Set-Membership Filtering Approach to Dynamic Event-Triggered Fault Estimation for a Class of Nonlinear Time-Varying Complex Networks

The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks,utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism(DETM).In order to optimize communication resource utilization,the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not.To guarantee a satisfactory estimation performance for the fault signal,an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals.The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains.The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator,ensuring that the specified performance requirements are met under certain conditions.Then,the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided.Finally,a numerical example is conducted to demonstrate the effectiveness of the designed estimator.

Global stabilizing controller design for linear time-varying systems and its application on BTT missiles

A parametric method for the gain-scheduled controller design of a linear time-varying system is given. According to the proposed scheduling method, the performance between adjacent characteristic points is preserved by the invariant eigenvalues and the gradually varying eigenvectors. A sufficient stability criterion is given by constructing a series of Lyapunov functions based on the selected discrete characteristic points. An important contribution is that it provides a simple and feasible approach for the design of gain-scheduled controllers for linear time-varying systems, which can guarantee both the global stability and the desired closed-loop performance of the resulted system. The method is applied to the design of a BTT missile autopilot and the simulation results show that the method is superior to the traditional one in sense of either global stability or system performance.

Stabilization of linear time-varying systems with state and input constraints using convex optimization

The stabilization problem of linear time-varying systems with both state and input constraints is considered. Sufficient conditions for the existence of the solution to this problem are derived and a gain-switched（gain-scheduled） state feedback control scheme is built to stabilize the constrained timevarying system. The design problem is transformed to a series of convex feasibility problems which can be solved efficiently. A design example is given to illustrate the effect of the proposed algorithm.

Sparse Recovery of Linear Time-Varying Channel in OFDM System

In order to improve the performance of linear time-varying（LTV）channel estimation,based on the sparsity of channel taps in time domain,a sparse recovery method of LTV channel in orthogonal frequency division multiplexing（OFDM）system is proposed.Firstly,based on the compressive sensing theory,the average of the channel taps over one symbol duration in the LTV channel model is estimated.Secondly,in order to deal with the inter-carrier interference（ICI）,the group-pilot design criterion is used based on the minimization of mutual coherence of the measurement.Finally,an efficient pilot pattern optimization algorithm is proposed by a dual layer loops iteration.The simulation results show that the new method uses less pilots,has a smaller bit error ratio（BER）,and greater ability to deal with Doppler frequency shift than the traditional method does.

TECHNICAL STABILITY OF NONLINEAR TIME-VARYING SYSTEMS WITH SMALL PARAMETERS

Technical stability:allowing quantitative estimation of trajectory behavior of a dynamical system over a given time interval was considered. Based on a differential comparison principle and a basic monotonicity condition, technical stability relative to certain prescribed state constraint sets of a class of nonlinear time-varying systems with small parameters was analyzed by means of vector Liapunov function method. Explicit criteria of technical stability are established in terms of coefficients of the system under consideration. Conditions under which the technical stability of the system can be derived from its reduced linear time-varying (LTV) system were further examined, as well as a condition for linearization approach to technical stability of general nonlinear systems. Also, a simple algebraic condition of exponential asymptotic stability of LTV systems is presented. Two illustrative examples are given to demonstrate the availability of the presently proposed method.

Real-Valued Discrete Gabor Transform of Linear Time-Varying Systems: Exact Representation and Approximation

An efficient algorithm for the representation and approximation of linear time-varying systems is presented via the fast real-valued discrete Gabor transform. Compared with the existing algorithm based on the traditional complex-valued discrete Gabor transform, the proposed algorithm runs faster, can more easily be implemented in software or hardware, and leads to a more compact representation. Simulation results are given for demonstration.

Application of Genetic Algorithms in Identification ofLinear Time-Varying System

By applying genetic algorithms (GA) to on-line identification of linear time-varying systems; a number of modifications are made to the Simple Genetic Algorithm to improve the performance of the algorithm in identification applications. The simulation results indicate that the method is not only capable of following the changing parameters of the system, but also has improved the identification accuracy compared with that using the least square method.

Modal Identification of Linear Time-varying Systems Using Continuous Wavelet Transform

A wavelet based identification method for linear time-varying systems is presented,and the ridge and skeleton of the continuous wavelet transform of free response is used to extract time-varying parameters. The stiffness and damping coefficients of single-degree-of—freedom systems,frequencies and damping ratios of multi-degree-of-freedom systems are estimated without any prior information of systems. The proposed method is applied to linear time-varying systems with both abrupt and smooth variation parameters. Gaussian white noise is added to the response to test the anti-noise performance of the algorithm. The simulation results show that the proposed method is capable of accurately tracking the variation of the systems.

Feedforward and feedback optimal control for linear time-varying systems with persistent disturbances

The optimal control problem for linear time-varying systems affected by external persistent disturbances with known dynamic characteristics but unknown initial conditions is consider and a design procedure of a feedforward and feedbaek optimal controller is presented. The condition of existence and uniqueness of the control law is given. The disturbanee observer is proposed to make the feedforward control law realizable physically. Simulation results demonstrate that the feedforward and feedbaek optimal control law is more effective and robust than the elassical state feedbaek control law with respect to external disturbanees.

Dynamic Stability Analysis of Linear Time-varying Systems via an Extended Modal Identification Approach

The problem of linear time-varying（LTV） system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the ＂frozen-time＂ assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stabilitypreserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system＇s stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam exper- imental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides anew way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.

Robust Stabilization for Linear Time-varying Uncertain Periodic Descriptor Systems

这个问题赶出为线性变化时间的不明确的周期的描述符系统的稳定被重游。为线性变化时间的不明确的周期的描述符系统基于柔韧的稳定性的概念，为柔韧的稳定性的一个必要、足够的条件被提出。柔韧的稳定问题也被学习，相应必要、足够的条件用双系统的符号被给。获得的矩阵不平等条件能被转变到与一些免费矩阵的介绍的线性矩阵不平等一，它使分析和设计过程简单、可靠。

Sufficient conditions of the various stabilities of the linear time-varying delayed differential equations

Due to the appearance and the study of the ornithopter and flexible-wing micro air vehicles, etc., the time-varying systems become more and more important and ubiquitous in the study of the mechanics. In this letter, the sufficient conditions of the uniform asymptotic stability are first presented for the delayed time-varying linear differential equations with any time delay by employing the Dini derivative, Lozinskii measure and the generalized scalar Halanay delayed differential inequality. They are especially based on the estimation of the arbitrary solutions but not the fundamental solution matrix since their solutions＇ space is infinite-dimensional. Then some sufficient conditions of the stability, asymptotic stability and uniform asymptotic stability of the delayed time-varying linear system with a sufficiently small time delay are reported by employing Taylor expansion and Dini derivative. It implies that these stabilities can be guaranteed by the Lozinskii measure of the matrix composing of the time delay and the coefficient matrices of the system.

Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model

为在批的产品质量的追踪的控制的最佳的反复的学习控制(ILC ) 策略处理的 batch-to-batch 被介绍。线性变化时间的不安(LTVP ) 模型在名字的轨道附近为产品质量被造。到模型植物失配的地址问题，在以前的成批处理的模型预言错误为当前的成批处理被加到模型预言。然后追踪错误转变模型能被造，并且有直接错误反馈的 ILC 法律明确地被获得。一条严密定理被建议，到证明在 ILC 下面追踪错误的集中。建议方法论在一个典型的批反应堆和轨道追踪的表演被 ILC 逐渐地改进的结果表演上被说明。

A Comparative Study of Nonlinear Time-Varying Process Modeling Techniques: Application to Chemical Reactor

This paper proposes the design and a comparative study of two nonlinear systems modeling techniques. These two approaches are developed to address a class of nonlinear systems with time-varying parameter. The first is a Radial Basis Function (RBF) neural networks and the second is a Multi Layer Perceptron (MLP). The MLP model consists of an input layer, an output layer and usually one or more hidden layers. However, training MLP network based on back propagation learning is computationally expensive. In this paper, an RBF network is called. The parameters of the RBF model are optimized by two methods: the Gradient Descent (GD) method and Genetic Algorithms (GA). However, the MLP model is optimized by the Gradient Descent method. The performance of both models are evaluated first by using a numerical simulation and second by handling a chemical process known as the Continuous Stirred Tank Reactor CSTR. It has been shown that in both validation operations the results were successful. The optimized RBF model by Genetic Algorithms gave the best results.

Study on the Application of Iterative Learning Control to Terminal Control of Linear Time-varying Systems

An iterative learning control algorithm based on shifted Legendre orthogonal polynomials is proposed to address the terminal control problem of linear time-varying systems. First, the method parameterizes a linear time-varying system by using shifted Legendre polynomials approximation. Then, an approximated model for the linear time-varying system is deduced by employing the orthogonality relations and boundary values of shifted Legendre polynomials. Based on the model, the shifted Legendre polynomials coefficients of control function are iteratively adjusted by an optimal iterative learning law derived. The algorithm presented can avoid solving the state transfer matrix of linear time-varying systems. Simulation results illustrate the effectiveness of the proposed method.

Guaranteed cost sampled-data control for uncertain nonlinear time-varying delay system

The robust guaranteed cost sampled-data control was studied for a class of uncertain nonlinear systems with time-varying delay. The parameter uncertainties are time-varying norm-bounded and appear in both the state and the input control matrices. By applying an input delay approach, the system was transformed into a continuous time-delay system. Attention was focused on the design of a robust guaranteed cost sampled-data control law which guarantees that the closed-loop system is asymptotically stable and the quadratic performance index is less than a certain bound for all admissible uncertainties. By applying Lyapunov stability theory, the theorems were derived to provide sufficient conditions for the existence of robust guaranteed cost sampled-data control law in the form of linear matrix inequalities (LMIs), especially an optimal state-feedback guaranteed cost sampled-data control law which ensures the minimization of the guaranteed cost was given. The effectiveness of the proposed method was illustrated by a simulation example with the asymptotically stable curves of system state under the initial condition of x(0)=[0.679 6 0].

Feedforward and feedback optimal control for linear time-varying systems with persistent disturbances

The optimal control problem was studied for linear time-varying systems,which was affected by external persistent disturbances with known dynamic characteristics but unknown initial conditions. To damp the effect of disturbances in an optimal fashion,we obtained a new feedforward and feedback optimal control law and gave the control algorithm by solving a Riccati differential equation and a matrix differential equation. Simulation results showed that the achieved optimal control law was realizable,efficient and robust to reject the external disturbances.

Event-based adaptive asymptotic tracking control of nonlinear time-varying systems with prescribed performance

This paper is concerned with the adaptive tracking control problem of nonlinear time-varyingsystems. Based on the backstepping technology, an event-based prescribed performance controlscheme is developed. And the time-varying uncertainties of the system are handled byutilising bound estimation method. The proposed controller not only ensures the prescribedtracking performance, but also reduces the communication burden. By using Lyapunov stabilityanalysis, it is proven that all of the closed-loop signals are bounded, and the tracking errorcan converge to zero. Simultaneously, Zeno behaviour is excluded. Finally, the simulation resultsare utilised to illustrate the effectiveness of the proposed adaptive control scheme.

Policy Iteration for Optimal Control of Discrete-Time Time-Varying Nonlinear Systems

Aimed at infinite horizon optimal control problems of discrete time-varying nonlinear systems,in this paper,a new iterative adaptive dynamic programming algorithm,which is the discrete-time time-varying policy iteration(DTTV)algorithm,is developed.The iterative control law is designed to update the iterative value function which approximates the index function of optimal performance.The admissibility of the iterative control law is analyzed.The results show that the iterative value function is non-increasingly convergent to the Bellman-equation optimal solution.To implement the algorithm,neural networks are employed and a new implementation structure is established,which avoids solving the generalized Bellman equation in each iteration.Finally,the optimal control laws for torsional pendulum and inverted pendulum systems are obtained by using the DTTV policy iteration algorithm,where the mass and pendulum bar length are permitted to be time-varying parameters.The effectiveness of the developed method is illustrated by numerical results and comparisons.

Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions

Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly expensive.Meanwhile,the number of samples collected from different health states is often unbalanced.To deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised conditions.In the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction results.In adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target domain.Comparative experiments on two case studies validated the superior performance of the proposed method.