This paper proposes a honeypot-driven active detection framework to address the growing cyber threats targeting smart grids. To address the limitations of existing detection methods and the lack of proactive defense capabilities, the framework integrates interpretable decision mechanisms with an adaptive modular architecture, through the dynamic weighting of the gated aggregation mechanism. It incorporates data preprocessing, honeypot interaction, dual-path detection, and decision fusion to more effectively capture diverse attack behaviors and strengthen overall detection reliability. The framework integrates a GBDT-based anomaly scorer with a dedicated Denial-of-Service (DoS) detector, and combines multidimensional evidence using a gated aggregation mechanism. Moreover, theoretical analyses establish a lower bound on feature distinguishability, prove model convergence, and demonstrate effective false-alarm suppression, providing formal guarantees for feasibility. Finally, experiments on real-world smart grid datasets show that the proposed framework accurately detects DoS, replay, and integrity attacks while maintaining strong overall performance. The results confirm its effectiveness as a theoretically grounded and proactive defense solution for smart grid security.

Safety guarantee is a fundamental requirement for numerous dynamical systems. In this work, the safe stabilization problem in complex scenarios is considered. First, we introduce the notion of the local safe set, a convex and compact set disjoint from unsafe regions, to construct a finite sequence of such sets. Next, we leverage Zubov’s theorem to propose the Zubov barrier function (Zubov BF), which provides safety guarantees on local safe sets. Compared with the existing ones, the Zubov BF reduces the conservatism and complexity in safety analysis. The relations between the Zubov BF and existing ones are discussed to show the generality of Zubov BF. The Zubov control barrier function (Zubov CBF) is further extended for control synthesis. Sufficient and necessary conditions are provided to verify the existence of the derived controller. To achieve the safe stabilization objective, we provide a modified quadratic programming (QP) framework with feasibility guarantee and demonstrate the continuity of the derived controller in closed form. Finally, a numerical example from the overtaking problem of two autonomous vehicles and a practical experiment on the safe navigation for a quadrotor are provided to illustrate the derived results.

Partial differential equations (PDEs) are commonly employed to model complex industrial systems characterized by multivariable dependence. However, existing physics-informed neural networks (PINNs) barely perform well in heterogeneous media modeling compared to their achievement for a homogeneous medium. Unknown PDE parameters due to insufficient prior knowledge with respect to physical attributes and unavailable time-varying interface caused by heterogeneous media may weaken PINNs feasibility. To this end, physically informed synchronic-adaptive learning (PISAL) is proposed. First, PISAL is proposed for learning the solutions and interface satisfying PDEs, in which$Net_{1}$,$Net_{2}$, and$Net_{I}$are constructed.$Net_{1}$and$Net_{2}$are for synchronically learning the solutions satisfying PDEs with diverse parameters;$Net_{I}$is for adaptively learning the interface to decompose the domain with heterogeneous media. Then, a criterion combined with the output of neural networks is introduced to adaptively distinguish the attributes of measurements and collocation points. Furthermore,$Net_{1}$,$Net_{2}$, and$Net_{I}$are integrated into a data-physics-hybrid loss function. Accordingly, a synchronic-adaptive learning (SAL) strategy is proposed to decompose the domain and optimize the three networks by iteratively erasing the training errors. Besides, we theoretically prove the proposed PISAL can iteratively approximate the fields with diverse physical attributes. Finally, extensive experimental results and comparisons with relevant state-of-the-art methods verify the feasibility and effectiveness of PISAL for industrial systems modeling in heterogeneous media. Note to Practitioners—The motivation behind this paper is to devise a method for industrial systems modeling, even in cases where unknown PDE parameters are caused by a lack of prior knowledge with respect to physical attributes and the unavailable time-varying interface is caused by heterogeneous media. Existing methods apply the domain decomposition technique under the assumption that the interface is available. To this end, a data-physics-hybrid method, PISAL, in which$Net_{1}$,$Net_{2}$, and$Net_{I}$with SAL strategy are proposed.$Net_{1}$,$Net_{2}$, and$Net_{I}$are first constructed.$Net_{1}$and$Net_{2}$are for synchronically learning the solutions satisfying PDEs with diverse parameters;$Net_{I}$is for adaptively learning the unavailable time-varying interface. Subsequently, a criterion combined with the output of neural networks is introduced, which is to adaptively distinguish different physical attributes of measurements and collocation points. Additionally, the three neural networks are integrated into a data-physics-hybrid loss function. Accordingly, a SAL strategy is proposed to decompose the domain and optimize the three neural networks. Besides, the approximation capability of PISAL is theoretically proved based on well-posedness and denseness. To validate the efficacy of the proposed PISAL, the two-phase Stefan problem and the mixed Navier-Stokes problem are employed. Meanwhile, some comparisons with relevant state-of-the-art approaches are given. Results highlight the feasibility of our method in industrial systems modeling with the above-mentioned challenges. Thus, the proposed method is suitable for industrial automation applications. In future research, we intend to conduct the proposed PISAL on an experimental platform to further verify the feasibility in practical scenarios and more complex applications.

Dynamic artificial neural network models refer to the dynamic model structures that include artificial neural networks such as multi-layer perceptrons, which are used to model nonlinear system dynamics.$H_{\infty }$tracking control for dynamic artificial neural network models with communication delays and external disturbance is investigated in this article. First, we formulate the dynamic artificial neural network model as a sampled-state error dependent model for networked control systems with the event-triggered mechanism, which is designed to reduce the consumption of network resources. The network-induced delay considered in this paper is time varying with a known upper bound. Furthermore, by Lyapunov-based techniques, we present sufficient conditions such that the closed-loop system satisfies the$H_{\infty }$tracking performance. In addition, we propose a method to co-design the tracking controller and event-triggering parameters in the form of linear matrix inequalities. The effectiveness of our proposed methods is validated through a simulation example and a turbofan engine hardware-in-the-loop experiment. Note to Practitioners—When mathematical models of the plant dynamics are not available, neural network modeling can serve as a useful method for controller design, provided we have numerical information about the system behavior. The novel stability conditions and the established controller design method can be adapted for different classes of dynamic artificial neural network models, including neural state space models, global input-output models and dynamic recurrent neural networks. This characteristic reduces the constraints on model selection, thus expanding the application scenarios. Instead of the stabilization problem,$H_{\infty }$tracking control problem is considered in this paper, which has a wider range of applications. In view of the growing need to reduce unnecessary consumption of communication resources in some digital control systems such as smart power grid, aircraft, industrial automatic production and so on, different from existing results, a discrete-time version of periodic event-triggered mechanism is adopted in analysis and control of dynamic artificial neural networks. In addition, disturbances and time delays which widely exist in engineering are also considered. Therefore, the proposed method is more suitable for practical applications. Finally, the established method is applied to the turbofan engine multivariable trajectory tracking control problem, and the effectiveness is illustrated by experiments. In future research, we will address the design problem of dynamic artificial neural network observer for the situation where the system states are unmeasurable.

This paper presents a robust model predictive control (RMPC) algorithm for nonlinear discrete-time systems subject to bounded disturbances and incremental control input constraints. To guarantee recursive feasibility, a terminal inequality constraint is integrated into the proposed RMPC algorithm. By employing constraint tightening techniques, we derive an upper bound on admissible disturbances that ensures the input-to-state stability (ISS) for the closed-loop system. The effectiveness of the proposed algorithm is validated through numerical simulations and practical experiments involving the control of a four-wheel mobile robot. The results demonstrate the capability of the proposed method to maintain system stability and optimize control performance in the presence of external disturbances. Note to Practitioners—In practical engineering, the prevalence of external perturbations and the necessity for incremental control input constraints significantly complicate the control system design process. Compared with traditional control methodologies, model predictive control (MPC) is better equipped to address disturbances and constraints, achieving enhanced control accuracy and safety. This paper introduces an enhanced RMPC method specifically designed to control a broad class of nonlinear systems in the presence of disturbances and input constraints. Additionally, we provide insights into the relationship between specific design parameters of the RMPC algorithm and the upper bounds of permissible disturbances, offering practical guidelines for implementation. The proposed method is validated through simulations and practical experiments with a four-wheeled mobile robot. The results confirm that the approach reliably maintains system stability while efficiently optimizing control inputs. Future work will focus on extending the algorithm to potential robotic systems and exploring alternative disturbance-handling methods, such as observer-based and set-membership approaches.

The active surge control has superiorities in expanding the stable working range and reducing the performance loss of aero-engines. Despite these benefits, ensuring fixed-time stability of the closed-loop system with model uncertainty remains a significant challenge. Conventional techniques to active surge control of aero-engines often struggle with model uncertainty and suffer from long-time surge fault. To deal with these issues, this article proposes a novel fixed-time active surge control scheme for enhancing the adaptability to the model change and extending the service life of aero-engines. First, considering the model uncertainty in aero-engines, a radial basis function (RBF) neural network is established for the approximation of complex system dynamic. Second, the adaptive law is proposed to optimize the weight vectors of the neural network. Third, a fixed-time controller is designed to ensure responsiveness and stabilize the compressor dynamics by tuning the intake air flow, where the fixed-time stability property guarantees less operation time under the surge fault. Finally, applications in the turbofan aero-engine validate the superiorities of the proposed method.

In data-driven mechanical fault diagnosis, domain adaptation is used to solve the problem of different distribution of collected data under varying operating conditions. However, the assumption of consistent label space between source and target domains is difficult to satisfy due to the types of faults that occur under different operating conditions not being completely identical. To address the problem of label inconsistency in partial domain adaptation, we propose a feature separation weight rectified network (FS-WRN). Shared domain features are extracted by feature separation, and the source and target domain distributions are aligned using the rectified local maximum mean discrepancy. The source-domain features are weight-rectified by capturing changes in the target domain distribution during training to minimize the effect of negative transfer due to private classes. Three public datasets are used, and the results show that FS-WRN has promising fault diagnosis performance under complex partial domain adaptation tasks.

It is well known that most position synchronization control schemes merely relay on position error dynamics, which are hard to fully explore the potential capability of synchronized plants. Therefore, this paper studies the synchronization motion control using both the position and velocity states for the dual-cylinder hydraulic system. This indicates that more states are involved in the synchronization regulation, which thus provides the ability of enhancing synchronous performance. Meanwhile, a novel fast finite-time observation approach is proposed and designed for each actuator to compensate the uncertainties of forces and flow rates, and eliminate the load variation caused by different loads and disturbances of two actuators. The proposed observer converges faster than traditional ones with exponential or asymptotic rates benefiting from the homogeneity technique. It is also proved that the proposed control scheme can ensure both synchronizing and tracking errors to exponentially converge to an adjustable compact set or zero when the system is only subject to constant disturbances. Finally, experiments on the aero-engine Hardware-in-the-loop (HIL) platform with hydraulic actuators are conducted to verify the effectiveness and practicability of the proposed synchronization control scheme.

This article investigates the event-triggered adaptive robust motion control for hydraulic actuators with parametric uncertainties and system nonlinearities. Under the continuous communication condition, the traditional adaptive robust motion controller is recursively presented. In order to reduce the unnecessary bandwidth consumption in the aero-engine networked control platform, an adaptive threshold triggered mechanism according to network resources is developed to synthesize the motion controller. Adjustable threshold parameters are involved to flexibly adjust the data transmission times depending on the network bandwidth occupation. It is proved that with the motion controller and the proposed adjustable threshold triggered mechanism, all the closed-loop system signals are globally bounded, and the hydraulic system output achieves asymptotic tracking to the reference trajectory by virtue of the adaptive technique, the Nussbaum-type and sign functions. Besides, the Zeno behavior is excluded, successfully. Finally, the proposed event-based control scheme is tested and discussed on the aero-engine hardware-in-the-loop (HIL) experiment platform with hydraulic actuators.

Generally, asymptotic tracking motion control algorithm can ensure the stability of the closed-loop system, but hardly exploits the potential system performance. As an alternative, a high-accuracy control method with finite-time regulation is studied in this paper for hydraulic actuators pertaining to complex unknowns and limited communication bandwidth. During system modeling, a hydraulic actuator system is expressed in the Brunovsky form with a lumped disturbance term, instead of the conventional semi-strict-feedback form, such that the tracking control can be transformed to the stabilizing problem. As a result, the homogeneity-based finite-time motion controller enables to be recursively constructed. This paper develops a finite-time observer to simultaneously estimate the partial unknown state and the lumped disturbance, with a short time, thereby improving the disturbance rejection and transient response. Moreover, a novel threshold triggering mechanism is proposed to alleviate the limited communication resources and ensure the relatively small amplitude fluctuation of control inputs via a minimum function. The stability and finite-time convergence of the closed-loop system are all proved. Meanwhile, the Zeno behavior is avoided. Finally, the proposed control scheme is tested and verified on the aero-engine Hardware-in-the-loop (HIL) test-rig with hydraulic actuators.Note to Practitioners—This paper displays how to utilize the event-triggered communication protocol and finite-time control for hydraulic actuators in the networked control systems. The designed event-triggered mechanism saves more communication resources while ensuring the satisfied motion control effect. Moreover, the internal and external uncertainties are all tackled with the developed finite-time observer. Experimental results verify that the proposed schemes are easily implemented, but with the plentiful performance improvements of motion control, disturbance attenuation (i.e., finite-time output tracking and unknown estimation), and the capability on saving network resources. In addition, the proposed event-driven control scheme is also feasible for other mechatronic systems, e.g., linear motor, DC motor and marine surface vehicles.

The anomaly detection of outliers and fault diagnosis in the imperfect time-series data is crucial in aero-engine industry. Early detection of the rotating stall is significant for the active stabilization control of the axial compressors, because the rotating stall and surge can cause enormous vibratory stresses in the blades and gives rise to surge to limit the performance of compressors. An aerodynamic instability inception and short-length-scale periodic anomaly prior to stall onset known as stall inception in axial compressors is observed in aero-engine. Based on deep learning theory, this paper conducts the accurate and rapid detection of stall precursors based on deep neural network. The deep dilated causal convolutional neural network combined with logistic regression(LR) named as LR-WaveNet is applied to the detection and prediction of stall inception in the imperfect time-series data of axial compressors with the rotating stall. The LR-WaveNet model can implement fast anomaly detection and stall prediction in long-time term series data. Furthermore, a single LR-WaveNet can be trained to capture and learn the time-domain statistical characteristics of many different stall inception training data with equal fidelity. The trained LR-WaveNet model can rapidly detect the occurrence of anomaly point and predict the probability of rotating stall and surge in axial compressors as an early warning signal. By comparing with the time domain analysis and the wavelet analysis, the calculation results are represented with experimental data to show the effectiveness and feasibility of the stall detection approach based on LR-WaveNet. Especially, the LR-WaveNet can detect the irregular and imperfect stall inception and capture the small fault features when the characteristics of stall inception sigals or data are not very unobvious, irregular and imperfect. Note to Practitioners—Note to Practitioners–This paper was motivated by the problem of detecting the stall and surge of axial compressor for aeroengine, but it also applies to other small fault diagnosis with imperfect data or time series prediction problem. The stall Inception is very difficult to detect for the reason of imperfect data with small fault or features. These existing detection methods of stall inception can only be applied under certain conditions, because they can only extract the pre-stall features partly. Meanwhile, the excessive number of compressor sensors is demanded in this method, so the application of these traditional methods are limited in practical situations in the stall inception detection. This paper suggests a new approach using Deep Dilated Causal Convolutional Neural Networks. The deep dilated causal convolutional neural network combined with logistic regression(LR) named as LR-WaveNet is applied to the detection and prediction of stall inception in the imperfect time-series data of axial compressors with the rotating stall. In this paper, the new approach is proved to be suitable and feasible to all the types of stall inception including the irregular stall inception with imperfect data. We then show how the deep learning method can be efficiently applied in the imperfect data situation with small fault and time series prediction. The experiments suggest that this approach is feasible. In future research, we will create the novel approach and theory for the detection of stall inception and small fault diagnostics with the imperfect data.

In this paper, we propose a switched model predictive control scheme based on the data-driven method without any terminal ingredients. Compared with conventional model-based model predictive control schemes, we utilize a series of input-output data to describe the system dynamics according to the behavioral systems theory. In order to improve system performance, multiple cost functions regulated by a suitable switching strategy are considered. For the nominal case with noise-free data, we prove that the proposed data-driven switched MPC schemes can ensure the exponential stability of the system provided that the switching signal satisfies the average dwell-time condition. Moreover, the robust stability is also analysed mathematically for the robust case considering bounded measurement noise. The effectiveness of the control algorithm is verified by the speed control of a commercial high bypass turbofan engine on a hardware-in-loop platform. The results of the experiment show the advantages of the proposed switched model predictive control scheme over conventional non-switched model predictive control schemes.Note to Practitioners—In this paper, we consider the problem of designing a control law to improve the performance of the system with multiple requirements that need a tradeoff. Since the performance criteria can easily be formulated in the cost function in model predictive control algorithm, it is a natural thought to design multiple cost functions which can be switched according to different performance requirements. However, the modelling process of the system is usually complicated and time-consuming for conventional model predictive control. In this paper, the data-driven method replaces the counterpart of the model in conventional model predictive control. The input-output measurements of the system can be directly used to forecast the dynamics of the system. The feasibility and stability of the control scheme are analyzed mathematically. We show that the proposed control law can ensure the above closed-loop performance of the controlled system, provided that the parameters of the controller are suitable and the switch of the cost function is sufficiently slow. The effectiveness of the proposed control scheme is demonstrated by the application of a high bypass ratio turbofan engine on a hardware-in-loop experiment platform. The results show that the turbofan engine can achieve a relatively rapid response with smaller overshoot.

Model predictive control (MPC) is popular in applications with slow dynamics because of its advantages in handling constraints and multivariable optimization. But for aeroengines, it is difficult to obtain an exact prediction model, which will lead to offsets in tracking. Besides, deploying MPC to embedded controllers for real-time control is a well-known challenge. Therefore, this paper presents a switched linear MPC, which incorporates the augmented prediction models with error integrator for offset-free tracking, the sparse-based quadratic programming formula for solving MPC, and a reset strategy for achieving bumpless transfer at the switching instant. Further, on the hardware board we developed, six hardware-related acceleration strategies are explored and evaluated for real-time performance. Then, eight cases of five objects are tested, whose results indicate a significant speedup of around 50 times. At last, the hardware-in-the-loop tests of the turbofan engine and the real bench tests of the micro-turbojet engine are performed, which verifies the superiority, real-time performance, and potential for practical applications.Note to Practitioners—This paper was motivated by the problem of applying the offset-free MPC to the embedded control system for aeroengines. The difficulty of accurate modelling and the requirement to compute the embedded MPC in a limited time are two major challenges. Accordingly, we explore six hardware acceleration strategies that are often ignored to ensure the real-time performance. Moreover, we investigate the switched MPC to obtain the desired dynamic response. However, the controller switching tends to induce fluctuations that are harmful to the engine. Hence, we present a reset strategy to ensure bumpless transfer performance. Preliminary physical experiments suggest that the proposed approaches are feasible to achieve the predetermined objectives. Further, the comparisons with the previous methods highlight our superiority. Overall, this paper contributes to optimize the computational performance of microcontrollers and enhance bumpless transfer performance for MPC. In our future work, the proposed approaches will be applied to flight experiments and more industrial devices.

Timely delivery of first aid supplies is significant to saving lives when an accident happens. Among the promising solutions provided for such scenarios, the application of unmanned vehicles has attracted ever more attention. However, such scenarios are often very complex, while the existing studies have not fully addressed the trajectory optimization problem of multiple unmanned ground vehicles (multi-UGVs) against the scenario. This study focuses on multi-UGVs trajectory optimization in the sight of first aid supply delivery tasks in mass accidents. A two-stage completely decoupling fuzzy multiobjective optimization strategy is designed. On the first stage, with the proposed timescale involved tridimensional tunneled collision-free trajectory (TITTCT) algorithm, collision-free coarse tunnels are build within a tridimensional coordinate system, respectively, for the UGVs as the corresponding configuration space for a further multiobjective optimization. On the second stage, a fuzzy multiobjective transcription method is designed to solve the decoupled optimal control problem (OCP) within the configuration space with the consideration of priority constrains. Following the two-stage design, the computational time is significantly reduced when achieving an optimal solution of the multi-UGV trajectory planning, which is crucial in a first aid task. In addition, other objectives are optimized with the aspiration level reflected. Simulation studies and experiments have been curried out to testify the effectiveness and the improved computational performance of the proposed design.

This article proposes a switching anti-windup strategy for linear, time-invariant (LTI) systems subject to asymmetric actuator saturation and$\mathcal{L}_{2}$-disturbances, the core idea behind which is to make full use of the available range of control input space by switching among multiple anti-windup gains. The asymmetrically saturated LTI system is converted to a switched system with symmetrically saturated subsystems, and a dwell time switching rule is presented to govern the switching between different antiwindup gains. Based on multiple Lyapunov functions, we derive sufficient conditions for guaranteeing the regional stability and weighted$\mathcal{L}_{2}$performance of the closed-loop system. The switching anti-windup synthesis that designs a separate anti-windup gain for each subsystem is cast as a convex optimization problem. In comparison with the design of a single anti-windup gain, our method can induce less conservative results since the asymmetric character of the saturation constraint is fully utilized in the switching anti-windup design. Two numerical examples, and an application to aeroengine control (the experiments are conducted on a semiphysical test bench), demonstrate the superiority and practicality of the proposed scheme.

In practical engineering applications, it is inevitable that production is often faced with different working conditions. Therefore, it is necessary to have a continual learning system, which can adapt to a sequence of tasks and keep learning over time. In this work, we propose a continual deep residual reservoir computing framework for the practical remaining useful life prediction task. Specifically, we propose a novel deep echo state network structure with residual blocks to effectively mitigate the performance degradation of the deep reservoir computing framework and reduce the difficulty of model training. Furthermore, the proposed framework is trained with the elastic weight consolidation method to alleviate the impact of catastrophic forgetting in continual learning systems. Extensive experiments are conducted with the FEMTO-ST bearing and high-intensity-radiated fields battery dataset. And the proposed framework is proven to be effective in multiple continual learning tasks compared with other state-of-the-art methods.

Variational mode decomposition (VMD) provides a feasible approach to decompose vibration signals obtained from complex machinery for further applications. The mode frequency bandwidth control parameter and the total number of modes are critical parameters for VMD. Thus, optimally and automatically setting the two parameters is an essential issue for VMD for various practical vibration signal sources. To this end, this work proposes an automatic parameter setting VMD for vibration signals. First, we use the bandwidth evaluation criterion and the mean mode-location distance to evaluate the sparsity of modes; we use the energy loss to evaluate the reconstruction from modes. Then, we synthesize the three criteria into a novel optimization model using logarithmic transformation. Accordingly, a genetic algorithm (GA)-based solver is developed for the optimization model. Finally, the artificial multicomponent signal and the practical rolling bearing vibration signal from Case Western Reserve University (CWRU) Laboratory are used to verify the performance of the proposed optimization method.

This article investigates the issue of distributed state estimation for a nonlinear hydraulic system. Our objective is to propose a distributed algorithm that enables individual nodes to obtain a global state estimation by utilizing local measurements and information gathered from neighboring nodes. We ensure global optimality by designing a mechanism for information exchange between neighboring nodes. The state estimation acquired by our distributed algorithm is equivalent to that of the centralized extended Kalman filter within a finite time. Our proposed algorithm is effective for acyclic network graphs and can be applied to cyclic network graphs through the loop removal approach. In addition, we provide a stability analysis of the proposed distributed algorithm. Finally, experiments on the practical platform are conducted to verify the feasibility and merits of the proposed algorithm.

Efficient fault-tolerant control (FTC) is necessary for the safe operation of aero-engine control system. In this paper, a high performance active FTC method based on zonotope for actuator fault in aero-engine control systems is proposed. Parameter uncertainties are considered to describe linearization error and identification error of system model for reducing the gap between theory and practice. Firstly, a zonotopic observer satisfying the peak-bounded index is proposed to reduce the influence of uncertainties and improve the accuracy of fault estimation. Moreover, with the aid of the zonotopic observer, the range of the sliding surface affected by the estimation errors and model uncertainties can be evaluated, and the dynamic quasi-sliding mode domain (QSMD) can be obtained. As a result, the dynamic QSMD can help design the parameters of the sliding mode fault-tolerant controller, ensure the stability and convergence of the entire closed-loop control system. Meanwhile, the conservative problem caused by manual parameters setting is avoided. Finally, the feasibility of the proposed method is verified by the aero-engine Hardware-in-the-loop (HIL) experiment platform.

This article studies a data-driven co-designing method for networked control systems under denial-of-service attacks and communication delays, using only noisy data of system trajectories instead of explicit system models. To this end, a delay system framework is first established by investigating the effect of both denial-of-service attacks and time-varying delays. In order to reduce the consumption of the network resource, a novel dynamic event-triggering scheme is proposed. The sensor is time-triggered while the controller and the actuator are event-triggered in the scheme. Based on the Lyapunov stability approach and a data-based representation of unknown systems, a data-driven method for co-designing the controller gain and event-triggering parameters is developed. In the presence of denial-of-service attacks with unknown patterns, the proposed data-driven co-designing method demonstrates the trade-off between the system performance metric and the denial-of-service resilience level. Furthermore, the impact of time-varying communication delays on the denial-of-service resilience level is investigated. Finally, several numerical experiments demonstrate the effectiveness of the proposed methods.

rgb0.00,0.00,0 Turbofan engine performance optimization is usually formulated as a single objective, closed-form optimization problem by employing a prior mechanism model with an additive, user-preference weight. However, in practical scenarios, the conventional single objective performance optimization may not satisfy the high-performance requirements. For instance, pursuing high-effective thrust will lead to high-turbine inlet temperature due to generating extra heat. Moreover, the system model may be inaccurate or even unavailable, mainly due to the degradation factor, manufacturing tolerance, or time-intensive experiments. Traditionally, the multiobjective optimization methods may require a certain amount of function evaluations, or the convergence properties may not be guaranteed explicitly. To tackle the above-mentioned issues, we formulate the performance optimization of turbofan engines as a multiobjective optimization problem and construct a gradient-free framework to deal with the issue of an inaccurate/unavailable turbofan engine model. Then, to ensure the safety requirement of the turbofan engine operating processes, a multiobjective optimization algorithm is proposed utilizing a gradient-free method, termed Hessian aware gradient estimation-based randomized search (HAGE-RS), and we analyze the corresponding convergence properties of the solved candidates. Finally, we illustrate the proposed algorithm on benchmarks and the performance optimization problem using real-world turbofan engine data under different operating conditions to show superior performance.

Adaptive robust control technique is investigated to devise trajectory tracking event-triggered controller for a class of uncertain nonlinear systems. Different from most existing results, this article divides the entire control input into two parts, including the model compensation input term and the robust term, which are separately dealt with by means of special triggered mechanisms. The advantage of separation is that the system reduces the magnitude of chattering amplitude within triggered inputs. To balance the information updating frequency and the magnitude of chattering amplitude, this article proposes the dynamic-threshold-triggered mechanism and provides a new Lyapunov function to guarantee the global boundedness of all the closed-loop signals as well as the convergence of the tracking error to an arbitrary small set around zero. This article also considers desired compensation adaptive robust event-triggered control strategy, thereby further saving bandwidth resources and smoothing the triggered inputs. The Zeno behavior is excluded for two triggering schemes. Finally, simulation and hardware-in-the-loop experiments are used to verify the effectiveness of the proposed strategies.

Since the output signal of the controller is prone to jump suddenly when a switching occurs, the practical system usually suffers from undesired bumps. To address this “bump” phenomenon, a parameters-free bumpless transfer structure based on multiple differentiators and a cointegrator is proposed in this article. The proposed structure is applicable for switching between manual and automatic modes, as well as switching during steady state and transient state. Unlike the previous compensator-based BT methods, our structure exploits the continuous property of the integrator, which prompts optimal performance and preserves original performance. Furthermore, sufficient conditions based on the dynamic dwell time are given to guarantee the stability of switched linear systems. At last, the designed BT approach is applied to a bivariate turbofan engine and a micro-turbojet engine, wherein the hardware-in-the-loop tests and real bench tests are performed, respectively, so as to demonstrate the significant improvement and potential for practical engineering.

This paper is concerned with the motion control for a quadrotor with a cable-suspended load (QCSL). A fixed-time control strategy is presented to improve the transient response and robustness of the QCSL with external disturbance. The overall control scheme is designed with a cascade structure to better cope with the underactuated property of the QCSL and the indirect effect of the control force on the load’s velocity through the tensile force on the cable. The simulation results are given to demonstrate the performance of the proposed scheme. Furthermore, actual flight tests were performed on a new experimental QCSL to validate the effectiveness of the proposed control strategy.

In order to reduce the possibility of structural fatigue and increase the lifetime of conveying fluid pipe, transverse vibration must be effectively eliminated. In this work, using parallel nonlinear energy sinks (NESs), a passive vibration controller is proposed to dissipate the vibration energy of the conveying fluid pipe. A high-order model of the conveying fluid pipe-parallel NESs system, in the form of partial differential equation, is derived and then converted into a quadratic form model containing the gradient information of a convex function. Combining the energy disturbance technique and first order convexity characteristic, the exponential stability of the closed-loop system is proved, which addresses the effectiveness of the proposed parallel NESs. Then, numerical simulations are given to verify the theoretical results and to illustrate the advantages of parallel NESs comparing with single NES. Finally, the reliability of the proposed approach is preliminarily verified through experiment.

In this article, classification problems for a class of tensor datasets with a noisy environment are investigated. To address such issues, a novel fuzzy support tensor machine (FSTM) dual model with robustness is established. First, for each input sample in the noisy tensor dataset, we define three kinds of fuzzy membership functions, such as linear, cosine, and exponential forms. In particular, the reconstruction process from one-dimensional (1-D) vector data to third-order tensor data is also derived in the Appendix. Second, the original optimization model of an FSTM on fuzzy membership is designed by constructing the vector pattern of the traditional support vector machine (SVM) models into a tensor pattern. Next, by introducing the Lagrangian multiplier method and tensor-Tucker decomposition method to the original FSTM model, an FSTM dual model without tensor inner product operation is obtained for the first time. Such a dual model with tensor-Tucker decomposition form can avoid conservativeness caused by the vectorization of tensor data in the traditional SVM model. Furthermore, an FSTM classifier is derived by the designed numerical algorithm, and the classification generalization error bound of the FSTM model with a general form is developed. It is worth noting that a linear least-squares FSTM (LLS-FSTM) equation with tensor-Tucker decomposition is also designed in the Appendix to further reduce the slightly time-consuming problem of the solving the dual optimization model FSTM. Finally, two numerical examples are presented to verify the feasibility and validity of the derived FSTM classifier.

In this article, the robust control problem of uncertain nonlinear systems subject to magnitude and rate saturation (MRS) is investigated via active disturbance rejection control. To this purpose, we propose a novel robust controller that involves an improved extended state observer (IESO), a composite loop, and two anti-windup loops. The IESO is designed to estimate the state and the total disturbance, which includes the uncertain dynamics and external disturbance. Compared with the traditional ESO, IESO absorbs an anti-windup loop to ensure accurate estimation under MRS. The convergence analysis of IESO is further carried out to theoretically verify the accurate estimation of IESO. The composite loop is generalized to cancel the total disturbance based on the output of IESO. Moreover, a rate anti-windup loop is designed to attenuate the effect of rate saturation on the estimation/cancellation of total disturbance. An algorithm for anti-windup gains computation is correspondingly established, in order to minimize the effect of external disturbance in terms of$L_{2}$gain while guaranteeing the local asymptotic stability. Finally, the proposed method is applied to aeroengine, which involves large uncertainty and suffers from disturbance and MRS. The experimental results verify the effectiveness of the proposed method.

Many fundamental studies have indicated that the vibration of conveying fluid pipe is more severe and complex at high subcritical fluid velocity. The control of vibration in this case, however, still remains a challenge for the general vibration absorbers. In this work, the inerter enhanced nonlinear energy sink (NES) is used to solve the severe vibration problem. The partial differential equation form model of the conveying fluid pipe-inerter enhanced NES system is derived and converted to an ordinary differential equation with an easy solution. Global stability of the conveying fluid pipe-inerter enhanced NES system is proved under the Lyapunov stability theory framework and functional analysis technique, to explain the effectiveness of the inerter enhanced NES. The influence of parameters on the conveying fluid pipe is discussed through a sensitivity analysis. The parameters of the proposed controller are optimized based on the energy functional. Finally, numerical examples are provided to verify the control effectiveness and the theoretical results, and also to show the advantages of inerter enhanced NES by comparing with the general NES.

To avoid the bumps of the control signal in the switching instants, this paper proposes a bumpless transfer control method which guarantees the continuous of control signal while keeping the asymptotic stability of the closed-loop system. Firstly, the presented bumpless transfer control method possesses a simple structure and meanwhile not have to redesign sub-controllers, which is easy to implement. Furthermore, the method is appropriate for either stable subsystems or unstable subsystems, and be suitable for either state feedback control or dynamic output feedback control. Moreover, we give the sufficient conditions of asymptotic stability for switched linear systems under bumpless transfer control for four cases. For the original switched system with stable subsystems under state or dynamic output feedback control, the stability conditions under mode-dependent average dwell time switching are derived. For the original switched system with unstable subsystems under state or dynamic output feedback control, which is stabilized by some certain switching laws, the stability conditions under the original switching law are given. Finally, a hardware-in-the-loop simulation of an aero-engine control system is employed to verify the effectiveness and superiority of the proposed method.

This article investigates a bumpless transfer control problem for switched systems. Since the control input signal of a controller switching system jumps when a switching occurs, the transient performance of the practical system may suffer bad effects. To avoid the damage to the system caused by the jump of control input signal, we propose a simple bumpless transfer control structure containing a differentiator and an integrator to smooth the control input signal and a common compensator designed to compensate the deviation from the original switching control input signal for eliminating the steady-state error. Furthermore, we give the sufficient conditions to guarantee asymptotic stability for linear and nonlinear bumpless transfer control systems, respectively. Moreover, we verify the recoverability and effectiveness of the proposed control strategy. Finally, a hardware-in-the-loop simulation for a variable cycle engine shows the feasibility and superiority of the proposed method.

In this article, a novel adaptive dynamic programming (ADP) approach is proposed for the optimal control problem of nonlinear continuous control systems with unknown dynamics. First, an alternating iteration algorithm based on Hamilton-Jacobi-Bellman equation is proposed for the optimal control of known nonlinear control systems. Then, the convergence results of the alternating iteration algorithm are obtained by using mathematical induction and monotone bounded convergence theorem. Moreover, the global asymptotic stability of the nonlinear closed-loop system is proved. Second, based on the scheme of alternating iteration algorithm, an ADP algorithm for the optimal control problem with unknown nonlinear dynamic model is developed by using the basis function approximation method and Newton-Leibniz formula, which can update the control strategy online by utilizing input and output information of the system. In addition, the convergence analysis of the proposed ADP algorithm is derived. Finally, the feasibility of the established results is verified by two examples, and the ADP method is applied to the optimal tracking fuel control problem of turbofan engines.

In this article, we investigate the output constrained control problem of uncertain nonlinear systems subject to magnitude and rate saturation. First, a novel output constrained controller is proposed based on the antiwindup approach and the active disturbance rejection control technique. Second, the stability is analyzed for the closed-loop system incorporating the proposed controller. Third, we establish the admissible set of initial states, which the initial state belongs to such that the output limit violation is prevented. An optimization algorithm is then presented for the antiwindup gain computation. The computed antiwindup gain guarantees a maximized admissible set of initial states and local asymptotic stability. Finally, the proposed method is applied to the aircraft engine control based on a semiphysical platform. The experimental results validate the effectiveness of the proposed method.

This paper deals with the problem of a quadrotor experiencing a total failure of one actuator. First, a nonlinear mathematical model for the faulty quadrotor is derived with three control inputs and six outputs, which includes the translational and rotational dynamics. Because of the limited inputs, the controllability of the yaw state is sacrificed, and the control inputs are reallocated to the other three healthy rotors. Second, a nonlinear controller is designed based on the proposed model. This presented controller includes two subcontrollers: 1) a roll angle, pitch angle, and altitude subcontroller and 2) a horizontal position subcontroller. In the controller design, the Moore-Penrose pseudoinverse of the coefficient matrix is applied to overcome the singularity of the roll angle. Third, the stability of both of the designed subcontrollers is verified by the Lyapunov stability theorem, and the convergence of the designed subcontrollers is analyzed. Finally, simulation results are provided to verify the effectiveness of the proposed model and the designed controller.

The stability problem is studied for hybrid systems with delays in this paper. Based on Lyapunov–Razumikhin approach, a novel theorem is presented for such system with the characteristic of homogeneity so that the system is globally preasymptotically stable. In particular, under the homogeneous assumption, we are able to obtain some rather weak conditions compared with general nonhomogeneous hybrid systems in this paper. Finally, two illustrative numerical examples are presented to demonstrate the applicability and the effectiveness of our theorems.

This paper investigates a stabilization problem for linear systems subject to saturation and time-varying delays in the control input, where the delay takes both large and small values in an alternating manner. Based on the delay values, the considered system is described by a switched system including a stabilizable subsystem and an unstabilizable subsystem. By using Lyapunov-Krasovskii functional method, a time-dependent switching rule orchestrating the proposed controllers is established to guarantee the regional stabilization of the closed-loop system. To obtain a domain of attraction as large as possible, an optimization problem is formulated in the form of linear matrix inequalities. Numerical examples and simulations are provided to demonstrate the effectiveness of the proposed method.

In this paper, the problems of stability and l2-gain analysis are investigated for discrete-time switched systems with mode-dependent average dwell time (DT). By proposing a novel multiple discontinuous Lyapunov function (MDLF) approach, the conditions of stability and weighted l2-gain are established for nonlinear systems. Next, the corresponding results for linear switched systems are also proposed by using MDLF. It turns out that our proposed results provide smaller bounds on the DT. Some simulation results are given to show the validity of our approaches.

This paper investigates a problem of stability analysis for a class of nonlinear systems with a time-varying delay taking both large and small values in an alternating manner. This problem is a delay-dependent stability analysis one but it shares some features with delay-independent ones. Hence, traditional stability analysis techniques for systems with delay are not applicable. By building upon our previous works, we first introduce the concept of delay-hybrid-dependent stability, which characterizes the delays described above. The considered system is first represented as a system with a switched delay. Then by using switching techniques and Lyapunov-Krasovskii functionals (LKFs), a new stability criterion is developed. Next, in the linear context, it is shown how the needed LKFs can be constructed by developing an original design of LKFs.

This paper investigates the regulation problem for a class of nonholonomic systems that includes power form systems and an approximated system of the rolling sphere as special cases. The basic idea is first to introduce a virtual periodic moving trajectory that satisfies certain persistent excitation condition (PE) and has the zero value at some time instants. Based on LaSalle invariance principle, the associated tracking problem is then solved under a necessary condition for stabilization and particularly true for the power form systems and the rolling sphere. With the help of virtual trajectory, the achieved tracking result is applied to the regulation problem and used to guarantee practical stability. The proposed controllers have a simple and explicit form, and hence are easily implemented. Simultaneously, fast convergence is guaranteed, thanks to the K-exponential convergence. More interestingly, the used approach is adding sufficiently exciting signals to the systems by considering virtual tracking signals so that the attractivity of the origin can be guaranteed based on LaSalle invariance principle. Thus, it is possible to extend the proposed results to more general systems. To verify the effectiveness of the proposed scheme, interesting simulation results are presented.

In this paper, a variation of the congestion situation is considered and a new game, the congestion game with player-specific (CGPS) utility functions, is proposed. This paper is motivated by some application scenarios that rule out the possibility of employing the existing game model to study such congestion situations. The CGPS game is characterized by adding a player-specific term and a weighted parameter with respect to the utility function. By using the semitensor product of matrices, the algebraic representation of the CGPS game is given and the existence of the weighted potential function is proved. Finally, the results are applied to solve the service chain composition problem in network function virtualization (NFV) and to analyze the effect of player-specific function on service chain configuration in NFV. Note to Practitioners-This paper is motivated by resource allocation problems in congestion networks where strategic users behave selfishly and aim at optimizing their own individual utility in the absence of a central controller. Compared with the centralized algorithms of poor reliability and scalability, game-theoretic control provides a promising distributed approach for resource allocation. In the game-theoretic framework, the existence and seeking of the desired solution are important issues. In this paper, a novel model is established to extend the utility functions space guaranteeing the existence of the solution. The developed utility design is used to capture users' different sensitivities to the effects of the network system. Simultaneously, it is more meaningful from the view of engineering to design the utility functions so that the desirable behavior is reachable. We also give an explicit scheme to seek the desired solution. The proposed model is finally applied to the service chain composition problem in NFV, of which the aim is to find the best service chain of users that accommodates their individual requirements. The proposed model shows reliable and effective.

The secure operation of electricity generation system (EGS) is essential to the quality of electricity and the security of energy Internet. In order to design secure control strategy for EGS, the behavior and capacity of adversary and possible attack should be studied. From this point of view and considering the actuator saturation and the false-data injection attack in control input channel of EGS, we analyze the behavior and stealthiness of adversary and design stealthy attack strategies in this paper. First, we prove that the magnitude of effective attack signal is smaller than the magnitude of injected attack signal because of actuator saturation. Second, we find that smaller actuator saturation limit makes the adversary easier to be detected. Finally, two stealthy attack strategies are designed for EGS which incorporates a χ2attack detector, and the effectiveness of them are illustrated through simulation.

This paper is concerned with the consensus problem of general linear dynamic multiagent systems with stochastic sampling. In this paper, the sampling intervals randomly switch between two different values. The communication topology between agents is fixed and directed. Full- and reduced-order observers are designed based on neighbor agents' relative output information. The algorithms to construct such observers are also provided. By using the estimated states of the agents, the observer-based consensus protocol with stochastic sampling are presented. Sufficient conditions to ensure consensus in mean square are derived by using Lyapunov stability theory. Finally, simulations are given to examine the effectiveness of the proposed methods.

This paper is concerned with the sampled-data-based consensus problem of heterogeneous multiagent systems under directed graph topology with communication failure. The heterogeneous multiagent system consists of first-order and second-order integrators. Consensus of the heterogeneous multiagent system may not be guaranteed if the communication failure always happens. However, if the frequency and the length of the communication failure satisfy certain conditions, consensus of the considered system can be reached. In particular, we introduce the concepts of communication failure frequency and communication failure length. Then, with the help of the switching technique and the Lyapunov stability theory, sufficient conditions are derived in terms of linear matrix inequalities, which guarantees that the heterogeneous multiagent system not only achieves consensus but also maintains a desired L2-gain performance. A simulation example is given to show the effectiveness of the proposed method in this paper.

This paper is concerned with the problem of adaptive tracking control for a class of uncertain nonlinear systems with nonsymmetric input saturation and immeasurable states. The radial basis function of neural network (NN) is employed to approximate unknown functions, and an NN state observer is designed to estimate the immeasurable states. To analyze the effect of input saturation, an auxiliary system is employed. By the aid of adaptive backstepping technique, an adaptive tracking control approach is developed. Under the proposed adaptive tracking controller, the boundedness of all the signals in the closed-loop system is achieved. Moreover, distinct from most of the existing references, the tracking error can be bounded by an explicit function of design parameters and saturation input error. Finally, an example is given to show the effectiveness of the proposed method.

This paper provides a design strategy for temperature control of the gas chromatograph. Usually gas chromatograph is modeled by a simple first order system with a time-delay, and a proportion integration (PI) controller is widely used to regulate the output of the gas chromatograph to the desired temperature. As the characteristics of the gas chromatograph varies at the different temperature range, the single-model based PI controller cannot work well when output temperature varies from one range to another. Moreover, the presence of various disturbance will further deteriorate the performance. In order to improve the accuracy of the temperature control, multiple models are used at the different temperature ranges. With a PI controller designed for each model accordingly, a delay-dependent switching control scheme using the dwell time technique is proposed to ensure the absolute exponential stability of the closed loop. Experiment results demonstrate the effectiveness of the proposed switching technique.

Event-triggered control (ETC) is a control strategy which can effectively reduce communication traffic in control networks. In the case where communication resources are scarce, ETC plays an important role in updating and communicating data. When network-induced delays are involved, two unsynchronized phenomena will appear if the existing ETC strategy, designed for networked control systems (NCSs) free of delays, is adopted. This paper deals with the ETC problem for NCS with delays existing in both sensor-to-controller and controller-to-actuator channels. A new predictive ETC strategy is proposed to solve both unsynchronized problems. It is shown that the stability of the resulting closed-loop system can be guaranteed under such an ETC strategy. Finally, both simulation studies and experimental tests are carried out to illustrate the proposed technique and verify its effectiveness.

Different from time-triggered control strategies, the event-triggered control (ETC) scheme takes action (e.g., sensing and actuation) when needed, which will greatly reduce the communication traffic in networked control systems (NCSs). On the other hand, due to reduced number of transmissions, the ETC is more vulnerable to communication dropouts compared with the periodic sampling. This paper focuses on a special Lyapunov-based ETC design with the consideration of packet dropouts. The closed-loop system with the ETC and packet dropouts can be rewritten as a switched system, switching between the normal transmission and the communication dropout. By adapting the well-known concept, average dwell-time in switched systems, the main result of this paper provides sufficient conditions in terms of transmission intervals that can ensure the closed-loop stability in the presence of transmission dropouts. Experimental results using dc-motor-based speed control have demonstrated the effectiveness of the proposed method.

This paper is concerned with the observer-based stabilization problem for linear systems with large delay periods (LDPs). The attention is focused on designing the full-order observers to guarantee the stability of the linear systems with LDPs. We use a switched system, which may include one subsystem that can not be stabilized, to describe such a model. First, the sufficient stability condition is presented for the system with small delay periods (SDPs). Then, the controller and the observer gains are designed based on the feasible solutions of LMIs. In the end, sufficient conditions guaranteeing the stability of the considered system with LDPs are presented.

The problem of guaranteed cost control for a class of networked control systems possessing uncertainties, network delays, and packet dropouts is solved in this paper. By means of introducing an auxiliary variable, a newly coupled, switched system model is derived first. Then, based on a predictive network control scheme, the conditions for guaranteed control performance of the overall system in terms of linear matrix inequalities are given. Next, a novel control design method, involving convex optimization technique to find solutions for the controllers that vary according to network delays and data-dropouts, is developed. It is shown from theory that the obtained criteria are much less conservative than existing ones. Finally, two illustrative examples, the second one being a laboratory-scale rig, are elaborated on to demonstrate the effectiveness of the proposed design method. Both numerical and simulation results appear favorable to this novel network control system synthesis.

This paper is concerned with analyzing input-to-state stability (ISS) for a class of switched nonlinear systems with time delays under asynchronous switching. Due to the existence of switching delay, the switching of the controller does not coincide accurately with the switching of the system.When the subsystem is stabilized with the matched controller, the subsystem is ISS; otherwise, the subsystem may be not ISS. We establish efficient condition, in terms of an upper bound on the switching delay, and in terms of a lower bound on the matched time intervals for the subsystem and the controller, which ensures ISS for the whole switched nonlinear system. Finally, an illustrative example is presented to demonstrate the efficacy of the results.

This correspondence considers the stability problem for a class of linear switched systems with time-varying delay in the sense of Hurwitz convex combination. The bound of derivative of the time-varying delay can be an unknown constant. It is concluded that the stability result for linear switched systems still holds for such systems with time-varying delay under a certain delay bound. Moreover, the delay bound of guaranteeing system stability can be easily obtained based on linear matrix inequalities (LMIs). As a special case, when the time-varying delay becomes constant, the criterion obtained in this correspondence is less conservative than existing ones. The reason for less conservativeness is also explicitly explained in this correspondence. Simulation examples illustrate the effectiveness of the proposed method.