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.

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.

This article studies integral-type event-triggered model predictive control (MPC) of continuous-time nonlinear systems. An integral-type event-triggered mechanism is proposed by incorporating the integral of errors between the actual and predicted state sequences, leading to reduced average sampling frequency. Besides, a new and improved robustness constraint is introduced to handle the additive disturbance, rendering the MPC problem with a potentially enlarged initial feasible region. Furthermore, the feasibility of the designed MPC and the stability of the closed-loop system are rigorously investigated. Several sufficient conditions to guarantee these properties are established, which is related to factors, such as the prediction horizon, the disturbance bound, the triggering level, and the contraction rate for the robustness constraint. The effectiveness of the proposed algorithm is illustrated by numerical examples and comparisons.

This article proposes a model predictive control as a secure service (MPCaaSS) framework for cyber–physical systems (CPSs) in the presence of both cyber threats and external disturbances. First, in order to take advantage of the cloud-edge computing, we design a double-layer controller architecture by using a novel control parameterization based on Gaussian radial basis functions. In this controller architecture, the cloud-side controller optimizes the controller parameters of the edge-side controller, whereas the edge-side controller implements the real-time control law using the generated controller parameters. Second, in order to securely transmit data packets, we integrate an encoding scheme and an elliptic curve cryptography (ECC)-based encryption into the proposed MPCaaSS framework. Then, the controller parameters and the state measurements can be encrypted such that no malicious attackers can corrupt and intercept the transmission. It is shown that the recursive feasibility of MPCaaSS is achieved under some sufficient conditions, and the robust stability of the closed-loop system is guaranteed if the optimization problem is recursively feasible. Simulated examples are conducted to demonstrate the effectiveness of the proposed method.

Traditional manual search for large-scale unknown areas is inefficient and hazardous, making multi-UAV (unmanned aerial vehicle) collaborative search a growing trend. However, existing methods still have shortcomings in complex environments or during sudden failures. This paper proposes a multi-UAV adaptive cooperative coverage search method based on area dynamic sensing. First, the search problem is modelled as an optimization problem, and a sensing set segmentation method is introduced along with performance metrics. Secondly, perception partitioning is used to avoid redundant detection, and dynamic path guidance technology drives the UAV swarm toward unexplored areas. Finally, an auction allocation task strategy is employed to plan optimal paths for each UAV in real-time, and collision avoidance algorithms are improved to enhance safety. Experiments demonstrate that this method excels in coverage rate, interference resistance, and environmental adaptability.