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非最小相位系统的组合前馈与模型辅助有源干扰抑制控制

Combined feedforward and model-assisted active disturbance rejection control for non-minimum phase system.

作者信息

Sun Li, Li Donghai, Gao Zhiqiang, Yang Zhao, Zhao Shen

机构信息

State Key Lab of Power Systems, Department of Thermal Engineering, Tsinghua University, Beijing 100084, China.

Center for Advanced Control Technologies, Cleveland State University, Cleveland, OH 44115, United States.

出版信息

ISA Trans. 2016 Sep;64:24-33. doi: 10.1016/j.isatra.2016.04.020. Epub 2016 May 8.

Abstract

Control of the non-minimum phase (NMP) system is challenging, especially in the presence of modelling uncertainties and external disturbances. To this end, this paper presents a combined feedforward and model-assisted Active Disturbance Rejection Control (MADRC) strategy. Based on the nominal model, the feedforward controller is used to produce a tracking performance that has minimum settling time subject to a prescribed undershoot constraint. On the other hand, the unknown disturbances and uncertain dynamics beyond the nominal model are compensated by MADRC. Since the conventional Extended State Observer (ESO) is not suitable for the NMP system, a model-assisted ESO (MESO) is proposed based on the nominal observable canonical form. The convergence of MESO is proved in time domain. The stability, steady-state characteristics and robustness of the closed-loop system are analyzed in frequency domain. The proposed strategy has only one tuning parameter, i.e., the bandwidth of MESO, which can be readily determined with a prescribed robustness level. Some comparative examples are given to show the efficacy of the proposed method. This paper depicts a promising prospect of the model-assisted ADRC in dealing with complex systems.

摘要

非最小相位(NMP)系统的控制具有挑战性,尤其是在存在建模不确定性和外部干扰的情况下。为此,本文提出了一种前馈与模型辅助的主动干扰抑制控制(MADRC)相结合的策略。基于标称模型,前馈控制器用于产生在规定的下冲约束下具有最短调节时间的跟踪性能。另一方面,标称模型之外的未知干扰和不确定动态由MADRC进行补偿。由于传统的扩展状态观测器(ESO)不适用于NMP系统,基于标称可观规范形提出了一种模型辅助ESO(MESO)。在时域中证明了MESO的收敛性。在频域中分析了闭环系统的稳定性、稳态特性和鲁棒性。所提出的策略只有一个调谐参数,即MESO的带宽,可以根据规定的鲁棒性水平轻松确定。给出了一些对比示例以展示所提方法的有效性。本文描绘了模型辅助ADRC在处理复杂系统方面的广阔前景。

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