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传统鲁棒自适应控制由于考虑了实际系统存在的不确定性,在一定程度上扩大了常规自适应控制的应用范围,但是传统鲁棒自适应控制大多只是从系统全局稳定性的角度出发来设计控制器而忽略系统动态和稳态性能,导致其无法在工况多变的实际被控系统中取得令人满意的效果。针对传统鲁棒自适应控制的不足,本文对由ARMA模型描述并包含未建模动态的系统设计了多模型鲁棒自适应控制器。首先采用正则化技术将系统未建模动态转化为系统有界扰动,并在系统降阶模型的基础上根据系统工况的变化设计了多个固定控制器和2个鲁棒自适应控制器,并根据性能指标函数选择最佳控制器作为当前系统控制器以提高系统性能。仿真实验说明当系统存在未建模动态以及系统工况发生变化时,本文设计的控制器能获得较好的控制效果。
Due to the uncertainty of the actual system, the traditional robust adaptive control has expanded the application range of conventional adaptive control to a certain extent. However, the traditional robust adaptive control is mostly designed from the perspective of the global stability of the system Controller ignores system dynamics and steady-state performance, making it impossible to achieve satisfactory results in an actual controlled system with varying operating conditions. In order to overcome the shortcomings of traditional robust adaptive control, a multi-model robust adaptive controller is designed for the system described by ARMA model and including unmodeled dynamics. Firstly, regularization is used to transform the unmodeled dynamics of the system into bounded disturbances of the system. Based on the reduced-order model of the system, several fixed controllers and two robust adaptive controllers are designed according to the changes of system operating conditions. And selects the best controller as the current system controller according to the performance index function to improve the system performance. The simulation results show that the controller designed in this paper can get a better control effect when the system has unmodeled dynamics and the system operating conditions change.