From the perspective of attackers, this article proposes a creative essential-information-aware (EIA) attack policy targeting remote state estimation in cyber-physical systems (CPSs), in which adversaries aim at maximizing the trace of estimation error covariance by selectively leveraging crucial information during data transmission. Through manipulating the transmitted signals, the attacker can recognize critical packets exchanged among sensing nodes and allocate higher attack energy level to them, thereby enlarging the attack success probability (ASP) and amplifying the disruptive effect. Then, a scientific power allocation scenario has been designed with the aid of signal-to-interference-plus-noise ratio (SINR), while explicitly accounting for the confinement imposed by limited channel transmission capacity. Furthermore, the relationships among the attack coefficient, ASP, attack cost, and the channel capacity are rigorously analyzed utilizing stochastic methods. The upper bounds for attack coefficient are derived to balance attack impact with resource limitations. Finally, two examples are provided to verify the effectiveness and practicality of the attack strategy.

In a large range of manufacturing tasks, the design objective is characterised as following a given path defined in space. In these applications, the tracking time of any particular position along the path is not specified, so an appropriate motion profile can be chosen among its admissible solutions to improve its tracking performance. This article develops an indirect reference update framework that maximizes accuracy while embedding practical constraints. An optimal path planning problem, incorporating system constraints, is formulated and can be solved using a discretized approach to derive a motion profile that minimizes control energy for a broad spectrum of industrial tasks. To satisfy robustness concerns, an iterative learning control (ILC) algorithm with an indirect reference update framework is designed to improve the accuracy and robustness of path following. It is evaluated on a gantry robot test platform, and the results illustrate superior levels of practical performance in terms of energy reduction and path following accuracy compared with existing approaches.

In practical applications of iterative learning control (ILC), the repetitive process may end up early by accident during the performance improvement along the trial axis, which yields the nonuniform trial length problem. For such practical systems, input signals are usually constrained because of some certain physical limitations. This article proposes an optimal ILC algorithm for linear time-invariant multiple-input–multiple-output (MIMO) systems with nonuniform trial lengths under input constraints. The optimal ILC framework is specifically modified for the nonuniform trial length problem, where the primal–dual interior point method is introduced to deal with the input constraints. Hence, the constraint handling capability are improved compared with the conventional counterparts for nonuniform trial lengths. Also, the monotonic convergence property of the proposed optimal ILC algorithm is obtained in the sense of mathematical expectation. Finally, the effectiveness of the proposed algorithm is verified on the numerical simulation of a mobile robot.

This paper concentrates on the dynamic event trigger based controller design for discrete-time Markov jump systems. The rich data transmission within the network channel may cause the collision and the waste of resources, which leads to some kinds of poor system performance. Firstly, a discrete time domain dynamic event trigger mechanism is proposed for controller design. Secondly, for discrete time Markov jump systems, a mode-dependent Lyapunov function is designed to ensure the system state to be exponentially mean-square ultimately bounded by linear matrix inequalities (LMIs). Finally, a numerical simulation example is conducted to illustrate the effectiveness of the provided method.