image processing methods
Chapter Three  Simulation Design of Aircraft Traction Cascade System
  3.1  Design of simulation function index
  3.2  Simulation parameters design
  3.3  Simulation system architecture
  3.4  Research methods and technical path
    3.4.1  Research methods
    3.4.2  Technical path
Chapter Four  PID Control and Stability of Aircraft Traction Cascade System
  4.1  Motion analysis and control mode of the cascade system
    4.1.1  Motion analysis
    4.1.2  Steady state control method
    4.1.3  PID control
  4.2  Tilt angle measurement
  4.3  Encoder velocity measurement
  4.4  DC motor PID control
    4.4.1  Position closed-loop control
    4.4.2  Speed closed-loop control
  4.5  Cascade system speed control and cascade PID
  4.6  PID traction control simulation
    4.6.1  Simulation experimental method
    4.6.2  Simulation experiments
    4.6.3  Simulation experimental results
  4.7  Conclusions
Chapter Five  Motion Control of Aircraft Traction Cascade System Based on Machine Learning
  5.1  Motion control based on machine learning
    5.1.1  Motion analysis
    5.1.2  Automatic control model
  5.2  Environment constrained motion control based on machine learning
    5.2.1  Characteristic environmental constraint
    5.2.2  The environment constraint of FOD
    5.2.3  The environment constraint of runway intrusion
  5.3  Results and discussion
  5.4  Conclusions
Chapter Six  High-Speed Data Transmission for the Aircraft Towing Tractor
  6.1  High-Speed communication architecture and environment construction
  6.2  High-Speed transmission link design
    6.2.1  Transmission link rule design
    6.2.2  Sub-packet design
  6.3  Results and discussion
    6.3.1  Millimeter wave transmission
    6.3.2  Full band communication
    6.3.3  Response time of control instructions
  6.4  Conclusions
Chapter Seven  Aircraft Tractor Automatic Control Simulation
  7.1  Data collection and annotation
    7.1.1  Traction platform configuration information
    7.1.2  Training data collection and labeling
    7.1.3  Environmental restraint control data collection and labeling
  7.2  Model training
    7.2.1  Model structure
    7.2.2  Model training process
  7.3  Model verification
    7.3.1  Traction process
    7.3.2  Pushing process
    7.3.3  Environment constrained motion control
    7.3.4  Results of high-speed data link test
  7.4  Conclusions
Chapter Eight  Summary and Prospect
  8.1  Summary
  8.2  Prospect
    8.2.1  Database construction
    8.2.2  Research and application of machine learning algorithms
    8.2.3  Automatic and driverless control of airport special vehicles
References