Generalized Disturbance Observer Synthesis Using a Two-Stage Heuristic Algorithm

Author(s):  
Guofei Xiang ◽  
Jianbo Su

Disturbance observer (DOB) based control has been widely applied in industries due to its easy usage but powerful disturbance rejection ability. However, the existence of innate structure constraint, namely the inverse of the nominal plant, prevents its implementation on more general class of systems, such as non-minimum phase plants, MIMO systems etc.. Furthermore, additional limitations exerted on Q-filter design, i.e., unity steady state gain and low-pass nature, which narrow down its solution space largely and prevent from achieving optimal performance even if it exists. In this paper, we present a novel DOB architecture, named generalized disturbance observer (G-DOB), with the help of nontraditional use of the celebrated Youla parametrization of two degree-of-freedom controller. Rigorous analyses show that the novel G-DOB not only inherits all the merits of the conventional one, but also alleviates the limitations stated before partially. By some appropriate system manipulation, the synthesis of Q-filter has been converted to the design of reduced-order controller. Thus, a heuristic two-stage algorithm has been developed with the help of Kalman-Yakubovich-Popov (KYP) lemma: firstly design a full information controller for the augmented system and then compute a reduced-order controller. Numerical examples are presented to demonstrate the effectiveness of the proposed G-DOB structure and design algorithm.

2021 ◽  
Author(s):  
Cong Wang ◽  
Hongwei Xia ◽  
Yanmin Wang ◽  
Shunqing Ren

Abstract In this paper, an adaptive discrete-time sliding mode control based on reduced-order disturbance observer is proposed for discretized multi-input multi-output systems subjected to unmatching condition. By using the designed discrete reduced-order disturbance observer, a new sliding surface is constructed to counteract the unmatched uncertainties. Then, to guarantee a smaller width of the quasi sliding mode domain, an adaptive reaching law is developed, whose switching gain is adaptively tuned to prevent overestimation of disturbance on the premise of ensuring the reaching condition of sliding surface; meanwhile, the ranges of the quasi sliding mode band and attractiveness region are deduced. The proposed control algorithm has low computational complexity and needs no information about the upper bound of unmatched disturbance. The simulations on the control of a bank-to-turn missile demonstrate that the proposed method can effectively reject unmatched disturbance, and provide higher accuracy in comparison with traditional methods.


2021 ◽  
pp. 1-1
Author(s):  
Amanda Spagolla ◽  
Cecilia F. Morais ◽  
Ricardo C. L. F. Oliveira ◽  
Pedro L. D. Peres

2021 ◽  
Vol 25 (5) ◽  
pp. 1169-1185
Author(s):  
Deniu He ◽  
Hong Yu ◽  
Guoyin Wang ◽  
Jie Li

The problem of initialization of active learning is considered in this paper. Especially, this paper studies the problem in an imbalanced data scenario, which is called as class-imbalance active learning cold-start. The novel method is two-stage clustering-based active learning cold-start (ALCS). In the first stage, to separate the instances of minority class from that of majority class, a multi-center clustering is constructed based on a new inter-cluster tightness measure, thus the data is grouped into multiple clusters. Then, in the second stage, the initial training instances are selected from each cluster based on an adaptive candidate representative instances determination mechanism and a clusters-cyclic instance query mechanism. The comprehensive experiments demonstrate the effectiveness of the proposed method from the aspects of class coverage, classification performance, and impact on active learning.


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