用PMSM驱动的自主电动汽车的自适应线性MPC与过的第三级通用集成器观察员.
Moustafa Magdi Ismail1, Mujahed Al-Dhaifallah2, Hegazy Rezk3
1Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES), King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.
本研究介绍了自主电动汽车的自适应线性模型预测控制 (AL-MPC). 这种新方法通过适应永磁同步电机非线性而显著改善了轨迹跟踪,超过了现有的MPC策略.
科学领域:
- 控制系统工程 控制系统工程
- 汽车工程 汽车工程
- 电气工程 电气工程
背景情况:
- 自动驾驶电动汽车需要精确控制轨迹跟踪和发动机协调.
- 永久磁铁同步电机 (PMSM) 呈现出挑战传统控制方法的非线性 (例如,电感变化).
- 固定参数模型预测控制 (MPC) 与动态过渡和操作点依赖的非线性作斗争.
研究的目的:
- 开发一种自适应线性MPC (AL-MPC) 战略,以提高自动驾驶电动汽车的轨迹跟踪性能.
- 在动态操作期间处理PMSM非线性时,解决传统MPC的局限性.
- 提高车辆控制系统的实时适应性和稳定性.
主要方法:
- 一个自适应线性MPC (AL-MPC) 策略,集成一个流量观察器,泰勒序列线性化和一个主动集二次编程优化器.
- 实时估计电磁扭矩和定子反应率,使用移动平均线过的第三阶概括集成器流量观测器.
- 制定一个统一的九态预测模型,动态更新和优化,使用二次编程来控制电压和方向盘.
主要成果:
- 与线性MPC相比,AL-MPC实现了显著的误差降低:99.9%的曲率MAE和65%的横向位置RMSE.
- 性能优于自适应非线性MPC,其向MAE低77.7%,横向MAE低94.6%.
- 在HIL测试中证明了实时可行性,控制周期执行时间为9.65ms.
结论:
- 拟议的AL-MPC有效地管理PMSM非线性,用于自动驾驶电动汽车的高级轨迹跟踪.
- 与现有的线性和自适应非线性MPC方法相比,AL-MPC提供了显著的性能改进.
- 通过模拟和HIL测试验证,AL-MPC是先进的车辆控制的计算效率高和实时可行的解决方案.
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