通过自学策略设计滑动模式模型预测双循环控制,以减轻电动汽车BLDC电机中的扭矩波动
N Prabhu1, Thirumalaivasan Rajaram2, Bragadeshwaran Ashok3
1School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, 632014, India.
结合滑动模式控制 (SMC) 和模型预测控制 (MPC) 的新双循环控制器显著降低了电动汽车 (EV) 无刷直流 (BLDC) 电机的扭矩波动. 这种先进的策略提高了电机效率和稳定性,提高了电动汽车的性能.
科学领域:
- 电气工程 电气工程
- 控制系统 控制系统
- 汽车技术 汽车技术
背景情况:
- 无刷直流 (BLDC) 电机对于电动汽车至关重要,但会受到扭矩波动的影响,影响性能.
- 现有的控制策略往往难以平衡稳定性,效率和波纹减少.
研究的目的:
- 开发和评估一种新的双循环控制策略,将BLDC电机的滑动模式控制 (SMC) 和模型预测控制 (MPC) 结合起来.
- 为了最大限度地减少扭矩波动,并提高电动汽车应用中的BLDC电机的效率和稳定性.
主要方法:
- 设计了一种合并SMC和MPC的双循环控制结构,用于内部循环电流和外部循环速度控制.
- 结合人工神经网络 (ANN) 和模糊逻辑的混合自学算法被整合到适应性错误减少中.
- 使用 MATLAB/SIMULINK 模型在循环 (MIL) 模拟和硬件在循环 (HIL) 测试来验证性能.
主要成果:
- 拟议的SMC-MPC控制器实现了快速0.01秒的上升时间,0.001%的稳定状态误差,0.02秒的沉降时间和0.066%的峰值超越.
- 与传统控制器相比,扭矩波动显著减少了28.57%.
- 实现了96.47%的最大电机效率.
结论:
- 新型SMC-MPC双循环控制器有效减少扭矩波动,提高BLDC电机效率.
- 这种先进的控制策略提高了电动汽车的运行范围和性能.
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