Fault Tolerant Sliding Mode Predictive Control for Uncertain Steer-by-Wire System.

Area of Science:

• Automotive Engineering
• Control Systems Engineering
• Robotics

Background:

• Steer-by-Wire (SbW) systems offer advanced steering capabilities but are vulnerable to actuator failures, potentially causing instability.
• Ensuring reliable steering performance in SbW systems necessitates robust fault-tolerant control strategies.

Purpose of the Study:

• To propose a novel fault-tolerant sliding mode predictive control (SMPC) strategy for Steer-by-Wire (SbW) systems.
• To enhance the robustness and fault-tolerant capabilities of SbW systems against uncertainties and actuator failures.

Main Methods:

• Sliding Mode Control (SMC) integrated with Model Predictive Control (MPC) to leverage robustness and fault tolerance.
• Chaos Particle Swarm Optimization (CPSO) for optimizing MPC parameters.
• A two-stage Kalman filter for simultaneous fault detection and state estimation.

Main Results:

• The proposed SMPC-CPSO controller demonstrated superior robustness against model uncertainties, disturbances, and actuator faults.
• Enhanced tracking performance was observed compared to various existing control methods (e.g., SCMP-PSOs, SMPC-DE, MPC, SMPC, MPC-PSO).
• Simulations validated the effectiveness of the integrated fault detection and state estimation using the two-stage Kalman filter.

Conclusions:

• The developed SMPC-CPSO strategy significantly improves the reliability and performance of SbW systems.
• This approach offers a promising solution for safe and stable operation of electronically controlled steering systems.
• The combination of SMC, MPC, CPSO, and Kalman filtering provides a comprehensive fault-tolerant control framework.