Adaptive Event-Triggered Fuzzy Control for Uncertain Active Suspension Systems.

Area of Science:

• Control Engineering
• Automotive Systems
• Fuzzy Logic Systems

Background:

• Active vehicle suspension systems require robust control strategies to handle uncertainties and actuator failures.
• Traditional control methods may not efficiently manage communication resources in dynamic systems.
• Event-triggered control offers potential for resource optimization but requires advanced mechanisms.

Purpose of the Study:

• To develop an adaptive event-triggered fuzzy control strategy for active vehicle suspension systems.
• To address the challenge of actuator failures within the control design.
• To enhance communication resource efficiency compared to traditional event-triggered schemes.

Main Methods:

• Application of Takagi-Sugeno fuzzy models to represent the active vehicle suspension system.
• Design of an adaptive event-triggered mechanism to optimize data transmission.
• Utilization of Lyapunov stability theory to guarantee system performance and constraints.

Main Results:

• The proposed adaptive event-triggered fuzzy control effectively manages system uncertainties and actuator failures.
• The adaptive mechanism demonstrates superior communication resource savings over constant-threshold schemes.
• Ensured desired performance and adherence to suspension constraints were validated.

Conclusions:

• The developed adaptive event-triggered fuzzy control is a feasible and effective approach for active vehicle suspension systems.
• This method offers significant advantages in communication resource management and system reliability.
• The findings contribute to the advancement of intelligent control strategies in automotive applications.