基于Lyapunov的神经网络模型的预测控制,使用元启发式优化方法
Chafea Stiti1, Mohamed Benrabah2, Abdelhadi Aouaichia1
1Laboratory of Electrical Systems and Remote Control, Blida1 University Blida, Ouled Yaïch, Algeria.
Scientific reports
|August 13, 2024
概括
一种新的基于Lyapunov的神经网络模型预测控制方法增强了对非线性系统的控制. 这种方法确保了稳定性,并在电机控制应用中展示了卓越的准确性和速度.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 非线性动力学是一种非线性动力学.
背景情况:
- 控制受约束的非线性系统在实现稳定性和性能方面存在重大挑战.
- 现有的模型预测控制 (MPC) 方法经常与复杂系统的计算复杂性和融合速度作斗争.
研究的目的:
- 引入一种新的基于Lyapunov的神经网络模型预测控制 (NN-MPC) 策略.
- 为了在MPC框架内利用一个元启发式优化算法来有效解决问题.
- 为了验证控制器在管理具有快速动态的受约束非线性系统中的有效性.
主要方法:
- 使用feedforward神经网络作为MPC框架内的预测模型.
- 使用基于驾驶培训的优化 (DTBO) 算法来解决受约束的优化问题.
- 将Lyapunov函数作为成本函数中的约束,以保证闭环稳定性.
主要成果:
- 拟议的基于Lyapunov的DTBO的NN-MPC在控制三相松鼠引擎的角度转速方面表现出卓越的准确性,速度和稳定性.
- 与其他先进的控制技术相比,包括平均绝对误差,根平均平方误差和增强百分比在内的性能指标得到了显著改善.
- 控制器表现出高效的计算性能,以减少计算时间为准.
结论:
- 使用DTBO开发的基于Lyapunov的NN-MPC是一种高效和高效的技术,用于控制受约束的非线性系统.
- 神经网络的集成和元启发式优化为先进的控制系统设计提供了一个有希望的方向.
- 控制器的稳定性和性能使其适用于需要精确和快速控制动态系统的应用.
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