This note concerns the problem of k-hop connectivity in a network of mobile agents, which is achieved if any pair of agents can communicate with each other through a link of k-1 or fewer intermediate nodes. We propose linear constraints involving binary optimization variables to ensure k-hop connectivity. Such constraints are then integrated into a Mixed-Integer Linear Programming (MILP) trajectory planning model. Simulation results illustrate the application of the proposed method and the effect of varying k in the context of a mission involving the visitation of multiple targets.

In recent works, well-known three-dimensional localization methods studied in the aerospace field have been revisited for applications on multirotor aerial vehicles (MAVs). However, most of these classic methods employ stochastic estimators that are asymptotically stable, in a stochastic sense, and exhibit high sensitivity to disturbances and model uncertainties. The goal of this paper is to propose and evaluate a novel solution to the localization problem of MAVs, employing multivariable robust observers based on sliding-mode techniques. Aiming to improve on the existing methods usually based on the extended Kalman filter, this paper investigates sliding-mode techniques to reach finite-time stabilization of the estimation error and provide robustness to disturbances and uncertainties. The super-twisting algorithm (STA) is considered as a starting point for its recognized performance when used to design differentiators. In particular, a modification of the STA is proposed, replacing one of its terms with a certain time-varying function that allows the upper bound of the settling time of the resulting algorithm to be a direct adjustable parameter. The proposed algorithm’s behavior is numerically evaluated and is shown to yield the predicted properties even in the presence of bounded disturbances and uncertainties. Additionally, an attitude determination problem employing the proposed algorithm is presented as an application. The three-dimensional attitude and angular velocity of an MAV are accurately estimated under strict settling-time restrictions, using only the vector measurements provided by an accelerometer and a magnetometer.

The malicious physical attacks from both sensor and actuator side make real threats to the security and safety of autonomous ground vehicles (AGVs). This paper focuses on the problem of neural-network-based event-triggered adaptive security control (ET-ASC) scheme for path following of AGVs subject to arbitrary abnormal actuator signal. Firstly, we assume that an arbitrary abnormal signal is caused by arbitrary malicious attacks or disturbances from actuators. Then, radial basis function neural network (RBF-NN) is used to reconstruct such abnormal actuator signal. Secondly, modelling issues on security path following control of AGVs with Sigmoid-like ETC scheme are shown when the AGV is suffering from abnormal actuator signal. In what follows, an ET-ASC scheme is developed to mitigate the adverse effects of abnormal actuator signal with the reconstructed abnormal signal based on a novel Sigmoid-like event-triggered communication scheme. By using the proposed RBF-NN-based ET-ASC scheme, H ∞ control performance can be guaranteed under arbitrary malicious actuator signal rather than such attacks following a specific probability distribution. Finally, some simulation experiments are provided to verify the effectiveness of proposed ET-ASC scheme.

The fixed time event-triggered control for high-order nonlinear uncertain systems with time-varying state constraints is investigated in this paper. First, the event-triggered control (ETC) mechanism is introduced to reduce data transmission in the communication channel. In consideration of the physical constraints and engineering requirements, time-varying barrier Lyapunov function (BLF) is deployed to make the system states confined in the given time-varying constraints. Then, the radial basis function neural networks (RBF NNs) is used to approximate the unknown nonlinear terms. Further, the fixed time stability strategy is deployed to make the system achieve semiglobal practical fixed time stability (SPFTS) and the convergence time is independent of the initial conditions. Finally, the proposed control scheme is verified by two simulation examples.

This paper considers the problem of adaptive control against deception attacks for a class of switched nonlinear cyber-physical systems (CPSs), in which each subsystem has more general and unknown nonlinearities. Specifically, an adaptive controller is designed for CPSs with unknown switching mechanisms to mitigate the impact of state-dependent sensor attacks and input-dependent actuator attacks. Compared with the existing researches, the actuator attacks considered in our paper are input-dependent, which means the controller is substantially attacked, besides, the signs of unknown time-varying gains caused by state-dependent sensor attacks and input-dependent actuator attacks are all unknown. To deal with these scenarios, Nussbaum-type functions are introduced. In addition, by constructing a common Lyapunov function for all subsystems, the closed-loop system signals are proved to be globally bounded under arbitrary switchings. Finally, we give a simulation example of a continuously stirred tank reactor system with state-dependent sensor attacks and input-dependent actuator attacks to illustrate the effectiveness of our results.

An exoskeleton robot is a sample of a wearable robot. One of the most critical challenges in developing wearable robots is the application of the interactive force between human and robot. Force sensors need to be placed on the robot. Consideration in using these sensors needs to be given to factors such as cost, noise, and weight. One way that can be used to help with the operation of the exoskeleton is to support the sensors with observers. This study will estimate the interactive force applied to a human arm model and the exoskeleton robot. The Sliding Mode Control (SMC) method will be employed to design a chattering-free robust fixed-time controller and observer, for estimating the states of the human arm and exoskeleton robot. Utilising this information from state observers, the interactive force is estimated. The state observer and the controller work together in real-time (online estimation). The Lyapunov theory is used to show the fixed-time stability analysis of the controller and the observer. Numerical simulation with three scenarios demonstrates the performance of the proposed design.

Digital twin (DT) has been around for many years, but there is no widely accepted standardized tool or method. In this study, system dynamics was proposed as a tool that can be integrated into multi-scale, multi-physics, and multi-disciplinary, which are continuously becoming issues in the DT field. Various heterogeneous data from multiple protocols or platforms could be integrated into one model. Through the five-step model building process, it was possible to integrate the theories and various models studied in the past. In this study, the operation and maintenance system of ROK Naval ships is implemented as a proposed method. Various physics, scales, and disciplines such as failures of ships, maintenance ability of repair shops and schedule pressure of mechanics were reflected. It was possible to observe non-intuitive correlations and potential problems caused by the latent effect of the high-fidelity DT model. The proposed method is also capable of updating through continuous data calibration or real-time interworking with external statistical analysis tools.