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The primary focus of this paper is to investigate the leader–follower consensus problem of multi-agent systems (MASs) with discrete and distributed delays in complex domains. We propose a new hybrid consensus protocol that incorporates a continuous-time protocol based on the communication topology of follower agents, along with an event-triggered pinning impulsive control (ETPIC) protocol. Using the Lyapunov functional method in complex domains, we establish delay-dependent sufficient conditions for leader–follower consensus of delayed complex-valued MASs. Our results demonstrate that the proposed hybrid protocol can ensure leader–follower consensus even when the size of discrete and distributed delays exceeds the length of intervals between two consecutive triggering instants. Furthermore, we prove that the Zeno phenomenon can be excluded under the proposed control protocol. In particular, as a special case, we derive the leader–follower consensus result for delay-free complex-valued MASs based on a reduced hybrid control protocol. Two numerical examples are presented to validate the effectiveness of the proposed control scheme. • This paper studies leader–follower consensus problem of multi-agent systems with discrete and distributed delays in the complex field. • It employs a hybrid consensus protocol with both continuous control and event-triggered pinning impulsive control. • Criteria on leader–follower consensus are derived based on the proposed protocols. • The pinning mechanism reduces transmission redundancy under leader-following structure. • Zeno phenomenon can be excluded under the proposed control protocol. [ABSTRACT FROM AUTHOR]

The primary focus of this paper centers on investigating unconstrained distributed optimization problems over undirected or directed graphs. One noteworthy departure from current distributed optimization algorithms in the continuous-time domain is the integration of nabla fractional calculus, which augments algorithmic performance by reducing iterative complexity. Through rigorous analysis, this paper demonstrates that the two algorithms presented converge at the Mittag–Leffler rate to the precise solution of a distributed optimization problem over a connected undirected graph or a connected balanced directed graph with strongly convex and smooth objective functions. The research findings provide valuable insights into the potential utility of nabla fractional calculus in distributed optimization problems, highlighting the possibility of enhancing the efficiency and effectiveness of distributed optimization algorithms. • Nabla fractional distributed optimization algorithm for undirected graphs. • Nabla fractional distributed optimization algorithm for directed graphs. • Convergence analysis via nabla fractional order system theory. [ABSTRACT FROM AUTHOR]

This paper addresses the stochastic communication protocol (SCP) based controller design problem for discrete delayed system with stochastic uncertainty and Markovian packet dropouts. This issue is frequently occurred in networked control system where the packet dropouts bring obstacle for controller design. Especially, the controller terms are not allowed to be transformed directly into actuator side under the SCP. Thus, the traditional approaches are not effective to solve this design issue. In this paper, a novel sliding mode control (SMC) mechanism is given to handle the influences caused by packet dropouts, time-varying delay and SCP. A protocol-based controller is designed to guarantee that the closed-loop system is mean-square asymptotically stable with satisfactory H ∞ performance. Moreover, an optimization algorithm is further provided to compute the protocol-based gain matrix. Finally, a simulation example is proposed to verify the effectiveness of the presented protocol-based SMC design procedure. [ABSTRACT FROM AUTHOR]

This paper proposes a cooperative target allocation method for efficiently utilizing air-sea heterogeneous unmanned vehicles to counter saturation attacks by low-cost UAVs. Specifically, a bilevel model that combines the advantages of heuristic algorithms and the coalition formation game (CFG) is constructed by integrating unmanned vehicles' motion and attack attributes. The upper layer model aims to utilize the advantages of the heuristic algorithm to generate a feasible target allocation scheme in a shorter time. A genetic algorithm constrained by inheritance conditions (GAC) is proposed to solve the problem of the low efficiency of traditional genetic algorithms in solving the upper model. The lower layer model is established based on a coalition formation game, which aims to continuously and stably optimize the target allocation scheme. A search method based on mobile players is designed to form a stable coalition structure faster when the number of unmanned vehicles is enormous. Simulation results show the feasibility and superiority of the algorithm proposed in this paper in dealing with allocation tasks of different complexity. [ABSTRACT FROM AUTHOR]

This paper studies the dynamic output feedback (OF) control problem for unmanned aerial vehicles (UAVs) with limited network bandwidth by a genetic-algorithm-assisted design approach. In order to make full use of the limited network bandwidth, only partial measurement information is transmitted to the controller via the sensor-to-controller network. Round-Robin protocol is introduced to administrate the transmission rule. Sufficient conditions are presented to make sure that the closed-loop UAV system is exponentially stable with the required L 2 -gain performance. However, due to the nonlinear coupling terms of the dynamic OF controller parameters and some redundant variables in the system modeling, the established conditions are non-convex and difficult to be solved. Fortunately, genetic algorithms (GA) show good performance in solving optimization problem with nonlinear and non-convex constraints. Therefore, this paper skillfully combines GA with linear matrix inequality techniques to solve the established conditions. Finally, all the dynamic OF controller parameters and the minimal L 2 -gain can be obtained simultaneously. At the end of this paper, an UAV networked control example and a numerical example are performed to verify the effectiveness and superiorities of our method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel procedure for discretizing uncertain time-invariant continuous-time linear systems in polytopic domains. The approach is based on multistep method theory and involves mixing models obtained with different multiples of a fixed step-size (sampling time), thereby increasing data availability. An optimization procedure is employed to determine the coefficients that minimize the residual discretization error, and then these coefficients are combined to construct an augmented structure for the discrete-time system. The discretization error is computed using a grid search and incorporated into the discrete-time system formulation. Using the resulting discrete-time system, a state feedback methodology is applied to synthesize digital controllers capable of stabilizing the original continuous-time system. The paper presents two sufficient Linear Matrix Inequality (LMI)-based conditions for designing robust controllers specific to the proposed structure. The first condition utilizes a constant Lyapunov function, while the second condition employs a parameter-dependent Lyapunov function. In both cases, the proposed conditions guarantee asymptotic stability for the continuous-time system in closed-loop. A numerical experiment is conducted to illustrate the validity and effectiveness of the proposed method. • Discretizatizing continuous-time time-invariant uncertain systems is an open problem. • Considering delayed steps in the discretization can provide more information. • Suitable discrete-time control design procedures are presented. [ABSTRACT FROM AUTHOR]

To solve the ship course control problem with nonlinear terms, input saturation, and complex noise, this paper proposes a saturated ship course control method with complex noise based on generalized fuzzy hyperbolic model (GFHM). Due to the characteristics of fewer identification parameters, GFHM can simplify the complexity of traditional ship fuzzy models. GFHM is more suitable for multi-variable nonlinear systems such as ships of which variables are limited and difficult to measure. Furthermore, to deal with the input rudder angle saturation problem caused by the limited capability to compensate the shipboard equipment controller, an auxiliary system is proposed. And the complex noise in the navigation environment is described by the random process. Then, a new type of ship fuzzy course controller is designed based on the theoretical framework of random differential equations (RDEs) and it is proved that the ship course system under the proposed GFHM-based controller is noise-to-state stable in probability (NSS-P) and the state is an asymptotic gain in probability (AG-P). The simulation results show that the proposed algorithm in this paper can effectively control the ship's course. [ABSTRACT FROM AUTHOR]

Concentrated on the marine surface vessel (MSV) with input saturation, model uncertainties and unknown disturbances, this paper proposes the error-constrained line-of-sight guidance-based prescribed performance dual event-triggered path following control (ECLOS-PPDETPFC) strategy which applies the dual relative threshold event-triggered mechanism (ETM) to both guidance and controller of the MSV path following. We apply the prescribed performance function (PPF) as the bounds of asymmetric modified barrier Lyapunov function (AMBLF) to construct the prescribed performance asymmetric modified barrier Lyapunov function (PPAMBLF). In guidance, event-triggered error-constrained line-of-sight (ETECLOS) is constructed by relative threshold ETM and AMBLF to reduce the calculation pressure and keep position errors within the constraint range; In controller, prescribed performance event-triggered path following control (PPETPFC) is based on the relative threshold ETM and PPAMBLF, which makes the heading error meet the prescribed performance requirements, while reducing the loss of actuator. At the same time, the adaptive auxiliary systems are used to compensate the influence of input saturation, radial basis function neural networks (RBFNNs) and adaptive laws are used to approximate the model uncertainties and composite disturbances. According to the Lyapunov stability theory, all signals are semi-globally uniformly ultimately bounded (SGUUB) and the Zeno phenomenon is avoided. Finally, the comparative experiment shows the effectiveness and superiority of the designed control strategy. • This paper proposes the error-constrained line-of-sight guidance-based prescribed performance dual event-triggered path following control (ETECLOS-PPETPFC) strategy. • This control strategy applies the dual relative threshold event-triggered mechanism (ETM) to both guidance and controller of the MSV path following, which can reduce controller calculation pressure and actuator loss. • This control strategy can reduce the update frequency of guidance heading angle and controller. • This control strategy can not only constrain the errors of MSV position, but also make the heading error meet the prescribed performance requirements. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time control and bumpless transfer control are investigated for switched systems under asynchronously switching. First, a class of dynamic output feedback controllers are designed to stabilize the switched system with measurable system outputs. Considering the improvement of transient performance, the bumpless transfer control and finite-time control are further studied in the controller design. To avoid the control bumps, a practical filter is introduced to make the control signal smoother and continuous. Furthermore, to derive a finite-time bounded system state over short-time intervals, the finite-time analysis is considered in managing the switching process with the average dwell time. New criteria are proposed to analyze the finite-time stability and finite-time boundedness for the closed-loop system and solvable conditions are newly proposed to optimize the controller gain. Finally, the superiorities of the proposed method are validated through an application to a boost converter. [ABSTRACT FROM AUTHOR]

This paper contributes to the problem of tracking control for nonlinearly vibrating structure supporting a vibrational mass-carrying string with unknown string tension. The control scheme is constructed based on optimally designed reaching law considering actuator force limit, parametric uncertainties and Lyapunov stability conditions. The reaching law is designed through real-time detection of stability margins and analyzing actuator capabilities such that system dynamics are either driven into reaching or attraction phase in finite time. The control algorithm boasts robustness against parametric uncertainty by narrowing stability bounds and features an online string tension estimation engine based on data analysis obtained from acceleration and proximity sensors measurements. In order to provide smooth tracking, string vibration suppression is achieved by incorporating corresponding dynamical terms in construction of control algorithm. The closed-loop system is simulated alongside conventional discrete sliding mode technique, the results of which are compared and analyzed to pinpoint viable advantages. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problems of finite-region boundedness and dissipativity analysis of two-dimensional (2-D) singular Roesser systems with mixed directional time-varying delays. The concepts of singular finite-region boundedness (SFRB), which deduces the property of singular finite-region stability (SFRS) when exogenous disturbance is ignored, and finite-region (Q , S , R)-dissipativity are first proposed. Then, by using an energy function constructed as a weighted 2-D Lyapunov–Krasovskii functional, and by utilizing zero-type free matrix equations, new delay- and time-region-dependent conditions, which guarantee the properties of finite-region stability and dissipativity with respect to a quadratic supplied rate function, are derived. The obtained results are shown to encompass and extend existing works in the literature. The effectiveness of the analysis results is validated by numerical examples. [ABSTRACT FROM AUTHOR]

This paper considers leader–follower consensus of linear multiagent systems with unknown inputs. The proposed technical framework consists of two processes, that is, estimation and control. For the estimation process, we design a complementary sector region based on a novel reset variable. In this region, a class of distributed extended state observers that depends on the above variables is constructed, which completes the estimation of system states and unknown inputs, simultaneously. This reset strategy also guarantees that the system states are quickly estimated. For the control process, the distributed output feedback controller associated with the reset strategy is designed. The unknown input is also compensated under the reset strategy, which achieves the objective of consensus. Finally, effectiveness of the presented method is validated by an example. [ABSTRACT FROM AUTHOR]

This paper investigates the design of fault-tolerant adaptive event-triggered integral sliding mode control (FTAETISMC) for a class of uncertain systems in the presence of partial actuator failures. The proposed control scheme guarantees the closed-loop system to operate properly in case of partial actuator failures and ensures the reachability of the sliding mode surface. In order to reduce the consumption of system resources, an adaptive event-triggered (ET) mechanism is introduced, where the threshold is based on a function of a non-negative adaptive parameter. By choosing the parameter of the integral sliding mode manifold, the asymptotic stability of the system can be achieved. To ensure that the system does not suffer from Zeno phenomenon, there exists a positive lower bound of the inter-event time. Finally, simulation results of a numerical example are presented to prove the benefits of the proposed method. • A networked control systems with matched and unmatched uncertainties under actuator failures is discussed. • A robust controller with integral sliding mode can eliminate uncertainties. • An adaptive event-triggered mechanism is proposed. • A minimum positive lower bound on the inter-event time to avoid Zeno phenomenon is obtained. [ABSTRACT FROM AUTHOR]

This paper presents an extension method of iterative learning control (ILC) to address the applications associated with non-repetitive time-varying systems (NTVSs). Conventional ILC approaches employ fixed nominal system models, but non-repetitive time-varying models may lead to accumulated model uncertainties, which fails to satisfy the robust convergence conditions. To tackle this issue, a novel ILC algorithm with parameter estimation is proposed using back propagation neural network. This algorithm incorporates an approach that utilizes Bayesian regularization training mechanism to accurately estimate non-repetitive time-varying parameters. Through comprehensive experiment on Monolithic XY Stage, the performance of proposed algorithm is validated to demonstrate its feasibility and effectiveness while handling tasks on NTVSs. [ABSTRACT FROM AUTHOR]

This paper considers a quantized consensus problem for nonlinear multi-agent systems (MAS) using iterative learning control (ILC). For actual digital communication networks, agents can only transmit state information with limited bandwidth. Therefore, a Sigma-Delta (Σ Δ) quantizer with a finite number of quantized bits is used to satisfy the communication network requirements. In addition, the introduction of network issues like input saturation and time delay make the problem more practically relevant. Due to the discontinuity caused by quantization, Filippov's non-smooth analysis theory is required to analyze the convergence performance of the MAS. The desired asymptotic consensus can be achieved with limited quantized information and possibly even a single bit between each pair of adjacent agents. Finally, numerical simulations are presented to illustrate the effectiveness of our theoretical analysis. [ABSTRACT FROM AUTHOR]

The Mittag–Leffler stability and stabilization of delayed fractional-order memristive neural networks(DFMNNs) are investigated in this paper. First, two new fractional Halanay inequalities are established by solving two fractional-order non-autonomous differential inequalities. Next, by using the proposed fractional Halanay inequalities, a novel Razumikhin-type theorem for Mittag–Leffler stability of delayed fractional-order systems is presented, which is an extension of the so-called Razumikhin theorem for integer-order delayed differential systems. Applying the Razumikhin-type theorem to the DFMNNs, several Mittag–Leffler stability and stabilization criteria are obtained. Finally, the validity of the proposed results is shown by two numerical examples. [ABSTRACT FROM AUTHOR]

Based on game theory, this paper considers the consensus problem of second-order hybrid multiagent systems (HMASs) in a leader–follower framework, which includes continuous-time multiagents and discrete-time multiagents. Firstly, the competitive relationship between two groups is modeled as a multi-player game type. To achieve consensus, we design different cost functions according to the rules of the game and assume that each player has a global purpose of minimizing their own cost. Moreover, the players adjust their states at the next moment according to the Nash equilibrium solution. Then, using matrix theory and stability theory, we analyze the necessary and sufficient conditions for the consensus problem of the second-order HMASs with different leaders, and show that the control parameters (sampling interval and feedback gain) and the eigenvalues of the network topology have essential impact on the consensus of the system. Finally, the validity of the results is tested by simulation examples. [ABSTRACT FROM AUTHOR]

This paper investigates the resilient leader-following consensus problem for a class of high-order multi-agent systems subject to unmatched lower triangular uncertainty dynamics and sensor–actuator attacks. Compared with previous work, the sensor and actuator attacks are modeled to be both time-varying and state-dependent, which makes the resilient consensus control more challenging. To deal with the unmatched uncertainties and sensor–actuator attacks, a new compensator with two online tuned dynamic gains is proposed relying only on the relative output measurements of neighboring agents. Then, using only the local compensation information, a fully distributed output feedback resilient protocol is designed to guarantee the leader-following consensus. Sufficient conditions in terms of matrix inequalities are derived to determine control gains and compensator parameters and simulation studies are presented to demonstrate the proposed theoretical results. [ABSTRACT FROM AUTHOR]

The localization is a very important task in many internet of things (IoT) based applications. Due to high requirements of communication cost and power consumption, however, existing localization methods are hard to apply for practical IoT applications with low power and narrow frequency. To answer this problem, in this paper, we propose a fully distributed low energy localization method by incorporating the distributed quantization aware least mean square (DQA-LMS) algorithm in a mobile diffusion narrowband IoT (NB-IoT) network. Each mobile network node in this case is assumed to use a low-power and low-resolution analog to digital converter (ADC). First, we address the performance analysis of the mobile networks in the presence of the quantized data. Second, we evaluate the localization performance of the proposed method with differently leveled quantization data and confirm that it can perform suitably even in the one-bit data case. Finally, we collate the localization performance of the Mobile DQA-LMS with the mobile distributed LMS (Mobile DLMS) and show that for all the quantized data cases, the performance of the mobile DQA-LMS is better than the conventional DLMS in terms of mean square deviation (MSD). [ABSTRACT FROM AUTHOR]

In this paper, the fixed-time control for a class of second-order nonlinear systems with matched disturbance is addressed by using terminal sliding mode control technique. To improve the control performance of traditional fixed-time control method, a new fixed-time stability theorem is constructed by introducing a simple linear term, and theoretical analysis shows that the proposed control method provides faster convergence speed and more accurate estimate of the upper bound of settling time. Moreover, the practical fixed-time stability is also discussed in details. Based on the proposed fixed-time stability theorem, a fixed-time terminal sliding mode controller for a class of second-order nonlinear system is designed to obtain a bounded settling time independently of the initial conditions of the system. Simulations are carried out to verify the feasibility of the proposed control method. [ABSTRACT FROM AUTHOR]

An input–output finite-time filtering design problem for a class of delayed Takagi–Sugeno fuzzy systems with external disturbance input is discussed in this paper. Specifically, the filter design uses the imperfect premise matching scheme that relaxes the requirement that the filter and system membership functions be the same. This method can improve the flexibility of the filter design by incorporating membership function information into the input–output finite-time stability analysis. Using the system and filter states, an augmented filtering system is obtained. The required stability criteria are then derived using the Lyapunov–Krasovskii stability theorem and a few recent inequality approaches. Following that, the desired filter gain matrices are determined. The developed filtering design method is validated through some demonstrative examples. [ABSTRACT FROM AUTHOR]

Channel estimation of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) is crucial for the application of wireless transmission. The signal system is susceptible to external noise, which reduces the accuracy of channel estimation. The denoising of the received signal is a research hotspot and challenge for channel estimation. Therefore, this paper proposes a quantum mechanics denoising-based channel estimation method. The proposed quantum mechanics denoising-based algorithm has the advantages of not relying on the original conditions, no grid error, and strong adaptive ability. The first part is that the received noisy signal is denoised. The conversion between the signal model of mmWave massive MIMO and the physical model of quantum mechanics needs to be solved. The received noisy signal is equivalent to the potential of the stationary Schrodinger equation. Then, the Hamiltonian matrix is constructed by the received signal and the corresponding eigenvalues and eigenvectors are calculated. The eigenvectors of the Hamiltonian matrix are related to the energy of Schrodinger equation, which are determined to the adaptive basis. In addition, the received signal is projected onto the adaptive basis to calculate the coefficients. The denoised received signal is reconstructed through the soft threshold processing of the coefficients. The second part is that channel estimation is performed on the denoised received signal using the l 1 / 2 -singular value decomposition (SVD)-based algorithm. The angle parameters are iteratively moved to the actual values by gradient descent. Besides, the initial values of the angles are obtained through the SVD preprocessing method. Simulation results show that the proposed quantum mechanics denoising-based method exhibits good estimation accuracy. [ABSTRACT FROM AUTHOR]

This paper introduces the signed graph into fractional-order coupled neural networks (FCNNs) and the bipartite synchronization is investigated for leaderless FCNNs. Instead of formulating leader's state or isolated node's state as the synchronization reference target, the bipartite synchronization of leaderless FCNNs is discussed by developing a direct error approach. First, an important fractional-order inequality is rigorously proved by contradiction. By virtue of fractional-order inequality, gauge transformation and several analytical tools, the criteria of bipartite leaderless synchronization are obtained for FCNNs with heterogeneous and homogeneous coupling weights. Specially, for the two types coupling weights, the adaptive pinning schemes are adopted which just rely on partial network information based on the spanning tree and connected dominating set, respectively. Eventually, the theoretical analysis is verified by two numerical simulations. [ABSTRACT FROM AUTHOR]

The path planning and tracking control problem of autonomous vehicle with model matched uncertainty and external disturbance is studied in this paper. A safe lane change zone is designed by considering lane change time, distance, and driving speed. Meanwhile, the candidate paths are generated using cubic quasi-uniform B-spline curve, and the optimal path is selected based on feasibility, comfort, and efficiency criteria. The proposed planning method ensures an efficient and comfortable lane change path without collision with surrounding vehicles. Model uncertainty and external disturbance may affect lane change tracking control of autonomous vehicle. Hence, a fault-tolerant path tracking controller is implemented by using deep neural networks and adjustable zonotope-tube model predictive control. A deep neural network-based controller is proposed to compensate for model matched uncertainty, thereby enhancing the fault tolerance of the control system. Then, an adjustable zonotope-tube model predictive controller is designed to enclose the actual trajectory within a zonotope-tube with the nominal state as the center and the disturbance set as the radius by using the feedback control to achieve the asymptotic stabilization of the closed-loop system. The effectiveness of this path planning and tracking control method is finally verified by numerical simulation. • A lane change path planning method suitable for structured roads is proposed, which is based on the utilization of a safe lane change zone and cubic quasi-uniform B-spline curves. The proposed method ensures an efficient and comfortable lane change path. • A fault-tolerant path tracking controller is presented for autonomous vehicle, which incorporates a deep neural network and adjustable zonotope-tube model predictive control. The controller effectively handles model uncertainty and external disturbances. • The computation of robust positive invariant set is performed using the adjustable zonotope-tube method, which enhances scalability and reduces computational cost. [ABSTRACT FROM AUTHOR]

This paper investigates the consensus problem for nonlinear multiagent systems (MASs) with unknown time-varying measurement sensitivity and infinite communication delays. Unlike existing works, the output measurement sensitivity is described as time-varying, and an infinite communication delay is considered between the agents. Under this condition, a novel output feedback control algorithm is developed. First, based on the hierarchical technique and Lyapunov-based time-domain method, the high-gain compensator and observer are constructed to tolerate the effect of infinite communication delays and accurately estimate the state of the leader. Subsequently, a new distributed k-filter and output feedback controller are proposed, which not only further relax the traditional Lipschitz conditional conservatism but also greatly simplify the design process. It is proved that the closed-loop system is globally stable and all agents can achieve consensus. Finally, the effectiveness of the proposed method is verified through the simulation of the single-link manipulators. [ABSTRACT FROM AUTHOR]

This paper investigates the detectability of Boolean networks (including Boolean control networks (BCNs) and probabilistic Boolean networks (PBNs)) and reveals the relationship between detectability and observability from the perspective of decompositions. Firstly, the single-experiment detectability and the arbitrary-experiment detectability of BCNs are converted into the consistent reachability and the any-input sequence reachability of a logica control network through an augmented approach. Then these two kinds of detectability are testified via two defined non-augmented matrix sequences. Moreover, some algorithms are given to obtain the current state and suitable input sequences satisfying corresponding detectability. Subsequently, the non-augmented approach is used to discuss the strong detectability of PBNs, and is generalized to analyze detectability and observability decompositions of BCNs. Finally, several examples are presented to illustrate these results. • Relationship between the detectability of a BCN and the reachability of a logical control network is established. • According to the non-augmented approach, two matrix sequences are given and several necessary and sufficient conditions are provided to testify the detectability. This method is generalized to the cases of BNs and PBNs. • Detectability decompositions and observability decompositions are proposed. Several conditions are designed to check the existence of these decompositions. [ABSTRACT FROM AUTHOR]

This paper delves into the robust H ∞ synchronization for diffusion-type parabolic complex networks (PCNs) characterized by second-order nonlinear partial differential equations (PDEs). Given that the potential deterioration of network performance or the loss of network synchronization caused by external disturbances and time delays, the analysis and synthesis results for the robust H ∞ synchronization of the PCNs with coupling time-varying delays are derived with and without the utilization of the distributed adaptive state feedback control protocol. A numerical example is employed to validate the effectiveness of the acquired H ∞ synchronization results and the adaptive protocol. [ABSTRACT FROM AUTHOR]

This paper proposes the hierarchical stability conditions for linear systems with an interval time-varying delay. In the matter of the delay, the upper and lower bounds are restricted, but there is no constraint on its derivative. Firstly, based on the state vectors and multiple integral state vectors involved in the delay-related generalized free-matrix-based integral inequalities (GFIIs), the hierarchical Lyapunov-Krasovskii functional (LKF) candidates are constructed. Then, the GFIIs are applied for the approximation of the integral quadratic terms, which will introduce the delay related nonlinear terms in the LKF differential. Next, the novel adjusted matrix-valued high and odd degree polynomial negative definite conditions (NDCs) are provided to acquire the hierarchical linear matrix inequality (LMI) conditions and cope with the nonlinear terms introduced by the GFIIs. Eventually, the advantages of the obtained stability conditions are checked through some numeric examples. [ABSTRACT FROM AUTHOR]

Radial Basis Function Neural Networks (RBFNN) methods have gained incredible efficiency and applicability in control. This paper presents a nested control strategy for robust trajectory tracking of a quadrotor using adaptive RBF compensation and NN-supervised control embedded with Integrator BackStepping (IBS). The approach addresses the robustness in the presence of modeling uncertainties, sensing noise, and bounded disturbances. The control design is derived from the decentralized inverse dynamics, using adaptive RBFNN for outer-loop disturbance approximation and compensation. In conjunction with an Inner-loop supervised control that stabilizes the quadrotor attitude, preventing initial instability during NN convergence. In addition, an adaptive Extended Kalman Filter (EKF) attenuates noisy signals. Simulation results demonstrate strong adaptability to changes in model parameters, and superior performance when compared to Proportional Integral Derivative (PID), Integrator BackStepping (IBS), and offline decentralized Multi-Layer Perceptron (MLP) algorithms, in terms of parameter convergence, disturbance compensation control, and noise attenuation. [ABSTRACT FROM AUTHOR]

We consider the inherent attack tolerance properties of resilient model predictive control (R-MPC) for cyber–physical systems (CPSs) modeled by discrete linear time-invariant (LTI) systems subjected to limited disruptions. In this paper, the relationship between the maximum allowable duration of Denial-of-Service (DoS) attacks, namely attacks that disrupt both sensor to controller (S–C) and controller to actuator (C–A) communication channels, and the upper bound of disturbances is deduced at length. Moreover, to achieve robust recursive feasibility and closed-loop stability of the system, we discuss that MPC with state, control and terminal constraints has a certain degree of inherent attack tolerance concerning the DoS attack duration and parameters like system matrices and the nominal terminal set. Finally, numerical simulation is given to substantiate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The paper focuses on the asynchronous filter design based on hidden Markovian model for Markovian jump systems with time-delay and external disturbances. Cyber nonlinearities in the communication environment are also considered. To further reduce the network bandwidth usage, an internal dynamic variable is introduced based on the previously extensively studied (static) event-triggered method, which is called dynamic event-triggered approach. The hidden Markovian model is utilized to characterize the asynchronous phenomenon caused by clock signals out of sync between system and filter mode. Then, according to the augmented filtering error system dynamics, some less conservative sufficient conditions are proposed to guarantee the stochastic stability with H ∞ performance. Consecutively, the filter parameter-solving problem is synthesized by a convex optimization problem. Finally, two numerical simulations are presented to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of fault estimation for cyber–physical systems (CPSs) with intermittent measurement transmissions. A novel hybrid observer-based fault estimation scheme is proposed to generate estimations of the system state and the fault. Specifically, the correction terms of the designed observer are updated while new output measurements are available. Moreover, the estimation performance of the designed observer is analyzed within a framework of hybrid systems, and sufficient conditions are established to guarantee exponential input-to-state stability with a prescribed convergence rate based on the elaborately constructed Lyapunov function candidate. In contrast to the existing estimation schemes, the requirements of continuous or periodical communication are eliminated, and therefore the proposed one is more applicable for the modern industrial systems integrated with data communication networks. Finally, the application to an aircraft model is presented to illustrate the effectiveness of the developed methods. • Compared with the existing fault estimation schemes depending upon continuous measurements, the proposed one is developed based on the sporadic measurements, and therefore more applicable for CPSs integrated with data communication networks. • Inspired by considerations from the efficient utilization of available resources, an intermediate variable and two output error correction terms updated while new measurements get available are incorporated with the designed observer. Specifically, by employing the elaborately constructed Lyapunov function candidate, it is proved that the estimation error system is exponentially input-to-state stable with a prescribed convergence rate. • The estimation performance of the designed observer is analyzed within a framework of hybrid systems, which reveals the intermittent innovation properties triggered by the arrived measurements and helps to construct computationally tractable observer synthesis conditions. [ABSTRACT FROM AUTHOR]

This paper studies the Inverse Optimal Control (IOC), aiming to identify the underlying cost functions using observed optimal control paths. An innovative IOC algorithm is developed in this paper by leveraging the closed-loop control law of optimal tracking control, without needing to consider any prior knowledge of the process noise. More explicitly, a convex optimization problem is formulated for the IOC problem by encompassing various linear constraints. The contributions of our work include: (i) Robustly handling process noise, ensuring accuracy without excessive data. (ii) Deriving linear conditions for optimal tracking control law, leading to a closed-form IOC solution that can yield the global optimal solution under sufficient conditions. (iii) No extra LMI constraints are needed when dealing with diverse reference signals. The paper concludes by demonstrating our approach's effectiveness through simulations and comparisons with baseline methods. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative approach to designing a distributed observer for fault and state estimations in Load Frequency Control (LFC) of time-delayed multi-area interconnected power systems. To reach this goal, the paper first provides exhaustive information and analysis on the structure, disturbances, and faults in multi-area power systems. Then, an L 1 observer with the capability to simultaneously estimate the system's states, process/actuator faults, and sensor faults is proposed. This observer exhibits robustness against various types of disturbances as well as time delays present within both the system and the communication network. The sufficient conditions for designing the proposed observer, guaranteeing the system stability, and minimizing the L 1 performance index, are expressed as optimization problems employing Linear Matrix Inequality (LMI) techniques. Additionally, the effectiveness and credibility of the proposed distributed observer in estimating all states and faults in all areas are validated through simulation results. [ABSTRACT FROM AUTHOR]

Iterative learning control (ILC) is widely used for trajectory tracking in networked dynamical systems, which execute repetitive tasks. Traditional norm optimal ILC (NOILC) based on the lifted approach provides an analytical expression for the optimal ILC update law, but it raises a computational complexity issue. As the trial length N (i.e., the number of sampling points in one trial) increases, the computational cost of the lifted approach increases exponentially, which is obviously impractical for long trials. To address this issue, this paper proposes a non-lifted norm optimal ILC (N-NOILC) approach by developing a new non-lifted cost function to improve computationally efficiency. The N-NOILC approach achieves monotonic convergence in the iteration domain, and the computational complexity decreases from O (N 3) of the lifted NOILC approach to O (N). Therefore, the proposed approach can be applied to large repetitive tasks. Based on the N-NOILC approach, this paper develops a centralized as well as a distributed algorithm for networked dynamical systems. Simulations are presented to validate the effectiveness of two algorithms and demonstrate the significant advantage of the N-NOILC approach in computational efficiency. [ABSTRACT FROM AUTHOR]

This paper mainly investigates the adaptive state feedback control problem for a class of stochastic nonlinear systems with time-delay and unknown control coefficients. The proposed control scheme leverages the dynamic gain technology and Nussbaum function to develop a state feedback controller, aiming to simplify its implementation and eliminate the influence of unidentified time-varying control coefficients. Through the utilization of suitable Lyapunov–Krasovskii (L–K) functionals and stochastic stability theories, it is demonstrated that the designed control mechanism guarantees the boundedness in probability for all closed-loop signals, ultimately regulating the state of the plant to zero. The availability and superiority of the developed adaptive management strategy are further certified through simulation results. • In contrast to traditional backstepping methods, this paper introduces dynamic gain at each step of the virtual controller design, utilizing adaptive laws in a novel recursive manner. This approach enhances the flexibility and adaptability of the control scheme, enabling effective compensation for uncertainties. • Our study extends the dynamic gain methodology from nonlinear time-delay systems to more complex nonlinear time-delay stochastic systems. We further innovate by integrating the Nussbaum function with the dynamic gain technique, effectively addressing the unknown control coefficients. • By selecting appropriate L–K functionals and dynamic gains, we eliminate the adverse effects of uncertainties and effectively regulate the system states to converge to zero. This ensures desirable system behavior and establishes the desired control objectives. [ABSTRACT FROM AUTHOR]

The fractional-order (FO) element has recently been included in the integer-order Robust Control Framework (RCF) through an infinite impulse response approximation. The primary focus of the previous results has been only on the robustness of its integer-order approximation. This paper proposes an end-to-end approach to design and numerically implement a fractional-order controller to ensure robustness during the entire flow, without commensurability assumptions. The robustness is ensured using the structured singular value (SSV) semi-metric at each step of the proposed approach. First, the design step ensures that both the fractional-order controller and its integer-order representation meet robustness criteria. Afterwards, the resulting high-order representation is simplified using a three-step order reduction procedure, without losing robustness, as detailed in the paper. Finally, the controller is numerically implemented using a sampling rate and a uniform quantization step computed for minimum transient and steady-state performance degradation. The end-to-end workflow is presented in a toolbox-oriented manner on a benchmark system. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to investigate the strong duality of non-convex semidefinite programming problems (SDPs). In the optimization community, it is well-known that a convex optimization problem satisfies strong duality if Slater's condition holds. However, this result cannot be directly generalized to non-convex problems. In this paper, we prove that a class of non-convex SDPs with special structures satisfies strong duality under Slater's condition. Such a class of SDPs arises in SDP-based control analysis and design approaches. Throughout the paper, several examples are given to support the proposed results. We expect that the proposed analysis can potentially deepen our understanding of non-convex SDPs arising in the control community and promote their analysis based on KKT conditions. [ABSTRACT FROM AUTHOR]

This study focuses on a class of distributed optimization problems with non-consistent local set constraints in time-varying unbalanced networks. Considering that each agent only has access to local gradient information with random errors, this paper proposes a distributed stochastic gradient projection algorithm. Under some conditions of random errors and a uniformly joint strongly connected topology, it is shown that the local decision states of all agents converge to a common optimal solution with a probability of one, achieving a sublinear convergence rate of O (ln k / k). Notably, under the condition that at least one local function exhibits strong convexity, the algorithm achieves a faster sublinear convergence rate of O (1 / k). These theoretical results are validated through numerical simulations. Furthermore, this paper applies the proposed algorithm to solve for the unknown parameters in distributed linear regression problems with incomplete data. Numerical results indicate that the algorithm not only aligns with the convergence properties reported in existing literature but also demonstrates significant superiority in convergence speed. This important discovery not only lays a solid theoretical foundation for the further application and extension of the algorithm but also reveals its immense potential in solving practical problems. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of engaging maneuvering targets in 3-D space when multiple missiles are launched simultaneously. To overcome this problem, a cooperative guidance law with a fixed time constraint is developed. The objective is to enable multiple missiles to attack targets simultaneously while adhering to impact angle limitations. Initially, a relative kinematics model is established for the interaction between multiple missiles and the target. This model establishes the relationship between the terminal line-of-sight (LOS) angle and the impact angle in 3-D space. Subsequently, guidance laws are devised for the longitudinal and normal directions of the LOS coordinate system. For the LOS direction, the guidance law is designed based on the finite time consensus theory of multi-agent systems. This design ensures that multiple missiles reach the target simultaneously at a predetermined time. To account for the target's acceleration, which is considered an unknown disturbance, a fixed time disturbance observer is incorporated into the system to handle it. In the normal direction of the LOS, a fixed time sliding mode cooperative guidance law is formulated using nonsingular terminal sliding mode technology. This law guarantees the fulfillment of terminal LOS angle requirements. Furthermore, the paper provides theoretical proof of fixed time consensus. Finally, to assess the effectiveness of the proposed method, simulations are conducted under various scenarios, demonstrating its capability to guide multiple missiles in attacking a moving target at a fixed time. • By considering the dynamics of complex nonlinear models, a cooperative guidance law with fixed-time convergence is developed. • To address the challenge of unknown disturbance, a fixed-time disturbance observer is introduced in order to measure the unknown variables. • Taking into consideration the impact angle constraint, the relationship between impact angles and terminal LOS angles in 3D space is derived. Subsequently, a fixed-time nonsingular terminal sliding mode guidance law is developed to ensure that the terminal LOS angles converge to the desired values. [ABSTRACT FROM AUTHOR]

The existing hyperspectral image data contain significant local and non-local spatial redundancy, as well as a large amount of spectral redundancy. However, current algorithms inadequately explore these redundant information, limiting the compression performance. To address this issue, this paper introduces a lossy compression algorithm for hyperspectral images, named THSIC(Transformer-based HyperSpectral Image Compression). This algorithm first utilizes a channel-spatial attention module to fully exploit spatial and spectral redundancies in hyperspectral images, resulting in a better latent representation. Subsequently, the Transformer and CNN-based hyperprior branches are employed to extract non-local and local redundant information from the latent representation, respectively. These two hyperprior information, along with the locally contextual prior extracted from the local context, are fused to construct multiple hyperprior information. Then, a more accurate entropy model is built using these priors, thereby enhancing the rate–distortion performance of lossy compression for hyperspectral images. [Display omitted] • The paper introduces a hyperspectral image compression algorithm based on multiple residual modules and channel-spatial attention to enhance the compression performance of the backbone network. • To explore local and non-local redundancy in the latent representation of hyperspectral images, the paper proposes a hybrid hyperprior network with dual branches, utilizing both Transformer and CNN-based hyperpriors. • Experimental results on three hyperspectral remote sensing image datasets show that the proposed hyperspectral remote sensing image compression algorithm based on Transformer super-prior has achieved good performance. [ABSTRACT FROM AUTHOR]

Due to the nature of the tele-robotic systems, some main problems are imposed, such as external disturbances, time-varying delays and external torques. However, some applications such as tele-surgery systems, require high precision, safety and accurate transmission of information between the leader and follower robots. In order to facilitate the imposed restrictions and to guarantee transient-state, and even steady-state performances in the presence of external disturbances and system uncertainties, the time-varying full state constrained control is employed by applying the Barrier Lyapunov function (BLF). In this regard, a new adaptive neural network torque observer is proposed to make the system independent from the force sensors. Moreover, the independence of the proposed algorithm from perfect knowledge of the manipulator dynamics and time-delay's derivative of communication channels are advantages of this paper. Furthermore, the key idea of this paper is to lessen the computational complexity in the backstepping-based adaptive neural controller by means of the command filter strategy and the BLF approach is combined to converge the synchronization error signals into a predefined constraint. In order to consider practical limitations, time-varying delays in the communication channel and input saturation constraint are also considered in the design mechanism. Finally, the stability analysis of the observer and controller together is conducted and the evaluation of the performance of the proposed method is performed through a series of various scenarios and comparisons. • Design of a new adaptive neural network disturbance observer to relax the system from force sensors. • Safety enhancement with equipping the proposed algorithm to the full state constraints. • Considering the input saturation and time-varying delay in the design mechanism. • Design an auxiliary system to deal with input saturation limitation. • Employing the command filter strategy with error compensation signals to handle the time delay's derivatives. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of prescribed-time adaptive fuzzy tracking control for pure-feedback stochastic systems with backlash-like hysteresis. To address the non-affine issue, the pure-feedback case is converted into strict-feedback form by introducing the mean value theorem. The key is that a non-scaling transformation method is introduced to decrease the computational burden such that the controller design is under the standard Lyapunov stability theoretical framework. In addition, the unknown nonlinear functions are approximated via fuzzy logic systems. At the same time, a novel prescribed-time tracking control scheme is proposed to ensure that the considered closed-loop systems are prescribed-time stability. Unlike existing results, the salient characteristic of the proposed control schemes is not only unrelated with the design parameters but also is independent of the initial states in this paper. Finally, simulation results are given to reveal the properties of the investigated control strategy. [ABSTRACT FROM AUTHOR]

This paper introduces a novel control theorem called multiple output response decoupling (MORD) and applies it to the vibration control of optical tables. Control design for multivariable systems is challenging because improving a particular performance might compromise others. Hence, this paper proposes the MORD theorem, which allows independent, simultaneous modifications of all output responses. First, we develop the output response decoupling (ORD) lemma, which can modify specified output responses while keeping others unchanged. Second, we derive the MORD theorem, which can adjust all output responses as individual ORD controls without cross-influences. Third, we apply the MORD theorem to optical table models, enabling the design of particular ORD controllers to suppress ground disturbances and other ORD controllers to minimize strut deflections. Integrating these controllers using the MORD structure provides concurrent optimization of these output responses. Finally, we perform experiments that illustrate the feasibility and effectiveness of the proposed MORD theorem. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of distributed optimal coordination over second-order multi-agent systems, where the dynamics are nonlinear and unknown. To solve this problem, a state-based observer is designed to handle the unknown matched and unmatched nonlinear terms. Using this observer and the proportional–integral control concept, a distributed optimal coordination algorithm with fixed control parameters is constructed through the consensus scheme and gradient flow method. With the help of the Routh–Hurwitz stability criterion, the exponential convergence proof is guaranteed under the condition that the nonlinear term and the convex local cost function are all locally Lipschitz. Finally, we present four simulation examples, including the vehicle platoon's cooperative control, to demonstrate the efficiency of our proposed algorithms. • This paper solves distributed consensus problems of nonlinear second-order systems. • A state-based observer is created to solve the matched and unmatched nonlinear item. • The Routh–Hurwitz criterion is used to prove the exponential convergence rate. • The created coordination method has been applied to the distributed vehicle platoon. [ABSTRACT FROM AUTHOR]

This paper explores the conditions for determining fixed nodes in structured networks, specifically focusing on directed acyclic graphs (DAGs). We introduce several necessary and sufficient conditions for determining fixed nodes in p -layered DAGs. This is accomplished by defining the problem of maximum disjoint stems, based on the observation that all DAGs can be represented as hierarchical structures with a unique label for each layer. For structured networks, we discuss the importance of fixed nodes by considering their controllability against the variations of network parameters. Moreover, we present an efficient algorithm that simultaneously performs labeling and fixed node search for p -layered DAGs with an analysis of its time complexity. The results presented in this paper have implications for the analysis of controllability at the individual node level in structured networks. [ABSTRACT FROM AUTHOR]

In this paper we formulate and solve a problem encountered in engineering applications when a linear-quadratic (LQ) optimal feedback controller uses state estimates obtained via a reduced-order observer. Due to the use of state estimates instead of the actual state variables, the optimal quadratic performance is degraded in a pretty complex manner. In the paper, we show how to find the exact formula for the optimal performance degradation (optimal performance loss) in linear time invariant systems for the steady state case (infinite horizon optimization problem). The optimal performance loss is obtained in terms of solution of a reduced-order algebraic Lyapunov equation whose dimension is equal to the dimension of the reduced-order observer. The quantities that impact the performance criterion loss are identified. Practical examples (an inverted pendulum on a cart and an aircraft) show that the optimal performance loss can be very significant in some applications, and even very high in the presented linear-quadratic optimal control of an inverted pendulum problem. We have shown, using the derived formula, how the optimal performance loss can be considerably reduced by properly choosing the reduced-order observer initial conditions via the least square method. [ABSTRACT FROM AUTHOR]