A QCGNN-Based Predictive Framework for a Common Sliding Mode Control for Enhanced Fault-Tolerant Performance of a Four-Wheel Independently Driven Electric Vehicle
The increasing popularity of electric vehicles is inevitable due to their lower dependence on conventional fuel and reduced air pollution. Among various drivetrain architectures, four-wheel independently driven electric vehicles (4WID-EVs) have gained significant attention owing to their superior load-carrying and dynamic performance. However, the distributed four-motor architecture makes them vulnerable to unpredictable motor failures, necessitating an effective fault-tolerant control strategy. This work proposes a common sliding mode controller (CSMC)-integrated quantum complete graph neural network (QCGNN) for adaptive tuning under one-, two-, and three-motor failure conditions at reference speeds of 20 and 40 m/s, ensuring stable operation through continuous state feedback. Simulation results demonstrate fault recovery within 3 s, a rise time of 1.2–1.3 s, a settling time below 5.5 s, a peak overshoot below 8%, and a steady-state error below 0.2%. Compared with the QCGNN-Optimal LQR, the proposed QCGNN-CSMC reduces the Mean Absolute Error (MAE) from 34 to 18, Root Mean Square Error (RMSE) from 41 to 23, and the steady-state error from 1.07 to 0.56, while maintaining R2 values above 0.90. Rapid controller prototyping further validates the robustness, reliability, and real-time applicability of the proposed fault-tolerant control framework for 4WID-EVs.