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1.
In this paper a new approach to algebraic parameter identification of the linear SISO systems is proposed. The standard approach to the algebraic parameter identification is based on the algebraic derivatives in Laplace domain as the main tool for algebraic manipulations like elimination of the initial conditions and generation of linearly independent equations. This approach leads to the unstable time-varying state-space realization of the filters for the on-line parameter estimation. In this paper, the finite difference and shift operators in combination with the frequency-shifting property of Laplace transform is applied instead of algebraic derivatives. Resulting state-space realization of the estimator filters is asymptotically stable and doesn’t require switch-of mechanism to prevent overflow of the estimator variables. The proposed method is especially suitable for applications in closed-loop on-line identification where the stable behavior of the estimators is a necessary requirement. The efficiency of the proposed algorithm is illustrated on three simulation examples.  相似文献   

2.
In this paper, we present a secure distributed estimation strategy in networked systems. In particular, we consider distributed Kalman filtering as the estimation method and Paillier encryption, which is a partially homomorphic encryption scheme. The proposed strategy protects the confidentiality of the transmitted data within a network. Moreover, it also secures the state estimation computation process. To this end, all the algebraic calculations needed for state estimation in a distributed Kalman filter are performed over the encrypted data. As Paillier encryption only deals with integer data, in general, this, in turn, provides significant quantization error in the computation process associated with the Kalman filter. However, the proposed estimation approach handles quantized data in an efficient way. We provide an optimality and convergence analysis of our proposed method. It is shown that state estimation and a covariance matrix associated with the proposed method remain with a certain small radius of those of a conventional centralized Kalman filter. Simulation results are given to further demonstrate the effectiveness of the proposed scheme.  相似文献   

3.
In this paper, the state estimation problem is studied for a class of discrete-time stochastic complex networks with switched topology. In the network under consideration, we assume that measurement outputs can be got from only partial nodes, besides, the switching rule of this network is characterized by a sequence of Bernoulli random variables. The aim of the presented estimation problem is to develop a recursive estimator based on the framework of extended Kalman filter (EKF), such that the upper bound for the filtering error convariance is optimized. In order to address the nonlinear functions, the Taylor series expansion is utilized and the high-order terms of linearization errors are expressed in an exact way. Furthermore, by solving two Ricatti-like difference equations, the gain matrix can be acquired at each time instant. It is shown that the filtering error is bounded in mean square under some conditions with the aid of stochastic analysis techniques. A numerical example is given to demonstrate the validity of the proposed estimator.  相似文献   

4.
In this paper, a constrained regularized least square (RLS) state estimator is developed for deterministic discrete-time nonlinear dynamical systems subject to a set of equality and/or inequality constraints. The stability of the estimation error is rigorously analyzed. The proposed estimator is then used to handle the important problem of secure communication. At the transmitting end, the output of the constrained unified chaotic system is used as a chaotic mask to achieve a satisfactory and typical secure communication scheme. The encrypted data signal is injected into the transmitter and simultaneously transmitted to the receiver through a public channel. At the receiving end, the constrained RLS estimator is used to reconstruct the states of the constrained unified chaotic system. Simulation results are presented to show the impact of the imposed constraints on the waveform and the pattern of the generated chaotic signal as well as the ability of the proposed estimator to synchronize the actual and estimated states of the constrained unified chaotic system. Moreover, the proposed estimator is applied to recover discrete signals such as digital images where computer simulation results are provided to show the effectiveness of the proposed estimation scheme.  相似文献   

5.
Unpredictable packet loss that occurs in the channel connecting a local sensor and a remote estimator will deteriorate the performance of state estimation. To relieve this detrimental impact, an online linear temporal coding scheme is studied in this paper. If the packet of the last step is lost, a linear combination of the current and the last measurements with proper weights is transmitted; otherwise, only the current data is sent. By virtue of the innovation sequence approach, a linear minimum mean-squared error estimation algorithm is designed. To optimize performance, a novel estimator is also proposed which provides a recursive expression of the error covariances. The proposed two algorithms are proved to be equivalent via a set of transformations. With the aid of some optimization techniques, a recursive algorithm is presented to obtain the optimal coding weight in terms of minimizing the average estimation error covariance.  相似文献   

6.
This paper deals with the distributed estimation problem for networked sensing system with event-triggered communication schedules on both sensor-to-estimator channel and estimator-to-estimator channel. Firstly, an optimal event-triggered Kalman consensus filter (KCF) is derived by minimizing the mean squared error of each estimator based on the send-on-delta triggered protocol. Then, the suboptimal event-triggered KCF is proposed in order to reduce the computational complexity in covariance propagation. Moreover, the formal stability analysis of the estimation error is provided by using the Lyapunov-based approach. Finally, simulation results are presented to demonstrate the effectiveness of the proposed filter.  相似文献   

7.
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8.
This paper is concerned with the robust state estimation problem for semi-Markovian switching complex-valued neural networks with quantization effects (QEs). The uncertain parameters are described by the linear fractional uncertainties (LFUs). To enhance the channel utilization and save the communication resources, the measured output is quantized before transmission by a logarithmic quantizer. The purpose of the problem under consideration is to design a full-order state estimator to estimate the complex-valued neuron states. Based on the Lyapunov stability theory, stochastic analysis method, and some improved integral inequalities, sufficient conditions are first derived to guarantee the estimation error system to be globally asymptotically stable in the mean square. Then, the desired state estimator can be directly designed after solving a set of matrix inequalities, which is robust against the LFUs and the QEs. In the end of the paper, one numerical example is provided to illustrate the feasibility and effectiveness of the proposed estimation design scheme.  相似文献   

9.
In this paper, a hierarchical estimator combined with the nonlinear observer and particle filter (PF) is proposed to accurately estimate the vehicle state and tire forces of distributed in-wheel motor drive electric vehicles (DIMDEVs) when the traditional tire models are not available. The proposed estimator consists of lower and upper layers. The lower layer, i.e. longitudinal tire force nonlinear observer (LTFNO) aims at estimating the longitudinal force based on the available drive/brake torques and rotational speed of wheels. The convergence of LTFNO is proved by the invariant set principle. The upper layer receives these estimated longitudinal tire forces from LTFNO and estimates the vehicle state including lateral tire forces based on an expert model (EM). The designed EM utilizes basic knowledge and rules about tire characteristics to approximate the unknown lateral tire force. The upper estimator combines with EM (EEM) to further improve the accuracy. The EEM takes the modeling errors and disturbances into account and avoids the usage of complex established tire models. Then PF is applied in the upper layer to complete the estimation, which only needs measurable longitudinal/lateral accelerations and yaw rate signals. Finally, the effectiveness of the designed hierarchical estimator is verified by Carsim and Simulink co-simulations. The results show the proposed strategy can accurately estimate the vehicle state and tire forces in real-time.  相似文献   

10.
A finite-time non-fragile state estimation algorithm is discussed in this article for discrete delayed neural networks with sensor failures and randomly occurring sensor nonlinearity. First, by using augmented technology, such system is modeled as a kind of nonlinear stochastic singular delayed system. Then, a finite-time state estimator algorithm is provided to ensure that the singular error dynamic is regular, causal and stochastic finite-time stable. Moreover, the states and sensor failures can be estimated simultaneously. Next, in order to avoid the affection of estimator’s parameter perturbation, a finite-time non-fragile state estimation algorithm is given, and a simulation result demonstrates the usefulness of the proposed approach.  相似文献   

11.
This paper studies the robust stochastic stabilization problem for a class of fuzzy Markovian jump systems with time-varying delay and external disturbances via sliding mode control scheme. Based on the equivalent-input-disturbance (EID) approach, an online disturbance estimator is implemented to reject the unknown disturbance effect on the considered system. Specifically, to obtain exact EID estimation Luenberger fuzzy state observer and a low-pass filter incorporated to the closed-loop system. Moreover, novel fuzzy EID-based sliding mode control law is constructed to ensure the stability of the closed-loop system with satisfactory disturbance rejection performance. By employing Lyapunov stability theory and some integral inequalities, a new set of delay-dependent robust stability conditions is derived in terms of linear matrix inequalities (LMIs). The resulting LMI is used to find the gains of the state-feedback controller and the state observer a for the resulting closed-loop system. At last, numerical simulations based on the single-link arm robot model are provided to illustrate the proposed design technique.  相似文献   

12.
细胞信号转导网络的结构复杂,规模庞大,建立的数学模型维数高,变量多,具有高度非线性。在复杂系统分析设计中,模型简化始终是主要的研究问题之一。提出一种基于混合推理方法的模型简化策略,利用代谢控制分析、敏感性分析、主元分析和通量分析相结合,降低系统模型维数,减少生化反应个数,简化系统结构。以NF-κB信号转导网络作为研究对象,原模型由24个常微分方程和64个参数组成,简化模型则包括17个常微分方程,1个代数方程和52个参数。仿真结果表明,简化模型能够准确地预测系统的动态特性,为模型分析和参数辨识提供方便,验证了模型简化策略的有效性。  相似文献   

13.
Many dynamical systems are continuous-time non-square with unknown mismatched input and output disturbances. For such systems, a universal on-line robust optimal tracking control is often desirable. In this paper, the conventional proportional-integral-differential (PID) controller is utilized as a fictitious PID filter to shape the tracking error in the frequency-domain using a quadratic performance index as a weighting function, such that the robust PID-shaped PI tracker integrated with the equivalent input disturbance (EID) estimator is established to carry out the on-line robust optimal tracking control of the general disturbed system. The benefits and discrepancies of the proposed compensation improvement mechanism over the conventional optimal trackers for continuous-time non-square systems with/without unknown mismatched input and output disturbances are listed as follows: (i) It develops a new net EID estimator without any previously established constraints on the dimensions of the system and on the disturbances; (ii) It provides an efficient estimated-state-feedback-based EID estimator in contrast to the conventional output-feedback-based EID estimators; (iii) It is able to carry out on-line EID estimation of the tracking errors for systems with endogenous/exogenous output disturbances; (iv) It is a universal tracker which can be simply implemented as a plug-in EID estimator for most servo systems, to improve the performance of any existing observers/trackers which are not allowed to be removed from the system. The advantages of the proposed method over two existing outstanding approaches reported in the literature are pointed out using illustrative examples.  相似文献   

14.
Detection and estimation of abnormalities for distributed parameter system (DPS) have wide applications in industry, e.g., battery thermal fault diagnosis, quality monitoring of hot-rolled strip laminar cooling process. In this paper, the abnormal spatio-temporal (S-T) source detection and estimation problem for a linear unstable DPS is first studied. The proposed methodology consists of two steps: first, an abnormality detection filter (ADF) which generates a residual signal for abnormality detection in the time domain is constructed using pointwise measurement; Then, an adaptive Luenberger-type PDE observer including an adaptive estimation algorithm is designed and triggered only when an alarm raises from the ADF. Theoretic analysis based on the spatial domain decomposition approach is presented to show the convergence of the estimation errors. Finally, an illustrative example is presented to show the performance of the proposed method.  相似文献   

15.
In this paper, the problem of finite-horizon H∞ state estimation is investigated for a class of discrete time-varying complex networks with multiplicative noises and random coupling strengths. The network nodes and estimators are connected via a constrained communication network which allows only one node to send measurement data at each transmission instant. The Round-Robin protocol is introduced to determine which node obtains the access to the network at certain transmission instant. The aim of the addressed problem is to design a set of time-varying estimator parameters such that the prescribed H∞ performance is guaranteed over a finite horizon. By using the stochastic analysis approach and completing-the-square method, sufficient conditions are derived for the existence of the desired estimators in terms of the solution to backward recursive Riccati difference equations. Finally, a numerical example is provided to validate the feasibility and effectiveness of the proposed results.  相似文献   

16.
This paper is concerned with a security problem about malicious integrity attacks in state estimation system, in which multiple smart sensors locally measure information and transmit it to a remote fusion estimator though wireless channels. A joint constraint is considered for the attacker behaviour in each channel to keep stealthiness under a residual-based detector on the remote side. In order to degrade the estimator performance, the attacker will maximize the trace of the remote state estimation error covariance which is derived based on Kalman filter theory. It is proved that the optimal linear attack strategy design problem is convex and finally turned into a semi-definite programming problem. In addition, the tendency of attack behaviour on recursive and fixed Kalman filter system is analyzed. Several examples are given to illustrate the theoretical results.  相似文献   

17.
In this study, a practical matrix method is presented to find an approximate solution of high-order linear Fredholm integro-differential equations with constant coefficients under the initial-boundary conditions in terms of Taylor polynomials. The method converts the integro-differential equation to a matrix equation, which corresponds to a system of linear algebraic equations. Error analysis and illustrative examples are included to demonstrate the validity and applicability of the technique.  相似文献   

18.
This paper investigates the distributed state estimation problem for a linear time-invariant system characterized by fading measurements and random link failures. We assume that the fading effect of the measurements occurs slowly. Additionally, communication failures between sensors can affect the state estimation performance. To this end, we propose a Kalman filtering algorithm composed of a structural data fusion stage and a signal date fusion stage. The number of communications can be decreased by executing signal data fusion when a global estimate is required. Then, we investigate the stability conditions for the proposed distributed approach. Furthermore, we analyze the mismatch between the estimation generated by the proposed distributed algorithm and that obtained by the centralized Kalman filter. Lastly, numerical results verify the feasibility of the proposed distributed method.  相似文献   

19.
This paper is concerned with the event-triggered H∞ state estimation problem for a class of discrete-time complex networks subject to state saturations, quantization effects as well as randomly occurring distributed delays. A series of Bernoulli distributed random variables is utilized to model the random occurrence of distributed delays. For the energy-saving purpose, an event-triggered mechanism is proposed to decide whether the current quantized measurement should be transmitted to the estimator or not. For the state-saturated complex networks, our aim is to design event-triggered state estimators that guarantee both the exponential mean-square stability of and the H∞ performance constraint on the error dynamics of the state estimation. Stochastic analysis is conducted, in combination with the Lyapunov functional approach, to derive sufficient conditions for the existence of the desired estimators whose gain matrices are obtained by solving a set of matrix inequalities. An illustrative example is exploited to show the usefulness of the estimator design algorithm proposed.  相似文献   

20.
This paper considers the output feedback sliding-mode control for an uncertain linear system with unstable zeros. Based on a frequency shaping design, a dynamic-gain observer is used for state estimation of an uncertain system. This paper confirms that (1) state estimation is globally stable in a practical sense, (2) the resultant error can be arbitrarily small with respect to the system uncertainties, and (3) the proposed sliding-mode control can drive the uncertain system state into an arbitrarily small residual set around the origin, such that the size of residual set is controlled by the filter design. Moreover, the proposed control design is inherently robust to measurement noise; the effect of measurement noise can effectively be attenuated without any additional work.  相似文献   

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