This paper presents a resilient model predictive control (MPC) framework to attenuate adverse effects of Denial-of-Service (DoS) attacks for Cyber-Physical Systems (CPSs), where the system dynamics is modeled by a Linear Time-Invariant (LTI) system. A DoS attacker targets on blocking the controller to actuator (C-A) communication channel by launching adversarial jamming signals. We show that, in order to guarantee exponential stability of the closed-loop system, several conditions for resilient MPC should be satisfied, which are related to the energy of DoS attacks and MPC parameters such as the prediction horizon and terminal constraints. Two key techniques including the -step positive invariance for the closed-loop system and the modified initial feasible set for resilient MPC are exploited for achieving exponential stability in the presence of DoS attacks. Finally, the effectiveness of the proposed MPC algorithm is verified by a simulated example.

Cyber-physical systems (CPSs) have attracted increasing attention in recent years due to their promise for substantial and long-term benefits to society, economy, environment, and citizens. In addition, the rapid advances in computing, communication, and storage technologies have resulted in a revolution in the information communication technology domain and domination in the industry context. The utilization of CPSs in industrial settings has led to industrial cyber-physical systems (ICPSs), which, in conjunction with the information-driven interactions, enables large-scale cooperation in industrial facilities and among all the stakeholders of the value chain. Hence, the research on ICPSs is essential, especially with respect to the engineering of such systems for industrial applications. This article presents an overview of recent developments in ICPSs. We first introduce the architecture of ICPSs. Then, we review the developments of ICPSs in relevant research domains. Finally, this article concludes by presenting some potential future research directions on ICPSs.

In this article, a robust nonlinear model predictive control (NMPC) scheme is proposed for the visual servoing of quadrotors subject to external disturbances. By using the virtual camera approach, the image moments are defined in the virtual camera plane and adopted as visual features to derive the decoupled image kinematics. As a result, the image-based visual servoing (IBVS) system model is established by integrating the image kinematics and quadrotor dynamics. To handle the visibility constraint, a robust NMPC scheme is developed for the IBVS of the quadrotor such that the visual target can stay within the field of view of the camera. In addition, based on the Lipschitz condition, the tightened state constraints are constructed to tackle external disturbances. The sufficient conditions on guaranteeing recursive feasibility of the proposed NMPC algorithm are derived. Furthermore, we theoretically show that the tracking error will converge to a small set around the origin in finite time under some derived conditions. Finally, simulation studies and experimental tests are conducted to verify the efficacy of the proposed method.

In this work, an adaptive learning model predictive control (ALMPC) scheme is proposed for the trajectory tracking of perturbed autonomous ground vehicles (AGVs) subject to input constraints. In order to estimate the unknown system parameter, we propose a set-membership-based parameter estimator based on the recursive least-squares (RLS) technique with the ensured nonincreasing estimation error. Then, the estimated system parameter is employed in MPC to improve the prediction accuracy. In the proposed ALMPC scheme, a robustness constraint is introduced into the MPC optimization to handle parametric and additive uncertainties. For the designed robustness constraint, its shape is decided off-line based on the invariant set, whereas its shrinkage rate is updated online according to the estimated upper bound of the estimation error, leading to further reduced conservatism and slightly increased computational complexity compared with the robust MPC methods. Furthermore, it is theoretically shown that the proposed ALMPC algorithm is recursively feasible under some derived conditions, and the closed-loop system is input-to-state stable (ISS). Finally, a numerical example and comparison study are conducted to illustrate the efficacy of the proposed method.

In this paper, considering the ubiquitously existing cyber attacks in cyber-physical systems (CPSs), we present a robust and resilient distributed model predictive control (MPC) strategy for CPSs with multi-agent architecture under denial-of-service (DoS) attacks to achieve the goal of cooperative regulation with all agents' states being regulated to their equilibrium. Each agent in the CPSs is subject to external disturbances, and the communication channels among agents might be affected by randomly occurring DoS attacks. To tackle these issues, firstly, a novel robustness constraint is designed to handle the uncertainties in the MPC algorithm. By adding this constraint, the state of the nominal system can be confined in a shrinking and tighter range compared to the classical MPC approach, thus resulting in enhanced robustness against uncertainties. Furthermore, a lengthened sequence transmission strategy is proposed to mitigate the effect of the lack of information in the communication channels induced by DoS attacks. At each time instant, the controller of each agent utilizes the predicted state information to compensate for the transmission block-out from one agent to another. Moreover, recursive feasibility for the control framework and the closed-loop stability for the overall system are guaranteed by theoretical analysis. Finally, simulation and comparison studies demonstrate the effectiveness of the proposed robust and resilient distributed MPC strategy.