This paper presents a universal and adaptive control framework for reconfigurable vehicles subject to composite motion disturbances, incorporating a collaborative fault-tolerant mechanism. A model-based cascaded control architecture is developed based on the vehicle’s kinematic and dynamic models. To ensure safety, an improved adaptive geofencing strategy is proposed which integrates capability constraints with barrier functions in the kinematic loop. For dynamic feedback, an adaptive gain-filtered extended state observer enables accurate disturbance estimation from noisy outputs and improves robustness via feedforward compensation. Meanwhile, a collaborative fault-tolerant mechanism further allocates control inputs to mitigate actuator faults. Finally, experimental results validate the proposed method’s effectiveness under complex interference scenarios.

Nonholonomic vehicles in distributed networks are prone to triggering nested velocity and acceleration saturation during reactive safety formations, exacerbating oscillations. This paper proposes a hybrid secure distributed collaborative frame-work, integrating compound adaptive anti-windup strategies with vehicle kinematics and safe geofences to achieve smooth and effective obstacle and collision avoidance while suppressing saturation-induced oscillations. The vehicle’s safe behavior for bypassing obstacles is formed via acceleration envelopes from safe geofences and input saturation, which generate constraint velocity commands. Additionally, a low-trigger and power-adjustable enhanced artificial potential field is integrated into the safety coordination to fine-tune vehicle maneuvers at extremely close distances to hazardous targets, ensuring high reliability. Safe acceleration envelopes and nested kinematic saturation are utilized to design a compound adaptive auxiliary dynamic system, smoothing oscillations induced by dual command constraints during formation. A distributed formation controller is further designed to enable multitasking collaboration in formations. The overall stability is mathematically analyzed, and the method’s superior smoothness and safety in task coordination are validated through simulations and experiments with vehicle clusters. Note to Practitioners—In response to the severe trajectory oscillations caused by saturation triggered by existing reactive avoidance approaches, this paper proposes a novel hybrid safety collaborative control based on the nonholonomic vehicle kinematics that markedly enhances the smoothness and safeness of formations in obstacle environments. The integration of safety acceleration envelopes, as well as low-trigger and adjustable artificial potential functions, markedly mitigates oscillations from reaction saturation compared with the solitary traditional artificial potential functions, as evidenced by simulations and experiments that demonstrate reduced oscillation amplitudes and shorter recovery times when evading hazardous targets using the proposed method. In addition, existing velocity/acceleration nested windups in actual applications are concurrently considered for the first time, and the corresponding compound adaptive anti-windup method is employed to smooth oscillations caused by control saturation. The security and smoothing strategies outlined allow for collaborative operations in more complicated obstacle environments and enable the deployment of larger vehicle clusters in confined spaces, significantly enhancing multi-vehicle collaboration’s economic viability and efficiency. Furthermore, the safety collaborative control framework designed for kinematics is conveniently structured for engineers as a standalone module, which is easily transferrable to commercial robotic products. The composite approach to safeness and smoothness can also be applied in other unmanned and manned collaborative scenarios.

The variations in camera view and hand spatial pose are the main reasons for the low accuracy and poor robustness of gesture recognition systems. In order to achieve accurate and stable gesture recognition with variable viewpoint, this article carries out research based on 3-D non-Euclidean vector graph features and graph neural networks. First, the 3-D information of hand joints is collected to construct a graph-structured gesture feature dataset, and a joint-based 3-D non-Euclidean vector graph method is proposed to solve the problem that similar gesture features are overly sensitive to spatial position and angle changes. Then, a Multi-Head graph attention network is designed and combined with graph convolutional neural network to explore the optimal hybrid graph neural network gesture recognition model. The experimental results show that, on the dataset processed by the joint-based 3-D non-Euclidean vector graph method, the training, testing, and validation accuracies of the optimal model reach 97.07%, 96.95%, and 87.06%, which are increased by 18.46%, 18.88%, and 44.23% compared to the original dataset, respectively. In conclusion, the method in this article is not only more robust to the variable viewpoint gesture recognition problem, but also has the advantages of low computational resource requirement and high real-time performance.

This article investigates the distributed control of a group of stochastic high-order nonlinear systems in which the subsystems are with unknown and time-varying control coefficients of unknown signs, inherent nonlinear drift and diffusion terms. To solve the control problem with unknown control directions, where traditional available Nussbaum functions are not applicable for the consensus of stochastic nonlinear systems with unknown and time-varying coefficients of unknown signs, a novel type of Nussbaum function is proposed with a new paradigm of stability analysis in probability. Global consensus control of stochastic multiagent systems is achieved by designing distributed controllers which integrate designed distributed filters and novel Nussbaum functions. In addition, it can be proved that all signals in the closed-loop system are bound in probability, and the transient consensus errors of the followers are bounded by positive constants which can be adjusted arbitrarily small. The effectiveness of the proposed control scheme is demonstrated by simulation results.

The existence of compound velocity and acceleration windups in clusters of nonholonomic mobile robots can seriously constrain the smoothness and stability of the overall motion. This article proposes a leader–follower-based distributed formation control framework for smooth and robust clustering of multiple nonholonomic mobile robots under compound windups of velocity and acceleration and unknown perturbations. The decoupled position and orientation kinematics and substrate wheel velocity dynamics are modularly devised via feedback linearization techniques to enable upper-level cooperative error regulation and lower-level wheel velocity trajectory tracking. The auxiliary dynamic system based on the velocity envelope generated by compound motion windups and the WMR kinematic is integrated into the collaborative error, adaptively mitigating the detrimental windup effects. The adaptive saturated extended state observer is utilized to flatly estimate unknown perturbations in the wheel velocity dynamics with enhanced robustness. Finally, the overall stability analyses are done based on Lyapunov’s theorem, and contrastive simulations and plentiful experiments are conducted to attest to the validity and availability.

This paper tackles the problem of event-triggered unified performance state estimation in neural networks with time-varying delays. A novel event-triggered methodology is introduced, aiming to balance the performance of the state estimator and the network's communication bandwidth. The proposed method leverages a triggered-parameter-dependent integral inequality with matrices that consider the event-triggered mechanism, capturing the interplay between the time-varying delay and system states. This innovative approach guarantees the asymptotic stability of the estimation error system, thereby meeting the$H$∞ performance criterion. The efficacy of the proposed condition is demonstrated by a numerical example.

This article develops the adaptive neural cooperative control scheme for a group of mobile robots with a limited sensing range in presence of input quantization by a dynamic surface control technique. First, to make the controller design feasible, the original robotic system is transformed into a new fully actuated system using a transverse function. Then, taking into consideration the effects of a hysteresis quantizer, an adaptive neural cooperative controller is developed based on the universal approximation property of the radial basis function neural networks and the connectivity preservation strategy. Furthermore, the proposed control scheme can guarantee that all closed-loop signals are semi-globally uniformly ultimately bounded. Meanwhile, desired constraints are not breached and tracking errors are within the predefined domains. Finally, several simulation results are carried out to testify the feasibility and efficiency of the theoretical findings revealed in this article.

In this article, the tracking problem of the adaptive sliding-mode control (SMC) design for human support robots based on a disturbance observer is investigated. First, a finite-time controller using nonsingular fast terminal SMC is proposed. Then, a robust disturbance observer is developed to estimate system uncertainties and disturbances. Simultaneously, to deal with the unknown bounded disturbance observer error, an adaptive control technology is developed. Furthermore, the proposed controller is synthesized to ensure that the tracking errors can be stabilized in finite time. Finally, simulations are performed to demonstrate that human support robots employing the proposed controller can converge to the desired trajectory.

In this article, the problem of integral sliding mode control (ISMC) for a class of nonlinear systems with stochastic characteristics under cyber-attack is investigated. The control system and the cyber-attack are modeled as an Itô-type stochastic differential equation. The stochastic nonlinear systems are approached by the Takagi-Sugeno fuzzy model. A dynamic ISMC scheme is applied and the states and control input are analyzed within a universal dynamic model. It is demonstrated that trajectory of the system can be confined to the integral sliding surface within finite time, and the stability of closed-loop system under cyber-attack will be guaranteed by using a set of linear matrix inequalities. Following a standard procedure of universal fuzzy ISMC, it is shown that all signals in the closed-loop system will be guaranteed bounded, and the states are asymptotic stochastic stable if some conditions are met. An inverted pendulum is applied to show the effectiveness of our control scheme.

Uncertainty estimation in real-world scenarios is challenged by complexities arising from peaking phenomena and measurement noises. This article introduces a novel scheme for practical uncertainty estimation to mitigate peaking dynamics and enhance overall dynamic behavior. A fusion estimation framework for lumped uncertainties using multiple extended state observers (ESOs) is constructed, and the low-frequency adaptive parameter learning technique is employed to approximate the optimal fusion. The adaptive fusion estimation not only attenuates transient peaks in uncertainty estimation but also attains fast convergence and high accuracy under the high-gain scheduling of ESOs. Furthermore, the robustness of uncertainty estimation against measurement noises is enhanced by cascading filters in the proposed adaptive fusion framework for multiple ESOs. Extensive theoretical analyses are executed to verify practical applicability in peak and noise rejection. Finally, simulations and experiments on the wheel velocity system of a mobile robot are conducted to test the validity and feasibility.

In this article, an adaptive fuzzy fixed-time slidingmode formation control scheme with prescribed performance is proposed for quadrotor unmanned aerial vehicles under uncertainties and external disturbances. First of all, for the position subsystem and attitude subsystem, in order to achieve the formation goal, the desired position and attitude of UAVs are generated by adaptive control method based on their neighbors’ information. Second, a nonsingular fixed-time sliding mode manifold incorporating with the prescribed performance function is put forward to achieve the predefined convergence performance, which has a faster convergence rate than the traditional counterpart and reduce the chattering phenomenon in the traditional counterpart. Then, in the design of the controller, adaptive fuzzy approximation characteristics are utilized to handle unknown terms, and Lyapunov stability theory is used to analyze the fixed time stability of the closed-loop system. Finally, the performance of proposed algorithms is demonstrated by simulation results.

This article addresses the composite hierarchical antidisturbance control issue for the front steering vehicle system with uncertain parameters. First, the front steering vehicle system is modeled as an interval type-2 Takagi–Sugeno (T–S) system, which contains the unknown disturbance matching the input channel and the norm bounded disturbance. Considering that the embedded membership functions of the interval type-2 fuzzy system are unknown, the definite part and the uncertain part of the interval type-2 T–S fuzzy system are separated to facilitate the subsequent analysis. Next, a novel disturbance observer design structure under the fuzzy framework is introduced. Through the novel design method, the expression of the unknown disturbance can be transformed into a new form containing only the uncertain parameter, and the estimation is realized by estimating the uncertain parameter. Then, a fuzzy integral sliding mode controller is adopted in combination with the disturbance estimation to ensure the stability of the composite system and the reachability of the specified fuzzy integral switching surface. Finally, simulation results of the front steering vehicle model that demonstrate the effectiveness of the proposed control scheme are provided.

This paper investigates the adaptive control for a class of uncertain nonlinear systems under denial-of-service (DoS) attacks. We analyze the closed-loop system stability under DoS attacks in terms of attack duration, attack frequency and resting time duration respectively. Three scenarios of DoS attacks against the system stability are considered. Firstly, it is shown that if the duration of each attack is less than a given constant, asymptotical convergence of system output is still preserved. Secondly, if the bounds on the frequency and duration of attacks with respect to overall intervals meet certain conditions, the proposed event-triggered control scheme guarantees that all the closed-loop signals are globally bounded and the stabilization error converges to a ball with a radius arbitrarily small. Thirdly, if resting time duration meets certain conditions after an arbitrarily long attack, closed-loop boundedness is still preserved. Simulation results are shown to illustrate the effectiveness of the proposed control schemes.

In this article, we propose a Swin Transformer and multilevel Feature Consistency based Network (STFC-Net), which is a multilevel cascade stereo matching method to predict the disparity in a coarse-to-fine manner. 1) To alleviate the problem of the limited receptive field of existing convolutional neural network (CNN)-based methods, inspired by the capability of modeling the large-scale dependence of transformer, we adopt a multilevel feature extraction module combining CNN and Swin Transformer to capture long-range context information; a multiscale cascaded cost aggregation module is used to cover different image regions with less memory consumption. 2) To make full use of the hierarchical features, we checked the multilevel left-right feature consistency in an unsupervised manner to improve the disparity accuracy. The experimental results show that our method outperforms some previous CNN methods on the Scene Flow and KITTI datasets with lower computational time complexity. Moreover, it generalizes well in some unknown and challenging real-world scenarios.

Unmanned aerial vehicles (UAVs) have been widely used in urban target-tracking tasks, where long-term tracking of evasive targets is of great significance for public safety. However, the tracked targets are easily lost due to the evasive behavior of the targets and the unstructured characteristics of the urban environment. To address this issue, this article proposes a hybrid target-tracking approach based on target intention inference and deep reinforcement learning (DRL). First, a target intention inference model based on convolution neural networks (CNNs) is built to infer target intentions by fusing urban environment information and observed target trajectory. Then, the prediction of the target trajectory can be inspired by the inferred target intentions, which can further provide effective guidance to the target search process. In order to fully explore the policy space, the target search policy is developed under a DRL framework, where the search policy is modeled as a deep neural network (DNN) and trained by interacting with the task environment. The simulation results show that the inference of the target intentions can effectively guide the UAV to search for the target and significantly improve the target-tracking performance. Meanwhile, the generalization results indicate that the proposed DRL-based search policy has high robustness to the uncertainty of the target behavior.

This article focuses on the finite-time fault estimation observer and controller design for switched random models subject to multiple time-varying delays. It is assumed that there exist actuator faults, model nonlinearities, external disturbances, and sensor faults in these systems. First, an observer is proposed to obtain the estimations of the system states, disturbances, as well as sensor and actuator faults. Compared with the existing results, the suggested observer estimates these sizes and shapes of states, exogenous disturbances, actuator, and sensor faults more accurately. This kind of unknown nonlinear dynamic can be approached by this generalized fuzzy hyperbolic model. Delay-dependent sufficient conditions of robust mean-square finite-time boundedness are obtained for the error system via a piecewise Lyapunov function. Observer matrices are obtained and finite-time fault estimation is realized. Second, we establish a novel controller based on fault estimation information. In addition, the piecewise function is developed to acquire delay-dependent adequate conditions of finite-time control. Controller matrices are obtained and finite-time fault-tolerant control can be achieved. At last, this validity of the method shown in this work is demonstrated via a practical simulation example.

Six-wheel skid-steer vehicles are widely used in the real world because of their special steering structure and load-carrying capacity. In this article, we aim to study the event-triggered fuzzy yaw control of six-wheel skid-steer vehicles. The nonlinear lateral dynamics of a six-wheel skid-steer vehicle is modeled using the Takagi-Sugeno (T-S) fuzzy system with irregular fuzzy rules, in which more membership function information can be captured. Considering the high-frequency transmission mode used in vehicle motion systems due to high-precision control requirements, an event-triggered scheme with a dynamic threshold is proposed to ensure control accuracy while reducing the data transmission. Then, a flexible yaw control scheme is constructed in which two adjustable weighting factors are introduced to improve the flexibility of the fuzzy yaw controller. By combining the flexible yaw control scheme and dynamic event-triggered scheme, we design a set of event-triggered fuzzy yaw controllers that satisfy the finite-time stability of lateral motion tracking for six-wheel skid-steer vehicles. The advantages and effectiveness of the proposed method are verified by conducting the TruckSim-MATLAB joint simulation.

In order to quickly and accurately detect the occurrence of accidental falls and save the lives of more elderly people living alone, this paper proposes a new convolutional network module Higher-order Coordinated Attention module (CAGn) based on the latest research, and uses it and the lightweight convolutional module (GSConv) to improve the YOLOv5s model to construct a new fall detection model YOLOv5s-CAGn-GSConv. In addition, a new fall detection dataset Fall-Dataset is produced, and a fall event triggering mechanism based on queue window is proposed to be applied to the fall detection system. Experimental results show that the CAGn module can capture cross-channel information and high-order spatial information at the same time, thereby effectively improving the accuracy of the detection model, and the introduction of GSConv module further improves the accuracy and greatly reduces the number of parameters of the detection model. The final YOLOv5s-CAGn-GSConv model only increases the number of parameters by 4.2% but improves the accuracy by 2.1% and the average accuracy by 0.9% ([email protected]) and 2.6% ([email protected]:0.95).

A multi-robot system can enhance the efficiency of a single task, and can thus be employed to construct large-scale environmental maps, by enabling each robot to individually build a map and then combine the information into a single map. A map fusion approach based on wavelet transform is proposed to achieve local map fusion in order to address the issue of many local maps developed at the same site. First, wavelet decomposition is applied to the local maps to obtain sub-maps representing high-frequency and low-frequency components of the map. The wavelet transform coefficients of the maps are generated in accordance with the frequency domain features of weighted ratio and weighted gradient-based fusion rules, which are introduced to the low-frequency and high-frequency coefficients, respectively. Second, the edge detection algorithm has been improved to produce a crisper edge of the fusion map. The inverse wavelet transform is then applied to finish the map fusion. According to the simulated trials and objective evaluation, the suggested strategy works.

The composite velocity and acceleration constraints suffered by nonholonomic mobile robots during motion severely hamper the stability and smoothness of the cluster. This paper presents an applicable control framework for the robust and smooth implementation of distributed formations based on nonholonomic mobile robot clusters against velocity and acceleration saturation. The decoupled feedback linearization control of cascaded position, heading and wheel velocity is used to achieve distributed time-varying formations with modular and scalable stabilization of the triple-level errors. The adaptive auxiliary dynamics are incorporated into the distributed coincident errors to reduce velocity and acceleration saturation, which improves the overall transient results and stability range. The adaptive saturation extended observer in the substrate dynamics is conceived to simultaneously estimate the lumped disturbances and suppress undesired peaks, smoothly enhancing the robustness of wheel velocity control. Ultimately, comparative simulations and extensive experiments for mobile robots are performed to test validity and practicality.

The reduced model-based coordinated design of fault detectors and controllers for discrete-time switching fuzzy systems is examined. First, the mean-square exponential stabilization of switching Takagi–Sugeno fuzzy systems is performed using the average dwell time method under an arbitrary switching law. Next, using segmented Lyapunov function techniques, a dynamic full- and reduced-order fault detector and controller is designed to ensure that the overall dynamic residual system is mean-square exponentially stable with a balanced$\mathcal {H}_{\infty }$performance level$(\xi, \beta)$. The solvability conditions for the fault detector and controller are derived using a linearization method, and the relevant parameters can be determined using the mathematical linear matrix solver toolbox. Two examples including a switching Chua’s circuit system are presented to demonstrate the effectiveness of the proposed fault detector and controller.

Multi-fisheye System has the advantages of sufficient overlap and the ability to capture a complete 360° scene, which is beneficial for the omnidirectional depth estimation task. However, due to the severe distortion of the fisheye images, it is hard for such systems to extract and match features to predict an accurate depth. In this work, on the basis of a multi-fisheye system, we present a novel end-to-end deep learning architecture for omnidirectional depth estimation: 1) to capture the reliable features of the distorted fisheye image, a multi-scale feature extraction and aggregation module is improved, which can adaptively obtain the global context information to represent the features; 2) to leverage more aligned features, especially those in the overlap between multi-fisheye images, we construct a fusion cost volume to combine similarity and semantic information, which can enhance the feature discriminability; and 3) to refine the omnidirectional depth map efficiently, a cascaded cost regularization architecture is proposed. Instead of several costly 3D convolutions, the 3D BSConv based on intra-kernel correlations is introduced to regularize the cost. The proposed method can incrementally predict the depth map from coarse to fine, and reduce the network computational complexity significantly. The experiments in several public indoor and outdoor synthetic datasets demonstrate that the proposed method outperforms some state-of-the-art methods in terms of a synthesis of accuracy and speed, with fewer model parameters.

By relabeling past experience with heuristic or curriculum goals, state-of-the-art reinforcement learning (RL) algorithms such as hindsight experience replay (HER), hindsight goal generation (HGG), and graph-based HGG (G-HGG) have been able to solve challenging robotic manipulation tasks in multigoal settings with sparse rewards. HGG outperforms HER in challenging tasks in which goals are difficult to explore by learning from a curriculum, in which intermediate goals are selected based on the Euclidean distance to target goals. G-HGG enhances HGG by selecting intermediate goals from a precomputed graph representation of the environment, which enables its applicability in an environment with stationary obstacles. However, G-HGG is not applicable to manipulation tasks with dynamic obstacles, since its graph representation is only valid in static scenarios and fails to provide any correct information to guide the exploration. In this article, we propose bounding-box-based HGG (Bbox-HGG), an extension of G-HGG selecting hindsight goals with the help of image observations of the environment, which makes it applicable to tasks with dynamic obstacles. We evaluate Bbox-HGG on four challenging manipulation tasks, where significant enhancements in both sample efficiency and overall success rate are shown over state-of-the-art algorithms. The videos can be viewed at https://videoviewsite.wixsite.com/bbhgg.

Model structure representation and fast estimation of perturbations are two key research aspects in adaptive control. This work proposes a composite local learning adaptive control framework, which possesses fast and flexible approximation to system uncertainties and meanwhile smoothens control inputs. Local learning, which is a nonparametric regression approach, is able to automatically adjust the structure of approximator based on data distribution from the local region, but it is sensitive to the outliers and measurement noises. To tackle this problem, the regression filter technique is employed to attenuate the adverse effect of noises by smoothing the output response and state features. In addition, the stable integral adaptation is integrated into local learning framework to further enhance the system robustness and smoothness of the estimation. Through the online elimination of uncertainties, the nominal control performance is recovered when the plant encounters violent perturbations. Stability analysis and numerical simulations are performed to demonstrate the effectiveness and benefits of the proposed control method. The proposed approach exhibits a promising performance in terms of rapid perturbation elimination and accurate tracking control.

This work investigates finite-time tracking control for a multi-input–multi-output plant with multisource uncertainties. A multivariable finite-time prescribed performance control scheme is proposed, where the norm of the tracking error vector is constrained with a prescribed bound. Due to the positiveness of the norm of the error vector, a novel error transformation is given to transform the “constrained” problem into an equivalent “unconstrained” problem. Meanwhile, the composite neural adaptive law is established to attenuate the effect of multisource uncertainties. The parametric perturbations are counteracted by neural adaptive terms. Time-varying uncertain control gains and external disturbances in the multivariable systems are compensated by adaptively estimating their bounds and applying the Lyapunov control design. To tackle the practical tracking problem, the aforementioned method is integrated into a dynamic-surface-based backstepping framework. Additionally, the practical quaternion-based attitude tracking problem is addressed, in which a quaternion-based form of error-norm constraint is constructed to express the generality and scalability of our proposed.

This article focuses on the problem of designing the reduced-order dynamic output feedback (DOF) controller for discrete-time T–S fuzzy plants. Differing from the existing methodologies, the reduced-order approximation technique is applied to simplify the pregiven high-order DOF controller. The key point is to construct a reduced-order closed-loop model to approximate the original high-order closed-loop system. First, a new error auxiliary system between the high-order closed-loop system and the reduced-order closed-loop model is obtained. The sufficient conditions to guarantee that the corresponding error system is asymptotically stable with a prescribed$\mathcal {H}_{\infty }$performance index are developed. Then, the parameters of the desired reduced-order controller are derived by utilizing the projection lemma and the cone complementary linearization algorithm. Finally, the advantages of the proposed technique are illustrated by a series of simulation analysis.

This article investigates the control of underactuated crane systems with unknown system parameters and nonsmooth input nonlinearities. To solve this problem, a novel filter-based adaptive control method is designed for the underactuated crane systems. First, since the backstepping control scheme cannot be directly applied to the crane systems, a group of filters is designed such that the crane systems become a class of strict-feedback nonlinear systems in each step of backstepping. Then, in order to ensure that the swing angle and position error converge to the origin, the variable transformation method is adopted. The result demonstrates that the tracking errors of the underactuated crane systems could be guaranteed to converge to a ball of origin with an arbitrarily small radius. Finally, the simulation results show that the proposed scheme is effective.

This article deals with the problems of stability and control of the interconnected system (IS) with unknown time-varying delays via decentralized time-delay control using partial variables with measurable states. First, the model of the IS with time delays is established, and the relevant control scheme is proposed. The control scheme just needs to control all or partial state variables corresponding to the elements on the main diagonal of the gain matrices, which can reduce the control cost and improve the flexibility of control. In addition, there are no additional restrictions in the process of designing the controller. Second, relevant lemmas are derived. The exponential boundedness and stability analysis of the IS with time delays are presented, respectively, by stability theory, and related results are derived. Meanwhile, the stability domain of the IS is estimated. Besides, the obtained results can also be used for many practical systems, such as the interconnected power system, the multislave teleoperation systems, the brushless dc motor (BLDCM) system, and the chaotic system. Finally, the effectiveness and application of the obtained results are verified by several examples.

In this article, the problem of observer-based sliding-mode control for dynamic fuzzy systems with bounded external disturbances is investigated. First, the sufficient conditions for codesign of an observer and a sliding-mode control law are presented, such that the closed-loop system is asymptotically stable and affords satisfactory dissipative performance. The event-triggering mechanisms are also applied in the observer-based controller design. Then, an integral sliding-mode surface and an observer-based sliding-mode controller are designed such that the system trajectories are steadily maintained on the sliding surfaces. Finally, a numerical example is given to demonstrate the effectiveness of the developed new design method.

This article deals with the fault detection filtering problem for a nonlinear dynamic system in the Takagi–Sugeno fuzzy framework. An event-triggered scheme considering network bandwidth utilization rate and fault occurrence probability is utilized, which can conserve communication resources and reduce computational burden. The fault detection fuzzy filter is used as a residual generator, and the proposed event-based fault detection scheme is formulated as a fuzzy filtering problem. Moreover, the generated residual is robust against exogenous disturbances while being sensitive to system faults, and the resulting fault detection system has asymptotic stability with a specified$\mathcal {H}_{\infty }$error property. The corresponding solvability conditions of the fault detection filter are constructed by converting the nonconvex feasibility problem into a series of optimization problems. Finally, a numerical simulation is conducted to demonstrate the feasibility and validity of the proposed scheme.

In this article, finite-time control is investigated for multiple time-delayed large-scale nonlinear systems against state and input constraints. There are multiple time-varying delays, intermittent actuator faults, and intermittent sensor faults in this model. Takagi–Sugeno fuzzy model is used to describe the nonlinear system. There are few tries to studying finite-time control for large-scale nonlinear systems. First, a dynamic output feedback controller is designed in this article to make the system finite-time bounded. Then, an augmented closed-loop model is constructed. Sufficient conditions of finite-time control are given by the fuzzy Lyapunov function. Finally, the feasibility of the approach is verified by two simulation examples.

In this article, the issue of multicenter and single-area humanitarian relief allocation with uncertain travel time and imprecise transportation information is investigated. Expert human knowledge using fuzzy control is employed to select rescue paths, and the considered problem is formulated as a fuzzy chance-constrained model to ensure that the allocated goods can be delivered to the disaster area on time within a desired probability. A new method is presented to transform chance-constrained programming into a mixed-integer model utilizing triangle fuzzy numbers and the robust optimization problem. A practical example is used to test the validity of the theoretic results obtained.

Reinforcement learning algorithms, such as hindsight experience replay (HER) and hindsight goal generation (HGG), have been able to solve challenging robotic manipulation tasks in multigoal settings with sparse rewards. HER achieves its training success through hindsight replays of past experience with heuristic goals but underperforms in challenging tasks in which goals are difficult to explore. HGG enhances HER by selecting intermediate goals that are easy to achieve in the short term and promising to lead to target goals in the long term. This guided exploration makes HGG applicable to tasks in which target goals are far away from the object's initial position. However, the vanilla HGG is not applicable to manipulation tasks with obstacles because the Euclidean metric used for HGG is not an accurate distance metric in such an environment. Although, with the guidance of a handcrafted distance grid, grid-based HGG can solve manipulation tasks with obstacles, a more feasible method that can solve such tasks automatically is still in demand. In this article, we propose graph-based hindsight goal generation (G-HGG), an extension of HGG selecting hindsight goals based on shortest distances in an obstacle-avoiding graph, which is a discrete representation of the environment. We evaluated G-HGG on four challenging manipulation tasks with obstacles, where significant enhancements in both sample efficiency and overall success rate are shown over HGG and HER. Videos can be viewed at https://videoviewsite.wixsite.com/ghgg.

In this article, the adaptive control for a class of strict-feedback nonlinear systems with uncertainties under injection and deception attacks is considered. An adaptive control scheme is proposed to deal with the injection and deception attacks meanwhile guarantee that regulation errors could be made arbitrarily small by adjusting control parameters. Compared with existing works whose models are linear or relatively simple, the model we consider in this article is nonlinear with parametric uncertainties. A new type of feedback control scheme is introduced to solve this problem. A simulation example is given to verify the effectiveness of our proposed control scheme.

The study focuses on the sliding mode output feedback control issues for Markovian jump systems via event-triggered scheme. First, combined the designed output feedback controller with sliding mode, the augmented closed-loop Markovian jump system dynamics are presented. Meanwhile, an event-triggered method is introduced to save the network resources. Second, the exponential stability of the closed-loop system is analyzed. Then, a singular value decomposition technique is utilized to synthesize the ${\mathcal{L}_2} - {\mathcal{L}_\infty }$ output feedback controller. Consequently, the controller gains expressions are derived. Moreover, the proposed sliding mode is proved to ensure the system to jump onto the sliding surface and maintain therein. Finally, simulation results are provided to show the proposed method is effective.

This paper presents a sliding mode dynamic output feedback controller design for Markovian jump systems under a communication network. For the inaccessible states and uncertain parts of Markovian jump systems, a novel integral-type sliding mode output feedback controller combined with system states and dynamic controller states is proposed. An event-triggered mechanism is introduced to reduce network bandwidth and resources requirements. The augmented dynamic system and complete control framework are presented together. The reachability of the novel integral-type sliding mode is proved. Then, based on delay-dependent Lyapunov functions, free-weighting matrices, and a singular value decomposition approach, a less conservative sufficient condition is proposed to ensure exponential stability with a weighted$\mathcal {L}_{2}$-$\mathcal{ L}_{\infty}$disturbance performance. Finally, a numerical and a DC-DC switched boost converter circuit simulations that verify the feasibility and effectiveness of the proposed controller are presented.

In this article, the power distribution and tracking problems of the distributed energy storage system (ESS) are addressed by designing a cooperative adaptive terminal sliding mode (CATSM) controller based on a multi-agent network topology for each ESS. First, a novel adaptive power allocation algorithm (APAA) is proposed to achieve a consistent state-of-charge (SOC) for each battery in a distributed ESS. Then, considering the capacity degradation of the battery during long-term charging and discharging, we use real-time current and SOC to estimate the current capacity of the battery, which ensures the accuracy of the algorithm. Then, based on the proposed algorithm, the CATSM controllers are designed for each ESS via a multi-agent network topology for the distributed ESS, and the projection operator is used to guarantee the boundedness of adaptive estimation. Finally, the simulation results are provided to validate the feasibility and effectiveness of the proposed control strategy.

This article addresses the problem of continuous-time dynamic sliding mode control for Takagi-Sugeno fuzzy nonlinear systems based on an event-triggered mechanism. First, a novel sliding mode dynamic system is established by introducing a corresponding sliding mode function and an efficient event-triggered mechanism. Then, based on a new Lyapunov function and the reciprocally convex method, sufficient conditions are presented to guarantee that the constructed network sliding mode dynamics is asymptotically stable. It is shown that the trajectories of the sliding mode dynamic system can converge into a bounded domain of a sliding mode surface in finite time. Furthermore, the explicit expression of the desired sliding mode controller is given in the form of linear matrix inequalities. Finally, the applicability and effectiveness of the proposed design techniques are demonstrated by a numerical example.

This article concerns the event-triggered fault detection filtering problem for nonlinear networked control systems. First, an event-triggered scheme with two trigger matrices is proposed by considering limited network resources; a fuzzy fault detection filter with a general structure is chosen as the residual generator. Then, we introduce a new event-based residual system by taking into consideration the interval time-varying delays. Sufficient conditions are obtained to guarantee that the event-based residual system is asymptotically stable and satisfies a prescribedH∞performance with the help of a reciprocally convex technique. Furthermore, some new relaxation matrices are introduced such that the corresponding solvability conditions can be established by means of a linearization procedure. Finally, examples are given to illustrate the effectiveness and advantages of the proposed new fault detection filtering techniques.

In this article, a distributed adaptive iterative learning control for a group of uncertain autonomous vehicles with a time-varying reference is presented, where the autonomous vehicles are underactuated with parametric uncertainties, the actuators are subject to faults, and the control gains are not fully known. A time-varying reference is adopted, the assumption that the trajectory of the leader is linearly parameterized with some known functions is relaxed, and the control inputs are smooth. To design distributed control scheme for each vehicle, a local compensatory variable is generated based on information collected from its neighbors. The composite energy function is used in stability analysis. It is shown that uniform convergence of consensus errors is guaranteed. An illustrative example is given to demonstrate the effectiveness of the proposed control scheme.

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.

In this article, the event-triggered adaptive output feedback control of multivariable system under nonsmooth actuator nonlinearities, including deadzone, backlash, and hysteresis is investigated. The nonsmooth actuator nonlinearities are handled with a unified framework by modeling them with a time-varying disturbance and a control input. With an event-triggered mechanism and an one-parameter estimation approach, the communication consumption for the control of multivariable system is significantly reduced. It is shown that all signals in the closed loop system are bounded. The Zeno behavior is also avoided. The simulation results demonstrate the effectiveness of the control scheme.

In this paper, a novel adaptive hybrid impedance control for a dual-arm robot is proposed to cooperatively manipulate unkown objects. This adaptive hybrid control scheme could be employed to improve motion tracking and internal force regulation performance when dual-arm cooperative robot grasping an unknown object. The controller has a master-slave structure, and we investigate the master-slave structure in three levels. The first level is an absolute-relative motion controller satifying the closed-chain constraints. The second level introduces an adaptive impedance control with variable stiffness to regulate the internal force. The motion and force control signals are fused in the third level by a select matrix. To verify the effectiveness of the algorithm, a dual-arm robotic system is set up with two UR5 arms and the experimental results illustrate the performance and feasibility of the proposed control strategy.

Abstract A novel optimal proportional integral derivative (PID) autotuning controller design based on a new algorithm approach, the “swarm learning process” (SLP) algorithm, is proposed. It improves the convergence and performance of the autotuning PID parameter by applying the swarm and learning algorithm concepts. Its convergence is verified by two methods, global convergence and characteristic convergence. In the case of global convergence, the convergence rule of a random search algorithm is employed to judge, and Markov chain modelling is used to analyse. The superiority of the proposed method, in terms of characteristic convergence and performance, is verified through the simulation based on the automatic voltage regulator and direct current motor control system. Verification is performed by comparing the results of the proposed model with those of other algorithms, that is, the ant colony optimization with a new constrained Nelder–Mead algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a neural network (NN). According to the global convergence analysis, the proposed method satisfies the convergence rule of the random search algorithm. With respect to the characteristic convergence and performance, the proposed method provides a better response than the GA, the PSO, and the NN for both control systems.

This paper investigates the problem of sliding mode observer design for a class of uncertain fuzzy time-delay systems based on an event-triggered strategy. The objective is to design an event-triggered mechanism by utilizing the information of system output and observer function. Based on the delay partitioning method and the Lyapunov-Krasovskii function approach, delaydependent sufficient conditions are proposed to guarantee the overall system, including sliding mode dynamics and error dynamics, to be asymptotically stable with an H∞ performance. Furthermore, an event-triggered sliding mode controller is synthesized to ensure that the system dynamics can be driven to the sliding region near the equilibrium point in finite time. Finally, a verification example is provided to demonstrate the feasibility and efficiency of the theoretical results presented.

This paper is concerned with the problem of generalized H2reduced-order filter design for continuous Takagi-Sugeno fuzzy systems using an event-triggered scheme. For a continuous Takagi-Sugeno fuzzy dynamic system, a reduced-order filter is designed to transform the original model into a linear lower order one. This filter can also approximate the original system with H2performance, with a new type of event-triggered scheme used to decrease the communication loads and computation resources within the network. By transforming the filtering problem to a convex optimization one, conditions are presented to design the fuzzy reduced-order filter. Finally, two illustrative examples are used to verify the feasibility and applicability of the proposed design scheme.

This paper examines the robust stabilization problem of continuous-time delayed neural networks via the dissipativity-learning approach. A new learning algorithm is established to guarantee the asymptotic stability as well as the (Q, S, R)-α-dissipativity of the considered neural networks. The developed result encompasses some existing results, such as H∞and passivity performances, in a unified framework. With the introduction of a Lyapunov-Krasovskii functional together with the Legendre polynomial, a novel delay-dependent linear matrix inequality (LMI) condition and a learning algorithm for robust stabilization are presented. Demonstrative examples are given to show the usefulness of the established learning algorithm.

This paper investigates the dissipativity-based fuzzy control problem for nonlinear dynamic systems based on the event-triggered mechanism. For the sake of reducing the number of transmissions while maintaining the closed-loop stability of the system, the event-triggered mechanism is considered. Moreover, the dissipativity is also taken into account in designing a controller that ensures the resulting closed-loop system is asymptotically stable and strictly (X, Y, Z)-θ-dissipative. In view of the fuzzy model, the stability of the resulting system is analyzed in terms of Lyapunov stability theory. According to the stability conditions, a fuzzy controller is designed. Additionally, the explicit expression of the desired controller is given in view of linear matrix inequalities. Finally, the corresponding simulation results are plotted by applying the standard software, and a practical example on a truck-trailer model is provided to verify and illustrate the effectiveness and applicability of the proposed fuzzy controller design scheme.

In this paper, the problem of event-triggered fuzzy-rule-dependent filter design for networked system is investigated. For the original system, our focus is on establishing a new fuzzy filtering error system, considering the event-triggered scheme, the network-induced delays. Then, constructing a desired Lyapunov-Krasovskii function, sufficient stability conditions can be obtained to ensure that the new fuzzy filtering error system is mean-square asymptotically stable with H∞ performance. Finally, transform these stability criteria into convex optimization problems, which can be calculated by the standard optimization toolbox.

This paper focuses on the problem of L2-L∞dynamic output feedback controller (DOFC) design for nonlinear switched systems with nonlinear perturbations in the Takagi- Sugeno fuzzy framework. First, the average dwell time approach is used to stabilize a nonlinear switched system exponentially under an arbitrary switching law. Then, based on the technique of piecewise Lyapunov functions, a fuzzy-rule-dependent DOFC is designed to ensure that the overall closed-loop system is exponentially stable with a weighted L2-L∞performance level (γ, α). The solvability condition for the desired DOFC is derived using a linearization technique. It is shown that the controller parameters can be obtained as solutions to a set of strict linear matrix inequalities that are numerically solvable with available standard software. Finally, two simulation examples illustrate effectiveness of the developed technique, including cognitive-radio systems.

This paper attempts to propose a new solution to the event-triggered fault detection problem for discrete-time Takagi-Sugeno fuzzy systems in a network environment. First, for the original Takagi-Sugeno fuzzy system, our focus is on constructing a new based-network residual system, considering the event-triggering mechanism, interval time-varying delays, and packet dropouts. Under the established system, a less conservative basis-dependent stability condition is obtained by using the reciprocally convex technique, which ensures that the corresponding residual system is mean-square asymptotically stable with a given $\mathcal{H}_{\infty }$ performance. Second, the desired fuzzy-rule-dependent fault detector and the controller scheme are established using a variable substitution approach. Furthermore, these criteria can be transformed into convex optimization problems and then calculated by the standard optimization toolbox. Finally, the advantages of the proposed fault detector and controller coordinated design technique are illustrated by the simulation results.

This paper investigates the problem of stability analysis and stabilization for Takagi-Sugeno (T-S) fuzzy systems with time-varying delay. By using appropriately chosen Lyapunov-Krasovskii functional, together with the reciprocally convex a new sufficient stability condition with the idea of delay partitioning approach is proposed for the delayed T-S fuzzy systems, which significantly reduces conservatism as compared with the existing results. On the basis of the obtained stability condition, the state-feedback fuzzy controller via parallel distributed compensation law is developed for the resulting fuzzy delayed systems. Furthermore, the parameters of the proposed fuzzy controller are derived in terms of linear matrix inequalities, which can be easily obtained by the optimization techniques. Finally, three examples (one of them is the benchmark inverted pendulum) are used to verify and illustrate the effectiveness of the proposed technique.

This paper considers H∞reduced-order filtering problem for discrete-time Takagi-Sugeno (T-S) fuzzy systems with time-varying delay in its state. Firstly, Based on the reciprocally convex methods and a novel fuzzy Lyapunov functional, the proposed basis-dependent condition is utilized to ensure that the resulted error system is asymptotically stable with a prescribed H∞performance and reduce the conservativeness. Then, By utilization of the convex linearization technique, the sufficient condition of reduced-order filter design can be casted into linear matrix inequality constraints. Finally, the desired filters can be obtained based on standard numerical algorithms.

This paper is devoted to investigate load frequency controller design problem for multi-area interconnected power systems via the event-triggered sliding mode control technique. The H∞performance is considered as a performance index to test the effect of the load disturbance attenuation for the augmented LFC scheme. Time-delay system analysis method is employed to demonstrate the asymptotical stability of the closed-loop system. A novel discrete-time sliding mode surface is designed to improve transient system performance and the robust controller is constructed to guarantee the fluctuation of frequency to converge to zero after a load and operation point variation.

In this paper, the model approximation problem is investigated for a Takagi-Sugeno fuzzy switched system with stochastic disturbance. For a high-order considered system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well with a Hankel-norm performance but translates it into a lower dimensional fuzzy switched system as well. By using the average dwell time approach and the piecewise Lyapunov function technique, a sufficient condition is first proposed to guarantee the mean-square exponential stability with a Hankel-norm error performance for the error system. The model approximation is then converted into a convex optimization problem by using a linearization procedure. Finally, simulations are provided to illustrate the effectiveness of the proposed theory.

In this paper, the problem of reliable filter design with strict dissipativity has been investigated for a class of discrete-time T-S fuzzy time-delay systems. Our attention is focused on the design of a reliable filter to ensure a strictly dissipative performance for the filtering error system. Based on the reciprocally convex approach, firstly, a sufficient condition of reliable dissipativity analysis is proposed for T-S fuzzy systems with time-varying delays and sensor failures. Then, a reliable filter with strict dissipativity is designed by solving a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, numerical examples are provided to illustrate the effectiveness of the developed techniques.

This paper is concerned with the problems of stability analysis and stabilization for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems with stochastic perturbation and time-varying state delay. By means of the delay-partitioning method and slack variables, a novel fuzzy Lyapunov-Krasovskii function is constructed to reduce the conservatism of stability conditions. Those conditions are converted to finite linear matrix inequalities, which can be readily solved by standard numerical software. Then, the delay-dependent stabilization approach, which is based on a nonparallel distributed compensation scheme, is introduced for the closed-loop fuzzy systems. Finally, illustrative examples are provided to illustrate the feasibility and effectiveness of the proposed methods.

This paper is concerned with the problem of induced l2 filter design for a class of discrete-time Takagi-Sugeno fuzzy Itô stochastic systems with time-varying delays. Attention is focused on the design of the desired filter to guarantee an induced l2 performance for the filtering error system. A new comparison model is proposed by employing a new approximation for the time-varying delay state, and then, sufficient conditions for the obtained filtering error system are derived by this comparison model. A desired filter is constructed by solving a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

This paper focuses on analyzing a new model transformation of discrete-time Takagi–Sugeno (T–S) fuzzy systems with time-varying delays and applying it to dynamic output feedback (DOF) controller design. A new comparison model is proposed by employing a new approximation for time-varying delay state, and then, a delay partitioning method is used to analyze the scaled small gain of this comparison model. A sufficient condition on discrete-time T–S fuzzy systems with time-varying delays, which guarantees the corresponding closed-loop system to be asymptotically stable and has an induced$\ell_{2}$disturbance attenuation performance, is derived by employing the scaled small-gain theorem. Then, the solvability condition for the induced$\ell_{2}$DOF control is also established, by which the DOF controller can be solved as linear matrix inequality optimization problems. Finally, examples are provided to illustrate the effectiveness of the proposed approaches.

The paper is concerned with the problem of distributed fuzzy filter design for a class of sensor networks described by discrete-time T-S fuzzy systems with time-varying delays and multiple probabilistic packet losses. In sensor network, each individual sensor can receive not only its own measurement but also its neighboring sensors' measurements according to the interconnection topology to estimate the system states. Our attention is focused on the design of distributed fuzzy filters to guarantee the filtering error dynamic system to be mean-square asymptotically stable with an average \mathscr H∞performance. Sufficient conditions for the obtained filtering error dynamic system are proposed by applying an comparison model and the scaled small gain theorem. Based on the measurements and estimates of the system states and its neighbors for each sensor, the solution of the parameters of the distributed fuzzy filters is characterized in terms of the feasibility of a convex optimization problem. Finally, an illustrative example is provided to illustrate the effectiveness of the proposed approaches in sensor networks.

In this paper, the problem ofl2-l∞filtering for a class of discrete-time Takagi-Sugeno (T-S) fuzzy time-varying delay systems is studied. Our attention is focused on the design of full- and reduced-order filters that guarantee the filtering error system to be asymptotically stable with a prescribedH∞performance. Sufficient conditions for the obtained filtering error system are proposed by applying an input-output approach and a two-term approximation method, which is employed to approximate the time-varying delay. The corresponding full- and reduced-order filter design is cast into a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

This paper is concerned with the problem of H(∞) model reduction for Takagi-Sugeno (T-S) fuzzy stochastic systems. For a given mean-square stable T-S fuzzy stochastic system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well with an H(∞) performance but also translates it into a linear lower dimensional system. Then, the model reduction is converted into a convex optimization problem by using a linearization procedure, and a projection approach is also presented, which casts the model reduction into a sequential minimization problem subject to linear matrix inequality constraints by employing the cone complementary linearization algorithm. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed methods.

This paper is concerned with the problem of H∞model approximation for discrete-time Takagi-Sugeno (T-S) fuzzy time-delay systems. For a given stable T- S fuzzy system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well in an H∞performance but is also translated into a linear lower dimensional system. By applying the delay partitioning approach, a delay-dependent sufficient condition is proposed for the asymptotic stability with an H∞error performance for the error system. Then, the H∞model approximation problem is solved by using the projection approach, which casts the model approximation into a sequential minimization problem subject to linear matrix inequality (LMI) constraints by employing the cone complementary linearization algorithm. Moreover, by further extending the results, H∞model approximation with special structures is obtained, i.e., delay-free model and zero-order model. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed methods.

This paper investigates the problems of stability analysis and stabilization for a class of discrete-time Takagi-Sugeno fuzzy systems with time-varying state delay. Based on a novel fuzzy Lyapunov-Krasovskii functional, a delay partitioning method has been developed for the delay-dependent stability analysis of fuzzy time-varying state delay systems. As a result of the novel idea of delay partitioning, the proposed stability condition is much less conservative than most of the existing results. A delay-dependent stabilization approach based on a nonparallel distributed compensation scheme is given for the closed-loop fuzzy systems. The proposed stability and stabilization conditions are formulated in the form of linear matrix inequalities (LMIs), which can be solved readily by using existing LMI optimization techniques. Finally, two illustrative examples are provided to demonstrate the effectiveness of the techniques proposed in this paper.