Disturbance and uncertainty rejection is a key objective in control system design, and active disturbance rejection control(ADRC)exactly provides an effective solution to this issue. To this end, this paper presents a...Disturbance and uncertainty rejection is a key objective in control system design, and active disturbance rejection control(ADRC)exactly provides an effective solution to this issue. To this end, this paper presents a generalized active disturbance rejection controller for a class of nonlinear uncertain systems with linear output. To be specific, a generalized reduced-order extended state observer(ESO) is proposed to reduce phase delay and complexity of the system, which can take full advantage of the system output. Also, this method includes the existing results with fewer assumptions, and can be applied to systems with any order measurable states or multiple states, even linear combination states. Furthermore, the stability of this approach is guaranteed and demonstrated through matrix transformation and Lyapunov method, and design examples and numerical simulations are given to show the effectiveness and practicability of the method.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61973175,61973172 and 62073177)the Key Technologies Research and Development Program of Tianjin(Grant No.19JCZDJC32800)Tianjin Research Innovation Project for Postgraduate Students(Grant No.2020YJSZXB02)。
文摘Disturbance and uncertainty rejection is a key objective in control system design, and active disturbance rejection control(ADRC)exactly provides an effective solution to this issue. To this end, this paper presents a generalized active disturbance rejection controller for a class of nonlinear uncertain systems with linear output. To be specific, a generalized reduced-order extended state observer(ESO) is proposed to reduce phase delay and complexity of the system, which can take full advantage of the system output. Also, this method includes the existing results with fewer assumptions, and can be applied to systems with any order measurable states or multiple states, even linear combination states. Furthermore, the stability of this approach is guaranteed and demonstrated through matrix transformation and Lyapunov method, and design examples and numerical simulations are given to show the effectiveness and practicability of the method.
