Neural-network hybrid control for antilock braking systems.

Area of Science:

• Automotive Engineering
• Control Systems
• Artificial Intelligence

Background:

• Antilock braking systems (ABS) aim to maximize wheel traction and steerability during braking.
• ABS performance is often compromised under harsh road conditions, necessitating improved control strategies.

Purpose of the Study:

• To develop a novel hybrid control system for antilock braking systems (ABS) incorporating a recurrent neural network (RNN) observer.
• To enhance ABS performance and stability, particularly under adverse road conditions.

Main Methods:

• A hybrid control system combining an ideal controller with an RNN uncertainty observer and a compensation controller was designed.
• Taylor linearization technique was applied to improve the RNN's learning capability.
• On-line parameter adaptation laws based on a Lyapunov function were derived to ensure system stability.

Main Results:

• Simulations demonstrated the effectiveness of the proposed RNN-based hybrid control system for antilock braking.
• The system showed improved performance in maintaining wheel traction and vehicle steerability across various road conditions.
• The RNN observer effectively estimated system uncertainties, contributing to robust control.

Conclusions:

• The developed RNN hybrid control system offers a promising solution for enhancing antilock braking system performance.
• The integration of RNNs provides superior dynamic response capabilities for uncertainty observation in ABS.
• The proposed control strategy guarantees system stability and improves braking control under diverse and challenging road environments.