Multi-channel quantized output feedback fault-tolerant tracking control for electric vehicle lateral dynamics under...

Area of Science:

• Control Systems Engineering
• Automotive Engineering
• Fuzzy Logic Systems

Background:

• Electric vehicles (EVs) require precise lateral dynamics control for safety and performance.
• Sensor faults and data quantization can significantly degrade control system reliability.
• Nonlinear variations in vehicle parameters (speed, mass, inertia) complicate control design.

Purpose of the Study:

• To investigate the multi-channel quantized output feedback tracking control for EV lateral dynamics.
• To address the challenges posed by sensor faults and parameter uncertainties.
• To ensure robust tracking performance and control output stability.

Main Methods:

• Modeling the EV lateral dynamics as a Takagi-Sugeno (T-S) fuzzy model with norm-bounded uncertainties.
• Designing a non-fragile fuzzy observer to handle additive random sensor faults.
• Developing a resilient controller to achieve H∞-tracking and L2-L∞ performance.

Main Results:

• The designed observer and controller effectively compensate for sensor faults and parameter variations.
• The system achieves guaranteed H∞-tracking performance for the tracking error.
• The control output satisfies the L2-L∞ performance criteria, ensuring stability.

Conclusions:

• The proposed non-fragile fuzzy observer and resilient controller provide an effective solution for robust EV lateral control.
• The methodology is validated through co-simulations under various challenging road conditions.
• This approach enhances the safety and reliability of autonomous driving systems in EVs.