This paper proposes a neural network-based optimized robust nonlinear Kalman filter for concurrent fault estimation of high-speed trains using descriptor systems. Considering the displacement and speed time delays and multi-source disturbances acting on every car, and regarding the concurrent actuator and sensor faults as the auxiliary variables of the train, a time-delay nonlinear descriptor system is established. Fuzzy and fuzzy inference layers are added to the hyper basis function neural network; meanwhile, an accelerated gradient algorithm is used for optimizing the basis function of the network. These strategies derive an improved fuzzy hyper basis function neural network to achieve a higher nonlinear multi-source disturbance approximation accuracy, and robust upper bounds are proposed to enhance the filtering accuracy of descriptor systems. Furthermore, a fusion intelligent optimization algorithm using a circle chaotic mapping method to improve the population initialization is proposed to better estimate the unknown system noise. These measures implement better Kalman filtering of descriptor systems to achieve better concurrent fault estimation, even under unknown noise. Simulation results show that, compared with the improved radial basis function neural network-based optimized robust nonlinear Kalman filter, using the proposed method, the displacement, speed, and fault estimation errors are comprehensively decreased, depending on more accurate multi-source disturbance and noise estimations.

Bearings are critical components of mechanical equipment, and predicting their remaining useful life (RUL) is important in industry. This paper proposes a RUL prediction method based on the assessment of a bearing’s health status. Features from the time, frequency, and time–frequency domains of the bearing’s vibration signal are extracted to construct a feature set. A multibranch encoder and restricted Boltzmann machine are used to improve the stacked autoencoder to reduce dimensionality. Local weights and health-sample means are introduced into the Mahalanobis distance to improve the health index. Subsequently, the weighted convolutional Euclidean distance serves as the distance metric in K -means clustering to achieve a more accurate health status assessment and provide historical data for RUL prediction. An improved self-attention (ISA) mechanism is proposed by incorporating depthwise separable convolutions and residual-like connections into self-attention mechanisms, enhancing the global and local dependencies of the temporal convolutional network (TCN). Thus, a more accurate RUL prediction is achieved. Comparative and ablation experiments confirm that the proposed ISA-TCN achieves superior predictive accuracy. Generalization experiments further demonstrate its strong adaptability, while anti-noise experiments demonstrate its strong robustness to uncertainties. Finally, experiments on multi-output RUL predictions validate the model’s effectiveness. This approach offers valuable insights for RUL prediction of rotating machinery under real-world scenarios involving variable operating conditions, noise interference, and multi-device environments.

SummaryIn this paper, the state estimation problem and structure identification problem are investigated for an array of complex networks with time‐varying delays and model uncertainty. Model uncertainty is generated by uncertain connections between nodes in complex networks. Combining probabilistic graph model and graph neural network, a probabilistic graph neural network is proposed to deal with the uncertainty, and the multiple linear terms of the errors between nodes are estimated. The purpose of the addressed state estimation problem is to design a state estimator such that, in the presence of the model uncertainty, the estimation error to converge to zero can be guaranteed and the explicit expression of the estimator parameters is given. Based on a Lyapunov function, the parameters of the estimator and the updating laws of the weights of probabilistic graph neural network can be obtained by the solutions to linear matrix inequalities. The purpose of the addressed structure identification problem is to infer a definite graph structure model based on Bayes' theorem in the presence of a stable probabilistic graph neural network and state estimator. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the developed state estimation and structure identification scheme.

The relative attitude estimation between chasers and uncooperative targets is an important prerequisite for executing in orbit service (OOS) tasks. Only by efficiently obtaining relative pose parameters can chasers design close-range rendezvous trajectories close to uncooperative targets. The focus of this article is on active systems, such as TOF cameras or LIDAR. This paper proposes an attitude estimation scheme to obtain relative attitude parameters between uncooperative targets. This scheme utilizes LIDAR to obtain three-dimensional point clouds of non-cooperative targets, extracts key points and simplifies the number of point clouds through joint farthest point sampling and point cloud feature analysis, and then uses point fast feature histograms (FPFHs) and robust iterative closest point algorithms to achieve point cloud registration between every two frames. Finally, a filtering framework was designed, whose scheme is an extended Kalman filter designed for updating measurements of relative position, velocity, attitude, and angular velocity estimation. The experimental results show that this method can effectively achieve point cloud registration for close range rotation and translation motion, and can estimate the motion state of the target.

In this paper, an optimized adaptive robust extended Kalman filter is proposed based on random weighting factors and an improved whale optimization algorithm for fault estimation of the dynamics of high-speed trains with constant time delays, drastically changing noise and stochastic uncertainties. Robust upper bounds are proposed to improve the performance of the extended Kalman filter by decreasing the influence of the linearization error on filtering for the dynamics of high-speed trains with constant time delays, and its robustness is proven to guarantee the feasibility of the proposed upper bounds. Furthermore, considering drastically changing noise with unknown statistics, a random weighting adaptive algorithm is proposed to implement unbiased noise estimation so that the robust extended Kalman filter can still be implemented well. In addition, a differential evolution algorithm and adaptive parameter are introduced to improve the performance of the whale optimization algorithm so that the stochastic uncertainties are optimized, and the influence of the stochastic uncertainties on filtering is further decreased. The simulation results in the three conditions show that, compared with the variational Bayes adaptive iterated extended Kalman filter, using the proposed method, the position, speed and fault estimation errors are decreased by 31.8%, 33.2% and 28.3%, respectively, on average, which depends on more accurate noise estimation.

针对具有状态定常时滞、测量丢失、执行器故障和噪声测量偏差的惯性系统,提出一种基于白鲸优化算法的优化鲁棒扩展Kalman滤波器.首先,视执行器故障为系统的附加状态变量,建立增广时滞系统.其次,设计鲁棒扩展Kalman滤波器.给出鲁棒上界,降低非线性状态项和非线性状态时滞项的线性化误差;考虑测量丢失环节对滤波精度的影响,在滤波器中引入调节系数降低估计误差.最后,考虑噪声真实值和测量值可能存在的偏差,基于白鲸优化算法优化噪声测量值的协方差矩阵,降低噪声测量偏差对滤波性能的影响.仿真结果表明,相较于基于鲸鱼优化算法和基于灰狼优化算法的优化鲁棒扩展Kalman滤波器,所提方法对状态变量估计的均方根误差分别平均降低了 50.2%和 67.2%,执行器故障估计的均方根误差分别降低了 12.8%和 35.4%;状态估计误差均值分别平均降低了 21.2%和31.4%,执行器故障估计均值分别降低了 9.3%和 16.9%,验证了所提方法的有效性.

In this paper, novel fault estimation and fault-tolerant control methods are proposed for dynamics of high-speed train based on descriptor systems with uncertainties in finite frequency domain. Dynamics of high-speed train is established based on multi-particle model considering that basic resistance is seen as the coefficient of state variables, and additive resistance and the operating noise are seen as multi-source disturbance. Concurrent actuator, sensor faults, and wind gust are considered simultaneously; wind gust is modeled as a disturbance generated by the exogenous system, and an uncertain descriptor system with actuator fault and the exogenous disturbance is established by seeing the sensor fault of high-speed train as the state variables. A robust disturbance-observer-based fault estimation method is proposed to decouple the non-linearity of the descriptor system, so that the combining estimation of the fault and wind gust is implemented. This observer has an unknown input structure, and its gain matrices are formulated as linear matrix inequalities. The observer not only guarantees the augmented state estimation error is asymptotic stable but also the actuator fault estimation and wind gust estimation errors are robust to the multi-source disturbance and the uncertainties. Based on the estimation results, the fault-tolerant controller associated with the state estimation, faults estimation, and wind gust estimation results is proposed to implement a stable close-loop fault-tolerant control for dynamics of high-speed train. Simulation examples are given to illustrate the effectiveness of this method.

When obtaining point cloud data of the measured object through 3D scanning, it is inevitable to encounter noise and outliers, which seriously affect the accuracy of estimating point cloud plane parameters and fitting planes. The Random Sample Consensus (RANSAC) algorithm can effectively estimate point cloud plane parameters and fit planes with certain robustness, but it has redundancy as it needs to distinguish inliers from outliers in each iteration, which has a certain impact on running efficiency. This article proposes an improved RANSAC algorithm based on Principal Component Analysis (PCA) method, combined with setting certain criteria to eliminate gross errors and outliers in point cloud data, in order to obtain ideal plane fitting parameters. Experiments show that compared with some traditional algorithms, this method can adapt well to the presence of gross errors and outliers in point cloud data, obtain better estimates of plane parameters, and is a robust plane fitting algorithm.

Control of nonlinear systems with time-varying delays is a common problem, particularly when the time-varying delays are unknown. This paper presents an adaptive compensation control method based on multiple neural networks. Firstly, considering the nonlinear systems with time-varying delays, a neural network method is adopted to approximate the unknown nonlinear structure with time-varying delays. On this basis, a backstepping controller is designed. Considering the unknown time-varying delays, the control effect of the neural network controller based on the backstepping method is not optimal. To deal with the problem, a double-layer adaptive compensator for the neural network is proposed. Furthermore, for the neural network adaptive compensation controller based on the backstepping method, the stability is also proved by a Lyapunov method. Finally, simulation results demonstrate the effectiveness of the scheme, especially when the nonlinear systems have an unknown time-varying delay.

In this paper, the state estimation problem is investigated for an array of complex networks with time-varying delays and model uncertainty. Model uncertainty is generated by uncertain connections between nodes in complex networks. Graph neural network is employed to deal with the uncertainty and the multiple linear terms of the errors between nodes are estimated. The purpose of the addressed state estimation problem is to design a state estimator such that, in the presence of the model uncertainty, the estimation error to converge to zero can be guaranteed and the explicit expression of the estimator parameters is given. By using Lyapunov function, the parameters of the estimator and the updating laws of the weights of graph neural network can be obtained by the solutions to linear matrix inequalities and the estimation error to converge to zero can be guaranteed. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the developed state estimation scheme.

针对具有状态时变时滞、系统不确定性、可建模扰动、运行噪声和执行器故障的卫星姿态控制系统,提出一种基于扰动观测器的自适应有限时间复合主动容错控制策略.针对可建模扰动设计扰动观测器,然后基于扰动估计误差设计了主动容错控制器.该时滞依赖控制器包含反馈控制项、扰动补偿项和快速自适应故障补偿项.提出的容错控制策略不仅保证闭环系统动态方程的有限时间有界性,而且保证闭环测量输出对于系统不确定性、运行噪声、执行器故障等的鲁棒性.给出控制器增益限制矩阵存在的充分条件及其线性矩阵不等式形式,进而给出仿真算例.仿真结果表明,基于扰动观测器方法,设计的自适应有限时间容错控制器能够快速估计可建模扰动,进而有效地实现系统的闭环容错控制.相较于基于非复合的自适应有限容错控制器,提出的方法对于状态变量的估计均方根误差分别降低了28.9％、4.7％和36.0％;对于可建模扰动估计的均方根误差降低了38.8％.仿真验证了所提方法的有效性.

The state estimation for nonlinear time-varying complex networks is a common problem, especially for the systems with time-varying sensor delay. This paper presents an artificial immune strategy based on particle filtering algorithm to estimate the states. Firstly, considering the nonlinear time-varying complex networks with time-varying sensor delay, this paper gives a theoretical proof that particle filter can be used for the state estimation. On this basis, this paper adopts the artificial immune algorithm to solve the problem which is particle impoverishment in particle algorithm. This paper gives the convergence of the proposed algorithm theoretically. Furthermore, for nonlinear time-varying complex networks with unknown noise distributions, this paper gives an improved algorithm which uses an improved kernel density estimation algorithm which has window width varying with probability density to estimate noise distributions. Simulation results demonstrate the effectiveness of the scheme, especially when nonlinear time-varying complex networks have time-varying sensor delay and noise distributions are unknown.

In on-orbit servicing missions, autonomous close proximity operations require knowledge of the target's pose and motion parameters. Due to the lack of prior information about the non-cooperative target in an unknown environment, the pose and motion estimation of an uncooperative target is a challenging task. In this paper, a relative position and attitude estimation method is proposed using consecutive point clouds. First, a fast plane detection method is used to extract the global features of non-cooperative targets. Compared with some other local feature-detection methods, the method mentioned in this paper is faster. Then a two-stage angle adjustment method and iterative closest point algorithm are used to register the two adjacent point clouds. Finally, an unscented Kalman filter is designed to estimate the relative pose and motion parameters (velocity and angular velocity) of the target. Experiments show that the proposed measurement method of pose and motion parameters has acceptable accuracy and good stability.

This paper is concerned with the problem of state estimation of memristor neural networks with model uncertainties. Considering the model uncertainties are composed of time-varying delays, floating parameters and unknown functions, an improved method based on long short term memory neural networks (LSTMs) is used to deal with the model uncertainties. It is proved that the improved LSTMs can approximate any nonlinear model with any error. On this basis, adaptive updating laws of the weights of improved LSTMs are proposed by using Lyapunov method. Furthermore, for the problem of state estimation of memristor neural networks, a new full-order state observer is proposed to achieve the reconstruction of states based on the measurement output of the system. The error of state estimation is proved to be asymptotically stable by using Lyapunov method and linear matrix inequalities. Finally, two numerical examples are given, and simulation results demonstrate the effectiveness of the scheme, especially when the memristor neural networks with model uncertainties.

This paper concerns the problem of adaptive compensation tracking control for a class of time-varying delay nonlinear systems with unknown structures and unknown actuator dead zones where time-varying delays are unknown. First, a variable separation approach is used to overcome the difficulty in dealing with a nonstrict-feedback structure, and multilayer neural networks are used to approximate the unknown nonlinear structures with time-varying delays. On this basis, we designed an adaptive multilayer neural-network compensation controller to reduce the error of multilayer neural networks. Furthermore, for unknown actuator dead zones, this paper separates the controller and adopts multilayer neural networks to deal with unknown actuator dead zones. In order to reduce the error of the dead-zone controller, wer designed an adaptive compensation controller for the dead zones. Lastly, this paper proves the stability of the systems with the Lyapunov method, and simulation results demonstrate the effectiveness of the scheme.

Aiming at the problem of simple and fast recognition of non-cooperative targets in 3D space, a simple recognition algorithm for point cloud targets is proposed. First, the point cloud data was divided into $n$ categories with the first K-means clustering. Second, the target class was identified with a coarse sieve, and the speed of the algorithm was improved with sparse processing. The more accurate target class was obtained with secondary clustering. The two types of point cloud data are processed by principal component analysis (PCA), which obtains the feature root matrices. Then cosine distance matching was applied to the feature root matrices and target library (trained by 12 groups of point cloud data). This type of data was retained when the similarity was greater than the upper threshold. Therefore, the center point coordinates, distances, and similarity of the target were outputted. The experimental test results of the 13th and 14th groups indicated that the target segmentation similarity of this algorithm could reach 95.75% and 96.98% respectively, and the accuracy reached 100%.

In this paper, a quasi-time-dependent (QTD) H ∞ filtering approach to discrete-time two-dimensional switched systems is presented, by applying the mode-dependent persistent dwell-time (MPDT) switching method. The Fornasini–Marchesini local state-space model is used to describe the interested system. Compared with the dwell-time and the average dwell-time switchings which are often used in the literature, a MPDT switching, which is a more general class of switching form, is studied in this paper. The objective is to design a full-order filter that ensures the filtering error system exponentially stable with a guaranteed H ∞ noise attenuation performance. By constructing a QTD switched Lyapunov-like function, the sufficient conditions for the existence of the filter are established by using the MPDT switching. The time-independent filter is actually a special case of QTD filter studied in this paper, which implies the developed results in this paper are more general with less conservativeness. Finally, two numerical examples are showed to validate the effectiveness and potential of the developed filter design method.

针对一类具有不确定性的广义系统,提出一种故障估计观测器设计方案.该观测器具有非奇异结构,不仅保证增广误差系统动态方程满足有限时间有界性条件,而且保证故障估计误差对于扰动在有限时间内具有鲁棒性.首先,视故障为系统部分状态变量,建立增广广义系统模型.之后,从有限时间分析增广误差系统的有界性,讨论故障估计误差对于扰动的有限时间鲁棒性;然后,给出观测器增益限制矩阵存在的充分条件,并给出该限制矩阵的线性矩阵不等式形式.最后,基于具有不确定性广义系统的卫星姿态控制系统,针对系统发生的执行器及传感器故障进行仿真,以验证设计方案的有效性.仿真结果表明,设计的有限时间鲁棒观测器不仅能够精确估计系统的故障值,而且能够有效估计耦合于故障的系统状态值:相较于指数稳定观测器,所提出的观测器对于状态变量的估计均方根误差降低了25％以上;对于故障估计的均方根误差降低了50％以上.仿真验证了该方法的有效性.

为解决目前智能微电网分布式云储能系统运行架设成本较高且难寻有效整体控制规律的问题,引入一种基于观测器的H∞控制器设计方法.在分段仿射Lyapunov函数的基础上,应用投影定理以及几个基本引理,寻求基于观测器且满足鲁棒H∞性能指标的反馈控制器设计方法,获得闭环控制系统的反馈控制器增益和观测器增益,并将云储能供电系统与传统电力系统进行比较.结果表明:采用该控制方法构建的电网使得新型能源的利用率更高,同时传统常规电力系统耗能有所下降.