Abstract:
We will apply dual three-phase permanent magnet synchronous motors (DTP-PMSM) to the road sensing unit of the steer-by-wire system to improve redundancy and fault toleran...Show MoreMetadata
Abstract:
We will apply dual three-phase permanent magnet synchronous motors (DTP-PMSM) to the road sensing unit of the steer-by-wire system to improve redundancy and fault tolerance performance, meeting the requirements of intelligent transportation systems. However, open circuit faults inevitably lead to periodic torque ripple, parameter mismatch, and current harmonics, which pose a huge threat to drivers and traffic participants. Therefore, this article proposes a dynamic balancing strategy that can operate stably under both motor health and fault conditions. Firstly, this strategy does not require diagnostic fault information, thereby fundamentally avoiding performance degradation caused by diagnostic accuracy, time, model reconstruction, and strategy switching. The torque control module (TCM) is based on adaptive iterative learning control with a forgetting factor, which achieves torque tracking and suppression of periodic ripple after faults. The current control module (CCM) is based on ultra local model free predictive current control and extended state observer to track current and suppress the effects of parameter mismatch and current harmonics. Based on the multi-variable multi-objective sliding mode extreme value search strategy, the internal parameters and external weights of the two modules are dynamically adjusted to achieve dynamic balance between health and fault operation of TCM and CCM. The experimental results show that this strategy effectively improves the robustness of the system in a healthy state and the fault tolerance performance in a faulty state, effectively suppressing the periodic torque ripple and current harmonics caused by faults. Compared with the dynamic balancing strategy based on NSGA-II, this strategy increases the THD of torque by 13.45% and reduces the THD of current harmonics by 9.83%.
Published in: IEEE Transactions on Intelligent Transportation Systems ( Early Access )