Unknown Input Observer Design for Takagi-Sugeno Fuzzy Stochastic System

Shenghui Guo,Fanglai Zhu,Liyun Xu 제어·로봇·시스템학회 2015 International Journal of Control, Automation, and Vol.13 No.4

This paper deals with the unknown input observer design for Takagi-Sugeno (T-S) fuzzy models subjected to measurement noise and stochastic noise. The method applies the singular system theory by considering the measurement noise as an augmented system state, and then an unknown input observer based on the techniques of singular systems is developed to estimate both the system states and measurement noise simultaneously. Under a necessary assumption, the error dynamic system of the observer is free from the unknown inputs. And the observer gain matrix is determined by means of minimum covariance matrix of state residual. Two simulation examples are given to illustrate the correctness and effectiveness of the proposed method.

Fault Detection and Reconstruction for Discrete Nonlinear Systems via Takagi-Sugeno Fuzzy Models

Shenghui Guo,Fanglai Zhu,Wei Zhang,Stanisław H. Zak,Jian Zhang 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.6

Observer-based actuator fault detection and sensor fault reconstruction for a class of discrete-time nonlinear systems with actuator and sensor faults are investigated in this paper. A descriptor Takagi-Sugeno (T-S) fuzzy model is employed to construct observer-based systems for the purpose of fault detection and sensor fault reconstruction. Two methods for observer design are proposed. In the first method, the observer gains are computed off-line. In the second method, the observer gains are computed on-line at each iteration. The observer designs are formulated using linear matrix inequalities. Sufficient conditions for the existence of the observer-based fault detection and sensor fault reconstruction systems are provided. Comparative simulation study to illustrate the validity of the proposed methods is performed.

Effect of polyethylene foam on dynamic cushioning energy absorption of paper corrugation tubes

Yanfeng Guo,Xuxiang Han,Yungang Fu,Shenghui Chen 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.4

The influence of the filled polyethylene foam on the cushioning energy absorption and impact resistance of the corrugation tube is analyzed through the axial drop impact experiment. Moreover, the compression deformation mode of the corrugation tube wall is predicted and experimentally verified. Results show that the X-direction tube has initial geometrical imperfections, which may reduce the initial impact force. The interaction between the polyethylene foam and the corrugation tube wall can improve the overall stability of the filled tube, and the structural stability of the double corrugation tube is the best. For the single tube, double tube, and single-filled tube, the volume (or mass) of the foam relative to the corrugation tube increases sequentially. The cushioning energy absorption of the paper corrugation tube is not improved, but the impact resistance is reinforced.

State and Faults Interval Estimations for Discrete-time Linear Systems

Weijie Ren,Shenghui Guo 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.7

This paper concerns state and fault simultaneous interval estimations for discrete-time linear systems with unknown but bounded uncertainties. Unlike most existing works on interval estimation, the system considered in this work is subject to actuator and sensor faults, while bounded uncertainties exist in both components. Thus, the method developed in this paper can be used for more general conditions. First, the considered system is reformulated as a descriptor one through the augmented vector method. An important lemma for descriptor systems is given in a more accurate description and proven in a very simple way. Then, by using the reachability analysis technique, a new proportional-integral observer-based interval estimation method is proposed. The H∞ technique reduces the influences of time-varying actuator faults and uncertainties. To build the zonotope of bounded uncertainties in the residual system, an equivalent description is introduced. Finally, a numerical example and an industrial system are simulated to demonstrate the efficacy and applicability of the developed method.

Proportional Integral Observer-based Consensus Control of Discrete-time Multi-agent Systems

Renyang You,Mingzhu Tang,Shenghui Guo,Guozeng Cui 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.5

The multi-agent consensus control is widely used in industrial application, such as industrial robot, satellite attitude control, etc. Therefore, this paper studies the consensus control problem of multi-agent systems. For discrete-time Lipschitz nonlinear multi-agent systems with unknown inputs, a consensus control algorithm based on proportional integral observer is proposed. Firstly, a proportional integral observer is designed for each subsystem to estimate the system states and unknown input signals simultaneously. Then, a consensus control protocol based on the system state estimation is designed, and H∞ technique is used to effectively suppress the unknown input signals. Furthermore, the gain matrices of the observer and consensus control are obtained by solving a linear matrix inequality. Finally, two simulation examples show the correctness and feasibility of the proposed method.

Extended State Observer Based on ADRC of Linear System with Incipient Fault

Xingxing Hua,Da-Rong Huang,Shenghui Guo 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.6

Owing to the fact that it is difficult to find the incipient fault and it is hard to deal with the disturbance, an extended state observer (ESO), which is based on the linear time invariant system is designed and analyzed. This method can effectively separate incipient fault as a new state variable from the disturbance. By using coordinate transformations, the original system is decoupled into two subsystems. Especially, an adaptive observer of faulty system is designed for the one subsystem with the incipient fault but not relevant to the disturbance, and an extended state observer (ESO) based on active disturbance rejection control (ADRC) is designed for the other one which not only has the disturbance but also the incipient fault. Two simulation results are presented to show the effectiveness of the presented methods.

Unknown Input Reconstruction via Interval Observer and State and Unknown Input Compensation Feedback Controller Designs

Fanglai Zhu,Wei Zhang,Jiancheng Zhang,Shenghui Guo 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.1

In this paper, we investigate the state estimation, unknown input and measurement noise reconstructionproblems and the feedback controller design issues for a linear discrete-time system with both unknown inputsand measurement noises. First, an augmented system is constructed and the state vector of the augmented systemconsists of the original system state and the measurement noise, and the preconditions between the original systemand the augmented system is discussed in detail. Second, for the augmented system, a reduced-order observeris designed so that the original system state estimates and the measurement noise reconstruction can be obtained. Third, in order to get the asymptotical unknown input reconstruction, an interval observer for part of the measurableoutput is proposed and an unknown input reconstruction method based on the interval observer is developed. Finally,an observer-based state feedback and unknown input controller is designed and the closed-loop system stability isanalyzed. We point out that the closed-loop system satisfies the so-called separation property. At last, two simulationexamples are given to verify the effectiveness of the proposed methods.

Prediction of bolted joint looseness based on feature weighted-multiple kernel support vector machine

Chuanbo Liu,Yanzhao Zhu,Kui Zhan,Shenghui Guo,Kang Liu 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.6

Given that traditional theoretical diagnosis is difficult to use for the accurate prediction of the looseness of bolted joints, this paper will use the support vector machine (SVM) method to solve this problem. The paper establishes a database by studying the loosening mechanism of bolted joints and by collecting the connection information of the fastening points of the vehicle chassis. Key influence features are determined by the information gain value. The average prediction accuracy of the standard SVM model is 76.94 %, which is better than the theoretical diagnosis method. To achieve optimization, the feature weighted support vector machine and the multiple kernel function support vector machine (MKL SVM) are established, and the average prediction accuracy is 82.18 % and 84.19 %, respectively. The fusion weighted support vector machine (FW-MKL SVM) is built by combining the advantages of two weighted optimization SVMs. The average prediction accuracy is 94.5 %, which meets practical engineering requirements.