Adaptive integral sliding mode control strategy for vehicular platoon with prescribed performance

Area of Science:

• Control Systems Engineering
• Robotics and Autonomous Systems
• Automotive Engineering

Background:

• Vehicular platoons face challenges with external disturbances and unknown nonlinearities affecting tracking performance.
• Existing control strategies often struggle with initial tracking errors and design complexity.

Purpose of the Study:

• To design a robust control strategy for vehicular platoons that ensures string stability and predefined tracking performance.
• To reduce controller design complexity by making it independent of initial tracking errors.

Main Methods:

• Development of a novel Tunnel Prescribed Performance Function (TPPF) to manage tracking error convergence.
• Implementation of an adaptive mechanism for estimating external disturbances.
• Design of a neural adaptive compensation term using Radial Basis Function Neural Networks (RBFNN) for unknown nonlinearities.
• Formulation of an adaptive Integral Sliding Mode Control (ISMC) strategy.

Main Results:

• The TPPF ensures a more compact convergence process for tracking errors.
• The adaptive mechanism and RBFNN effectively handle external disturbances and nonlinearities.
• The proposed adaptive ISMC strategy achieves predefined tracking performance and ensures string stability.

Conclusions:

• The novel adaptive ISMC strategy significantly enhances vehicular platoon control.
• The approach demonstrates superior performance and effectiveness compared to existing methods through numerical simulations.