Adaptive Switching Iterative Learning Control Strategy of Vehicle-Mounted Flywheel Battery System Based on Error Compensation of Road Conditions

It is difficult to avoid the complex interference caused by road conditions when the vehicle-mounted flywheel battery works. The system structure and dynamic model parameters will change in real time, which obviously increases the control difficulty to achieve robust stability. Therefore, an adaptive switching iterative learning control (ASILC) strategy based on road conditions is proposed to improve the robustness of the control system. First, the structure and parameters of the flywheel cell with multifunction air gap are introduced. The relationship between the flywheel displacement and the control current is obtained by the derivation of its suspension force model. Second, an adaptive switching iterative learning method is designed to switch between two iterative learning methods based on road conditions. Then, error compensation is added to the iterative learning control (ILC) to correct the error. In the process of error compensation, according to the offset of the flywheel under different road conditions, the road condition characteristic compensation factor is proposed. Finally, the performance and comparison tests are carried out on the experimental platform to verify the effectiveness of the proposed strategy. Good experimental results show that the proposed strategy can reduce the average deviation to 76.6% when the flywheel is subjected to continuous disturbances, making it more reliable and robust than the traditional sensor-based control strategy.