Data-driven control for switched systems over a vulnerable and resource-constrained network

To alleviate the restriction of system model on control design, data-driven model-free adaptive control (MFAC) is an excellent alternative to model-based control methods. This paper studies event-triggered data-driven control for switched systems over a vulnerable and resource-constrained network. The system is transformed into an equivalent switched data model through dynamic linearization. Resource constraints and denial of service (DoS) attacks in the network are concerned, and a novel joint anti-attack method including resilient event-triggering mechanism and prediction scheme is presented. Furthermore, new event-triggered MFAC algorithms are proposed. In this scenario, by constructing a Lyapunov functional on tracking error, sufficient conditions to ensure its boundedness are derived. This is the first time in the literature to give a complete solution to data-driven control of switched systems. At last, the validity of new algorithms and theoretical results is confirmed by simulations.     

Fault-tolerant control for the linearized spacecraft attitude control system with Markovian switching

In this article, the fault-tolerant control is investigated for the spacecraft attitude control system described by a linearized model with Markovian switching. First, the evolution of sudden failures of the spacecraft’s actuators is described by a Markov process. Then, the mathematical model of the spacecraft attitude control system with the Markov jump characteristic fault is established. Taking the uncertainty of the system model and external interference into consideration, a fault-tolerant control scheme is proposed for the established spacecraft attitude control system with the Markov jump characteristic fault by using the sliding mode control technique. Compared with some existing sliding mode controller design methods, the proposed method requires a less total number of LMIs to be solved. The stability and reachability of the resulting closed-loop system under the presented sliding mode control scheme are proven by applying the Lyapunov stability theory. Finally, some simulation results are provided to show the effectiveness and advantages of the proposed control method for spacecraft attitude control.     

Distributed RISE control for spacecraft formation reconfiguration with collision avoidance

In this paper, we investigate the distributed formation reconfiguration problem of multiple spacecraft with collision avoidance in the presence of external disturbances. Artificial potential function (APF) based virtual velocity controllers for the spacecraft are firstly constructed, which overcome the local minima problem through introducing auxiliary inputs weighted by bump functions. Then, based on the robust integral of the sign of the error (RISE) control methodology, a distributed continuous asymptotic tracking control protocol is proposed, accomplishing both formation reconfiguration and the collision avoidance among spacecraft and with obstacles. Furthermore, using tools from graph theory, Lyapunov analysis and backstepping technique, we show the stability and collision avoidance performance of the closed-loop multiple spacecraft system. Numerical simulations for a spacecraft formation are finally provided to validate the effectiveness of the proposed algorithm.     

黑龙江省高速公路联网监控系统升级改造方案研究

随着黑龙江省高速公路建设步伐的不断加快,联网收费里程不断增加,2012年底纳入联网收费的高速公路已达3875公里.然而,随着黑龙江省高速公路物理路由的进一步形成,各个高速公路管理中心与省联网中心间在联网过程中产生的各种问题日益突出,现有监控系统已无法很好的适应路网管理需要.本方案将在原有系统的基础上,对省联网中心的监控系统进行改造和整合,从而完成对整个系统设备的升级更新,完善机电系统软、硬件配置.     

Data-driven control of consensus tracking for discrete-time multi-agent systems

This paper investigates the consensus tracking problem of leader-follower multi-agent systems. Different from most existing works, dynamics of all the agents are assumed completely unknown, whereas some input-output data about the agents are available. It is well known from the Willems et al. Fundamental Lemma that when inputs of a linear time-invariant (LTI) system are persistently exciting, all possible trajectories of the system can be represented in terms of a finite set of measured input-output data. Building on this idea, the present paper proposes a purely data-driven distributed consensus control policy which allows all the follower agents to track the leader agent’s trajectory. It is shown that for a linear discrete-time multi-agent system, the corresponding controller can be designed to ensure the global synchronization with local data. Even if the data are corrupted by noises, the proposed approach is still applicable under certain conditions. Numerical examples corroborate the practical merits of the theoretical results.     

Barrier function-based prescribed-performance adaptive attitude tracking control for spacecraft with uncertainties

This paper addresses the problem of robust adaptive attitude tracking control for spacecraft with mismatched and matched uncertainties. The idea of disturbance estimation and compensation is introduced into the control design. First, finite-time disturbance observers are developed for different channels of spacecraft based on barrier functions for achieving finite-time asymptotic estimates of unknown bounded uncertainties in the system. Second, a class of prescribed performance functions is considered in the design of the barrier function. The spacecraft attitude adaptive tracking control strategy with finite-time convergence capability and prescribed performance is proposed based on the designed finite-time disturbance observers and barrier function. Finally, the theoretical findings are verified by numerical simulations and compared with the simulation results of existing methods.     

Data-driven iterative feedforward control with rational parametrization: Achieving optimality for varying tasks

In precision motion systems, well-designed feedforward control can effectively compensate for the reference-induced error. This paper aims to develop a novel data-driven iterative feedforward control approach for precision motion systems that execute varying reference tasks. The feedforward controller is parameterized with the rational basis functions, and the optimal parameters are sought to be solved through minimizing the tracking error. The key difficulty associated with the rational parametrization lies in the non-convexity of the parameter optimization problem. Hence, a new iterative parameter optimization algorithm is proposed such that the controller parameters can be optimally solved based on measured data only in each task irrespective of reference variations. Two simulation cases are presented to illustrate the enhanced performance of the proposed approach for varying tasks compared to pre-existing results.     

Data-driven optimal tracking control for discrete-time systems with delays using adaptive dynamic programming

In this paper, the data-driven adaptive dynamic programming (ADP) algorithm is proposed to deal with the optimal tracking problem for the general discrete-time (DT) systems with delays for the first time. The model-free ADP algorithm is presented by using only the system’s input, output and the reference trajectory of the finite steps of historical data. First, the augmented state equation is constructed based on the time-delay system and the reference system. Second, a novel data-driven state equation is derived by virtue of the history data composed of input, output and reference trajectory, which is considered as a state estimator.Then, a novel data-driven Bellman equation for the linear quadratic tracking (LQT) problem with delays is deduced. Finally, the data-driven ADP algorithm is designed to solve the LQT problem with delays and does not require any system dynamics. The simulation result demonstrates the validity of the proposed data-driven ADP algorithm in this paper for the LQT problem with delays.     

Event-triggered backstepping control for attitude stabilization of spacecraft

To decrease the communication frequency between the controller and the actuator, this paper addresses the spacecraft attitude control problem by adopting the event-triggered strategy. First of all, a backstepping-based inverse optimal attitude control law is proposed, where both the virtual control law and the actual control law are respectively optimal with respect to certain cost functionals. Then, an event-triggered scheme is proposed to realize the obtained inverse optimal attitude control law. By designing the event triggering mechanism elaborately, it is guaranteed that the trivial solution of the closed-loop system is globally exponentially stable and there is no Zeno phenomenon in the closed-loop system. Further, the obtained event-triggered attitude control law is modified and extended to the more general case when the disturbance torque cannot be ignored. It is proved that all states of the closed-loop system are bounded, the attitude error can be made arbitrarily small ultimately by choosing appropriate design parameters and the Zeno phenomenon is excluded in the closed-loop system. In the proposed event-triggered attitude control approaches, the control signal transmitted from the controller to the actuator is only updated at the triggered time instant when the accumulated error exceeds the threshold defined elaborately. Simulation results show that by using the proposed event-triggered attitude control approach, the communication burden can be significantly reduced compared with the traditional spacecraft control schemes realized in the time-triggered way.     

Multi-objective output feedback control for autonomous spacecraft rendezvous

In this paper, the problem of robust output feedback control for a class of spacecraft rendezvous systems is investigated, which contains parameter uncertainty, external disturbance, poles assignment, _H∞-norm$H_{\infty }\mathrm{-}\mathrm{norm}$, variance and input constraints. The aim of this problem is to design a dynamic output feedback controller such that the closed-loop poles are placed within a specified disc, the _H∞$H_{\infty }$ norm of the transfer function from disturbance to output is ensured to be less than a prespecified disturbance attenuation level, the steady-state variance for each state of spacecraft rendezvous system is guaranteed to be less than the prespecified individual upper bound, and the actual control input is confined into a certain range simultaneously. Based on the Lyapunov theory, the existence conditions of such controller are derived in terms of linear matrix inequalities (LMIs). An illustrative example is given to demonstrate the effectiveness of the proposed control design method.     

Data-driven fault diagnosis for an automobile suspension system by using a clustering based method

This paper concerns the issues of fault diagnosis and monitoring for an automobile suspension system where only accelerator sensors in the four corners of the car body are available. A clustering based method is proposed to detect the fault happened in the spring, and the Fisher discriminant analysis is applied to isolate the root factor for the fault. Different from most of the existing approaches, the pure data-driven characteristic enables this method to serve as an on-line fault diagnosis and monitoring tool without suspension model or fault features known as a prior. Moreover, this method can classify different reductions in the spring coefficient into one fault rather than different faults. The effectiveness of the proposed method is finally illustrated on an automobile suspension benchmark.     

Vision transformer attention with multi-reservoir echo state network for anomaly recognition

Anomalous event recognition requires an instant response to reduce the loss of human life and property; however, existing automated systems show limited performance due to considerations related to the temporal domain of the videos and ignore the significant role of spatial information. Furthermore, although current surveillance systems can detect anomalous events, they require human intervention to recognise their nature and to select appropriate countermeasures, as there are no fully automatic surveillance techniques that can simultaneously detect and interpret anomalous events. Therefore, we present a framework called Vision Transformer Anomaly Recognition (ViT-ARN) that can detect and interpret anomalies in smart city surveillance videos. The framework consists of two stages: the first involves online anomaly detection, for which a customised, lightweight, one-class deep neural network is developed to detect anomalies in a surveillance environment, while in the second stage, the detected anomaly is further classified into the corresponding class. The size of our anomaly detection model is compressed using a filter pruning strategy based on a geometric median, with the aim of easy adaptability for resource-constrained devices. Anomaly classification is based on vision transformer features and is followed by a bottleneck attention mechanism to enhance the representation. The refined features are passed to a multi-reservoir echo state network for a detailed analysis of real-world anomalies such as vandalism and road accidents. A total of 858 and 1600 videos from two datasets are used to train the proposed model, and extensive experiments on the LAD-2000 and UCF-Crime datasets comprising 290 and 400 testing videos reveal that our framework can recognise anomalies more effectively, outperforming other state-of-the-art approaches with increases in accuracy of 10.14% and 3% on the LAD-2000 and UCF-Crime datasets, respectively.     

Data-driven discrete terminal sliding mode decoupling control method with prescribed performance

This paper proposes a data-driven terminal sliding mode decoupling controller with prescribed performance for a class of discrete-time multi-input multi-output systems in the presence of external disturbances and uncertainties. First, utilizing a discrete-time extended state observer and a compact form dynamic linearization data model, we derive a new data-driven mothod and establish the relationship between the input and output signals of controlled plant. Moreover, the disturbances, uncertainties, and couplings are suppressed owing to the application of the terminal sliding mode technique. Combined with the principle of prescribed performance control, the terminal sliding mode law with prescribed performance is derived. With the proposed data-driven method, the tracking error is lower, and the decoupling ability is improved. Furthermore, the stability of the control system is proven. Finally, a simulation is conducted on a three-tank system to demonstrate the effectiveness of the proposed scheme.     

Adaptive neural network dynamic surface control of uncertain strict-feedback nonlinear systems with unknown control direction and unknown actuator fault

In this paper, the tracking control problem of a class of uncertain strict-feedback nonlinear systems with unknown control direction and unknown actuator fault is studied. By using the neural network control approach and dynamic surface control technique, an adaptive neural network dynamic surface control law is designed. Based on the neural network approximator, the uncertain nonlinear dynamics are approximated. Using the dynamic surface control technique, the complexity explosion problems in the design of virtual control laws and adaptive updating laws can be overcome. Moreover, to solve the unknown control direction and unknown actuator fault problems, a type of Nussbaum gain function is incorporated into the recursive design of dynamic surface control. Based on the designed adaptive control law, it can be confirmed that all of the signals in the closed-loop system are semi-global bounded, and the convergence of the tracking error to the specified small neighborhood of the origin could be ensured by adjusting the designing parameters. Finally, two examples are provided to demonstrate the effectiveness of the proposed adaptive control law.     

Detecting collusive spammers with heterogeneous graph attention network

Detecting collusive spammers who collaboratively post fake reviews is extremely important to guarantee the reliability of review information on e-commerce platforms. In this research, we formulate the collusive spammer detection as an anomaly detection problem and propose a novel detection approach based on heterogeneous graph attention network. First, we analyze the review dataset from different perspectives and use the statistical distribution to model each user's review behavior. By introducing the Bhattacharyya distance, we calculate the user-user and product-product correlation degrees to construct a multi-relation heterogeneous graph. Second, we combine the biased random walk strategy and multi-head self-attention mechanism to propose a model of heterogeneous graph attention network to learn the node embeddings from the multi-relation heterogeneous graph. Finally, we propose an improved community detection algorithm to acquire candidate spamming groups and employ an anomaly detection model based on the autoencoder to identify collusive spammers. Experiments show that the average improvements of precision@k and recall@k of the proposed approach over the best baseline method on the Amazon, Yelp_Miami, Yelp_New York, Yelp_San Francisco, and YelpChi datasets are [13%, 3%], [32%, 12%], [37%, 7%], [42%, 10%], and [18%, 1%], respectively.     

Practical predefined-time attitude coordination control for multiple rigid spacecraft

This paper investigates the practical predefined-time attitude cooperation control problem for a group of rigid spacecraft under an undirected communication graph. First, since the leader is accessible to only a subset of the group members, a distributed practical predefined-time state observer is proposed to estimate the leader’s states by using a time-varying scaling function. Second, a distributed practical predefined-time attitude coordination controller is designed to guarantee that the attitude tracking errors of all follower spacecraft converge to the neighborhood of the origin within a preset time. Finally, the effectiveness of the proposed control law is demonstrated by illustrative numerical examples.     

Adaptive fuzzy finite-time control for spacecraft formation with communication delays and changing topologies

This paper aims to solve the finite time consensus control problem for spacecraft formation flying (SFF) while accounting for multiple time varying communication delays and changing topologies among SFF members. First, in the presence of model uncertainties and external disturbances, the coupled dynamics of relative position and attitude are derived based on the Lie group SE(3), in which the position and attitude tracking errors with respect to the virtual leader whose trajectory is computed offline are described by exponential coordinates. Then, a nonsingular fast terminal sliding mode (NFTSM) constructed by the exponential coordinates and velocity tracking errors is developed, based on which adaptive fuzzy NFTSM control schemes are proposed to guarantee that the ideal configurations of the SFF members with respect to the virtual leader can be achieved in finite time with high accuracy and all the aforementioned drawbacks can be overcome. The convergence and stability of the closed-loop system are proved theoretically by Lyapunov methods. Finally, numerical simulations are presented to validate the effectiveness and feasibility of the proposed controllers.     

Adaptive coordinated attitude control for spacecraft formation with saturating actuators and unknown inertia

In this paper, an adaptive attitude coordination control problem for spacecraft formation flying is investigated under a general directed communication topology containing a directed spanning tree with a leader as the root. In the presence of unknown time-varying inertia, persistent external disturbances and control input saturation, a novel robust adaptive coordinated attitude control algorithm with no prior knowledge of inertia for spacecraft is proposed to coordinately track the common time-varying reference states. Aiming at optimizing the control algorithm, a dynamic adjustment function is introduced to adjust the control gain according to the tracking errors. The effectiveness of the proposed control scheme is illustrated through numerical simulation results.     

Optimal residual generation for fault detection in linear discrete time-varying systems with uncertain observations

This work deals with the problem of optimal residual generation for fault detection (FD) in linear discrete time-varying (LDTV) systems subject to uncertain observations. By introducing a generalized fault detection filter (FDF) with four parameter matrices as the residual generator, a novel FDF design scheme is formulated as two bi-objective optimization problems such that the sensitivity of residual to fault is enhanced and the robustness of residual to unknown input is simultaneously strengthened. A generalized operator based optimization approach is proposed to deduce solutions to the corresponding optimization problems in operator forms, where the related H_∞/H_∞ or $H_{?}/H_{\infty }$ FD performance index is maximized. With the aid of the addressed methods, the connections among the derived solutions are explicitly announced. The parameter matrices of the FDF are analytically derived via solving simple matrix equations recursively. It is revealed that our proposed results establish an operator-based framework of optimal residual generation for some kinds of linear discrete-time systems. Illustrative examples are given to show the applicability and effectiveness of the proposed methods.