New design of active disturbance rejection control for nonlinear uncertain systems with unknown control input gain

2021 ◽  
Vol 65 (4) ◽  
Author(s):  
Sen Chen ◽  
Zhixiang Chen ◽  
Yi Huang ◽  
Zhi-Liang Zhao
2020 ◽  
pp. 002029402091521 ◽  
Author(s):  
Sen Chen ◽  
Zhixiang Chen ◽  
Zhiliang Zhao

The paper studies the control problem for nonlinear uncertain systems with the situation that only the current reference signal is available. By constructing a memory structure to save the previous reference signals, a novel error-based active disturbance rejection control with an approximation for the second-order derivative of reference signal is proposed. The transient performance of the proposed method is rigorously studied, which implies the high consistence of the closed-loop system. More importantly, to attain the satisfactory tracking performance, the necessary condition for nominal control input gain is quantitatively investigated. Furthermore, the superiority of the proposed method is illuminated by contrastively evaluating the sizes of the total disturbance and its derivative. The proposed method can alleviate the burden of the estimation and compensation for total disturbance. Finally, the experiment for a manipulator platform shows the effectiveness of the proposed method.


2021 ◽  
Author(s):  
Ping Liu ◽  
Sen Chen ◽  
Zhi-Liang Zhao

Abstract The paper investigates the control problem for a class of lower-triangular nonlinear uncertain systems with mismatched uncertainties and unknown values of control coefficients. Based on the signs of control coefficients rather than the nominal values or the approximative mathematical expressions, a new active disturbance rejection control is proposed. The design procedure can be concluded by three steps: determining the equivalent integrators chain form, constructing the extended state observer to estimate the total disturbance, and designing a dynamical system to let the actual input track the ideal input. Then under a mild assumption for mismatched uncertainties and unknown control coefficients, the paper rigorously analyzes the bounds of tracking error, estimating error and the error between the actual and ideal inputs. The presented theoretical results reveal the strong robustness of the proposed method to mismatched uncertainties and uncertain control input coefficients. Moreover, the tuning law of observer parameter and the parameter of dynamical input design is theoretically shown.


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