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The effects of the tube wall on the fluid flow, permeability and streaming potential are reported for a porous transducer for use in a liquid circular angular accelerometer. Fluid flow and pressure near the surface of the transducer are modeled and validated numerically. We show that the differential pressure across the transducer is not affected by the tube wall. A capillary bundle model is employed to represent the transducer to obtain the radial porosity, permeability and streaming potential distributions due to wall effects. A simulated spherical packing is generated from measured particle parameters to specify the model by calculating the radial porosity distribution. The theoretical permeability and streaming potential coupling coefficient are located within one standard deviation from the mean of measurements. Due to the radial distribution of streaming potential, the electrode can be configured into a large streaming potential region to measure the signal more efficiently. Three electrode configurations are described based on these results, which can be applied to increase the signal of the sensor. [ABSTRACT FROM AUTHOR]

In this paper, a complete mathematical model for a half-tracked vehicle with eight degrees of freedom is established. It is difficult to control a suspension system owing to complexity of system itself and irregular excitations from road surfaces. A road excitation model is analyzed in which a relationship between the vehicle body and the gun is taken into account. Active disturbance rejection controllers are proposed in all channels of six independent subsystems by decoupling the original system. With switch extended state observers, total disturbances are eliminated for the half-tracked vehicle. Simulation results are presented to illustrate effectiveness of the proposed method in this paper. [ABSTRACT FROM PUBLISHER]

A capillary bundle model is used to evaluate the permeability and streaming potential coupling coefficient of a porous transducer, which is the key element of a fluid-based angular accelerometer. The capillary bundle is specified with structural parameters and a method to transform the particle size distribution into the capillary radius distribution. Together with a theoretical model of zeta potential and specific surface conductance, the model is applied to determine permeability and streaming potential prediction. Three types of transducers are fabricated and experiments validate the proposed model, which is an important part of the sensor model for engineering application. [ABSTRACT FROM AUTHOR]

This paper is concerned with the permeability modeling for a kind of porous transducer, which is sintered by glass microspheres. This kind of transducer is used to measure the streaming potential in the liquid-circular angular accelerometer. Kozeny-Carman (KC) equation is improved by taking into account the shape of fluid channel, the porosity of porous transducer, and the particle size distribution (PSD) of glass microspheres in the transducer. Under the assumption of star-like fluid channels, the shape factor is analyzed numerically and further evaluated. The notion of equivalent diameter of microspheres is applied to avoid the error of permeability calculation resulting from the usage of the average diameter. By implementing the experiments of transducers with 9 different PSDs, a relationship among the equivalent diameter, the tortuosity, the average diameter, the porosity and the skewness of the PSD is obtained. The comparison results illustrate that the proposed permeability model considerably reduces the large prediction errors of the previous models when dealing with the mixture of different microspheres and the large skewness of PSD. [ABSTRACT FROM AUTHOR]

The Kalman filter has been found to be useful in vast areas. However, it is well known that the successful use of the standard Kalman filter is greatly restricted by the strict requirements on a priori information of the model structure and statistics information of the process, and measurement noises. Generally speaking, the covariance matrix of process noise is harder to be determined than that of the measurement noise by routine experiments, since the statistical property of process noise cannot be obtained directly by collecting a large number of sensor data due to the intrinsic coupling of process noise and system dynamics. Considering such background of wide applications, this paper introduces one algorithm, recursive covariance estimation (RCE) algorithm, to estimate the unknown covariance matrix of noise from a sample of signals corrupted with the noise. Based on this idea, for a class of discrete-time linear-time-invariant systems where the covariance matrix of process noise is completely unknown, a new Kalman filtering algorithm named, Kalman filter with RCE, is presented to resolve this challenging problem of state estimation without the statistical information of process noise, and the rigorous stability analysis is given to show that this algorithm is optimal in the sense that the covariance matrix and state estimations are asymptotically consistent with the ideal Kalman filter when the exact covariance matrix of process noise is completely known a priori. Extensive simulation studies have also verified the theoretical results and the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

We present a novel method to localize the vehicle on an easily accessible geo-referenced satellite image based on LiDAR. We first design a neural network to extract and compare the spatial-discriminative feature maps of the satellite image patch and the LiDAR points, and obtain the probability of correspondence. Then based on the outputs of the network, a particle filter is used to obtain the probability distribution of the vehicle pose. This method can use LiDAR points and any type of odometry as input to localize the vehicle. The experimental results show that our model can generalize well on several datasets. Compared with other methods, ours is more robust in some challenging scenarios such as the occluded or shadowed area on the satellite image. [ABSTRACT FROM AUTHOR]

Integrated navigation of unmanned ground vehicles (UGV) is significant for many advanced intelligent transportation system applications. Adaptive information fusion technique based on observability analysis has a great potential to enhance UGV integrated navigation systems for the capability of high-precision positioning and navigation. In an integrated navigation system, the tolerance against unknown and time-varying observation conditions is a key factor to satisfy the specific requirements of high-precision, self-adaption, and high reliability. Thus, a novel adaptive federated Kalman filter (FKF) is proposed with time-varying information sharing factors based on the criteria for the degree of observability. In addition, an error-state cascaded integration architecture is designed for UGV integrated navigation. Simulation with real datasets gathered from road tests in urban areas showed that the new adaptive integrated navigation system can autonomously update FKF information sharing factors according to the measurement quality and the observability of each navigation error-state. Therefore, the accuracy, robustness, and fault-tolerance ability of the whole system can be effectively improved in a high dynamic environment. [ABSTRACT FROM AUTHOR]

In this paper, the modeling and tracking control problem of unmanned turret system (UTS) is addressed. To guarantee the feasibility of modeling and control, three-axis stabilization is analyzed. A complete mathematical model for UTS is then established based on robotic analysis technique. The coupling model also takes inner parameters perturbation and external disturbances into consideration. To achieve rapid response and high tracking precision, a fast nonsingular terminal sliding mode control scheme is designed compounding with extended state observer (ESO). Owing to the estimating ability of ESO, this control scheme can reduce the chattering phenomena remarkably. Finally, simulation results are presented to illustrate effectiveness of the control strategies. [ABSTRACT FROM PUBLISHER]

The attitude stabilization problem for rigid spacecraft in the presence of inertial uncertainties, external disturbances, actuator saturations, and actuator faults is addressed in this paper. First, a novel fast terminal sliding mode manifold is designed to avoid the singularity problem while providing high control ability. In addition, fast terminal sliding mode control laws are proposed to make the spacecraft system trajectory fast converge onto the fast terminal sliding mode surface and finally evolve into small region in finite time, which cannot be achieved by the previous literatures. Based on the real sliding mode context, a practical adaptive fast terminal sliding mode control law is presented to guarantee attitude stabilization in finite time. Also, simulation results are presented to illustrate the effectiveness of the control strategies. [ABSTRACT FROM AUTHOR]

The digital filtering technology has been widely applied in a majority of signal processing applications. For the linear systems with state‐space model, Kalman filter provides optimal state estimates in the sense of minimum‐mean‐squared errors and maximum‐likelihood estimation. However, only with accurate system parameters and noise statistical properties, the estimation obtained by standard Kalman filter is the optimal state estimate. Most of time, the exact noise statistical properties could not be obtained as a priori information or even wrong statistical properties may be captured by the offline method. This may lead to a poor performance (even divergence) of Kalman filtering algorithm. In this study, a novel real‐time filter, named as fast minimum norm filtering algorithm, has been proposed to deal with the case when the covariance matrices of the process and measurement noises were unknown in the linear time‐invariant systems with state‐space model. Tests have been performed on numerical examples to illustrate that the fast minimum norm filtering algorithm could be used to obtain acceptable precision state estimation in comparison with the standard Kalman filter for the discrete‐time linear time‐invariant systems. [ABSTRACT FROM AUTHOR]

Two finite‐time control algorithms are developed for distributed cooperative attitude synchronisation of multiple spacecraft with a dynamic virtual leader. Each spacecraft is modelled as a rigid body incorporating with model uncertainty and unknown external disturbance. The virtual leader gives commands to some of the follower spacecraft, and the communication network between followers can be an undirected or a directed graph. By using two neighbourhood synchronisation error signals, a finite‐time control algorithm is designed associated with adaptive mechanism such that all follower spacecraft synchronise to the virtual leader in finite time. Then a novel estimator‐based finite‐time distributed cooperative control algorithm is developed by using the followers' estimates of the virtual leader, and the convergence of the attitude and angular velocity errors can be guaranteed in finite time. Moreover, both of the control strategies are chattering‐free for their continuous design. Simulation examples are illustrated to demonstrate the validity of the two algorithms. [ABSTRACT FROM AUTHOR]

Regulator stations are widely used in gas transmission and distribution systems. Their state monitoring is of great significance to the safe operation of gas pipe networks. Due to the complexity of the working environment and the limitation of sensors, the acquired information is uncertain, which makes the state monitoring result prone to errors. The evidence theory has the ability to solve the uncertainty problem effectively. Most of the improvement methods of the evidence theory are in the spatial domain. These methods are not applicable to the fusion of the time-domain information. In this article, an improved method of the evidence theory is proposed for the state monitoring of a gas regulator station. It can meet the requirement of the dynamic fusion of the time-domain information. First, the back-propagation neural network is used to judge whether the evidence conflicts with each other. The simulation results demonstrate that it can judge the conflicts well. On this basis, the relative conflict factor is proposed to modify the evidence, and the calculation method of the adaptive time attenuation factor is proposed to reduce the accumulated error. The dynamic fusion of the time-domain information is realized by combining the time attenuation factor and the relative conflict factor. Finally, the proposed method is applied to the state monitoring of the gas regulator station. The feasibility and effectiveness of the method are verified by experiments. It verifies that the variation of the support degree of the proposed method for the correct proposition is 0.1478 higher than that of the temporal evidence combination based on relative reliability factor when the evidence is strongly conflicting. [ABSTRACT FROM AUTHOR]

Summary: To address the difficulties of under‐actuation, nonlinearity, strong coupling, disturbances and measurement noise in four‐wheel steering (4WS) vehicle tracking control, this paper presents an innovative control strategy based on differential flatness and linear active disturbance rejection controller (LADRC). The flatness property of 4WS vehicle is derived from a simplified dynamic model that ignores some complicated dynamics and external disturbances. The impact of these neglected components on the system is treated as part of the total‐disturbance, which is estimated by the observer and compensated by LADRC. Three flatness‐based control schemes, including feedback linearization controller with PID corrective term, traditional LADRC and LADRC with robust generalized proportional integral observer (RGPIO), are designed and investigated in this paper. Moreover, an overtaking scenario is selected as an application example for the tracking controllers, and the corresponding trajectory planner is designed. The tracking performances of these schemes for the overtaking trajectory are simulated on a three‐degree‐of‐freedom dynamic model and a more complete dynamic model of the 4WS vehicle, respectively. Results show the effectiveness of these schemes, especially the excellent tracking performance of LADRC with RGPIO in the presence of strong external disturbances and measurement noise. [ABSTRACT FROM AUTHOR]

Many practical non‐linear systems can be described by non‐linear auto‐regressive moving average (NARMA) system models, whose stabilisation problem is challenging in the presence of large parametric uncertainties and non‐parametric uncertainties. In this work, to address this challenging problem for a wide class of discrete‐time NARMA systems, in which there are uncertain periodic parameters as well as uncertain non‐linear part with unknown periodic time delays, we develop adaptive predictive control laws using the key ideas of 'future outputs prediction' and 'nearest‐neighbour compensation', among which the former is carried out to overcome the non‐causalness problem and the latter novel idea is proposed to completely compensate for the effect of non‐linear uncertainties as well as unknown time delays. To achieve the desired asymptotic tracking performance in the presence of semi‐parametric uncertainties with time delays, an 'n ‐step parameter update law' is first designed, based on which an 'one‐step update law' is then elaborately constructed to obtain smoother closed‐loop signals. This study in general develops a systematic adaptive control framework for periodic NARMA systems with guaranteed boundedness stability and asymptotic tracking performance, which are established by rigorous theoretic proof and verified by simulation studies. [ABSTRACT FROM AUTHOR]

The problem of attitude stabilization for a spacecraft system which is nonlinear in dynamics with inertia uncertainty and external disturbance is investigated in this paper. An adaptive law is applied to estimate the disturbances, where a sliding mode controller is designed to force the state variables of the closed-loop system to converge to the origin. Then, the spacecraft system subjected to control constraints is further considered, and another adaptive sliding mode control law is designed to achieve the attitude stabilization. No prior knowledge of inertia moment is required for both of the proposed adaptive control laws, which implies that the designed control schemes can be applied in spacecraft systems with a large parametric uncertainty existing in inertial matrix or even in unknown inertial matrix. Also, simulation results are presented to illustrate the effectiveness of the control strategies. [ABSTRACT FROM AUTHOR]

This paper deals with a sliding mode control for linear systems with input and state time-varying delays. Two different time delays are considered for the sliding mode control of a continuous linear system. Delay-dependent sufficient conditions are given for the existence of sliding manifolds in terms of linear matrix inequalities. A reaching motion controller is also proposed for such systems by means of both reaching law and inequality approach. An inverted pendulum system is utilized to illustrate the effectiveness of the developed techniques. [ABSTRACT FROM AUTHOR]

The problem of attitude control for a spacecraft model that is nonlinear in dynamics with inertia uncertainty and external disturbance has been investigated. Adaptive law and extended state observer are applied to estimate the disturbance, by which sliding-mode controllers are designed to combine the two approaches in order to force the state variables of the closed-loop system to converge to the reference attitude states. Also, simulation results are presented to illustrate the effectiveness of the control strategies. [ABSTRACT FROM AUTHOR]

Gravity-matching algorithm is a key to the gravity-aided inertial navigation system (INS). The traditional particle filter-based matching algorithm with a gravity sample vector is efficient. However, the range of particle filter and the probability model of the actual location are not specified in the algorithm. An improved particle filter-based matching algorithm with a gravity sample vector is proposed. Because of the high short-term accuracy of INS, the error range of INS in a short period is analyzed in a polar coordinate system in this algorithm. First, the attitude error angle model of INS is established. Relative angle error is proposed to calculate latitudes and longitudes of particles in the fan area at any position. Then a particle filter embedded in a particle filter is proposed to calculate the error range of the real position and establish the probability model of this position. Finally, in order to reduce the matching error, the relative displacements of the positions of the particles and the upper matching positions are added to the weights of the particles. Simulation results show that the proposed method has higher accuracy and better robustness. [ABSTRACT FROM AUTHOR]

The matching area selection algorithm is one of the key technologies for underwater gravity‐aided inertial navigation system, which directly affects the positioning accuracy and matching rate of underwater navigation. The traditional matching area selection algorithms usually use the statistical characteristic parameters of gravity field. However, the traditional algorithms are difficult to reflect the spatial relation characteristic of gravity field, which always miss some latent matching areas with obvious change of gravity field. In order to solve this problem, the matching area selection algorithm based on co‐occurrence matrix is proposed. The proposed algorithm establishes gravity anomaly co‐occurrence matrix and extracts spatial relation characteristic parameters to reflect the gravity field. The comprehensive spatial characteristic parameter is built by entropy and is used to select the matching area by maximization of inter‐class variance. The experimental results show that the proposed algorithm can select more effective matching areas than the traditional algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a traversability assessment method and a trajectory planning method. They are key features for the navigation of an unmanned ground vehicle (UGV) in a non-planar environment. In this work, a 3D light detection and ranging (LiDAR) sensor is used to obtain the geometric information about a rough terrain surface. For a given SE(2) pose of the vehicle and a specific vehicle model, the SE(3) pose of the vehicle is estimated based on LiDAR points, and then a traversability is computed. The traversability tells the vehicle the effects of its interaction with the rough terrain. Note that the traversability is computed on demand during trajectory planning, so there is not any explicit terrain discretization. The proposed trajectory planner finds an initial path through the non-holonomic A*, which is a modified form of the conventional A* planner. A path is a sequence of poses without timestamps. Then, the initial path is optimized in terms of the traversability, using the method of Lagrange multipliers. The optimization accounts for the model of the vehicle's suspension system. Therefore, the optimized trajectory is dynamically feasible, and the trajectory tracking error is small. The proposed methods were tested in both the simulation and the real-world experiments. The simulation experiments were conducted in a simulator called Gazebo, which uses a physics engine to compute the vehicle motion. The experiments were conducted in various non-planar experiments. The results indicate that the proposed methods could accurately estimate the SE(3) pose of the vehicle. Besides, the trajectory cost of the proposed planner was lower than the trajectory costs of other state-of-the-art trajectory planners. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL) has been successfully applied to end-to-end autonomous driving, especially in simulation environments. However, common DRL approaches used in complex autonomous driving scenarios sometimes are unstable or difficult to converge. This paper proposes two approaches to improve the stability of the policy model training with as few manual data as possible. For the first approach, reinforcement learning is combined with imitation learning to train a feature network with a small amount of manual data for parameters initialization. For the second approach, an auxiliary network is added to the reinforcement learning framework, which can leverage the real-time measurement information to deepen the understanding of environment, without any guide of demonstrators. To verify the effectiveness of these two approaches, simulations in image information-based and lidar information-based end-to-end autonomous driving systems are conducted, respectively. These approaches are not only tested in the virtual game world, but also applied in Gazebo, in which we build a 3D world based on the real vehicle model of Ranger XP900 platform, the real 3D obstacle model, and the real motion constraints with inertial characteristics, so as to ensure that the trained end-to-end autonomous driving model is more suitable for the real world. Experimental results show that the performance is increased by over 45% in the virtual game world, and can converge quickly and stably in Gazebo in which previous methods can hardly converge. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we propose a simultaneous state estimation and fault estimation approach for a class of first‐order hyperbolic partial integral differential equation systems. Specifically, we consider the multiplicative boundary actuator and sensor faults, ie, unknown fault parameters multiplying by the boundary input or boundary state (ie, output). As a consequence, two difficulties arise immediately: (1) simultaneous estimation of both plant state and faults is a nonlinear problem due to the multiplication between fault parameters and plant signals; (2) no prior information is available to determine the type (actuator or sensor) of faults. To overcome these difficulties, this paper develops adaptive fault parameter update laws and embeds the resulting laws into the plant state observer design. First, we propose new approaches to estimate actuator fault and sensor fault, respectively. Next, we develop a novel method to simultaneously estimate actuator and sensor faults. The proposed observer and update laws, designed using only one boundary measurement, ensure both state estimation and fault parameter estimation. By choosing appropriate Lyapunov functions, we prove that the estimates of state and fault parameters converge to an arbitrarily small neighborhood of their true values. Numerical simulations are used to demonstrate the effectiveness of the proposed estimation approaches. [ABSTRACT FROM AUTHOR]

The gravity matching algorithm is a key of the gravity-aided inertial navigation system (INS). The traditional matching algorithm connects only the measured gravity anomaly data to the position of the carrier according to a certain method to correct the inertial error. The gravitational field characteristic parameters should also be considered in the matching algorithm to improve the matching accuracy and reduce the number of mismatching. Therefore, based on the vector matching algorithm, a characteristic parameter matching algorithm is proposed. This paper presents new methods to calculate the characteristic parameters and the range of particle filters to increase the accuracy of matching. The gravity anomaly value of each particle is calculated more accurately by the proposed method of continuous gravity anomaly. Due to the high short-time accuracy of inertial navigation, the final trajectory is obtained by rigid transformation of a series of positions indicated by the INS corresponding to the trajectory. Simulation results prove that when compared with the vector matching algorithm, the proposed method makes the matching results more accurate and more reliable. [ABSTRACT FROM AUTHOR]

In this paper, the problem of optimal state estimation is studied for fusion of asynchronous multirate multiscale sensors with unreliable measurements and correlated noise. The noise of different sensors is cross-correlated and coupled with the system noise of the previous step and the same time step. The system is described at the highest sampling rate with different sensors observing a single target independently with multiple sampling rates. An optimal state estimation algorithm based on iterative estimation of the white noise estimator is presented, which makes full use of the observation information effectively, overcomes the packet loss, data fault, unreliable factors, and improves the precision and the robustness of the system state estimation. A numerical example is used to illustrate the effectiveness of the presented algorithm. [ABSTRACT FROM PUBLISHER]

This paper proposes a stable adaptive fuzzy fault-tolerant attitude-tracking controller for a rigid spacecraft in the presence of unavailable velocities, external disturbance, actuator faults, and actuator saturation. Fuzzy logic systems are applied to approximate the unknown nonlinear function vector, and a fuzzy adaptive observer is designed to estimate the unmeasured velocity of the rigid body. By using the backstepping technique, a novel adaptive fuzzy attitude-tracking fault-tolerant control scheme is developed. It has been testified that this control approach guarantees that all signals of the rigid spacecraft are bounded and the tracking error between the system output and the reference signal converges to a small neighborhood of zero. Simulation examples with constant faults and time-variant faults are provided to show the fault-tolerant effectiveness of the control method. [ABSTRACT FROM PUBLISHER]

This article presents an efficient scene matching method robust to noise and occlusion. The method combines a coarse matching method with a fine matching method by iterative correction. Both coarse matching method and fine matching method, inspired by sparse representation for face recognition, are resistant to noise and occlusion inherently. In each step of iterative matching, the result of coarse matching is introduced into fine matching as prior knowledge, which gives a rough range about the possible positions. Then, the fine matching finds the most reasonable result based on the rough range given by coarse matching. Finally, the result of fine matching is brought back to coarse matching as post knowledge to correct it. Experiments demonstrate that the robustness to noise and occlusion is improved compared with the matching methods without iterative correction. [ABSTRACT FROM AUTHOR]

Particle filters have been proven to be very effective for nonlinear/non-Gaussian systems. However, the great disadvantage of a particle filter is its particle degeneracy and sample impoverishment. An improved particle filter based on Pearson correlation coefficient (PPC) is proposed to reduce the disadvantage. The PPC is adopted to determine whether the particles are close to the true states. By resampling the particles in the prediction step, the new PF performs better than generic PF. Finally, some simulations are carried out to illustrate the effectiveness of the proposed filter. [ABSTRACT FROM AUTHOR]

Out-of-sequence measurements (OOSMs) can create difficulties for discrete time filtering. A number of procedures have been developed for extending the Kalman filter to handle these different types of delay. A common characteristic of these methods is the policy of always incorporating, or fusing delayed measurements at the time they finally become available. In this paper, we proposed a selective threshold (ST) to determine the value of the OOSMs, and based on a proposed information filter (ST-IF) to update the estimate with the useful OOSMs. By the simulation, we show that selective fusion can reduce computational costs while maintaining a good performance. [ABSTRACT FROM AUTHOR]

This article is concerned with state estimation and data fusion of a linear dynamic system observed by multirate sensors in the environment of wireless sensor networks. The sampling, estimation, and transmission rates in the wireless sensor networks are different. We derive the optimal linear estimation in the centralized, sequential, and distributed forms. A numerical example is given to show the feasibility and effectiveness of the proposed algorithms. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper investigates the control problem of finite-time attitude synchronization and tracking for a group of rigid spacecraft in the presence of environmental disturbances. A new fast terminal sliding manifold is developed for multiple spacecraft formation flying under the undirected graph topology. On the basis of the finite-time control and adaptive control strategies, two novel decentralized finite-time control laws are proposed to force the spacecraft attitude error dynamics to converge to small regions in finite time, and adaptive control is applied to reject the disturbance. The finite-time convergence and stability of the closed-loop system can be guaranteed by Lyapunov theory. Simulation examples are provided to illustrate the feasibility of the control algorithm. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper is concerned with state estimation of a linear system by fusion of asynchronous, multirate, multisensor data. The system is described at the finest scale with multiple sensors observing a common thing independently at different scales with different sampling rates. Unreliable measurements are available. The main results are: the multiscale system is properly formulated, a credibility measure is constructed and used to determine whether the measurement is normal or faulty, and an optimal recursive fusion algorithm is presented. The effectiveness and stability of the algorithm under unreliable measurements are analyzed. A numerical example is given to show the feasibility and effectiveness of the presented algorithm. [ABSTRACT FROM PUBLISHER]

Navigation accuracy of an inertial navigation system can be significantly enhanced by rotating inertial measurement unit with gimbals. Therefore, nonorthogonal angles of gimbals, which are coupled into the navigation error during rotation, should be calibrated and compensated effectively. In this paper, the relationship model of nonorthogonal angles and navigation error is established. Then, the calibration scheme and observation equation during gimbals rotation is proposed. Proved by a piecewise constant system method, all of the error parameters are observable and can be estimated by an extended Kalman filter. Experimental results show that compared with the traditional method, the proposed method can substantially reduce velocity error on static base. Moreover, the position accuracy of long-term navigation under moving base is also significantly increased. [ABSTRACT FROM PUBLISHER]

In order to compensate errors of inertial measurement unit which is the core of rotational inertial navigation system, self-calibration is utilized as an effective way to reduce navigation error. Error model of navigation solution and initial alignment is used to establish the relationship between navigation errors and inertial measurement unit (IMU) errors. A second order damper is added to the vertical velocity channel to suppress the divergence and then the vertical velocity error can be regarded as an effective observation to estimate the error parameters. Since the accuracy of the self-calibration method is susceptible to the positioning error of gimbals, total least squares (TLS) method is utilized in identification of the error parameters. Experimental results show that all of the twenty-one error parameters can be estimated with the proposed rotation scheme. Compared to least squares (LS) method, TLS method can improve the position accuracy of 8 h by 46.2%. [ABSTRACT FROM AUTHOR]

Dynamic deformation of a vehicle and relative position of inertial navigation systems will cause large errors during information sharing. Such errors should be estimated and compensated effectively, or it will cause large errors to navigation information output. Therefore, the model between deformation angle and dynamic lever arm is established to verify that the influence is not coaxial but decussate. Then, estimation and compensation methods for deformation angle and dynamic lever arm are proposed with real-time closed-loop correction of the lever-arm length. Simulation results demonstrate that, for the estimation of misalignment angle, the proposed method has a faster convergence than the traditional method. Moreover, the deformation angle and dynamic lever-arm estimation also takes less time with a higher accuracy than the traditional method. [ABSTRACT FROM PUBLISHER]

Abstract: This paper is concerned with the optimal state estimation for linear systems when the noises of different sensors are cross-correlated and also coupled with the system noise of the previous step. We derive the optimal linear estimation in a sequential form and for distributed fusion. They are both compared with the optimal batch fusion, suboptimal batch fusion, suboptimal sequential fusion, and the suboptimal distributed fusion where the cross-correlation between the noises are neglected. The comparison is in terms of theoretical filter mean square error and the real root mean square error. Simulation on a target tracking example is given to show the effectiveness of the presented algorithms. [Copyright &y& Elsevier]

This article is concerned with the state estimation problem for a kind of non-uniform sampling dynamic system. The system is described at a certain sampling rate in discrete form. A sensor observes the target successively, while the samples are obtained non-uniformly and the sampling points in each sampling periods may be different. To generate the optimal state estimate, state prediction and innovation are carried out step-by-step similar to Kalman filter (KF), but before innovation in each sampling period, measurements should be properly disposed and augmented. It is shown that our main results improve and extend the existing KF for which the samples are obtained multirate non-uniformly. Measurements randomly missing with Bernoulli distribution are also considered in this article. Finally, the feasibility and efficiency of the presented algorithm is illustrated by a numerical simulation example. [ABSTRACT FROM AUTHOR]

SUMMARY This paper is devoted to the attitude control of a quaternion missile model, which is nonlinear in aerodynamics with atmospheric moment uncertainties, inertia uncertainties, bounded disturbances and actuator failures. By employing the back-stepping technique, the corresponding sliding mode controller is designed to guarantee the state variables of the closed loop system to converge to a small region of the reference states with the help of the adaptive law by estimating the total uncertainties, and be chatter-free. Also, simulation results are presented to illustrate the effectiveness of the control strategy. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• Construct projectiles fast in-flight alignment model with K. • Define noise evaluation index to judge the abnormal degree of measurement noise. • Propose two sets of online adjustment measurement variance matrix schemes. Initial in-flight alignment is the basis of accurate navigation for projectiles strap-down inertial navigation system (SINS). Due to complex and highly dynamic flight environment of projectiles, inertial sensors and GNSS are susceptible to interference, which causes measurement noise appear as non-Gaussian noise, resulting in low alignment accuracy and long alignment time. Thus, this paper proposes statistical confidence domain data driven based fast in-flight alignment method. Firstly, the K matrix is used as state variables to construct projectiles initial in-flight alignment filter model. The noise evaluation indexes are defined according to measurement information and estimation results to judge abnormal degree of measurement noise. Based on this, we propose an adaptive robust matrix Kalman filter (ARMKF) method. The measurement variance matrix formulas are derived based on additive noise, which provides theoretical support for parameter selection in practical applications. Simulation and test results show that alignment accuracy and alignment time of the proposed method are better than traditional methods. [ABSTRACT FROM AUTHOR]

SUMMARY This paper considers the containment control problems for both continuous-time and discrete-time multi-agent systems with general linear dynamics under directed communication topologies. Distributed dynamic containment controllers based on the relative outputs of neighboring agents are constructed for both continuous-time and discrete-time cases, under which the states of the followers will asymptotically converge to the convex hull formed by those of the leaders if, for each follower, there exists at least one leader that has a directed path to that follower. Sufficient conditions on the existence of these dynamic controllers are given. Static containment controllers relying on the relative states of neighboring agents are also discussed as special cases. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned with the design and stability analysis of networked control systems with quantization effect and noisy measurement. Quantization strategy used here is a traditional uniform one, and Kalman filter is applied to obtain the estimated states. Under some assumptions and conditions, a sufficient condition is given for the closed-loop system to be stable based on Lyapunov theory and invariable set theory. Two examples are given to show the effectiveness of the main results. [Copyright &y& Elsevier]

SUMMARY This paper is concerned with the state estimation for a kind of nonlinear multirate multisensor asynchronous sampling dynamic system. There are N sensors observing a single target independently at multiple sampling rates, and the dynamic system is formulated at the highest sampling rate. Observations are obtained asynchronously, and each sensor may lose data randomly at a certain probability. The fused state estimate is generated using multiscale system theory and the modified sigma point Kalman filter. It is shown that our main results improve and extend the existing sigma point Kalman filter for which the samples are obtained multirate nonuniformly. Measurements randomly missing with Bernoulli distribution could also be allowed in this paper. Finally, the feasibility and efficiency of the presented algorithm is illustrated by a numerical simulation example.Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This article considers a centralised data fusion system, in which the measurements of the local sensors are time-stamped, and then transmitted through the network to the fusion centre. The system will suffer measurements delay or loss due to the unreliability of the network. Based on the Kalman filter and information filter in a single channel, a centralised data fusion algorithm with buffers is proposed to solve this problem. A probabilistic metric to evaluate the performance of the system is presented. Simulation results show the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper investigates the problem of stability for a class of linear uncertain Markovian jump systems over networks via the delta operator approach. The sensor-to-controller random network-induced delay and arbitrary packet losses are considered for mode-dependent time delays. That is, a Markov process is used to model the time-varying delays which are dependent on the system mode. Based on the Lyapunov-Krasovskii functional in the delta domain, a new sufficient condition for the solvability of the stability problem is presented in terms of linear matrix inequalities. A numerical example is given to illustrate the effectiveness of the techniques developed. [ABSTRACT FROM AUTHOR]