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Related Concept Videos

PD Controller: Design01:26

PD Controller: Design

157
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
157
Load-frequency control01:28

Load-frequency control

104
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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Feedback control systems01:26

Feedback control systems

261
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...
261
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
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...
93
Control Systems01:10

Control Systems

979
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...
979
Controller Configurations01:22

Controller Configurations

78
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
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Machine learning based adaptive traffic prediction and control using edge impulse platform.

Manoj Tolani1, G E Saathwik2, Ayush Roy2

  • 1Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India. manoj.tolani@manipal.edu.

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|May 17, 2025
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Summary
This summary is machine-generated.

This study introduces an autonomous traffic control system that uses sensors and machine learning to dynamically adjust traffic signal timings. This intelligent system reduces traffic congestion and delays without human intervention.

Keywords:
Artificial intelligenceInternet of thingsMachine learningTinyMLTraffic controlTraffic prediction

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Area of Science:

  • Intelligent Transportation Systems
  • Machine Learning Applications
  • Autonomous Control Systems

Background:

  • Traffic congestion and delays are significant issues in current vehicle traffic control.
  • Conventional traffic signal systems with fixed timers are inadequate for unpredictable traffic conditions.

Purpose of the Study:

  • To develop an automated traffic control system that adapts signal timings based on real-time vehicle density.
  • To mitigate traffic congestion and reduce delays through dynamic signal adjustment.

Main Methods:

  • Utilizing proximity sensors to detect approaching vehicles, monitoring their speed and density.
  • Implementing an Edge-Impulse-based machine learning model for predicting vehicle density and arrival times.
  • Dynamically adjusting traffic signal timings based on predicted and real-time traffic data.

Main Results:

  • The proposed system effectively reduces traffic congestion and delays by optimizing signal timings.
  • Machine learning algorithms enable accurate forecasting of traffic conditions.
  • Automation of the traffic scheduling process minimizes human error and enhances road safety.

Conclusions:

  • The developed methodology offers an intelligent, efficient, and autonomous solution for modern traffic control.
  • The system demonstrates reliability and accuracy in real-world traffic scenarios.
  • This approach has the potential to significantly improve existing traffic management systems.