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Related Concept Videos

Controller Configurations01:22

Controller Configurations

352
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...
352
Feedback control systems01:26

Feedback control systems

687
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...
687
Root-Locus Method01:19

Root-Locus Method

479
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.
This system can be represented by a block...
479
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

375
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
375
PD Controller: Design01:26

PD Controller: Design

624
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,...
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Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

393
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
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    This study introduces a novel control framework to mitigate the "butterfly effect" in connected and automated vehicles (CAVs). The approach ensures smoother vehicle dynamics by suppressing fluctuations in spacing, velocity, and acceleration for enhanced stability.

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    Area of Science:

    • Control Systems Engineering
    • Robotics
    • Automotive Engineering

    Background:

    • Connected and automated vehicles (CAVs) have seen progress in platoon control.
    • Existing methods often overlook the
    • butterfly effect
    • leading to unpredictable fluctuations in nonlinear platoons.
    • Individual vehicle and string stability do not prevent uncomfortable velocity and acceleration variations.

    Purpose of the Study:

    • To propose a parallel error-fluctuation suppression control framework for CAV platoons.
    • To address and mitigate the unpredictable
    • butterfly effect
    • in nonlinear vehicle platooning.
    • To ensure stable and comfortable vehicular motion despite potential spacing changes.

    Main Methods:

    • Development of tunable triple-layered error boundaries for spacing, velocity, and acceleration.
    • Integration of a Barbalat-lemma-enhanced filtering-compensating mechanism.
    • Application of an adaptive approach using radial basis function neural networks (RBFNNs) for asymptotic error tracking.
    • Proposal of an adaptive backstepping control integrating proactive and reactive suppression strategies.

    Main Results:

    • The proposed framework effectively confines propagated errors within predefined envelopes.
    • Asymptotic error tracking proactively suppresses potential fluctuations in vehicle dynamics.
    • The adaptive backstepping control mitigates the unquantifiable
    • butterfly effect
    • .

    Conclusions:

    • The developed control approach significantly enhances the stability and comfort of CAV platoons.
    • Theoretical analysis and simulations validate the effectiveness and superiority of the proposed method.
    • This work offers a robust solution for managing complex dynamics in nonlinear vehicle platoons.