scholarly journals Resilient set-membership state estimation for nonlinear complex networks with time-delay and incomplete measurements

2021 ◽  
Vol 336 ◽  
pp. 08017
Author(s):  
Ning Yang ◽  
Dongyan Chen ◽  
Jun Hu
Keyword(s):  
Complex Networks ◽  
State Estimation ◽  
Taylor Series Expansion ◽  
Interval Matrix ◽  
Linear Matrix ◽  
Convex Optimization Problem ◽  
Set Membership ◽  
Linearization Errors ◽  
Time Invariant ◽  
Bounded Uncertainties

Taking the incomplete measurements and the weighted try-once-discard (WTOD) protocol into account, this paper develops a novel resilient set-membership state estimation (RSMSE) method for time-varying nonlinear complex networks with time-invariant delay. A classic interval matrix technique is utilized to describe incomplete measurements. The Taylor series expansion is applied to dispose the nonlinearities, where the high-order terms of the linearization errors are described by norm-bounded uncertainties. To mitigate the communication burden, the WTOD protocol is introduced, where only one node can send updated data through a shared communication network at each certain transmission step. Using the recursive linear matrix inequalities (RLMIs), a series of ellipsoidal sets including the state vector can be determined. The desirable estimator gain and a smallest possible estimation ellipsoid can be calculated via solving the convex optimization problem. Lastly, we use an illustrative example to show the feasibility of the introduced RSMSE technique.

scholarly journals Simultaneous Robust Fault and State Estimation for Linear Discrete-Time Uncertain Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Feten Gannouni ◽  
Fayçal Ben Hmida
Keyword(s):  
Convex Optimization ◽  
State Estimation ◽  
Discrete Time ◽  
Upper Bound ◽  
Optimization Problem ◽  
Estimation Error ◽  
Original System ◽  
Error Variance ◽  
Linear Matrix ◽  
Convex Optimization Problem

We consider the problem of robust simultaneous fault and state estimation for linear uncertain discrete-time systems with unknown faults which affect both the state and the observation matrices. Using transformation of the original system, a new robust proportional integral filter (RPIF) having an error variance with an optimized guaranteed upper bound for any allowed uncertainty is proposed to improve robust estimation of unknown time-varying faults and to improve robustness against uncertainties. In this study, the minimization problem of the upper bound of the estimation error variance is formulated as a convex optimization problem subject to linear matrix inequalities (LMI) for all admissible uncertainties. The proportional and the integral gains are optimally chosen by solving the convex optimization problem. Simulation results are given in order to illustrate the performance of the proposed filter, in particular to solve the problem of joint fault and state estimation.

scholarly journals Adaptive event-triggered state estimation for complex networks with nonlinearities against hybrid attacks

2022 ◽  
Vol 7 (2) ◽  
pp. 2858-2877
Author(s):  
Yahan Deng ◽  
◽  
Zhenhai Meng ◽  
Hongqian Lu
Keyword(s):  
Complex Networks ◽  
State Estimation ◽  
Denial Of Service ◽  
Sufficient Conditions ◽  
Cyber Attacks ◽  
Linear Matrix ◽  
System State ◽  
Dos Attacks ◽  
Augmented System ◽  
Event Triggered

<abstract><p>This paper investigates the event-triggered state estimation problem for a class of complex networks (CNs) suffered by hybrid cyber-attacks. It is assumed that a wireless network exists between sensors and remote estimators, and that data packets may be modified or blocked by malicious attackers. Adaptive event-triggered scheme (AETS) is introduced to alleviate the network congestion problem. With the help of two sets of Bernoulli distribution variables (BDVs) and an arbitrary function related to the system state, a mathematical model of the hybrid cyber-attacks is developed to portray randomly occurring denial-of-service (DoS) attacks and deception attacks. CNs, AETS, hybrid cyber-attacks, and state estimators are then incorporated into a unified architecture. The system state is cascaded with state errors as an augmented system. Furthermore, based on Lyapunov stability theory and linear matrix inequalities (LMIs), sufficient conditions to ensure the asymptotic stability of the augmented system are derived, and the corresponding state estimator is designed. Finally, the effectiveness of the theoretical method is demonstrated by numerical examples and simulations.</p></abstract>

Guaranteed Asymptotic Stability for Some Linear Systems With Bounded Uncertainties

1979 ◽  
Vol 101 (3) ◽  
pp. 212-216 ◽  
Author(s):  
G. Leitmann
Keyword(s):  
Asymptotic Stability ◽  
State Feedback ◽  
Linear Feedback ◽  
Linear Matrix ◽  
Compact Sets ◽  
Nonlinear Controller ◽  
Linear Dynamical Systems ◽  
Linear Matrix Equation ◽  
Time Invariant ◽  
Bounded Uncertainties

We consider a class of linear dynamical systems in which the system and input matrices, as well as the input, are uncertain. The nominal system is time-invariant, while the uncertainties are assumed to be measurable functions of time whose values may range in given compact sets. Utilizing solely the knowledge of the sets from which uncertain quantities take their values, we derive a state feedback controller that guarantees global uniform asymptotic (Lyapunov) stability of the zero state in the presence of admissible uncertainties. The controller is nonlinear, namely componentwise switching; however, its construction requires only the solution of a linear matrix equation. Unlike linear feedback, this nonlinear controller assures asymptotic stability for any admissible realization of the system; this is illustrated by means of a simple example.

Adaptive state estimation of Markov switched neural networks driven by Lévy noise

2019 ◽  
Vol 42 (2) ◽  
pp. 330-336
Author(s):  
Dongbing Tong ◽  
Qiaoyu Chen ◽  
Wuneng Zhou ◽  
Yuhua Xu
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Sufficient Conditions ◽  
Lyapunov Stability Theory ◽  
Lévy Noise ◽  
Linear Matrix ◽  
Delayed Neural Networks ◽  
Switched Neural Networks ◽  
Inequality Technique ◽  
Levy Noise

This paper proposes the [Formula: see text]-matrix method to achieve state estimation in Markov switched neural networks with Lévy noise, and the method is very distinct from the linear matrix inequality technique. Meanwhile, in light of the Lyapunov stability theory, some sufficient conditions of the exponential stability are derived for delayed neural networks, and the adaptive update law is obtained. An example verifies the condition of state estimation and confirms the effectiveness of results.

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