This paper investigates the preset trajectory tracking control problem of vehicular platoon systems (VPS) with state constraints and preassigned performance requirements using reinforcement learning (RL). Specifically, both velocity and acceleration constraints are simultaneously considered along with performance requirements, thus providing a more comprehensive guarantee of system safety and stability. Existing approaches based on barrier Lyapunov functions (BLF) can tackle these issues but are prone to nonlinear growth and singularity problems, which complicates controller design. To overcome these limitations, a novel two-step state transformation strategy is proposed. First, a nonlinear mapping function (NMF) is employed to reconstruct the states, embedding velocity and acceleration constraints directly into the transformed state space. Second, a preset-trajectory-based preassigned performance control (PPC) strategy is adopted to convert the performance requirements into a tracking task. This strategy effectively mitigates nonlinear growth and singularity issues, simplifying controller design and stability analysis. On this basis, a simplified RL algorithm based on neural networks (NNs) within the actor–critic framework is integrated with the backstepping method, to enhance the overall control performance. The proposed method guarantees internal stability and string stability of the platoon. Simulation results validate the effectiveness and advantages of the proposed method.

This article focuses on the formation reconfiguration mechanism for an aerial cable towed system (ACTS), which is composed of three quadrotors manipulating a point-mass payload. The system dynamics are analyzed in depth, which yields a refined decoupled model under actuator faults by utilizing the variation linearization technique and coordinate transformations. Based on this model, a fixed-time sliding mode observer is applied to estimate the degree of thrust degradation. The estimates are used for analyzing and optimizing the capacity margin under thrust loss fault, which reconfigures the desired formation. Leader-follower controllers are then designed to track the planned trajectory with the desired formation. Finally, simulation results demonstrate the effectiveness of the proposed method.

This article investigates the state estimation and security control problem for discrete-time dual-rate cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. The asynchrony predicament between different signals of dual-rate CPSs, exacerbated by the impact of cyber attacks on the sensor-to-controller channel, substantially increases the complexity of state estimation and control processes. Based on the signal-to-interference-plus-noise ratio and two-channel probability descriptions, an improved sample-and-hold (SAH) estimator is applied to dual-rate CPSs, ensuring favorable state estimates while enduring low-frequency sampling and DoS attacks. Furthermore, to solve the performance degradation problem posed by the SAH algorithm, an alternating-sampling-prediction (ASP)-based estimation method is proposed. At each fast-update moment, the predictor generates virtual outputs. The estimator can reconstruct complete state information by alternately using incomplete sampling data and iterative predictive information. Compared with the SAH method, the proposed ASP-based approach significantly enhances the control performance of dual-rate CPSs. Building on two valid estimation methods, the corresponding security control inputs are designed, guaranteeing both ideal control performance and resilience against attacks. Using convex optimization analysis, both estimator and controller gains are calculated to realize the stochastic stability of closed-loop dual-rate CPSs. Finally, the effectiveness and intercomparisons of the two estimation methods are shown by simulating a satellite yaw-angle control system and a quadrotor landing control experiment.

Tracking accuracy is critical for practical applications. However, due to the presence of various disturbances and intermittent information caused by packet losses or network attacks, ensuring tracking precision and speed becomes extremely challenging. Consequently, this article proposes a novel control architecture for output tracking with intermittent information, which includes an intermittent information-based disturbance observer (IIBDO) and a flexible antidisturbance control strategy based on conditional disturbance utilization (CDU). The novel IIBDO addresses the issue of information intermittency by compensating for lost information using available signals and their trends. Building on this, a disturbance diagnosis condition (DDC) is introduced to assess whether disturbances are beneficial to system performance. Through integration of DDC and disturbance estimation, the CDU-based flexible antidisturbance control is designed, enabling the system to utilize disturbances rather than merely rejecting them. Sufficient conditions are derived to ensure both tracking and antidisturbance performance, and the potential stabilizing effects of disturbances on the system are also analyzed using Lyapunov stability theory. Finally, comprehensive simulations and experiments confirm the effectiveness of the IIBDO, and the improvement in tracking performance brought about by CDU is also verified.

High-precision and safety control in face of disturbances and uncertainties is a challenging issue of both theoretical and practical importance. In this article, new adaptive anti-disturbance control schemes are proposed for a class of uncertain nonlinear systems with composite disturbances, including additive disturbances, multiplicative actuator faults, and implicit disturbances deeply coupled with system states. Both the cases with known and unknown control/fault directions are investigated. By properly fusing the techniques of disturbance observers and adaptive compensation, it is shown that all closed-loop signals are globally uniformly bounded and the tracking error converges to zero asymptotically, no matter the control/fault directions are known or not. In the case of known directions, the proposed control scheme, for the first time, guarantees asymptotic tracking andL$_{\infty}$tracking performance simultaneously in face of disturbances and actuator faults. Moreover, novel Nussbaum functions and a contradiction argument are introduced, which allow the system to have multiple unknown nonidentical control directions and unknown time-varying fault direction. Simulation results illustrate the effectiveness of the proposed control schemes.

Underwater autonomous navigation has long been a challenging problem due to the scarcity of information sources. The polarization navigation offers a feasible solution to this problem. The existing polarization navigation schemes, however, require that the horizontal attitude is known, which can only be used for 2-D orientation. To address the limitation, a 3-D attitude determination strategy is developed in this article by exploiting the underwater downwelling radiance fields (light intensity and polarization). In particular, the horizontal attitude information contained in the Snell's window, a unique underwater optical phenomenon induced by refraction, is extracted via an improved edge recognition method. On this basis, underwater polarization is exploited to calculate solar position for orientation. By this means, the 3-D attitude is acquired independently using underwater downwelling radiance fields. The effectiveness of the proposed strategy is validated via experiments in both the water tank and open sea environments.

The safety and high-precision attitude control of launch vehicles are threatened by degradation fault and multiple disturbances (such as model uncertainty, uncertain inertia, and external disturbance) during the reentry stage. To address these challenges, an adaptive sliding mode observer (ASMO)-based composite control scheme is proposed in this article for launch vehicles to achieve simultaneous compensation and suppression of the degradation fault and multiple disturbances. Since the mismatched model uncertainty that coupled with the system state exhibits strong uncertainty, an ASMO is designed to estimate it by adaptively learning the upper bound of the derivative of the mismatched model uncertainty. In order to attenuate the effect of the degradation fault, uncertain inertia, and external disturbance, three adaptive laws are accordingly designed to identify them online. By combining the ASMO and the designed adaptive laws, a composite controller is constructed, and the degradation fault and multiple disturbances are simultaneously compensated and suppressed. The coordinated optimization performance and refinement of antidisturbance control and fault-tolerant control are effectively enhanced. Moreover, by introducing a prescribed performance function, the attitude tracking error response is constrained within a predefined range. Simulation and experiments validate the effectiveness of the proposed scheme.

Polarization navigation is an emerging autonomous navigation technology suitable for unmanned underwater vehicles (UUVs). Nonetheless, light refraction poses challenges for underwater polarization navigation as it alters the direction and amplitude of the polarization electric vector (E-vector). In this article, we propose a novel underwater heading determination method with inertial navigation system/polarization sensor integration in consideration of light refraction. In view of light deflection and energy attenuation under refraction, the direction correction matrix and amplitude compensation factor are established for the E-vector. On this basis, the refracted E-vector is introduced into the heading measurement model, which can reduce modeling errors induced by refraction, thereby producing a better prediction of heading information. In addition, a dual-filter algorithm is constructed to handle fusion models with different characteristics, improving the reliability and efficiency of heading estimation. Numerical simulation is conducted to confirm the feasibility of the proposed method, while ocean navigation experiments on UUV are carried out to evaluate its accuracy.

Solar-tracking is one of the key issues in underwater polarization navigation. Most existing studies are based on the Angle of Underwater Polarization (AoUP). As water depth increases, however, the AoUP is disturbed by multiple scattering, while the Degree of Underwater Polarization (DoUP) maintains a stable relative relationship. To address the less robustness of polarization navigation, a solar-tracking and integrated orientation method based on DoUP is proposed. In consideration of the refraction, the relationship between the solar position and DoUP is established based on the maximum Degree of Polarization. Moreover, an integrated navigation model is developed aided by the solar position. Results from the static experiment in water tank and dynamic sea trials demonstrate that the proposed method exhibits improved accuracy and robustness compared to the AoUP method. This article presents a potential approach to improve the adaptability of underwater autonomous orientation.

Depth estimation problem for the perspective vision systems has been extensively studied in the literature and the depth estimation can be achieved under the assumption that the exact camera motion parameters are available. However, in practice, it is hard to obtain the camera motion parameters exactly. For instance, the exact camera velocity is always unavailable and instead only the roughly estimated one can be obtained. This introduces the significant difficulties to achieve the depth estimation with partially uncertain camera motion parameters. In this article, we consider the depth estimation problem for the perspective vision system in the case that the camera angular velocities are accurate, while partial camera linear velocities are contaminated by some disturbances. This problem is theoretically formulated and solved for the first time in this article by proposing a new depth observer in the presence of the partially uncertain camera linear velocities. Local exponential convergence of the depth and disturbance estimates is achieved in presence of the partially uncertain camera linear velocities such that the estimation errors of states and disturbances converge to zero for constant disturbances or to a small error bound for bound time-varying disturbances. Simulations and experiments are carried out to verify the performance of the proposed observer.

We propose a reliable state feedback control method for uncertain linear systems with a terminal reliability constraint. By modeling the uncertainties from system parameters and disturbances with multiple probabilistic intervals whose probabilities sum to 1, multiple corresponding probabilistic intervals of control responses are approximately estimated through the system dynamics as the uncertainty propagation bridge. This not only mitigates the need for large sample data required by probabilistic methods for robust control, but also avoids the large error caused by the first-order Taylor expansion required by nonprobabilistic methods in the case of large radiuses of uncertain parameters. Considering the different characteristics of transient and steady-state phases, a prescribed performance threshold function is established for the concerned control response. Based on probabilistic intervals and threshold, the failure rate model and reliability model for the concerned control response are formulated. Further, an optimization problem for the reliable state feedback control with the terminal reliability constraint is formulated and solved by the simulated annealing algorithm. An illustrative example is presented to demonstrate the feasibility and effectiveness of the reliable state feedback control.

The proportional-integral (PI) controller has proven to be a remarkably effective control strategy in the servo field. However, it is difficult for PI to handle the problem of multiple disturbances degrading the velocity-tracking capability of the gimbal system in the control moment gyro (CMG). In this article, we propose a solution to this problem while preserving the advantages of PI from an engineering standpoint. Starting from the refined disturbance analysis based on a 5 Nms CMG gimbal system experimental tests, multiple disturbances of the gimbal system can be classified according to frequency distribution in terms of 0$\sim $5 and 128 Hz. Subsequently, under the composite hierarchical anti-disturbance control frame, a peak filter and a disturbance observer addition improve the anti-disturbance capability of the PI-based gimbal system at the frequencies of 0$\sim $5 and 128 Hz. Notably, the proposed method has a relatively low order, which is appropriate for the processing power limit. Finally, the experimental results reveal that compared to three effective control schemes (PI, extended state observer-based composite controller, and PI-resonant controller), the proposed method can strikingly enhance the anti-disturbance ability and velocity-tracking performance of the gimbal system.

This article is concerned with a quasiperiodic disturbance estimation problem for dynamic control systems without prior knowledge on frequency. As a major challenge of our work, the quasiperiodic disturbance to be treated is always submerged by untargeted waves, leading to complicated coupling between disturbance separation and frequency identification. Existing approaches on quasiperiodic disturbance rejection have circumvented, rather than overcome, this challenge by assuming either a known frequency or a measurable disturbance signal. In this work, an expectation-maximization (EM) framework is proposed where disturbance signal separation and frequency identification are carried out in an iterative manner. In the E-step, the expected log-likelihood function is evaluated via reconstruction of the quasiperiodic signal based on the latest frequency estimate; and in the M-step, the frequency estimate is updated by maximizing the log-likelihood function obtained in the E-step. To facilitate recursive frequency estimation, an online EM algorithm is also developed based on the forward-only smoothing techniques. Furthermore, we show that the proposed method can be easily extended to deal with nonlinear system models and time-varying frequencies.

How to use multiple optical satellite images to recover the 3D scene structure is a challenging and important problem in the remote sensing field. Most existing methods in literature have been explored based on the classical RPC (Rational Polynomial Coefficients) camera model which requires at least 39 GCPs (ground control points), however, it is non-trivial to obtain such a large number of GCPs in many real scenes. Addressing this problem, we propose a hierarchical reconstruction framework based on multiple optical satellite images, which needs only 4 GCPs to fully-automated reconstruct the 3D scene structure. The proposed framework is independent from the RPC model and composed of a dense affine reconstruction stage and a followed affine-to-Euclidean upgrading stage: At the dense affine reconstruction stage, a dense affine reconstruction approach is explored for pursuing the 3D affine scene structure without any GCP from input satellite images. Then at the affine-to-Euclidean upgrading stage, the obtained 3D affine structure is upgraded to a Euclidean one with 4 GCPs. Experimental results on two public datasets demonstrate that the proposed method significantly outperforms several state-of-the-art methods in most cases.

Singularly perturbed systems (SPSs) wildly exist in practical engineering practice; however, the disturbance rejection in SPSs is still a problem. As well-known disturbance rejection methods, composite hierarchical anti-disturbance control (CHADC) can effectively compensate and suppress disturbance, which improves the control performances of complex systems. To enhance the capability of anti-disturbance and control precision, this article proposes a novel composite hierarchical anti-disturbance (CHAD) dynamic surface control (DSC) for the fast subsystem considering highly dynamic disturbance, and CHAD proportion-integration (PI) control for the slow subsystem with slow-varying harmonic disturbance. The disturbances are supposed to be generated by an exogenous system based on partially known information with model uncertainty, and the stabilities of both fast and slow closed-loop subsystems are analyzed. Meanwhile, the compensability of disturbances is discussed. Finally, simulations for an F-8 aircraft are given to demonstrate the effectiveness of the proposed methods.

In this article, a composite anti-disturbance control of disturbance rejection and attenuation for a class of nonlinear positive systems is proposed for the first time to achieve both positivity and stability. The disturbance observer-based control (DOBC) method is utilized to compensate for the effects of the modeled disturbances, while the$L_{1}$control strategy is adopted to suppress the effects of the disturbances which are not compensable to satisfy the$L_{1}$index performance. Besides, two analysis schemes of the closed-loop system are presented. The first scheme is commonly used and is easy for analysis, but it has some conservativeness. The second scheme is proposed novelly with less conservativeness, which releases the positivity constraint of disturbance estimation errors. The corresponding theorems and algorithms are proposed for necessary conditions and solutions. Finally, two examples are presented to illustrate the effectiveness of the proposed control schemes.

Adversarial attack is an important research direction of neural network security, and the black-box attack in the unknown model is an important application scenario of adversarial attack. The adversarial samples generated by existing black-box attack algorithms often have drastic hue changes, are easy to be perceived by human eyes, and the algorithm computational efficiency is generally low. Therefore, this paper proposes a multi-scale grid search for the image gray attack strategy, performing perturbations of different intensity on image areas of different scales, and then makes the generated sample hue change more natural. Experiments show that the performance of the method is better than the baseline algorithm, and greatly improves the attack efficiency, increasing the neural network query number over 50% comparing with the fastest baseline algorithm.

In this paper, a composite velocity-tracking control scheme is presented for improving dynamics response and control accuracy of the flexible gimbal system in the control moment gyro (CMG). Specifically, an${H}_{\infty } $optimal vibration controller (HOVC) is developed to weigh multi-performance of the gimbal system, such as stability, fast-tracking performance, and attenuation ability of multi-frequency-band disturbances. Meanwhile, a robust disturbance observer (RDO) designed on optimization technology is utilized to improve the nonlinear friction rejection ability of the gimbal system. Moreover, rigorous theoretical analysis based on the small-gain theory is provided to demonstrate the stability of the closed-loop gimbal system under multiple disturbances. Finally, experimental studies are presented to verify the effectiveness of the proposed method.

Precise attitude control of space manipulators plays an important role in advanced on-orbit assembly tasks. The vibration of flexible appendage and inertial uncertainties encountered in the operating process, however, may cause attitude error or even safety threats to the space manipulator system. In this article, a high-precision attitude control scheme of a space manipulator system is designed via a combination of disturbance observer (DO), prescribed performance-based H∞control, and iterative learning control (ILC) techniques. The proposed control scheme consists of three portions: 1) a DO that estimates the vibration disturbance caused by flexible appendage of base satellite; 2) a robust H∞controller with prescribed performance to attenuate the inertial uncertainties resulting from capture of an unknown object; and 3) an ILC for improving the transient and steady-state process in the presence of a repetitive on-orbit assembly task. This novel control scheme can not only handle the flexible vibration and inertial uncertainty of the space manipulator but also achieve satisfactory tracking performance. Both simulation and experimental results confirm the superiority of the proposed control strategy.

In this paper, a hybrid anti-disturbance composite control scheme is presented to address the problem subject to systems remarkably affected by external disturbances and discrete dynamics of actuators. By explicitly considering the hybrid feature resulting from discrete actuation signals acted on a continuous system, the hybrid disturbance observer is designed to estimate the effect of external disturbance. Meanwhile, H∞technique is used to mitigate the effect of continuous command executing error caused by discontinuous operation, which cannot be modeled in priori. Within the proposed composite control scheme, the combination of the disturbance estimation information and the H∞technique enables the entire hybrid system precisely performing. The stability of the whole continuous-discrete control system is analyzed. Finally, based on the Mars lander dynamics, comparative simulation studies illustrate that the developed control scheme can preserve a satisfactory level of performance, even in the presence of external disturbances and actuator quantization errors.

In this article, a novel spacecraft composite attitude stabilization scheme based on dual disturbances observers (DDOs) and high-precision nonsingular terminal sliding mode control is presented, with explicit consideration of reaction wheel dynamics and multiple disturbances. First, spacecraft attitude coupling dynamics model is constructed covering reaction wheel dynamics and multiple disturbances. These disturbances include matched disturbances caused by motor counter electromotive force and equivalent mismatched disturbance caused by reaction wheel friction and environment disturbances. DDOs are designed to estimate both equivalent matched and mismatched disturbances, respectively. Subsequently, the high-precision control scheme is designed to compensate the estimated disturbances and attenuate the influence of estimated errors via composite nonsingular terminal sliding mode attitude controller. The closed-loop stability and convergence are proved based on the Lyapunov stability theory. Finally, hardware-in-the-loop experiments are conducted to verify the effectiveness of proposed spacecraft attitude stabilization scheme.

In this paper, a novel adaptive neural network (NN) control scheme is proposed for a class of nonlinear systems with unknown control direction. By introducing some differentiable functions and high-order Lyapunov functions, the obstacle caused by unknown control direction in NN control is successfully circumvented and all closed-loop signals are shown to be uniformly bounded up to infinite time. Meanwhile, by introducing an error transformation technique, it is rigorously proved that the argument of the unknown nonlinearities remains within a compact set which can be explicitly calculated a priori, making the NN approximation always valid. Moreover, with the aid of a bound estimation approach, we effectively compress the impact of approximation errors and external disturbances and steer the tracking error into a predefined small residual set. Simulation results illustrate the effectiveness of the proposed scheme.

The authors introduce their design, implementation and formally verification of a Trusted Execution Environment (TEE)‐based trusted storage system (TSS) in mobile devices, which conforms to GlobalPlatform specifications. The authors’ TSS provides not only authenticating the integrity and freshness of data but also many security storage operation properties like atomicity operations of a persistent object. To improve data store efficient when a big persistent object is read or written, a new mechanism that dynamic allocate continuous memory in REE's kernel memory space and map the address to the TEE through a communication pipe is proposed. This method can reduce switching times, allocating memory times and copy memory overloads between two worlds. A formal method is used in their design and development to guarantee the correctness and security of TSS. They consider the functional correctness mainly in this study, and use traditional formal verification tool – VCC verify the functional correctness of TSS. Their evaluation demonstrates its advantage compared to existing systems in addition.

The paper studies on the modeling and prediction method for multi-attribute events, which exist widely in practical application. In view of the features of multi-attribute events, the paper proposes an event processing framework including the construction of training sample space based on multi-attribute event grid (MAEG), bi-direction SVR (Support Vector Regression) modeling method for prediction based on longitudinal modeling and transverse modeling, and model updating strategy based on ?-insensitive error. The performance of the proposed methods of the framework is demonstrated by extensive experiments with real datasets.

In practical application, GPS trajectories are often sparse due to the sampling points lost or a new user appearing, which makes the accuracy of location prediction low based on a single user data. To solve this problem, this paper proposes a novel Markov location prediction approach based on user mobile behavior similarity clustering. First, this paper proposes a region partitioning method based on Voronoi diagram, transforms the GPS trajectories into region trajectories and predicts the locations over region trajectories. Second, this paper proposes a new approach to measure the similarity of users' mobile behavior by considering users' transferring features and regional features. Third?based on the mobile behavior similarity, this paper divides users into various groups and employs the first-order Markov model on the groups for location prediction. The experiments over real GPS trajectory dataset indicate that the proposed method is effective for location prediction.

The virtual machine (VM) live migration could achieve VM redistribution among distributed system hosts without reducing normal working performance. Post-copy is one of the wildly used VM live migration algorithm and has lots of advantages, such as less total migration time, less downtime, lower network overhead and so on. Its disadvantage is that the VM will be suspended frequently due to the page faults caused by the incomplete memory while VM is restored to run on destination host, which may lead to an extremely negative affect on VM work efficiency. To solve this problem, this paper proposes the pre-record algorithm. Pre-record extends the VM execution on source host, records the accessed memory pages during this period to obtain pre-recorded page set (PPS), and preferentially completes the migration of PPS to avoid page faults as much as possible. It also proposes PDoPMP algorithm to analysis the trend of the trajectory of memory address in PPS, in order to further expand the predict range of memory pages. The experimental results show that the pre-record has better efficiency than traditional post-copy, especially after combining with PDoPMP. It could obviously reduce the page faults number and then the total VM migration time without prolonging the downtime, and could improve VM migration efficiency under different workload and network conditions.

Nowadays, many applications return to the user a set of results that take the query as their nearest neighbor, which are commonly expressed through reverse nearest neighbor (RNN) queries. When considering moving objects, users would like to find objects that appear in the RNN result set for a period of time in some real-world applications such as collaboration recommendation and anti-tracking. In this work, we formally define the problem of interval reverse nearest neighbor (IRNN) queries over moving objects, which return the objects that maintain nearest neighboring relations to the moving query objects for the longest time in the given interval. Location uncertainty of moving data objects and moving query objects is inherent in various domains, and we investigate objects that exhibit Markov correlations, that is, each object's location is only correlated with its own location at previous timestamp while being independent of other objects. There exists the efficiency challenge for answering IRNN queries on uncertain moving objects with Markov correlations since we have to retrieve not only all the possible locations of each object at current time but also its historically possible locations. To speed up the query processing, we present a general framework for answering IRNN queries on uncertain moving objects with Markov correlations in two phases. In the first phase, we apply space pruning and probability pruning techniques, which reduce the search space significantly. In the second phase, we verify whether each unpruned object is an IRNN of the query object. During this phase, we propose an approach termed Probability Decomposition Verification (PDV) algorithm which avoid computing the probability of any object being an RNN of the query object exactly and thus improve the efficiency of verification. The performance of the proposed algorithm is demonstrated by extensive experiments on synthetic and real datasets, and the experimental results show that our algorithm is more efficient than the Monte-Carlo based approximate algorithm.

Complex event detection in stream is an important problem in event stream processing field. In this paper, we propose a new complex event detection algorithm in probabilistic stream, Instance Pruning and Filter-Detection Algorithm (IPF-DA). This algorithm is based on a kind of data structure called Chain Instance Queues (CIQ), to detect complex events satisfying query requirements with single-scanning probabilistic stream. In the process of complex event detection, IPF-DA prunes unnecessary event instances with query requirements and achieves filter for complex events with the given threshold. And it further improves the efficiency by setting proper tolerance, while insuring high recall. In addition, we construct Bayesian network to express and infer the probability distribution of uncertain events. Conditional Probability Indexing-Tree (CPI-Tree) is defined to store conditional probabilities of Bayesian network, saving query time compared with traditional Conditional Probability Table (CPT). Experimental results show that a series of strategies proposed by this paper are effective for complex event detection in probabilistic stream.

The key of distributed computing is to fully utilize computing resources. However, the efficiency of distributed computing can always be affected by volatility of available nodes and uncertainty of network environment. In order to enhance resource allocation efficiency in distributed computing, this paper presents an inframarginal analysis based resource allocation method, which distributes computing task to the node with more comparative advantage carrying on the task. Simulation results proved that it is effective on resource allocation in the distributed computing environment.