Fangyuan Li, Pengfei Li, Shijie Wang, Junqi Gao, Jianxing Liu, Biqing Qi, Yuqiang Li. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Graph Retrieval Augmented Generation (GraphRAG) effectively enhances external knowledge integration capabilities by explicitly modeling knowledge relationships, thereby improving the factual accuracy and generation quality of Large Language Models (LLMs) in specialized domains.However, existing methods suffer from two inherent limitations: 1) Inefficient Information Aggregation: They rely on a single agent and fixed iterative patterns, making it difficult to adaptively capture multi-level textual, structural, and degree information within graph data.2) Rigid Reasoning Mechanism: They employ preset reasoning schemes, which cannot dynamically adjust reasoning depth nor achieve precise semantic correction.To overcome these limitations, we propose Graph Counselor, an GraphRAG method based on multi-agent collaboration.This method uses the Adaptive Graph Information Extraction Module (AGIEM), where Planning, Thought, and Execution Agents work together to precisely model complex graph structures and dynamically adjust information extraction strategies, addressing the challenges of multi-level dependency modeling and adaptive reasoning depth.Additionally, the Self-Reflection with Multiple Perspectives (SR) module improves the accuracy and semantic consistency of reasoning results through self-reflection and backward reasoning mechanisms.Experiments demonstrate that Graph Counselor outperforms existing methods in multiple graph reasoning tasks, exhibiting higher reasoning accuracy and generalization ability.Our code is available at Graph-Counselor.

This paper presents an improved generalized super-twisting control (IGSTC) scheme for neutral-point-clamped (NPC) converters subject to the line-inductance parameter uncertainties. By optimizing the algorithm structure, the proposed method accelerates transient response. An online fixed-time inductance estimation algorithm is integrated into the current-tracking loop, significantly enhancing the system’s robustness against inductance variations. Through Lyapunov-based analysis, the practical finite-time stability of the IGSTC and the practical fixed-time convergence of the identification algorithm are rigorously established. Finally, comprehensive simulations under both nominal and varying inductance scenarios validate that the superior performance of the proposed strategy.

This paper proposes a novel non-singular predefined-time control method for permanent magnet synchronous motor (PMSM) servo systems under disturbances. A non-singular predefined-time sliding mode manifold is designed to eliminate singularity issues while enabling adaptive switching under varying conditions, with stability achieved within a user-defined time frame. Based on this sliding mode manifold, a controller is developed to ensure predefined-time convergence in both reaching and sliding phases. Lyapunov-based analysis guarantees predefined-time stability and practical predefined-time stability, reducing the convergence domain. Simulation results validate the method’s effectiveness, robustness, and practical applicability in achieving predefined-time performance.

Developing robotic systems for unstructured and contact-rich environments presents significant challenges, necessitating advanced dexterous motion planning, compliant interaction control, and spatio-temporal coordination. To address these, we introduce PACR (Point-Axis Constraint Reasoning), an unified framework that encodes robot trajectories and impedance profiles via constraint functions parameterized by point-axis primitives, extracted from multi-view RGB-D camera observations. This enables joint optimization of motion and impedance within a shared mathematical framework. For enhanced robustness, we implement a dual-agent Vision-Language Model (VLM) system: a Generator employs Chain-of-Thought reasoning to formulate constraints, while an adversarial Critic validates them, significantly mitigating hallucination risks. Integrated with the dual-agent system, the framework also features an error backtracking mechanism, enabling dynamic adaptation by learning from failures. Extensive experiments across diverse manipulation tasks reveal that PACR achieves a 61% success rate (compared to 37% for baseline methods) and reduces the average contact forces, demonstrating broad applicability through zero-shot generalization without task-specific training.

This paper is focused on the event-based secure control issue for cyber-physical systems (CPSs) under aperiodic denial-of-service (DoS) attacks. Malicious DoS attacks disrupt the communication between the controller and the actuator. The finite attack resources of malicious attackers are taken into consideration, and the DoS attacks are characterized using an aperiodic model. In contrast to prior results, the present study tackles the issue of secure controller design by considering the attributes of the DoS attack, instead of employing a switched system approach to address the aforementioned concerns. More specifically, under aperiodic DoS attacks, sufficient criteria are established to guarantee that the closed-loop CPSs can achieve bounded stability. Then, within a time-varying attack period, the relationship between the attack active interval and the attack silent interval is derived. Without satisfying the derived conditions, the system’s stability will deteriorate. Moreover, an event-based secure control scheme under aperiodic DoS attacks is designed. To verify the efficacy of the derived theory, a wheeled mobile robot system under aperiodic DoS attacks is illustrated. Note to Practitioners—CPSs have been widely utilized in various domains, such as aerospace and intelligent transportation. However, the openness of networks provides attackers with numerous opportunities for malicious assaults, consequently leading to a degradation in system performance. Consequently, researching the security issues of CPSs under malicious attacks is of utmost urgency. This paper focuses on the issue of event-triggered secure control for CPSs in the presence of energy-constrained aperiodic DoS attacks. The event-triggered communication mechanism is introduced to reduce the computational burden. The criteria for ensuring the bounded stability of CPSs under aperiodic DoS attacks are proposed. The relationship between the attack active interval and the attack silent interval is derived, which is incorporated into the proposed criteria. A wheeled mobile robot system is given to validate the effectiveness of the proposed method. In the future, an active defense control method will be proposed to counter malicious attacks.

In this paper, a generalized proportional integral observer (GPIO)-based finite-time sliding mode control scheme is proposed to enhance the convergence rate and disturbance rejection capacity of the grid-connected three-level neutral-point-clamped (NPC) converter. Firstly, a generalized super-twisting algorithm (GSTA) is designed in the voltage regulation loop and the instantaneous power loop of the NPC converter. Compared with the conventional super-twisting algorithm (STA), this method provides a faster convergence speed while the system trajectories are far from the origin, which is applied to the NPC converter to improve its dynamic performance and robustness. Additionally, by introducing a generalized proportional integral observer (GPIO) combined with the GSTA in the voltage regulation loop, the unknown time-varying disturbances can be rejected effectively. The stability of the proposed control scheme is proved. Finally, a set of comparative experiments between the proposed controller and the classic STA-based method have been carried out based on a three-level NPC converter prototype, and the results confirmed the efficacy of the proposed control strategy.

Few-Shot Class-Incremental Learning (FSCIL) has gained considerable attention in recent years for its pivotal role in addressing continuously arriving classes. However, it encounters additional challenges. The scarcity of samples in new sessions intensifies overfitting, causing incompatibility between the output features of new and old classes, thereby escalating catastrophic forgetting. A prevalent strategy involves mitigating catastrophic forgetting through the Explicit Memory (EM), which comprise of class prototypes. However, current EM-based methods retrieves memory globally by performing Vector-to-Vector (V2V) interaction between features corresponding to the input and prototypes stored in EM, neglecting the geometric structure of local features. This hinders the accurate modeling of their positional relationships. To incorporate information of local geometric structure, we extend the V2V interaction to Graph-to-Graph (G2G) interaction. For enhancing local structures for better G2G alignment and the prevention of local feature collapse, we propose the Local Graph Preservation (LGP) mechanism. Additionally, to address sample scarcity in classes from new sessions, the Contrast-Augmented G2G (CAG2G) is introduced to promote the aggregation of same class features thus helps few-shot learning. Extensive comparisons on CIFAR100, CUB200, and the challenging ImageNet-R dataset demonstrate the superiority of our method over existing methods.

This article proposes an event-triggered generalized super-twisting sliding mode disturbance rejection control strategy for permanent magnet synchronous motors (PMSMs), aiming to ensure precise position tracking while efficiently conserving communication resources. A unified event-triggering mechanism facilitates the simultaneous transmission of measurement and actuator signals, effectively implementing feedback-control bidirectional triggering. Furthermore, the entire control strategy is updated based on triggered state information. It incorporates a generalized super-twisting controller characterized by a simple structure and smooth dynamic performance, in addition to a neural network observer and a high-gain observer designed to estimate lumped disturbances and enhance system robustness. The practical stability of the closed-loop system, the boundedness of the signals, and the Zeno-free execution of triggering sequences are rigorously verified. Finally, experimental results on a PMSM validate the superiority of the proposed control approach in terms of transient-state position tracking, steady-state smoothness, communication efficiency, and robustness.

This article explores a novel variable-gain super-twisting observer (VGSTO)-based fixed-time sliding mode control (FTSMC) method for the dc-link voltage control of three-level neutral-point-clamped (NPC) active front-end (AFE) converters. Among the literature review, the super-twisting observer is an attractive solution for disturbance estimation. However, its convergence velocity is limited when the trajectories are far away from the origin. To overcome this drawback, a novel VGSTO is proposed in this work, whose innovation lies in adopting dynamically adapted exponent coefficients in the conventional super-twisting observer; thus, it turns into a linear observer outside a ball around the origin, which increases the convergence rate of conventional super-twisting observer, and the disturbance rejection ability is enhanced significantly. In addition, an FTSMC is designed for the NPC converter and its settling time is independent of the initial states, which breaks through the limitation of the traditional finite-time sliding mode controllers and assures the dynamic performance of the NPC converter. The proposed control scheme is assessed and compared with other representative observer-based sliding mode control methods based on a three-level NPC AFE converter test bench, and the experimental results verify its feasibility and effectiveness.

Advanced life forms, sustained by the synergistic interaction of neural cognitive mechanisms, continually acquire and transfer knowledge throughout their lifespan. In contrast, contemporary machine learning paradigms exhibit limitations in emulating the facets of continual learning (CL). Nonetheless, the emergence of large language models (LLMs) presents promising avenues for realizing CL via interactions with these models. Drawing on Complementary Learning System theory, this paper presents a novel Interactive Continual Learning (ICL) framework, enabled by collaborative interactions among models of various sizes. Specifically, we assign the ViT model as System1 and multimodal LLM as System2. To enable the memory module to deduce tasks from class information and enhance Set2Set retrieval, we propose the Class-Knowledge-Task Multi-Head Attention (CKT-MHA). Additionally, to improve memory retrieval in System1 through enhanced geometric representation, we introduce the CL-vMF mechanism, based on the von Mises-Fisher (vMF) distribution. Mean-while, we introduce the von Mises-Fisher Outlier Detection and Interaction (vMF-ODI) strategy to identify hard examples, thus enhancing collaboration between System1 and System2 for complex reasoning realization. Comprehensive evaluation of our proposed ICL demonstrates significant resistance to forgetting and superior performance relative to existing methods. Code is available at github.com/ICL.

Insulator cleaning robot operates in challenging and unpredictable conditions, whereas the conventional control algorithms are not easy to adapt to the complex disturbances. To deal with this issue, in this paper, a two-degree-of-freedom Lagrangian dynamics model is derived and a radial basis function (RBF) neural network based non-singular fast terminal sliding mode (NFTSM) controller is designed for the insulator cleaning robot. The NFTSM is designed for tracking the robot's reference trajectory, and RBF neural network is introduced to compensate external disturbances and internal unmodelled dynamic, which further improves both the dynamic and steady state performance of the robot. Finally, simulation results demonstrate the advantages of the proposed controller in insulator cleaning robot trajectory tracking control.

This paper introduces a fixed-time sliding mode control (FTSMC) scheme that utilizes a fixed-time disturbance observer (FTDOB) for three-level neutral-point-clamped (3L-NPC) converters. Therein, a FTSMC with adaptive exponential coefficient is developed to regulate the dc-link voltage. Compared with existing methods, the proposed approach features a settling time upper bound that is independent of the initial error, which ensures the dynamic performance of the 3L-NPC converters. Furthermore, a FTDOB is employed to estimate external disturbances and act as feedforward compensation of the 3L-NPC converters to enhance its disturbances rejection ability. Finally, a series of simulation results illustrate the advantages of the proposed scheme by compared with several representative methods used in 3L-NPC converters.

From the perspective of information bottleneck (IB) theory, we propose a novel framework for performing black-box transferable adversarial attacks named IBTA, which leverages advancements in invariant features. Intuitively, diminishing the reliance of adversarial perturbations on the original data, under equivalent attack performance constraints, encourages a greater reliance on invariant features that contributes most to classification, thereby enhancing the transferability of adversarial attacks. Building on this motivation, we redefine the optimization of transferable attacks using a novel theoretical framework that centers around IB. Specifically, to overcome the challenge of unoptimizable mutual information, we propose a simple and efficient mutual information lower bound (MILB) for approximating computation. Moreover, to quantitatively evaluate mutual information, we utilize the Mutual Information Neural Estimator (MINE) to perform a thorough analysis. Our experiments on the ImageNet dataset well demonstrate the efficiency and scalability of IBTA and derived MILB. Our code is available at github.com/IBTA.

This article aims to solve a hierarchical multi-UAV Dubins traveling salesman problem (HMDTSP). Optimal hierarchical coverage and multi-UAV collaboration are achieved by the proposed approaches in a 3-D complex obstacle environment. A multi-UAV multilayer projection clustering (MMPC) algorithm is presented to reduce the cumulative distance from multilayer targets to corresponding cluster centers. A straight-line flight judgment (SFJ) was developed to reduce the calculation of obstacle avoidance. An improved adaptive window probabilistic roadmap (AWPRM) algorithm is addressed to plan obstacle-avoidance paths. The AWPRM improves the feasibility of finding the optimal sequence based on the proposed SFJ compared with a traditional probabilistic roadmap. To solve the solution to TSP with obstacles constraints, the proposed sequencing-bundling-bridging (SBB) framework combines the bundling ant colony system (BACS) and homotopic AWPRM. An obstacle-avoidance optimal curved path is constructed with a turning radius constraint based on the Dubins method and followed up by solving the TSP sequence. The results of simulation experiments indicated that the proposed strategies can provide a set of feasible solutions for HMDTSPs in a complex obstacle environment.

Multiple batteries in uninterruptible power supply (UPS)-microgrid systems based on multiagents composed of multiple electric vehicles (EVs) can encounter state of charge (SoC) consistency problems. To solve this differential expansion and controller saturation problem, an adaptive command filter sliding-mode control strategy based on virtual synchronous generators (VSGs) and considering the power allocation principle is proposed. First, based on directed graph theory, an SoC consistency algorithm and power allocation strategy for multiple EVs were proposed, forming a dc power system with a fixed communication topology. Second, the rotor motion equation of synchronous generator (SG) is introduced into the inverter control algorithm to form the mathematical model of VSG. Third, a low-pass filter (LFP) was introduced in the voltage control process to simulate the excitation attenuation characteristics of the SG. Based on the above, a backstepping control strategy, including a command filter and sliding mode controller is proposed, which improves the operating stability of the system based on the system errors of angle, frequency, and power output. Finally, the UPS-microgrid system based on multiagents is simulated to demonstrate the stability of the system and the effectiveness of the proposed control strategy.

In this article, a novel adaptive fixed-time fuzzy control algorithm is designed for uncertain Euler–Lagrange (EL) systems with actuator control input saturation. In contrast to existing algorithms, this article explores a faster fixed-time backstepping control algorithm. It enables the system to achieve fixed-time convergence with a faster convergence rate and obtain a smaller upper bound of the convergence time. To address the problem of actuator control input saturation, a novel fixed-time auxiliary system is constructed, involving coordinate transformation of the system's error variables to mitigate the effects of saturation. In response to the unknown dynamics (including model uncertainty, external disturbance, etc.) of the EL system, this article designs an adaptive interval type-2 fuzzy neural network for estimation and compensation. Stability analysis confirms that the tracking error can achieve faster fixed-time convergence. Simulation and experimental results demonstrate that the proposed control algorithm can enhance dynamic and steady-state tracking control performance.

In this article, on the basis of an existing dual layer nested (DLN) adaptive sliding-mode control (ASMC) strategy, an observer-based sliding-mode control strategy with an improved DLN adaptive tuning mechanism (IDLN-ASMC) is proposed for a three-level neutral-point-clamped converter. The proposed controller not only ensures good system performance but also mitigates two problems of the existing DLN-ASMC strategy. Meanwhile, three objectives are achieved. First, an adaptive supertwisting algorithm is utilized in the power tracking loop to converge power tracking errors to bounded regions in finite time. Next, a disturbance observer-based IDLN-ASMC strategy is proposed in the dc-link voltage regulation loop to regulate the dc-link voltage to its reference value. Finally, a simple proportional-integral controller is used in the dc-link voltage-balancing loop to reduce the voltage difference between the two dc-link capacitors. The results of simulation and experiment demonstrate the effectiveness and superiority of the proposed strategy.

In this article, an observer-based prescribed performance speed control method is proposed for permanent magnet synchronous motors. A transformed speed error is introduced and a suitable controller is designed to make it converge to zero, while guaranteeing the original speed error evolves strictly within a prescribed region. The controller is designed based on a backstepping approach. A linear extended state observer is applied to estimate and feed forward the external constant load disturbance to improve robustness. A data-driven radial-basis function neural network is proposed to approximate the nonlinear dynamic caused by parameter uncertainties and periodic-changing disturbance by deploying real-time and historical data. The stability analysis is based on Lyapunov's control theory. Experimental results verify the effectiveness and advantages of the proposed control scheme.

Deep neural networks (DNNs)-based natural language processing (NLP) systems are vulnerable to being fooled by adversarial examples presented in recent studies. Intent detection tasks in dialog systems are no exception, however, relatively few works have been attempted on the defense side. The combination of linear classifier and softmax is widely used in most defense methods for other NLP tasks. Unfortunately, it does not encourage the model to learn well-separated feature representations. Thus, it is easy to induce adversarial examples. In this article, we propose a simple, yet efficient defense method from the geometric constraint perspective. Specifically, we first propose an M-similarity metric to shrink variances of intraclass features. Intuitively, better geometric conditions of feature space can bring lower misclassification probability (MP). Therefore, we derive the optimal geometric constraints of anchors within each category from the overall MP (OMP) with theoretical guarantees. Due to the nonconvex characteristic of the optimal geometric condition, it is hard to satisfy the traditional optimization process. To this end, we regard such geometric constraints as manifold optimization processes in the Stiefel manifold, thus naturally avoiding the above challenges. Experimental results demonstrate that our method can significantly improve robustness compared with baselines, while retaining the excellent performance on normal examples.

This paper studies the problem of trajectory tracking control for robotic manipulators subject to model uncertainties, external disturbance and actuator saturation. At first, a nonsingular fixed-time sliding mode variable is developed based on a novel fixed-time stable system, which solves the singularity problem brought by the terminal sliding mode control and obtains a fast convergence rate. After that, a novel fixed-time sliding mode observer is designed to estimate and compensate the model uncertainties and external disturbance. The fixed-time stability of the system under the proposed composite control is proved by Lyapunov stability theory. Finally, the effectiveness of the method is verified by comparative simulation.

This paper presents a model-free predictive current controller for permanent magnet synchronous motors. The dynamic model of the motor currents is represented using an ultra-local model, where all system parameters, nonlinear terms, and other unmodeled dynamics are encapsulated into two lumped functions. These functions are estimated online using a second-order fixed-time sliding mode observer, allowing for system model estimation without requiring exact knowledge of the system parameters in advance. Subsequently, a predictive controller is designed by taking into consideration the inherent one-step delay. Simulation results are presented to showcase the effectiveness and efficacy of the proposed approach.

In this paper, a fixed-time active disturbance rejection-based sliding mode control scheme is proposed for the three-phase three-level neutral-point-clamped (NPC) active front-end (AFE) converter to regulate the dc-link voltage. In order to further improve the disturbance rejection ability of the active disturbance rejection control (ADRC) when applied in NPC converter, a fixed-time disturbance observer is used to estimate the disturbances instead of the traditional extended state observer in ADRC. In addition, a sliding mode controller with constant plus proportional rate reaching law (CPPRL) is designed to regulate the dc-link voltage, and the dynamic performance and control accuracy of NPC converter is improved without increasing the chattering. Finally, the performance of the proposed control scheme is evaluated by a class of comparative simulations, and the results confirm the effectiveness and feasibility of the proposed approach.

In this paper, the fixed-time control problem of DC-DC buck converter systems with mismatched disturbances is investigated. Firstly, the sliding mode fixed-time observers are constructed to estimate the matched and mismatched disturbances of the systems. Secondly, a novel segmented terminal sliding mode control (TSMC) variable considering the mismatched disturbance of the system is designed based on the observations. To improve the tracking performance, a new second-order fixed-time reaching law is proposed. Then, in order to make the DC-DC buck converter systems with mismatched disturbances achieve accurate control in a fixed-time independent of the initial state, a novel fixed-time nonsingular TSMC based on the fixed-time observers is proposed. Finally, comparative experiments are conducted to verify the effectiveness and practicality of the proposed control strategy.

This article develops a new controller design approach to stabilize system states onto the equilibrium at an arbitrarily selected time instant irrespective of the initial system states and parameters. By the stabilization approach, the actual convergence time (not the bound of actual convergence time) is independent of the initial value of system states. This feature differentiates our proposed prescribed-instant stability from conventional fixed, predefined, and prescribed time stability. In this work, we propose the controller design method for the prescribed-instant stability of n -order integrator systems. The proposed control is bounded and can gradually go to zero at an arbitrarily selected time instant, at which the system states reach zero simultaneously. This special stability of the controlled system is analyzed by reduction to absurdity. In simulations, an example of comparison with frequently used prescribed-time control is presented to show the difference. Moreover, the proposed stabilization method is validated by a magnetic suspension system with matched disturbances.

Efficient and simple modulation of output power is demanding for radio frequency plasma and wireless power transmission,magnetic resonance imaging systems. In this article, a power modulation strategy that can improve the linearity, power capability and efficiency simultaneously is proposed. The control modes ofon/offand Outphasing are combined to ensure shallow phase depth in a wide power range. Hybrid modules composed of load independent class Φ2structure and symmetrical class Φ2structures are adopted ason/offmodules and PS module to match the proposed control scheme, which are able to provide constant output voltage while remaining zero voltage switching (ZVS) state under resistive-inductive and resistive-capacitive load separately. Theon/offmodule is uniquely designed to behave as a static load when turned off. With the cooperation of control strategy and circuit architecture, the proposed system is able to regulate power linearly with optimal efficiency and minimal voltage and current stress. An implementation with four modules, 13.56 MHz, 25–200 W inverter system is demonstrated, the output power can be seamlessly and linearly controlled with efficiency always within 82%–93.5%. The heat dissipation in prototype is uniform, and is feasible to expand to a higher power compared with other control and circuit structures.

In this paper, the prescribed-instant stability control is proposed and utilized in the leader-follower multiagent system with a directed communication topology. The proposed prescribed-instant stability ensures the settling time is exactly equal to the prescribed time instant. Therefore, the specific time instant at which the system containment is established can be manipulated as per our will. This result is of great significance in some complex multiagent system cooperation tasks. The proof of the main result is demonstrated by the Lyapunov method, Specifically, the settling time is clamped from two sides to the selected time instant. Apart from the theoretical proof, a numerical simulation is also presented to validate the proposed method.

Electrochemical impedance spectroscopy (EIS) Nyquist plot modeling has been attached great importance to fault diagnosis of proton exchange membrane fuel cell (PEMFC) system. This paper applies Gaussian process regression (GPR) and Bayesian optimization (BO) to the problem of building an adaptive EIS Nyquist plot model of PEMFC. The experimental results show that GPR performs better than multivariate polynomial regression and equivalent circuit model (ECM) method for this task when a small number of training samples are available. Therefore, this method can be a suitable approach for online adaption of EIS Nyquist plot model for fault diagnosis application.

To handle the common existing constraints, that is, limited energy supplies and limited communication bandwidth in multiagent systems (MASs), this article investigates the consensus problem in MASs with event-triggered communication (ETC) and state quantization. In order to compensate for the effect brought by mismatched disturbances, we also propose a novel multiple discontinuous sliding-mode surface, and the corresponding sliding-mode control law is constructed by considering the event-triggered and dynamic quantized mechanisms jointly. Under such a scheme, it is shown that the state trajectories of all the agents will be regulated to achieve consensus asymptotically and the Zeno behavior can be avoided completely. We further extend this work to self-triggered and periodic event-triggered cases. Particularly, in a periodic event-triggered approach, the new form of triggering conditions and upper bound of the sampling periods are provided explicitly. As a result, all agents can reach bounded consensus. Moreover, the upper bound of the consensus error can be arbitrarily adjusted by appropriately selecting parameters, and the periodic event-triggered case will be reduced to the event-triggered case when the bound approaches 0 (sampling periods approach 0 at the same time). A numerical example is illustrated to verify the effectiveness of the proposed algorithms.

This article proposes a sliding-mode-observer (SMO)-based control strategy to regulate the dc-link voltage for a three-phase two-level active front end (AFE). The SMO is designed for the voltage control loop to estimate the external load which is abruptly connected to the AFE dc-link and consequently causes the dc-link voltage fluctuation. The estimated load value is used to compensate the voltage loop controller, therefore, the voltage loop gains more robustness against the load perturbation and its disturbing effect is greatly reduced. The effectiveness and advantage of the proposed control strategy has been verified through theoretical analysis, simulations, and the real-application experiments conducted on a 5 KVA laboratory AFE. The results show that the proposed control strategy provides obvious improvement of the dc-link voltage control performance comparing with the conventional PI controller and demonstrates stronger robustness against the operating point variations caused by the changes in external load and dc-link capacitance.

In order to obtain a simple and efficient control strategy for three-phase two-level grid-connected power converters with improved system performance including reduced computation and communication burdens, an event-triggered continuous control set-model predictive control (CCS-MPC) is proposed in this paper. In the DC-link voltage regulation loop, a simple but efficient controller is designed to track the DC-link voltage to its reference value. In the power tracking loop, a simple event-triggered CCS-MPC is utilized to ensure that the active power and reactive power also track their reference values. The control signals to the converter are updated and transmitted only when the triggering condition is satisfied. Compared with periodic sampling control strategies, which require the calculation and transmission of control signals in each sampling period, the proposed controller reduces the utilization of limited computation and communication resources while maintaining good tracking performance. By comparing with the periodic sampling proportional-integral strategy, the effectiveness and superiority of the proposed strategy are shown through simulation results.

Machining parameter optimization can be an effective way to reduce the energy consumption of machining processes and contribute to sustainable manufacturing. In this paper, the energy-aware machining parameter optimization problem is described and formulated. Based on the description and formula, an approach which considers multiple machining parameters including spindle speed, feed rate, depth of cut and width of cut simultaneously is developed. This approach consists of two key steps: modelling the energy consumed in the machining process and searching for the optimal machining parameters. In the modelling step, a hybrid modelling method which integrates regression and artificial neural networks (ANN) is developed to characterize the relationship between energy consumption and the machining parameters and obtain the energy consumption prediction model. In the searching step, an improved flower pollination algorithm (FPA) is presented to achieve the further searching for the optimal machining parameters with energy consideration. The approach has been tested and validated on multiple groups of experimental data. The results demonstrate that the approach can lead to a more accurate energy consumption prediction and an optimal solution for machining parameters.

This article investigates the problem of optimal tracking control for cyber-physical systems (CPSs) when the cyber realm is attacked by Denial-of-Service (DoS) attacks which can prevent the control signal transmitting to the actuator. Attention is focused on how to design the optimal tracking control scheme without using the system dynamics and analyze the impact of DoS attacks on tracking performance. First, a Riccati equation for the augmented system, including the system model and the reference model is derived under the framework of dynamic programming. The existence and uniqueness of its solution are proved. Second, the impact of the successful DoS attack probability on tracking performance is analyzed. A critical value of the probability is given, beyond which the solution to the Riccati equation cannot converge. The tracking controller cannot be designed. Third, reinforcement learning is introduced to design the optimal tracking control schemes, in which the system dynamics are not necessary to be known. Finally, both a dc motor and an F16 aircraft are used to evaluate the proposed control schemes in this article.

In this paper, a high-order nonlinear disturbance observer-based fractional-order sliding mode control (FOSMC) strategy is proposed for DC/DC buck converters in the presence of mismatched disturbances, ensuring the properties of both stability and dynamic performance. Traditional sliding mode control can deal with matched disturbances while achieving desired closed-loop performance, unfortunately it is particularly sensitive to mismatched disturbances. In the proposed control structure, two nonlinear disturbance observers are constructed to estimate both matched and mismatched disturbances in finite time. Then, the estimated variables are used for the fractional-order sliding mode surface design, where a super twisting sliding mode controller is designed to drive the states of the system to track their desired values. Comparing with the traditional SMC, the proposed method not only reduces the sensitivity of the system to mismatched disturbance, but also improves the transient performance of the system. Simulation and experimental results have comprehensively illustrated the feasibility and effectiveness of the proposed strategy.

This paper deals with the problem of dissipativity-based filtering for switched genetic regulatory networks (GRNs) with stochastic perturbation and time-varying delays. By choosing an appropriate piecewise Lyapunov function and using the average dwell time method, we propose a new set of sufficient conditions in terms of Linear matrix inequalities (LMIs) for the existence of dissipative filter, which ensures that the resulting filtering error system is mean-square exponentially stable with dissipativity performance. The filter gains are provided by solving feasible solutions to a certain set of LMIs. A simulation example is given to demonstrate the effectiveness of the desired dissipativity-based filter design approach.

This paper presents a tracking control scheme with quantization mechanism for hypersonic flight vehicles (HFVs) with prescribed performance using an interval type-2 fuzzy neural network (IT2FNN). A parameterized tracking error model of the HFV is derived with some considered uncertainties, which are approximated by an IT2FNN. The tracking control of the velocity and altitude of the HFV is designed by using a prescribed performance control technique. It allows that transient characteristics of the tracking errors can be improved and adjusted by some prescribed performance functions. According to an adaptive backstepping control design procedure, novel continuous control laws of the fuel equivalency ratio, canard deflection, and elevator deflection are designed with logarithmic quantization mechanism, for the sake of avoiding inadvertently increasing the effective gains of continuous controllers as well as reducing loads of the communication from controller unit to actuator unit. Besides, the limited tracking errors of the flight path angle and angle-of-attack can be achieved by applying the designed controllers. Finally, the presented tracking controllers with quantization mechanism are validated by comparative simulations.

In this paper, the event-triggered filter design problem is investigated for a class of discrete-time repeated scalar nonlinear systems. An admissible filter is introduced and sufficient conditions to ensure the asymptotical stability of the controlled system with a given performance are established. The solution of the corresponding event-triggered filter problem is settled by the projection lemma, and the cone complementary linearization technique is introduced to derive the filter parameters. An example is provided to demonstrate the effectiveness and potential of the proposed new design method.

This paper proposes a dynamic sliding mode control (SMC) approach to the robust voltage regulation of dc-dc boost converters by using interval type-2 fuzzy neural networks (IT2FNNs). First, uncertainties caused by the perturbation of the input inductor and the output capacitor are represented with some bounded approximation errors, by the utilization of a Takagi-Sugeno (T-S) fuzzy modeling approach. Based on the represented model of the boost converter, a new type of sliding surface is designed depending on the duty cycle and reference inputs of the converter. Then, a dynamic SMC law is designed, by considering that the perturbation of the uncertain parameters, including input inductor, output capacitor, load resistor, and input voltage, is bounded. Meanwhile, we adopt an exponential plus power approaching law in the sliding mode controller for fast reachability of the sliding surface and a small chattering in the duty cycle input. Moreover, in terms of the considered uncertainties, a novel IT2FNN-based dynamic SMC law is derived, by applying simplified ellipsoidal-type membership functions in the type-2 fuzzy neural network. To improve the capacity to manage the uncertainties, some online learning algorithms for the updating of the IT2FNN are designed by a gradient descent method (GDM), without the requirement of the boundedness of the uncertainties. The resulting tracking error system is synthesized to be bounded stable based on the designed IT2FNN-based dynamic SMC. Finally, the effectiveness of the proposed adaptive IT2FNN-based dynamic SMC method is verified by some comparative simulation results.

This paper presents voltage regulation and current tracking control strategies for three phase two-level grid-connected power converters. By using power-invariant Park's transformation, an averaged mathematical model of power converters is obtained in dq synchronous reference frame. Then a novel control strategy using adaptive control and H∞technique is proposed to regulate the dc-link output voltage as well as track a desired current reference for three-phase power rectifiers. More specifically, an efficient adaptive controller is established in the external loop for regulating dc-link output voltage in the presence of external disturbances. A set of H∞controllers are designed in the internal loop to force the input currents track their desired values. Finally, simulation results obtained from the proposed control method are presented, analyzed, and compared with that of sliding mode control, and the superiority of the proposed control laws is verified.

This paper investigates the problem of the resilient control for cyber-physical systems (CPSs) in the presence of malicious sensor denial-of-service (DoS) attacks, which result in the loss of state information. The concepts of DoS frequency and DoS duration are introduced to describe the DoS attacks. According to the attack situation, that is, whether the attack is successfully implemented or not, the original physical system is rewritten as a switched version. A resilient sliding mode control scheme is designed to guarantee that the physical process is exponentially stable, which is a foundation of the main results. Then, a zero-sum game is employed to establish an effective mixed defense mechanism. Furthermore, a defense-based resilient sliding mode control scheme is proposed and the desired control performance is achieved. Compared with the existing results, the differences mainly lie in two aspects, that is, one where a switched model is obtained, based on which the average dwell-time like approach is utilized to derive the resilient control scheme, and the other where the zero-sum game in employed to make the attacks satisfy the concepts of DoS frequency and DoS duration. Finally, simulation results are given to demonstrate the effectiveness of the proposed resilient control approach.

In this article, a linear extended state observer (LESO) based second-order sliding mode (SOSM) control strategy with the direct power control is proposed for a three-phase neutral-point-clamped (NPC) power converter connected to a dc microgrid. Comparing with the PI control method, the proposed approach implements the advanced SOSM controller into the voltage regulation loop and instantaneous power tracking loop to enhance the dynamic and steady state performance. Furthermore, saturation function is applied in the SOSM method to weaken the chattering phenomenon. On the other hand, since the dc load is regarded as an external disturbance, an efficient LESO is designed in the voltage regulation loop to reject this disturbance. The design process of the proposed control strategy is shown based on the continuous averaged model of the NPC converter. Finally, comparison experiments among PI, LESO-based PI, and proposed LESO-based SOSM control strategies are implemented, which validate the superiority of the proposed approach.

With rapid development of deep integration of computation, control, and communication, Cyber-Physical Systems (CPSs) play an important role in industrial processes. Combined with the technology of fog computing, CPSs can outsource their complicated computation to the fog layer, which in turn, may bring security threats with regard to data privacy. To protect data privacy in a control framework, this paper investigate observer-based secure control problem towards fog computing aided CPSs (FCA-CPSs) by utilizing data perturbation method. Firstly, security inputs are designed to encrypt the transmitted states to realize specific confidentiality level. Then, sufficient conditions are established to ensure the stability of considered FCA-CPSs. Finally, a numerical example is provided to illustrate the effectiveness of the secure estimation scheme.

This paper presents an adaptive sliding mode observer (ASMO) to estimate three-phase grid voltage parameters, including both positive and negative sequences of voltage and grid frequency under unbalanced grid faults. First, the dynamic of three-phase voltage is reformulated as the second-order uncertain system, which can transform the traditional phase locked loop problem to the observer design problem. Based on the obtained dynamic system, an ASMO is constructed to estimate three-phase grid voltage parameters, using the adaptive and sliding mode techniques. The stability of the overall system including the observer estimation errors, sliding variable and adaptive estimation errors is rigorously proved by Lyapunov stability theory. The performance of proposed observer is assessed for estimation of three-phase grid voltage parameters by simulation in which two types of faults, i.e., the amplitude of voltage and grid frequency variations. are considered.

In this paper, a distributed filtering scheme is presented to deal with the fault detection problem of nonlinear stochastic systems with wireless sensor networks (WSNs). The nonlinear stochastic systems, which are of discrete-time form, are represented by interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy models. Each sensor of the WSN can receive measurements from itself and its neighboring sensors subject to a deterministic interconnection topology. Independent random variables obeying the Bernoulli distribution are formulated to characterize the randomly occurred packet losses between the WSN and the filter unit. To generate residual signals for evaluation functions of the fault detection mechanism, a novel type of IT2 T-S fuzzy distributed fault detection filter is proposed corresponding to each sensor node. Additionally, a fault reference model is adopted for improving the performance of the fault detection system. A new overall fault detection system is formulated in an IT2 T-S fuzzy model framework. Applying Lyapunov functional approach, we concentrate on the analysis of stability and performance of the resulting fault detection system. New techniques are utilized to handle the decoupling problem in design procedure. The desired parametric matrices of the fuzzy filters are designed subject to a developed criterion, which is a sufficient condition of the robust mean-square asymptotic stability for the overall fault detection system with a disturbance attenuation performance. Finally, a truck-trailer system with a four-node WSN is established for simulation validation. In simulations, the mincx function of the MatLab 2017a in Windows 10 OS is used to optimize the level of the disturbance attenuation performance, and to obtain the filter gains for the established system. By comparing the different time instants when the residual evaluation functions exceed their respective thresholds, simulation results successfully validate the effectiveness and applicability of the presented distributed fault detection scheme.

Cascaded H-bridge (CHB) and modular multilevel converter (MMC) are effective solutions for medium and high voltage motor drive applications. However, the choice between the two converters is not particularly clear. This paper presents a loss evaluation of cascaded H-bridge and modular multilevel converter as a reference for loss calculation. A brief description of CHB and MMC are provided and the explicit expression of conduction loss and switching loss for MMC and CHB is derived respectively to show the relationship between the loss and the main variables of MMC and CHB directly based on carrier phase-shifting PWM (CPS-PWM). In the derivation of switching loss, a novel switching loss calculation method based on PWM is proposed to simplify the theoretical calculation process. Finally, the total loss comparison of the two converters based on different conditions are shown for a better selection in motor drive application.

This paper presents a novel control scheme for DC-DC boost converter, maintaining the desirable voltage regulation performance under high load variation and large change of voltage reference. The model of converter is reformulated, in which the unknown equivalent load, input voltage, model uncertainties and unmodeled dynamics are lumped as external disturbance. The control strategy is designed with backstepping control technology, similar to the cascade control method in which the intermediate variable is introduced to fast respond the control demand, effectively dealing with the nonlinearity of the boost converter dynamics. The disturbance observers are established to estimate the lumped disturbances, rejecting disturbances and removing steady-state errors to improve the closed-loop performance. The simulation results demonstrate that the proposed control strategy, backstepping control combined with disturbance observer, provides lots of advantages superior to the conventional PI control such as faster dynamic response and less output voltage drop.

This paper is concerned with the problem of secure state reconstruction for cyber-physical systems (CPSs). CPSs are more vulnerable to the cyber world yet to attackers, who can attack any sensor of the considered systems and modify values of attacked sensors to be arbitrary ones. In the design process, both malicious attacks on sensors and unknown input are taken into consideration. First, a linear discrete-time state-space model is utilized to describe such systems, and then a sparse vector is adopted to model attacks. By collecting sensor measurements and using an iterative approach, a new model in descriptor form is obtained, which paves the way for estimating system states under an unknown input situation. Second, the problem of secure state estimation is transformed into an optimal version. A novel sliding-mode observer is proposed to estimate system states from collected sensor measurements corrupted by malicious attacks. In order to guarantee the estimations to be sparse, a projection operator is designed. Third, a projected sliding-mode observer-based estimation algorithm is developed to reconstruct system states, where an event-triggered scheme is integrated to save limited computational resource. In addition to propose such an algorithm, the effectiveness of both projection operator and sliding-mode observer is analyzed. Furthermore, the convergence of the proposed secure estimation algorithm is proved. Finally, some simulation results are given to demonstrate the effectiveness of the proposed algorithm.

This paper studies an output-based adaptive fault-tolerant control problem for nonlinear systems with nonstrict-feedback form. Neural networks are utilized to identify the unknown nonlinear characteristics in the system. An observer and a general fault model are constructed to estimate the unavailable states and describe the fault, respectively. Adaptive parameters are constructed to overcome the difficulties in the design process for nonstrict-feedback systems. Meanwhile, dynamic surface control technique is introduced to avoid the problem of "explosion of complexity". Furthermore, based on adaptive backstepping control method, an output-based adaptive neural tracking control strategy is developed for the considered system against actuator fault, which can ensure that all the signals in the resulting closed-loop system are bounded, and the system output signal can be regulated to follow the response of the given reference signal with a small error. Finally, the simulation results are provided to validate the effectiveness of the control strategy proposed in this paper.

This paper is concerned with the exponential stability analysis of genetic regulatory networks (GRNs) with switching parameters and time delays. In this paper, a new integral inequality and an improved reciprocally convex combination inequality are considered. By using the average dwell time approach together with a novel Lyapunov-Krasovskii functional, we derived some conditions to ensure the switched GRNs with switching parameters and time delays are exponentially stable. Finally, we give two numerical examples to clarify that our derived results are effective.

A new approach to the control of three-phase two-level grid-connected power converters is proposed in this paper. The proposed control is an extended state observer (ESO)based second order sliding mode (SOSM), which comprises two control loops: the outer loop is a voltage regulation loop, as well as inner loop is an instantaneous power tracking loop. The outer loop is accomplished by an H∞controller plus an ESO, which is designed to regulate dc-link capacitor voltage of the converter and asymptotically reject external disturbances and parameter perturbations. The SOSM strategy is employed in the inner loop to drive the active and reactive power convergence to their desired values. Control objectives of nearly unity power factor and dc-link capacitor voltage regulation are simultaneously satisfied. Availability of the ESO-based SOSM is compared with the classic proportional-integral control in simulations, and the comparison implies that the proposed strategy not merely achieves an almost perfect tracking performance, but also provides a complete robustness against resistance load variation.

This paper employs second-order sliding mode control (SOSMC) to carry out the current tracking and voltage regulation tasks of a grid-connected three-phase two-level power converter. For dc-link voltage regulation, a disturbance observer is adopted to improve the whole control performance. In this paper, four different types of disturbance observers are proposed for comparison, which are conventional linear observer (LINO), second-order sliding mode observer (SOSMO), linear extended state observer (LESO) and nonlinear extended state observer (NESO). To ensure fair comparison, the parameters of the observers are tuned to make the power converter achieve almost same current total harmonic distortion (THD) and steady error of dc-link voltage. The performances are compared in the term of transient response of the dc-link voltage. The simulation results show: first, the disturbance observers improve the control performance of SOSMC; second, SOSMO outperforms the other three observers.

In this paper, a novel scheme of disturbance observer based second order sliding mode (SOSM) control for DC-DC buck converters is proposed. A cascade-control structure is established to regulate the output voltage and force the inductor current to track its reference, which comprises two control loops. The voltage regulation loop which is based on an SOSM controller combined with extended state observer (ESO) is the external loop. The current tracking loop also accomplished by SOSM controller is the internal loop. The fast motion is dominated by the dynamics of the current tracking loop whereas the slow motion stems from the dynamics of the output voltage. In addition, the load resistance that influences significantly the dynamics of whole system is regarded as the external disturbance in this papper. A disturbance observer, ESO, aims at asymptotically rejecting disturbances to converter. The proposed control strategy is verified using simulation test.

This paper studies the problem of reliable filter problem for a category of sensor networks in the framework of interval type-2 fuzzy model. In the filter design, the random link failures, which are caused possibly by missing measurements as well as by probabilistic communication failures, are considered to illustrate more realistic dynamical behaviors of sensor networks. In order to tackle the uncertainties existing in systems, interval type-2 (IT2) fuzzy approach is utilized to establish the model, wherein upper and lower membership functions together with weighting coefficients are employed to express the uncertainties. An distributed IT2 fuzzy filter model is constructed to estimate system states. Using the Lyapunov theory, sufficient conditions have been given to ensure that the filtering error system is mean-square asymptotically stable and satisfies the predefined average $ \mathcal {H}_{\infty }$ performance level. Moreover, the criteria to design the filter parameters are developed through using cone complementary linearization approach. Finally, a practical example is given to validate the proposed method.

This paper studies the problem of adaptive fuzzy control for a category of single-input single-output nonlinear networked control systems with network-induced delay and data loss based on adaptive backstepping control approach. Fuzzy logic systems are used to approximate the unknown nonlinear characteristics existing in the system, while Pade approximation is introduced to handle network-induced delay. Data loss occurs intermittently and stochastically in the data transmitting process, which is regarded as the delay in the controller design. In the framework of adaptive fuzzy backstepping technique, a novel state-feedback adaptive controller is constructed to ensure all signals in the resulting closed-loop system to be bounded and the state variables can be regulated to the origin. Finally, two examples are given to show the validity of the proposed results.

In this paper, a novel adaptive sliding mode controller is designed against the actuator saturation for a PEM fuel cell air-feed system. To deal with the saturation nonlinearity, a diagonal matrix with unknown elements is introduced for adaptive design in terms of sliding mode control strategy. An integral-type sliding mode surface is designed with constrained conditions. The designed SMC law can guarantee the finite-time convergence against the actuator saturation of the plant. The effectiveness of the proposed saturated SMC scheme is validated in the application to air-feed PEM fuel cell systems.

In this paper, a model based robust control for three-phase three-level Neutral Point Clamped (NPC) power converters is studied. Based on the continuous averaged model of the system, a Second Order Sliding Mode (SOSM) technique is employed to the control design. The control objectives are to achieve desired dc-link capacitor voltage regulation, voltage balance in the two dc-link capacitors and the instantaneous active and reactive power tracking. In order to achieve fast dynamic response of the proposed controller in the presence of external disturbances, an Extended State Observer (ESO) is employed to asymptotically reject the disturbances which are integrated in the controller design. The proposed control strategy has a cascaded structure which consists of power tracking (inner loop) ESO-based dc-link voltage regulation and capacitors voltage balance (outer loop). Multi-rate simulation illustrates the effectiveness and robustness of the proposed controller under parametric uncertainties and load variations.

Support vector machine has obtained more and more attentions as a new method of machine learning based on the statistic learning theory. At the same time, there are increasing concerns about the fault diagnosis for practical engineering systems. Firstly, many kinds of SVM algorithms will be introduced, such as LS-SVM, LSVM and PSVM and so on. Besides, the advantages and disadvantage of those methods will be introduced. Finally, we discuss fault diagnosis and other aspects' recent applications and the directions we should research in the future.