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This study introduces a learning-based control method for autonomous vehicles using Adaptive Neuro-Fuzzy Inference System (ANFIS) to create a Takagi-Sugeno (TS) controller. This approach reduces computational load for real-time implementation in autonomous driving systems.

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence
  • Fuzzy Logic Systems

Background:

  • Autonomous vehicles require efficient and reliable control systems.
  • Model Predictive Control (MPC) offers advanced control but can be computationally intensive.
  • Fuzzy logic systems, particularly Takagi-Sugeno (TS) models, provide a framework for complex system control.

Purpose of the Study:

  • To develop a learning-based control approach for autonomous vehicles.
  • To reduce the computational burden associated with traditional control methods like MPC.
  • To enable real-time implementation of advanced control strategies for autonomous systems.

Main Methods:

  • Utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm to learn an explicit Takagi-Sugeno (TS) controller from existing controller data.
  • Identifying the vehicle's dynamic model in the TS form.
  • Assessing closed-loop stability using Lyapunov theory and Linear Matrix Inequalities (LMIs).
  • Learning the control law from a Model Predictive Control (MPC) controller to eliminate online optimization.

Main Results:

  • Successfully learned an explicit TS controller from MPC data without online optimization.
  • Demonstrated a significant reduction in computational load compared to traditional MPC.
  • Validated the approach through simulations on a small-scale autonomous racing car model.
  • Confirmed closed-loop stability through theoretical analysis.

Conclusions:

  • The proposed learning-based approach effectively creates an explicit TS controller for autonomous vehicles.
  • Eliminating online optimization via ANFIS-based learning facilitates real-time implementation and reduces computational demands.
  • The method shows promise for enhancing the efficiency and practicality of autonomous vehicle control systems.