Reduced-Order Multiple Observer for Aircraft State Estimation during Landing

2016 ◽  
Vol 841 ◽  
pp. 253-259 ◽  
Author(s):  
Mihai Lungu ◽  
Romulus Lungu
Keyword(s):  
State Estimation ◽  
Finite Time ◽  
Design Procedure ◽  
Lyapunov Theory ◽  
Multiple Model ◽  
Reduced Order ◽  
Reduced Order Observer ◽  
Multiple Observer ◽  
Aircraft Motion

The paper presents a new reduced-order multiple observer which can achieve the finite-time reconstruction of the system’s state associated to a multiple-model. This observer is a combination of a reduced-order observer and a full-order multiple observer. The design of the new observer involves the usage of the Lyapunov theory, the solving of a linear matriceal inequality, and a variables’ change. The steps of the design procedure have been software implemented in order to validate the new reduced-order multiple observer for the case of an aircraft motion during landing.

scholarly journals Design of Reduced-Order Multiple Observers for Uncertain Systems with Unknown Inputs

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Mihai Lungu
Keyword(s):  
Linear Time ◽  
Design Procedure ◽  
Lyapunov Theory ◽  
Design Algorithm ◽  
Reduced Order ◽  
Unknown Inputs ◽  
Linear Time Invariant ◽  
Reduced Order Observer ◽  
Multiple Observer ◽  
Takagi Sugeno

The paper presents the design of a new reduced-order multiple observer for the estimation of the state associated with Takagi-Sugeno systems with unknown inputs, this being only the second reduced-order multiple observer ever designed. The design of reduced-order multiple observers which can achieve the finite-time state reconstruction for nonlinear systems described by multiple models is a niche area problem; the author of this paper continuing his work started with the introduction of the reduced-order multiple observer concept. The new multiple observer is a combination of a typical reduced-order observer for linear-time invariant multivariable systems and a full-order multiple observer for Takagi-Sugeno systems. The sufficient stability conditions of the observer are derived via the Lyapunov theory and its robustness is improved by means of a novel and efficient method which cancels the negative effect of the uncertainties appearing in the system. To validate the suggested design algorithm, the steps of the design procedure have been summarized and software implemented for the concrete case of a light aircraft lateral-directional motion.

scholarly journals State Estimation with Reduced-Order Observer and Adaptive-LQR Control of Time Varying Linear System

2020 ◽  
Vol 26 (2) ◽  
pp. 24-31
Author(s):  
Omer Aydogdu ◽  
Mehmet Latif Levent
Keyword(s):  
Optimal Control ◽  
Linear System ◽  
State Estimation ◽  
Servo System ◽  
Variable Load ◽  
Time Varying ◽  
Load Effect ◽  
Reduced Order ◽  
Lqr Control ◽  
Reduced Order Observer

In this study, a new controller design was created to increase the control performance of a variable loaded time varying linear system. For this purpose, a state estimation with reduced order observer and adaptive-LQR (Linear–Quadratic Regulator) control structure was offered. Initially, to estimate the states of the system, a reduced-order observer was designed and used with LQR control method that is one of the optimal control techniques in the servo system with initial load. Subsequently, a Lyapunov-based adaptation mechanism was added to the LQR control to provide optimal control for varying loads as a new approach in design. Thus, it was aimed to eliminate the variable load effects and to increase the stability of the system. In order to demonstrate the effectiveness of the proposed method, a variable loaded rotary servo system was modelled as a time-varying linear system and used in simulations in Matlab-Simulink environment. Based on the simulation results and performance measurements, it was observed that the proposed method increases the system performance and stability by minimizing variable load effect.

scholarly journals Robust Functional Interval Observer for Multivariable Linear Systems

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Luc Meyer
Keyword(s):  
Linear System ◽  
Linear Function ◽  
Continuous Time ◽  
State Vector ◽  
Internal State ◽  
Design Procedure ◽  
Reduced Order ◽  
Reduced Order Observer ◽  
Interval Observer ◽  
Interval Observers

The study of a continuous-time multivariable linear system may not need the knowledge of the entire internal state vector, but only of a linear function of it. In this case, instead of designing a complete observer, only a functional (also called reduced order) observer is used. In this field of research, this paper focuses on robust functional cooperative interval observers. Such an observer is proposed and its properties (in particular, its convergence) are established. Then, a design procedure is given for practical use. Finally, the theoretical contributions are illustrated in examples.

scholarly journals A Reduced-Order TS Fuzzy Observer Scheme with Application to Actuator Faults Reconstruction

2012 ◽  
Vol 2012 ◽  
pp. 1-25 ◽  
Author(s):  
Dušan Krokavec ◽  
Anna Filasová
Keyword(s):  
Design Procedure ◽  
Matching Condition ◽  
Lyapunov Theory ◽  
Observer Design ◽  
Linear Matrix ◽  
Actuator Fault ◽  
Actuator Faults ◽  
Reduced Order ◽  
Fuzzy Observer ◽  
Fault Reconstruction

This paper focuses on the principle for designing reduced-order fuzzy-observer-based actuator fault reconstruction for a class of nonlinear systems. The problem addressed can be indicated as an approach for a kind of reduced-order fuzzy observer design with special gain matrix structure that depends on a given matching condition specification. Using the Lyapunov theory, the stability conditions are obtained and expressed in terms of linear matrix inequalities, and the conditions for asymptotic estimation of actuator faults are derived. Simulation results illustrate the observer design procedure and demonstrate the actuator fault reconstruction effectiveness and performance.

scholarly journals The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2085
Author(s):  
Xue-Bo Jin ◽  
Ruben Jonhson Robert RobertJeremiah ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Future Development ◽  
Data Driven ◽  
Estimation Methods ◽  
Multiple Model ◽  
Development Trends ◽  
Research Results ◽  
Model Driven ◽  
Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

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