Objective: Hybrid cardiovascular mock circuits (HMC), designed for dynamic testing of Ventricular Assist Devices (VAD), offer physiologic accuracy by sequestering model complexity in silico and ease of construction by reducing number of model elements in vitro. Despite superior response time and precision, pneumatic actuation is avoided in HMCs due to nonlinear dynamics and noise. We tested the hypothesis that a HMC consisting of a variable elastance-driven numerical circuit coupled to a pneumo-hydraulic physical circuit can be controlled without linearizing system dynamics. Methods: Reference left ventricular and aortic pressures generated in silico were tracked, respectively, in in vitro preload and afterload reservoirs by controlling non-linear pneumatic dynamics using the Lyapunov stability criterion. A centrifugal pump, the speed (i.e. flow) of which was adjusted using PID control, was interposed between the reservoirs and mimicked the VAD under evaluation. The flow of a recirculating gear pump was controlled by the backstepping method to equalize reservoir fluid volumes by rejecting pressure and flow disturbances. Sensor noise was reduced with discrete-time Kalman filtering. Results: Our results showed that normal, failing and assisted cardiovascular physiologies were numerically simulated and tracked at physical VAD terminals with high accuracy. Reservoir volumes remained stable at various combinations of heart rate, pressure, and VAD flow. Conclusion: The HMC described here offers a stable performance testing platform for VAD prototypes. Significance : This is the first proof that hybrid systems using pneumatic actuation at hydraulic interfaces can optimally be regulated with nonlinear controllers to achieve precise reference tracking and robust disturbance rejection.

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.

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.

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.

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.

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.

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.

In this paper, an event-triggered nearly optimal tracking control method is investigated for a class of uncertain nonlinear systems by integrating adaptive dynamic programming (ADP) and integral sliding mode (ISM) control. By introducing a neural network (NN) adaptive term, the designed ISM-based discontinuous control law is employed to eliminate the influence of the uncertainties and obtain the tracking error system constructed from the sliding mode dynamics, as well as relax the known upper-bounded condition of uncertainties. In order to guarantee the stability of tracking error system and improve the control performance, under the ADP technique, a critic NN is applied to approximate the optimal value function for solving the event-triggered Hamilton-Jacobi-Bellman equation and the event-triggered nearly optimal feedback control is obtained. The feedback control law is updated and transmitted to plant only when events occur, thus both the communication and the computational resources can be saved. Furthermore, the stability of tracking error is proven thanks to Lyapunov’s direct method. Finally, we provide two simulation examples to validate the developed control scheme.

This article researches the trajectory tracking problem for unmanned marine vehicles (UMVs) with disturbances and under denial-of-services (DoS) attacks in the wireless channel. By applying the partial form dynamic linearization algorithm, an equivalent data-driven model of the UMVs with ocean disturbances is firstly established. And the disturbances are estimated by using extended state observer, which improves the immunity of the UMVs to disturbances in the environment, and the robustness of the UMVs systems is better. It is the first time that the DoS attacks are considered under the data model for UMVs, and a novel data-driven adaptive trajectory tracking control framework is constructed. When the proposed equivalent data model suffers from DoS attacks which follows the Bernoulli distribution, an attack predictive compensation mechanism is devised to relieve the influence of DoS attacks. Based on it, the data-driven adaptive trajectory tracking controller is designed such that the error of trajectory tracking is convergent under DoS attacks and external disturbances. Finally, the effectiveness of the proposed data-driven control scheme and the predictive compensation mechanism is validated through the simulations.

This investigation proposes a dynamic event-triggered-based anti-disturbance control technique for the uncertain linear parameter varying (LPV) systems subject to multiple disturbances. The disturbances are comprised of two parts including the unavailable modeled disturbances and the available unmodeled disturbances. First, an observer is constructed to capture the unavailable modeled disturbances. Then, a dynamic event-triggered-based feedback controller is proposed. Further, under the developed event-triggered controller, sufficient conditions are presented for the uncertain LPV systems to achieve the multiple disturbances suppression and communication transmission resources saving. In the end, the reasonability of the raised dynamic event-triggered based anti-disturbance control scheme is verified by an example of a turbofan.

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 this article, the switching-like adaptive event-triggered dynamic output feedback H ∞ control for the interval type-2 (IT-2) Takagi-Sugeno (T-S) fuzzy system over networks with hybrid attacks and Markovian packet losses is investigated. The switching-like adaptive event-triggered mechanism (SAETM) is involved with the characteristic of dynamically adjusting trigger threshold according to the change of the system states and appropriately selecting the event-triggered scheme in the light of the packet loss process is creatively proposed. The hybrid attack which is consisted of deception attack and aperiodic Denial-of-Service (DoS) attack is considered, and the deception attack is expressed as the Bernoulli random variable, while the aperiodic DoS attack is characterized by the frequency and duration in view of the average dell-time (ADT) technique. By applying the packet loss dependent multiple Lyapunov function technique and iterative method, the sufficient condition is derived to address the stability of NCS, and the existence condition is established to guarantee the prescribed performance of the H ∞ controller. Finally, two simulation examples are applied to show the availability of the provided algorithm.

This article explores recursive algorithms for parameter identification issues of Hammerstein output-error systems. The proposed approach includes the key term separation auxiliary model recursive gradient algorithm, which utilizes the gradient search and the key term separation. To enhance computational efficiency, the system is decomposed into two or three subsystems through the hierarchical identification principle. Based on this, a key term separation auxiliary model two-stage recursive gradient algorithm and a key term separation auxiliary model three-stage recursive gradient algorithm are presented. The simulation results verify the validity of the obtained algorithms.

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.

For the nonlinear parameter-varying (NPV) model of unmanned surface vehicle (USV) with the consideration of the velocities on yaw and surge as well as wave disturbances, a robust H ∞ control method is proposed based on extended homogeneous polynomial Lyapunov function (EHPLF) to regulate heading for the superior performance on the rapidity, accuracy and robustness. Firstly, a NPV model of heading error is established to design a general form of a state feedback controller with a robust H ∞ performance. Secondly, a Lyapunov matrix with full states and varying parameter is constructed to derive the robust H ∞ global exponential stability conditions by Euler’s homogeneity relation for the NPV system, known as the EHPLF stability conditions. Thirdly, since the EHPLF stability conditions consist of a set of nonlinear coupled inequalities that cannot be directly solved by sum of squares (SOS) toolboxes, they are decoupled with matrix transformations to obtain the EHPLF-SOS stability conditions, which is solved for the parameters of the state feedback controller. Finally, the simulation results indicate that EHPLF method exhibits a superior performance on dynamic, steady-state and robustness.

This paper studies the practical fixed-time average consensus problem for general continuous linear multi-agent systems under switching topologies. Firstly, a distributed fixed-time consensus control protocol based on dynamic event-triggered mechanism is designed by using the local information exchange between individual agents. The protocol introduces auxiliary dynamic variables, and the triggering condition changes in real time based on the dynamic variable obtained online, which can significantly reduce the number of triggering events, effectively decrease the energy dissipation of the system and the update frequency of the controller. Then, under the designed control protocol, the sufficient conditions for the multi-agent system to solve the practical fixed-time average consensus problem are given, and it is proved that there is no Zeno behavior in the system. Finally, the simulation results verify the effectiveness of the conclusions.

Under the framework of the step-function method, the stability of a nonlinear fuzzy hybrid control system combining an impulsive controller and a continuous state feedback controller is investigated. Both the two controllers are assumed to be subject to both actuator saturation and time-varying delays, which has received little attention if any, in the existing studies. A new assumption is established enabling the use of generalized sector conditions to tackle the double saturation, and the conservatism of the stability results is remarkably reduced thanks to the improved step-function method. The stability theorem proposed in this paper removes restriction on the time delays of both controllers, which can be also applied to wider scopes of systems, including hybrid control systems with both stabilizing and instabilizing impulses, systems with varying impulsive gain, and systems with Zeno behavior. Numerical simulations of stabilization for different systems by delayed saturated hybrid control have been conducted, which demonstrate the validity of proposed theorems.

Health monitoring is critical for the maintenance and risk management of reinforced concrete (RC) structures. In this paper, a robust adaptive Kalman filter is proposed for an interstory drift estimation problem to show the health condition of RC structures in the case that the statistics or internal dynamics describing the signals and measurements are not known precisely. More precisely, we build an adaptive current Jerk model (ACJM) where the model parameters are updated in each time step to presuppose the statistics characterization of the RC dynamic, while the unknown measurement noise covariance is adapted based on a fixed-lag innovation with respect to measurements. Moreover, a robust adaptive Kalman filter is designed for the modeling mismatch in each time increment by solving a minimax game: one “hostile” player tries to select a worst model far from the proposed ACJM with an exponential decay tolerance, while an optimum filter is designed by minimizing the estimation error according to this worst model. Finally, some simulation and experimental results show the effectiveness of the proposed algorithm.