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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Feedback control systems01:26

Feedback control systems

259
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
259
Reinforcement Schedules01:24

Reinforcement Schedules

120
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
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Control Systems01:10

Control Systems

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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
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Load-frequency control01:28

Load-frequency control

98
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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Root-Locus Method01:19

Root-Locus Method

116
A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
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Related Experiment Video

Updated: May 17, 2025

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
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Enhancing Airport Traffic Flow: Intelligent System Based on VLC, Rerouting Techniques, and Adaptive Reward Learning.

Manuela Vieira1,2,3, Manuel Augusto Vieira1,2, Gonçalo Galvão1,3

  • 1Electronics Telecommunication and Computer Department, Instituto Superior de Engenharia de Lisboa-Instituto Politécnico de Lisboa, 1949-014 Lisboa, Portugal.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered airport traffic system using Visible Light Communication (VLC) and Deep Reinforcement Learning (DRL) for optimized navigation. The system enhances safety and efficiency by intelligently managing pedestrian and Autonomous Guided Vehicle (AGV) traffic flow.

Keywords:
adaptive reward mechanismsautonomous guided vehicles (AGVs)deep reinforcement learning (DRL)indoor localizationintelligent rerouting techniquesmulti-agent systemsroute optimizationtraffic flow simulationvisible light communication (VLC)wayfinding assistance

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Area of Science:

  • Intelligent Transportation Systems
  • Artificial Intelligence in Mobility
  • Wireless Communication Networks

Background:

  • Airports require efficient localization and traffic management for pedestrians and Autonomous Guided Vehicles (AGVs).
  • Existing systems face challenges in optimizing complex airport traffic flows and ensuring safety.
  • Seamless indoor navigation without GPS is crucial for operational efficiency.

Purpose of the Study:

  • To develop an AI-driven airport traffic management system integrating Visible Light Communication (VLC) and Deep Reinforcement Learning (DRL).
  • To optimize traffic flow, reduce congestion, and enhance safety for both pedestrians and AGVs.
  • To enable accurate indoor localization and improve overall airport mobility.

Main Methods:

  • Implementation of a hybrid mesh network using tetrachromatic LEDs with On-Off Keying (OOK) modulation and SiC optical receivers for VLC.
  • Development of AI agents utilizing Deep Reinforcement Learning (DRL) and Q-learning algorithms for traffic analysis and decision-making.
  • Integration of rerouting techniques and adaptive reward mechanisms for dynamic traffic load balancing and bottleneck avoidance.

Main Results:

  • Achieved a more balanced green time allocation, reducing vehicle-prioritized phases by up to 43% to accommodate pedestrian flows.
  • Demonstrated improved route planning, reduced halting times, and enhanced coordination between AGVs and pedestrian traffic.
  • Confirmed preserved traffic flow responsiveness and stable critical clearance phases despite pedestrian prioritization.

Conclusions:

  • The proposed AI-driven VLC system significantly enhances airport traffic management by optimizing pedestrian and AGV coordination.
  • The integration of DRL and adaptive strategies leads to safer, more efficient, and human-centered airport mobility.
  • The system provides accurate indoor localization, supporting seamless operations without GPS reliance.