Estimation Error Vector Research Articles

Dynamic event‐triggered distributed interval functional observers for interconnected systems

AbstractIn this paper, we design dynamic event‐triggered interval functional observers (FOs) for interconnected systems comprising subsystems where each subsystem is subject to nonlinearities and output disturbances. Our design method consists of two main steps. First, we design decentralized dynamic event‐triggered mechanisms (ETMs) which use only locally measured output information. We then consider the design of distributed interval FOs by using the newly proposed ETMs. Their existence conditions are established and formulated in terms of linear programming. We also derive a bound on the estimated error vector and show that this bound is the smallest. Thus, this ensures that the unknown linear functional state vector can be estimated within an upper and lower bound of its true value by the designed interval observers. Finally, we apply the obtained results to design dynamic event‐triggered interval observers for linear functions of the state vectors of an ‐machine power system.

Adaptive Parameter Identification for Nonlinear Sandwich Systems with Hysteresis Nonlinearity Based Guaranteed Performance

The paper presents an adaptive identification algorithm via data filtering and improved prescribed performance function for Sandwich systems with hysteresis nonlinearity. By developing a filter in which the filter is simple and easy to realize online and several variables, the estimation error vector can be derived. To improve the transient performance of estimator, a modified prescribed performance function is proposed to constrain the estimation error data through the usage of the predefined domain. For the constrained estimation error condition, the error transformation technique is utilized to simplify the design of the estimator thanks to that the restricted condition is transformed into unconstrained condition. To achieve the convergence of the parameter estimation and assure the predetermined property, a fresh adaptive law is developed. Moreover, the theoretical analysis indicates that the error can converge to a small region based on martingale difference theorem. According to the numerical verification and experimental results, the advantage and practicability of the invented estimator are inspected by comparing the estimators with unconstrained condition.

Identification of nonlinear Wiener-Hammerstein systems by a novel adaptive algorithm based on cost function framework

This paper investigates parameter identification of nonlinear Wiener-Hammerstein systems by using filter gain and novel cost function. Taking into account the system information is corrupted by noise, the filter gain is exploited to extract the system data. By using several auxiliary filtered variables, an extended estimation error vector is developed. Then, based on the discount term of the extended estimation error and the penalty term on the initial estimate, a novel cost function is developed to obtain the optimal parameter adaptive law. Compared with the conventional cost function which is composed of the square sum of output error, the proposed algorithm based on the cost function of this paper can provide faster convergence rate and higher estimation accuracy. Furthermore, the convergence analysis of the proposed scheme indicates that the parameter estimation error can converge to zero. The effectiveness and practicality of the proposed scheme are validated through the simulation example and experiment on the turntable servo system.

Robust Transmission Design for Multicell D2D Underlaid Cellular Networks

This paper investigates the robust transmission design (RTD) of a multi-cell device-to-device (D2D) underlaid cellular network with imperfect channel state information (CSI). The bounded model is adopted to characterize the CSI impairment and the aim is to maximize the worst-case sum rate of the system. To protect cellular communications, it is assumed that the interference from all D2D transmitters to each base station (BS) is power-limited. It is first shown that the worst-case signal-to-interference-plus-noise ratio (SINR) of each D2D link can be obtained directly, while that of cellular links cannot be similarly found since the channel estimation error vectors of cellular links are coupled in the SINR expressions. To solve the nonconvex problem, the objective function of the original problem is replaced with its lower bound, and the resulted problem is decomposed into multiple semidefinite programming (SDP) subproblems which are convex and have computationally efficient solutions. An iterative RTD algorithm is then proposed to obtain a suboptimal solution. Simulation results show that D2D communication can significantly increase the performance of the conventional cellular systems while causing tolerable interference to cellular users. In addition, the proposed RTD algorithm outperforms the conventional non-robust transmission design greatly in terms of network spectral efficiency.

Low-Complexity CS-Aided MPA Detector for SCMA Systems

In this letter, we present a novel low-complexity multiuser detection for sparse code multiple access systems, combined message passing algorithm (MPA), and the compressed sensing (CS) technique, referred to as a CS-aided MPA detector. Specifically, the detector is divided into a two-step procedure, i.e., initial detection and sparse error correction. In the first step, MPA with a few iterations is employed as initial detection. In the second step, based on the observation that the symbol error produced by initial detection tends to be sparse, we propose a CS-based greedy pursuit algorithm, called threshold-aided adaptive subspace pursuit, to estimate the error accurately. Then, the final estimated vector is obtained by adding the initial estimated vector produced by MPA to the estimated error vector. Simulation results demonstrate that the proposed CS-aided MPA detector provides very similar symbol error rate performance compared with the original one with substantially reduced complexity.

Error Recovery Based Low-Complexity Detection for Uplink Massive MIMO Systems

In this letter, we introduce a new approach based on error recovery for detection in uplink massive multiple-input multiple-output (MIMO) systems. In the proposed work, first, the non-sparse massive MIMO system is converted into a quasi-sparse error (i.e., error vector consists of a small fraction of significant elements) system by using a rough initial estimate of the transmitted symbol vector. Next, the error is estimated by a low-complexity error recovery technique which detects the error elements in an ordered pattern. Finally, to obtain the output symbol vector, the estimated error vector is combined with the initial solution vector. Furthermore, in order to enhance performance of the algorithm, multiple iterations of error recovery technique are performed. Simulation results reveal that the proposed algorithm outperforms the recently reported methods with approximately equal complexity.

Automatic control of aircraft lateral-directional motion during landing using neural networks and radio-technical subsystems

The paper presents a new automatic architecture for the control of aircraft lateral-directional motion during landing; the system controls the lateral angular deviation of aircraft longitudinal axis with respect to the runway, by using a classical controller, a radio-navigation system, a system for the calculation of the distances between aircraft and the runway radio-markers, and an adaptive controller mainly used for the control of aircraft roll angle and its deviation with respect to the runway. The adaptive control system uses the dynamic inversion concept, a dynamic compensator, a neural network trained by the system׳s estimated error vector (signal provided by a linear observer), and a Pseudo Control Hedging block. The new designed adaptive architecture is software implemented and validated by complex numerical simulations; the obtained characteristics are very good and prove the new architecture׳s stability and its small overshoots.

Robust H2 Fault Diagnosis Observer Design for Continuous-Time Multi-Agent Dynamic Systems with Disturbances

The distributed fault estimation observer (DFEO) is studied for continuous-time multi-agent systems in this paper. For each agent, a fault estimation observer is designed using neighborhood output estimation errors. By denoting global estimation error vectors, the global error dynamics is constructed for the multi-agent systems. Then, the existence condition of the presented DFEO is further discussed to present its feasibility. Based on the decomposition technique, the result of H2 DFEO are given to calculate the distributed gain matrix of DFEO. Finally, an example is presented to illustrate the effectiveness of the proposed design techniques.

DECODING OF 2-D CONVOLUTIONAL CODES BASED ON ALGEBRAIC APPROACH

In this paper, we apply the decoding matrix for 2-D convolution codes to reconstruct information sequences. It is suitable for non-square matrices with multivariate polynomial elements. Next, development of a syndrome decoder for 2-D convolutional codes based on Grobner bases is introduced. The computation of the syndrome vector employs the computation of the syzygy module, found by means of the Grobner basis of a certain module. Then, estimated error vector can be identified by using m-variate division algorithm. Simulation results show error-correcting capability of decoding process. AMS Subject Classification: 94B10, 94B35

Magnetometer-only attitude and angular velocity filtering estimation for attitude changing spacecraft

This paper presents an improved real-time sequential filter (IRTSF) for magnetometer-only attitude and angular velocity estimation of spacecraft during its attitude changing (including fast and large angular attitude maneuver, rapidly spinning or uncontrolled tumble). In this new magnetometer-only attitude determination technique, both attitude dynamics equation and first time derivative of measured magnetic field vector are directly leaded into filtering equations based on the traditional single vector attitude determination method of gyroless and real-time sequential filter (RTSF) of magnetometer-only attitude estimation. The process noise model of IRTSF includes attitude kinematics and dynamics equations, and its measurement model consists of magnetic field vector and its first time derivative. The observability of IRTSF for small or large angular velocity changing spacecraft is evaluated by an improved Lie-Differentiation, and the degrees of observability of IRTSF for different initial estimation errors are analyzed by the condition number and a solved covariance matrix. Numerical simulation results indicate that: (1) the attitude and angular velocity of spacecraft can be estimated with sufficient accuracy using IRTSF from magnetometer-only data; (2) compared with that of RTSF, the estimation accuracies and observability degrees of attitude and angular velocity using IRTSF from magnetometer-only data are both improved; and (3) universality: the IRTSF of magnetometer-only attitude and angular velocity estimation is observable for any different initial state estimation error vector.

Fault-Tolerant Master–Slave Synchronization for Lur'e Systems Using Time-Delay Feedback Control

This paper deals with fault-tolerant master-slave synchronization for Lur'e systems using time-delay feedback control. Taking a general nature of fault in the master system into account, a new synchronization scheme, namely, fault-tolerant master-slave synchronization, is proposed, by which the master-slave synchronization can be achieved no matter if the fault occurs or not. By making use of an observer-based fault estimator and a modified time-delay feedback controller, the fault-tolerant master-slave synchronization is formulated so as to discuss the global asymptotic stability of the error system and the bound of energy gain from fault to state and fault estimation error vectors. Some new delay-dependent criteria are derived to analyze the synchronization error system, and based on the analysis results, a sufficient condition on the existence of such a master-slave synchronization scheme and a solution to the controller and fault-estimator gain matrices are obtained in terms of linear matrix inequalities. Finally, a Chua's circuit is used to illustrate the effectiveness of the proposed method.

Adaptive model predictive control for constrained nonlinear systems

In this paper, a method is proposed for the adaptive model predictive control of constrained nonlinear system. Rather than relying on the inherent robustness properties of standard NMPC, the developed technique explicitly account for the transient effect of parametric estimation error by combining a parameter adjustment mechanism with robust MPC algorithms. The parameter estimation routine employed guarantees non-increase of the estimation error vector. This means that the controller employs a process model which approaches that of the true system over time. These estimates are used to update the parameter uncertainty set at every time step, resulting in a gradual reduction in the conservative and/or computational effects of the incorporated robust features.

Recursive parameter estimation by means of the SG-algorithm

Recursive parameter estimation in linear regression models by means of the Stenlund-Gustafsson algorithm is considered. The manifold of stationary solutions to the parameter update equation is parameterized in terms of excitation properties. It is shown that the parameter estimation error vector does not diverge under lack of excitation, therefore achieving the purpose of anti-windup. Furthermore, an elementwise form of the parameter vector estimate is suggested revealing the effect of individual matrix entries in the Riccati equation on the parameter estimation updates. Simulations are performed to illustrate the loss of convergence rate in the estimates versus the decrease of computational power needed for two specific approximations of the Riccati equation in the elementwise form.

A new two-dimensional block adaptive FIR filtering algorithm and its application to image restoration

This paper presents a new two-dimensional (2-D) optimum block stochastic gradient (TDOBSG) algorithm for 2-D adaptive finite impulse response (FIR) filtering. The TDOBSG algorithm employs a space-varying convergence factor for all the filter coefficients, where the convergence factor at each block iteration is optimized in a least squares sense that the squared norm of the a posteriori estimation error vector is minimized. It has the same order of computational complexity as another 2-D optimum block adaptive (TDOBA) algorithm. Computer simulations for image restoration show that the TDOBSG algorithm outperforms the TDOBA algorithm and other related algorithms in terms of objective and/or subjective measures.

Conditional simulation of multi-variate Gaussian fields via generalization of Hoshiya's technique

This paper generalizes conditional simulation technique of uni-variate Gaussian random fields by the stochastic interpolation proposed by Hoshiya, to multi-variate random fields. The kriging estimation of multi-variate Gaussian fields is proposed, and basic formulation for conditional simulation of multi-variate random fields is established. For the particular case of uncorrelated components of multi-variate field, the formulation reduces to that of uni-variate field given by Hoshiya. The paper also provides proofs of some important properties of the estimation error vector, which guarantee that the conditional simulation of the multi-variate field can be implemented by separately computing its kriging estimate and simulating the error vector. An analytical example of two-variate field is elucidated and some numerical results are discussed.

MUSIC and maximum likelihood techniques on two-dimensional DOA estimation with uniform circular array

The authors describe the application of multiple signal classification (MUSIC) and maximum likelihood (ML) techniques to the joint azimuthal and elevational directions-of-arrival (AEDOA) estimation with a uniform circular array. Both deterministic and random source signal models are considered. The asymptotic statistical properties of MUSIC and ML estimation error vectors for AEDOA parameters are investigated. In particular, explicit analytical expressions are derived for asymptotic MUSIC and ML covariance matrices as well as Cramer-Rao lower bounds (CRLBs). These analytical formulae are employed in the theoretical performance study. Computer simulation results are presented to validate theoretical predictions and compare the performance of MUSIC and ML methods. It is shown that the performance of unconditional ML is superior to that of deterministic ML, which is, in turn, better than that of MUSIC.

Comments on "A fast block FIR adaptive digital filtering algorithm with individual adaptation of parameters

For the original article see ibid., vol.36, no.1, p.1-10 (1989). The optimum block adaptive algorithm with individual adaptation of parameters (OBAI) presented in the above-mentioned work was derived by minimizing the squared norm of the a priori estimation error vector with an assumption. The commenters show that the OBAI algorithm can also be obtained by minimizing the squared norm of the a posteriori estimation error vector without any assumption. >

Information-theoretical optimal smoothing estimators

The optimal smoothing estimators are derived based on information theory. For a continuous-time stochastic linear system it is proved that minimizing the mutual information between the smoothing estimate vector and the smoothing error vector is equivalent to minimizing the entropy of the smoothing estimation error vector. By minimizing the entropy of the smoothing estimation error vector we derive the basic equations for the optimal smoothing estimators. Using the basic equations, three kinds of optimal smoothing estimators are derived. Furthermore, the information structure of optimal-smoothing problems is clarified.