State observer for linear system with unknown input disturbance and sampled and delayed output with measurement noise

2018 ◽  
Vol 41 (3) ◽  
pp. 749-759
Author(s):  
Abdeldafia M Mohammed ◽  
Haoping Wang ◽  
Yang Tian
Keyword(s):  
Linear System ◽  
Measurement Noise ◽  
Fault Estimation ◽  
Unknown Input ◽  
Time Varying ◽  
Luenberger Observer ◽  
Input Disturbance ◽  
Continuous State ◽  
The Stability ◽  
Time Linear

This paper addresses the sampled and unknown time-varying delayed output problem of the continuous-time linear system with unknown input disturbance and measurement noise. Based on the piecewise continuous hybrid system and extended functional observer, a robust hybrid extended observer is proposed to estimate the non-delayed continuous state from the sampled and delayed measurements when the time delay is unknown time-varying. The advantages of the designed observer are the quite simple structure, robustness to dealing with measurement noise, and ability to estimate the system state with accuracy under the influence of unknown input disturbance. Furthermore, the proposed observer is able to estimate the non-delayed continuous state and unknown input disturbance, in particular for the issue of fault estimation and perturbation disturbance simultaneously. The stability of the robust hybrid extended observer is illustrated and analyzed. Moreover, to show the effectiveness of the proposed observer, a comparison with the delayed Luenberger observer is performed via a numerical example. Finally, the simulation results are demonstrated.

Observer-Based Fault Diagnosis of Satellite Systems Subject to Time-Varying Thruster Faults

2006 ◽  
Vol 129 (3) ◽  
pp. 352-356 ◽  
Author(s):  
Wen Chen ◽  
Mehrdad Saif
Keyword(s):  
Fault Diagnosis ◽  
Estimation Error ◽  
Fault Estimation ◽  
Time Varying ◽  
Satellite Systems ◽  
Adaptive Observers ◽  
Error Dynamics ◽  
The Stability ◽  
Estimation Of Time ◽  
Identification Strategy

This paper presents a novel fault diagnosis approach in satellite systems for identifying time-varying thruster faults. To overcome the difficulty in identifying time-varying thruster faults by adaptive observers, an iterative learning observer (ILO) is designed to achieve estimation of time-varying faults. The proposed ILO-based fault-identification strategy uses a learning mechanism to perform fault estimation instead of using integrators that are commonly used in classical adaptive observers. The stability of estimation-error dynamics is established and proved. An illustrative example clearly shows that time-varying thruster faults can be accurately identified.

scholarly journals Observer-Based Controllers for Two-Wheeled Inverted Robots with Unknown Input Disturbance and Model Uncertainty

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Khanh G. Tran ◽  
Nam H. Nguyen ◽  
Phuoc D. Nguyen
Keyword(s):  
Linear System ◽  
Taylor Series ◽  
Model Uncertainty ◽  
Disturbance Observer ◽  
System Stability ◽  
Unknown Input ◽  
Input Disturbance ◽  
Equivalent Linear ◽  
Compound Disturbance ◽  
Equivalent Linear System

In this paper, two controllers with a compound disturbance observer are proposed for a two-wheeled inverted robot (TWIR) with model uncertainty and unknown input disturbance. First, an equivalent linear model of the TWIR with uncertainty and input disturbance is proposed using the Taylor series expansion for the nonlinear model of the TWIR at an equilibrium point, in which the nonlinear part of the Taylor series and the model uncertainty are combined with unknown input disturbance as compound input disturbance. Then, the compound input disturbance is estimated by using the Newton method and reference model. As the estimated compound disturbance is used to compensate for the compound disturbance, the equivalent linear system becomes closely definite without compound input disturbance. Finally, two controllers are proposed using the equivalent linear system. Stability analysis of the proposed control methods is also given. To illustrate the proposed methods, some simulations for the TWIR are performed and compared with the existing methods. The main contribution of this work includes the following: (i) simple controllers based on compound input disturbance observer for trajectory tracking and balancing of TWIRs with unknown input disturbance and model uncertainty are proposed; (ii) the stability of proposed closed-loop control systems is proved; (iii) our proposed methods are simulated and compared with the existing methods.

scholarly journals Linear time-varying Luenberger observer applied to diabetes

2017 ◽  
Author(s):  
Onofre Orozco López ◽  
Carlos Eduardo Castañeda Hernández ◽  
Agustín Rodríguez Herrero ◽  
Gema García Saéz ◽  
María Elena Hernando
Keyword(s):  
State Space ◽  
Linear Time ◽  
Time Varying ◽  
Data Set ◽  
Luenberger Observer ◽  
Meal Plan ◽  
The Stability ◽  
Real Patients ◽  
Selection Of

We present a linear time-varying Luenberger observer (LTVLO) using compartmental models to estimate the unmeasurable states in patients with type 1 diabetes. The LTVLO proposed is based on the linearization in an operation point of the virtual patient (VP), where a linear time-varying system is obtained. LTVLO gains are obtained by selection of the asymptotic eigenvalues where the observability matrix is assured. The estimation of the unmeasurable variables is done using Ackermann's methodology. Additionally, it is shown the Lyapunov approach to prove the stability of the time-varying proposal. In order to evaluate the proposed methodology, we designed three experiments: A) VP obtained with the Bergman minimal model; B) VP obtained with the compartmental model presented by Hovorka in 2004; and C) real patients data set. For experiments A) and B), it is applied a meal plan to the VP, where the dynamic response of each state model is compared to the response of each variable of the time-varying observer. Once the observer is evaluated in experiment B), the proposal is applied to experiment C) with data extracted from real patients and the unmeasurable state space variables are obtained with the LTVLO. LTVLO methodology has the feature of being updated each instant of time to estimate the states under a known structure. The results are obtained using simulation with MatlabTM and SimulinkTM. The LTVLO estimates the unmeasurable states from in silico patients with high accuracy by means of the update of Luenberger gains at each iteration. The accuracy of the estimated state space variables is validated through fit parameter.

scholarly journals Sensor Fault Estimation Filter Design for Discrete-time Linear Time-varying Systems

2014 ◽  
Vol 40 (10) ◽  
pp. 2364-2369 ◽  
Author(s):  
Zhen-Hua WANG ◽  
Mickael RODRIGUES ◽  
Didier THEILLIOL ◽  
Yi SHEN
Keyword(s):  
Discrete Time ◽  
Filter Design ◽  
Linear Time ◽  
Fault Estimation ◽  
Time Varying ◽  
Sensor Fault ◽  
Time Varying Systems ◽  
Sensor Fault Estimation ◽  
Time Linear

scholarly journals Linear time-varying Luenberger observer applied to diabetes

2017 ◽  
Author(s):  
Onofre Orozco López ◽  
Carlos Eduardo Castañeda Hernández ◽  
Agustín Rodríguez Herrero ◽  
Gema García Saéz ◽  
María Elena Hernando
Keyword(s):  
State Space ◽  
Linear Time ◽  
Time Varying ◽  
Data Set ◽  
Luenberger Observer ◽  
Meal Plan ◽  
The Stability ◽  
Real Patients ◽  
Selection Of

We present a linear time-varying Luenberger observer (LTVLO) using compartmental models to estimate the unmeasurable states in patients with type 1 diabetes. The LTVLO proposed is based on the linearization in an operation point of the virtual patient (VP), where a linear time-varying system is obtained. LTVLO gains are obtained by selection of the asymptotic eigenvalues where the observability matrix is assured. The estimation of the unmeasurable variables is done using Ackermann's methodology. Additionally, it is shown the Lyapunov approach to prove the stability of the time-varying proposal. In order to evaluate the proposed methodology, we designed three experiments: A) VP obtained with the Bergman minimal model; B) VP obtained with the compartmental model presented by Hovorka in 2004; and C) real patients data set. For experiments A) and B), it is applied a meal plan to the VP, where the dynamic response of each state model is compared to the response of each variable of the time-varying observer. Once the observer is evaluated in experiment B), the proposal is applied to experiment C) with data extracted from real patients and the unmeasurable state space variables are obtained with the LTVLO. LTVLO methodology has the feature of being updated each instant of time to estimate the states under a known structure. The results are obtained using simulation with MatlabTM and SimulinkTM. The LTVLO estimates the unmeasurable states from in silico patients with high accuracy by means of the update of Luenberger gains at each iteration. The accuracy of the estimated state space variables is validated through fit parameter.

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