A Persistent-Excitation-Free Method for System Disturbance Estimation Using Concurrent Learning

• Published in: IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2023-08, Vol.70 (8), p.1-11
• Main Authors: Zhang, Zengjie, Liu, Fangzhou, Liu, Tong, Qiu, Jianbin, Buss, Martin
• Format: Article
• Language: English
• Subjects: concurrent learning; disturbance estimation; Disturbance observers; disturbance resistant control; Dynamical systems; Epidemics; Estimation; Excitation; fault detection and identification; History; Integrated circuit modeling; Learning; networked epidemic model; persistent excitation; Predictive models; Robust control; System identification; unknown-input observer
• ISSN: 1549-8328; 1558-0806
• DOI: 10.1109/TCSI.2023.3274558

Observer-based methods are widely used to estimate the disturbances of different dynamic systems. However, a drawback of the conventional disturbance observers is that they all assume persistent excitation (PE) of the systems. As a result, they may lead to poor estimation precision when PE is not ensured, for instance, when the disturbance gain of the system is close to the singularity. In this paper, we propose a novel disturbance observer based on concurrent learning (CL) with time-variant history stacks, which ensures high estimation precision even in PE-free cases. The disturbance observer is designed in both continuous and discrete time. The estimation errors of the proposed method are proved to converge to a bounded set using the Lyapunov method. A history-sample-selection procedure is proposed to reduce the estimation error caused by the accumulation of old history samples. A simulation study on epidemic control shows that the proposed method produces higher estimation precision than the conventional disturbance observer when PE is not satisfied. This justifies the correctness of the proposed CL-based disturbance observer and verifies its applicability to solving practical problems.