The paper devises a H ∞ -norm theory for the CSVIU (control and state variations increase uncertainty) class of stochastic systems. This system model appeals to stochastic control problems to express the state evolution of a possibly nonlinear dynamic system restraint to poor modeling. Contrary to other H ∞ stochastic formulations that mimic deterministic models dealing with finite energy disturbances, the focus is on the H ∞ control with infinity energy disturbance signals. Thus, the approach portrays the persistent perturbations due to the environment more naturally. In this regard, it requires a refined connection between a suitable notion of stability and the systems’ energy or power finiteness. It delves into the control solution employing the relations between H ∞ optimization and differential games, connecting the worst-case stability analysis of CSVIU systems with a perturbed Lyapunov type of equation. The norm characterization relies on the optimal cost induced by the Min-Max control strategy. The rise of a pure saddle point is linked to the solvability of a modified Riccati-type equation in a form known as a generalized game-type Riccati equation, which yields the solution of the CSVIU dynamic game. The emerging optimal disturbance compensator produces inaction regions in the sense that, for sufficiently minor deviations from the model, the optimal action is constant or null in the face of the uncertainty involved. A numerical example illustrates the synthesis.

In this paper, by considering the existence of unavailable state, external disturbance, and denial of service (DoS) attacks, an anti-disturbance trajectory tracking controller is proposed for the air-ground system composed of unmanned autonomous helicopter (UAH) and unmanned ground vehicle (UGV). Initially, by combining the advantages of conventional event-triggered scheme (ETS) and memory ETS, a switching-like event-triggered mechanism is put forward, which can cause less data transmissions without reducing control performance, and effectively restrain the DoS attacks. Secondly, by dividing the DoS attacks into active intervals and sleep ones, the concept of acknowledgement character technology (ACK) is presented to determine the suitable type of time intervals. Thirdly, the switching-like dynamic ETS is introduced for the sleep intervals of DoS attacks while the ETS with fixed triggered-threshold is exploited for the active intervals. Fourthly, based on the occurrence of the DoS attacks, a switching-like observer and a normal one are respectively proposed to estimate the UGV state, the UAH state, the reference input of the UGV, and the disturbance of the UAH, which are utilized to design the anti-disturbance tracking controller. Fifthly, an augmented closed-loop system consisting of observation errors and tracking error is established and its stability is analyzed by using Lyapunov stability theory. Then, a sufficient condition on co-designing the parameters of switching-like ETS, observers, and tracking controller is presented in terms of linear matrix inequalities (LMI). Finally, the validity and superiority of the proposed control scheme are verified by resorting to simulations and comparisons.

The paper focuses on the problem of formal synthesis of controllers for control-affine nonlinear systems against complex properties. Our goal is to design a closed-form control policy that guarantees the satisfaction of complex properties that are expressed using ( ω)-regular languages and equivalently recognized by Non-deterministic Büchi Automata (NBA). We propose leveraging a funnel-based control approach to provide a closed-form solution to the problem. Our approach decomposes the specification represented by NBA into a sequence of reachability problems which we solve using a funnel-based control approach. Controllers associated with each reachability problem are then combined to design a hybrid control policy enforcing the desired ( ω)-regular property. We demonstrate the effectiveness of the proposed results on room temperature control and mobile robot motion control case studies.

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.

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.

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.

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.

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.

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.