一种利用并发学习实现系统扰动估计的无持续激励方法
A Persistent-Excitation-Free Method for System Disturbance Estimation Using Concurrent Learning
AI总结：
针对传统扰动观测器依赖持续激励条件的问题，提出基于时变历史栈的并发学习扰动观测器，并证明误差有界收敛，仿真验证其在无PE条件下精度更高。
摘要：
基于观测器的方法被广泛用于估计不同动态系统的扰动。然而，传统扰动观测器的一个缺点是它们都假设系统满足持续激励（PE）。因此，在PE无法得到保证时，例如当系统的扰动增益接近奇异点时，它们可能导致较差的估计精度。本文提出一种基于并发学习（CL）且采用时变历史栈的新型扰动观测器，即使在无PE情形下也能保证较高的估计精度。该扰动观测器分别在连续时间和离散时间下进行设计。利用Lyapunov方法证明了所提方法的估计误差收敛至一个有界集合。文中还提出一种历史样本选择程序，以减少由旧历史样本累积造成的估计误差。一项关于流行病控制的仿真研究表明，在PE不满足时，所提方法比传统扰动观测器产生更高的估计精度。这证明了所提出的基于CL的扰动观测器的正确性，并验证了其在解决实际问题中的适用性。
英文摘要：
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.
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