This paper addresses the control of a continuous-time system with possibly large uncertainty of unknown internal dynamics or external disturbance. A novel control scheme is proposed to estimate and cancel the system uncertainty effectively so as to enhance disturbance rejection (DR) performance. Unlike asymptotic analysis with infinite gain in the literature, the estimation transient analysis is carried out for the proposed scheme with a finite estimator gain and the precise error formulas are derived, based on a classical low-order plant description. The control performance associated with a realizable gain is quantified by tight bounds with respect to the ideal case, which enables easy parameter tuning. The necessary and sufficient condition for the internal stability of the control system is established, along with a D-decomposition method for determining the complete set of the gain intervals that could internally stabilize the plant. In the presence of measurement noise, a low-pass filter is introduced to attenuate its adverse effect. Simulations and semi-realistic experiments are performed to demonstrate the effectiveness of the proposed scheme, which shows evident improvement on DR performance over the well-known active DR control.

本文讨论了一个连续时间系统的控制，该系统可能具有未知的内部动力学或外部干扰的较大不确定性。提出了一种新颖的控制方案，可以有效地估计和消除系统的不确定性，从而提高抗干扰性能。与文献中具有无限增益的渐近分析不同，本文针对具有有限估计器增益的拟议方案进行了估计瞬态分析，并基于经典的低阶工厂描述推导了精确的误差公式。相对于理想情况，与可实现增益相关的控制性能通过严格的边界进行量化，从而可以轻松进行参数调整。建立了控制系统内部稳定性的充要条件，连同D分解方法来确定可以在内部稳定工厂的整套增益间隔。在存在测量噪声的情况下，引入了一个低通滤波器以减轻其不利影响。通过仿真和半现实实验证明了该方案的有效性，与已知的主动DR控制相比，DR性能得到了明显的改善。