Application of PID Control Principles in the Tasks of Modeling the Movement of Wheeled Vehicles Equipped with an Anti-Lock Braking System

Boyko, Grigory; Fedin, Alexey; Redl, Jozef

doi:10.1007/978-3-031-35875-3_24

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…The Proportional-Integral-Derivative (PID) is the widely used method in industry to control both linear and non-linear dynamic systems. This control method has been used by many researchers to design ABS system [23,[30][31][32][33].…”

Section: Proportional-integral-derivative Controllermentioning

confidence: 99%

Anti-lock braking system control design using a non-linear multi-body dynamic model for a two wheeled vehicle

Khadr,

Houidi,

Romdhane

2024

Eng. Res. Express

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This paper investigates the design of an Antilock Braking System for a Two Wheeled Vehicle using a non-linear multi-body dynamic model developed by an approach widely used in robotics. The Antilock Braking System is controlled using Matlab/Simulink by applying the Bang-Bang, Proportional-Integral-Derivative and Fuzzy Logic controllers in order to set the longitudinal slip to a desired value. The developed model is used to study the braking performances of a Two Wheeled Vehicle in a straight-line maneuver under different road conditions: dry, wet and a transition between both. The simulation results indicate that the non-linear dynamic model can effectively simulate the behavior of the Two Wheeled Vehicle under emergency braking conditions and with the different types of controllers. The braking performance of the Two Wheeled Vehicle with Antilock Braking System was improved by decreasing the stopping distance, avoiding wheel lockup and therefore contributes to ensure the vehicle control and stability and to reduce the risk of falling. Also, it can be seen through comparative analysis that the Fuzzy logic is one of the most efficient controllers that improve the braking safety.

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Section: Proportional-integral-derivative Controllermentioning

confidence: 99%

Anti-lock braking system control design using a non-linear multi-body dynamic model for a two wheeled vehicle

Khadr,

Houidi,

Romdhane

2024

Eng. Res. Express

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Prediction of automotive response using supervised machine learning in antilock braking systems and comparison with different models for improved vehicle safety

Gunjate,

Khot

2024

Eng. Res. Express

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Previous research on predicting automotive response in antilock braking systems faced several common drawbacks. Limited availability of high-quality datasets and computational constraints hindered the models' ability to generalize and perform effectively in real-time. Additionally, integrating these models into existing vehicle systems is challenging, often requiring extensive calibration and testing to ensure reliability and safety. This study focuses on enhancing automotive safety through the integration of machine learning with advanced braking systems. An antilock braking system model is developed using MATLAB, accounting for key parameters such as speed, mass, wheel radius, and moment of inertia. Various probability ranges are simulated to evaluate the system's real-time response in dynamic environments. A supervised machine learning approach is applied, with lasso regression used for model validation. Braking response was evaluated at three speeds: 25 km/h, 45 km/h, and 65 km/h. To assess model accuracy, error analysis was conducted using metrics such as mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE). Results showed a significant improvement in prediction accuracy, with MAPE reduced to 3.2%, MAE to 0.15 seconds, RMSE to 0.25 seconds, and MSE to 0.06 seconds.A comparative analysis is done using the Python Jupyter Framework, further validating the findings. The machine learning model successfully forecasts braking response times, and validates its prediction against random datasets.

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Application of PID Control Principles in the Tasks of Modeling the Movement of Wheeled Vehicles Equipped with an Anti-Lock Braking System

Cited by 2 publications

References 23 publications

Anti-lock braking system control design using a non-linear multi-body dynamic model for a two wheeled vehicle

Anti-lock braking system control design using a non-linear multi-body dynamic model for a two wheeled vehicle

Prediction of automotive response using supervised machine learning in antilock braking systems and comparison with different models for improved vehicle safety

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