This paper studies the secure state estimation and control problem of cyber-physical systems (CPSs), where there are multiple heterogeneous sensing nodes and one actuator which are both under the threat of intermittent false data injection attacks. A specific filter gain is proposed by solving the equality constrained optimization problem so that the attack on the actuator does not affect the a posteriori estimation error of the system. A new method to construct X2 detection variables is proposed, which does not require that the covariance matrix be invertible, and then the actuator and sensor attack detection schemes are developed, respectively. Based on the detection results, a multiple information fusion algorithm is developed and an estimator based controller with the compensator, which can compensate for the actuator attack in previous step, is further designed. A necessary and sufficient condition for complete compensation is provided, and the stability of the filter and the control system is proved. Finally, the effectiveness of the estimation algorithm and control algorithm is verified by a numerical simulation.

Consensus-based distributed Kalman filters for estimation with targets have attracted considerable attention. Most of the existing Kalman filters use the average consensus approach, which tends to have a low convergence speed. They also rarely consider the impacts of limited sensing range and target mobility on the information flow topology. In this article, we address these issues by designing a novel distributed Kalman consensus filter (DKCF) with an information-weighted consensus structure for random mobile target estimation in continuous time. A new moving target information-flow topology for the measurement of targets is developed based on the sensors’ sensing ranges, targets’ random mobility, and local information-weighted neighbors. Novel necessary and sufficient conditions about the convergence of the proposed DKCF are developed. Under these conditions, the estimates of all sensors converge to the consensus values. Simulation and comparative studies show the effectiveness and the superiority of this new DKCF.

This article studies the detection of discontinuous false data-injection (FDI) attacks on cyber–physical systems (CPSs). Considering the unknown stochastic properties of the process noise and measurement noise, deep reinforcement learning is applied to designing an FDI attack detector. First, the discontinuous attack detection problem is modeled as a partially observable Markov decision process (POMDP) and a neural network is used to explore the POMDP. In the network, sliding observation windows which are composed of the offline fragment historical data are used as the input. An approach to designing the reward in POMDP is provided to ensure the precision of the detection when there are even some state recognition errors. Second, sufficient conditions on attack frequency and duration to guarantee the applicability of the detector and the expected estimation performance are further given. Finally, simulation examples illustrate the effectiveness of the attack detector.

In this paper, a motion planning for autonomous driving on highway is studied. A high-level motion planning controller with discrete action space is designed based on deep Q network (DQN). An occupancy grid based state presentation aiming at specific scenarios is proposed and then a novel attention mechanism named external spatial attention (ESA) is designed for occupancy grid to improve the network performance. Con-sidering both computational complexity and interpretability, a lightweight data-driven safety layer consisting of two-dimensional linear biased support vector machine (2D-LBSVM) is proposed to improve safety. The advantages of this controller and the role of each module are illustrated by experiments. In addition, the superior performance of occupancy grid state and the interpretability of safety layer are further analyzed.

In this paper, the secure output consensus control problem for discrete-time heterogeneous multi-agent systems against two types of cyber-attacks is studied. The attackers continuously launch attack signals into the actuators and randomly launch attack signals into the sensors with unknown Markovian attack probabilities. A novel observer-based cooperative output regulation consensus controller is designed to mitigate the detrimental influence of attack on consensus performance. To avoid corrupted sensor's signal being used by the observer, an attack detector based observer is developed for each follower to estimate both the attack signal in actuator and the system state. By converting the estimation error system subject to an unknown Markov modeled attack to a Markov switching system with unknown transition probabilities, a sufficient condition of the observer gain is given. Moreover, a discrete-time auxiliary system is designed to estimate the leader state. Finally, a numerical example and a practical example of heterogeneous mobile vehicles show the effectiveness of the proposed control strategy.

This paper studies the consensus of a group of linear dynamic agents with a uniform communication delay and focuses on searching an allowable delay bound. As long as the delay is less than this bound, there exist linear feedback consensus protocols driving the multi-agent system to achieve consensus. Both fixed and switching topology cases are investigated. In both cases, the consensus problem is converted to the robust stability problem of corresponding uncertain state-delayed systems. By using Lyapunov–Krasovskii functional analysis, consensus conditions which contain the feedback gain conditions and delay conditions are proposed for systems over fixed and switching topologies, respectively. Furthermore, allowable delay bounds are obtained for both systems by solving the optimal robust stabilisation problems. Numerical examples are given to illustrate the results.