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This paper studies the problem of obstacle-avoidance tracking control for a class of uncertain nonlinear robot systems in multiple-dynamic-obstacles environment. The main challenge focuses on how to simultaneously ensure tracking performance and obstacle avoidance. The existing collision-avoidance tracking control schemes cannot guarantee the tracking performance inside the obstacle detection region, since the use of additive Lyapunov-Barrier function (LBF) generates the dynamic mismatching. To overcome this difficulty, a novel integral-multiplicative LBF (IMLBF) is constructed. The adaptive mechanism is designed to compensate for the mismatching uncertainties. By incorporating the barrier function into backstepping procedure, a secure adaptive tracking control scheme is proposed. Compared with the existing results, the proposed control scheme can simultanesouly achieve the obstacle avoidance and global tracking performance regardless of being inside the obstacle sensing region and unknown nonlinear uncertainties.

This article investigates the problem of remote state estimation for nonlinear systems via a fading channel, where the packet losses may occur over the sensor-to-estimator communication network. The risk-sensitive (RS) approach is introduced to formulate the estimation problem with intermittent measurements such that an exponential cost criterion is minimized. Based on the reference measure method, the closed-form expression of the nonlinear RS estimator is derived. Moreover, stability conditions for the designed estimator are established by extending the contraction analysis of the linear cases. In contrast to the linear cases, a novel cost function is designed to obtain the finite-dimensional nonlinear estimate, which counteracts the linearization errors by treating them as model uncertainties. Simulation results illustrate that the proposed nonlinear estimator achieves better estimation qualities compared with the existing nonlinear minimum mean square error methods.

This article is concerned with the secure state estimation problem for cyber-physical systems under intermittent denial-of-service (DoS) attacks. Based on a switching scheme and the cascade observer technique, a novel resilient state observer with a switched compensation mechanism is designed. Moreover, a quantitative relationship between the resilience against DoS attacks and the design parameters is revealed. Compared with the existing results, where only the boundedness of the estimation error is guaranteed under DoS attacks, the exponential convergence of the estimation error is achieved by employing the proposed observer scheme, such that the estimation performance is improved. More specifically, in the disturbance-free case, it is proven that the state estimation error converges exponentially to 0 despite the existence of DoS attacks. Finally, simulation results are provided to illustrate the effectiveness and merits of the proposed methods.

This article investigates the resilient event-triggered (ET) distributed state estimation problem for nonlinear systems under denial-of-service (DoS) attacks. Different from the existing results mainly considering linear or specified nonlinear systems, more general nonlinear systems are considered in this study. Moreover, the considered DoS attacks are able to compromise different communication links among estimators independently. In this context, by resorting to the techniques of incremental homogeneity, a nonlinear ET distributed estimation scheme is designed to estimate the states and regulate the data transmission. Under this scheme, the resilient state estimation is achieved by employing a multimode switching estimator, and the problem of efficiency loss of the ET mechanism caused by DoS attacks is solved by designing a dynamic trigger threshold with switched update laws. Then, based on the decay rates of the Lyapunov function corresponding to different communication modes, sufficient conditions are given to guarantee the stability of the estimation error system under DoS attacks. Finally, simulation results are provided to verify the effectiveness of the proposed method.

This article is concerned with the cooperative tracking control problem for heterogeneous multiagent systems in a leader-following form under a directed graph. The dynamics of each following agent is unknown, obeying a strict-feedback form. With the help of fuzzy-logic systems, input filters, and constraint-handling schemes, a fully distributed output-feedback control algorithm is proposed to achieve output synchronization with prescribed performance and guarantee boundedness of signals in the closed-loop systems. In addition, the algorithm exhibits a simplicity control attribute in the sense that: 1) the control design utilizes only relative output measurements, and no extra information needs to be transmitted via the network and 2) the issue of explosion of complexity is addressed, without employing command filters or dynamic surface control techniques. Finally, the simulation results clarify and verify the established theoretical findings.

This article investigates the event-triggered distributed state estimation problem for a class of cyber-physical systems (CPSs) with multiple transmission channels under denial-of-service (DoS) attacks. First, an observer-based event-triggered transmission scheme is proposed to improve the transmission efficiency, and the corresponding distributed Kalman filter is designed to estimate the system states. Under the collective observability condition, a relationship between estimation error covariance, attack intensity, and transmission efficiency is established by utilizing the covariance intersection fusion method and the property of matrix congruent transformation rank. The important features that distinguish our work from others are that the considered DoS attacks compromise each channel independently and do not have to satisfy the probabilistic property of the packet loss process. Furthermore, an event-triggered communication scheme is considered to improve the utilization of network resources between filters, and a sufficient condition for the parameter design is given which takes into account the influence of DoS attacks. Finally, simulation results are provided to verify the effectiveness of the proposed methods.

This paper focuses on investigating the tradeoff problem between the attack stealthiness and the attack performance in cyber-physical systems (CPSs). The notion of (,)-stealthiness is utilized to characterize the stealthiness level and the corrupted error covariance of the remote estimator is adopted to measure the attack performance. The aim is to design an off-line stealthy attack strategy to maximize the attack performance. Different from the existing results, where the relationship between attack performance and system stability is not provided, we prove that the corrupted error covariance has an upper bound for stable systems and can be arbitrarily large for unstable systems. To maximize the attack effect under a given (,)-stealthiness constraint, a non-convex optimization problem is formulated. By applying convex analysis and monotonic optimization techniques, an upper bound of the attack performance is presented for stable systems. Subsequently, a (,)-stealthy attack signal is constructed to achieve the upper bound. Finally, simulations on a stable numerical example and experiments on the permanent magnet synchronous machine (PMSM) monitoring system are performed to demonstrate the theoretical results.

This paper studies a class of distributed attack strategies against state estimation of wireless sensor networks. The attack objective is to corrupt the least numbers of sensors so that the state estimate error approaches to a target bias while avoiding being detected and a high attacking lost. First, with precise knowledge of measurement model and applying a sparsity projection operation with average linear complexity rather than the popular brute force search and approximation techniques, a distributed optimization scheme is proposed which provides to an exact and low-complexity algorithmic solution to finding the optimal attack strategy. Furthermore, by introducing dead-zone type projection and region projection operators, a distributed optimization algorithm for robust sparse undetectable attacks is proposed with imprecise model knowledge. A distinguishing point of the proposed algorithms is that a trade-off between computational complexity and attacks impact can be achieved by properly designing a cost function. Simulation results on an unmanned ground vehicle are presented to substantiate the algorithm.

This article investigates the problem of secure state estimation for continuous-time linear systems in the presence of sparse sensor attacks. Compared with the existing results, the attacked sensor set can be changed by adversaries against secure estimation. To address the more erratic attacks, a novel supervisory state observer is proposed, which employs a bank of candidate nonlinear subobservers and a switching logic administrated by a monitoring function to select the active subobserver at every instant of time. Based on the stability analysis of switched systems, it is proven that the supervisory observer asymptotically converges to a neighborhood of the true system state in the presence of the sensor attacks and bounded disturbances. A simulation example is given to substantiate the theoretical results.

This paper investigates a distributed optimization problem for high-order strict-feedback nonlinear multiagent systems. A virtual system is built for each agent using a distributed proportional-integral (PI) optimization algorithm to estimate the global optimal solution online. The estimate is then input into a prefilter to generate an alternative estimate and its high-order derivatives. Based on these signals, a backstepping controller is used to make the actual system track the alternative estimate asymptotically, which thereby realizes exact global consensus optimization. Compared to the existing result, the proposed algorithm is fully distributed under undirected topologies with much weaker assumptions on local and global objective functions, lower computation and communication cost. Exact global optimal consensus can also be reached in the cases with directed topologies and intermittent communications. The effectiveness of the method is verified by simulations.