Fuzzy Logic-Enhanced Neuroadaptive Fault-Tolerant Control for Vehicular Platoons With Stochastic Disturbances and...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Automotive Engineering

Background:

• Vehicular platoons face challenges including nonlinear dynamics, disturbances, and actuator faults.
• Maintaining stringent asymmetric spacing constraints is critical for safety and efficiency.
• Existing neuroadaptive control methods may struggle with complex uncertainties and fault tolerance.

Purpose of the Study:

• To introduce a novel fuzzy logic-enhanced neuroadaptive sliding mode control (FLENNSMC) framework for vehicular platoons.
• To address nonlinear dynamics, stochastic disturbances, actuator faults, and asymmetric spacing constraints.
• To improve robustness, adaptation speed, and computational efficiency in control systems.

Main Methods:

• Integration of fuzzy logic's interpretive strengths with neural networks' adaptive learning.
• Utilizing a Takagi-Sugeno (T-S) fuzzy model and a fuzzy logic-enhanced RBFNN (FLERBFNN).
• Incorporating fault-tolerant control, asymmetric barrier Lyapunov function (BLF), and Nussbaum function for stability and constraint satisfaction.

Main Results:

• The FLENNSMC framework effectively handles nonlinear dynamics, stochastic disturbances, and actuator faults.
• Strict enforcement of asymmetric spacing constraints was achieved using the BLF.
• Stochastic Lyapunov-Krasovskii stability analysis confirmed uniform ultimate boundedness (UUB) of tracking errors and mixed H-infinity/passivity performance.

Conclusions:

• The proposed FLENNSMC demonstrates superior performance over conventional methods in simulations.
• Fuzzy logic integration enhances the learning process and handling of complex uncertainties in vehicular platoons.
• The framework offers improved robustness, adaptation speed, and computational efficiency for autonomous vehicle control.