In this paper, we discuss how to synthesize stabilizing Model Predictive Control (MPC) algorithms based on convexly parameterized Integral Quadratic Constraints (IQCs), with the aid of general multipliers. Specifically, we consider Lur’e systems subject to sector-bounded and slope-restricted nonlinearities. As the main novelty, we introduce point-wise IQCs with storage in order to accordingly generate the MPC terminal ingredients, thus enabling closed-loop stability, strict dissipativity with regard to the nonlinear feedback, and recursive feasibility of the optimization. Specifically, we consider formulations involving both static and dynamic multipliers, and provide corresponding algorithms for the synthesis procedures. The major benefit of the proposed approach resides in the flexibility of the IQC framework, which is capable to deal with many classes of uncertainties and nonlinearities. Moreover, for the considered class of nonlinearities, our method yields larger regions of attraction of the synthesized predictive controllers (with reduced conservatism) if compared to the standard approach to deal with sector constraints from the literature.

This paper presents a Youla-Kucera based interpolation between a set of Linear Parameter-Varying (LPV) controllers, each one being a gain-scheduled of Linear Time-Invariant (LTI) controllers designed separately for different operating points. The gain-scheduling is achieved based on Youla-Kucera (YK) parameterization. A generalized LPV-YK control structure is designed to interpolate between various LPV controllers. The closed-loop system is proved to guarantee the quadratic stability for any continuous/discontinuous interpolating signals in terms of a set of Linear Matrix Inequalities (LMIs). The proposed method can help multi-variable and multi-objective systems to achieve high performances at different operating conditions and different critical situations regardless of the interpolation rate. A numerical example is simulated to show the importance of the proposed method to achieve different objectives for lateral control of autonomous vehicles. In addition, the approach has been tested on a real Renault ZOE vehicle to validate its real performance, and compare it with a standard polytopic LPV controller.

In this paper, an enclosing control problem is investigated for nonholonomic mobile agents with a moving target of unknown velocity. An adaptive observer containing two internal variables is first designed for each agent to compensate for the lack of the target velocity information. One variable is designed to estimate the unknown target velocity and further its estimation error is assessed by the other internal variable to subsequently guarantee the control performance. Then using the estimated information from the adaptive observer, a dynamic control law for circular formation of nonholonomic agents around the moving target is designed by a backstepping process. The global asymptotical stability of the closed-loop system is achieved under the proposed dynamic control law with the adaptive observer. Finally, a simulation is conducted to demonstrate the effectiveness of the proposed approach.

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.

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.