This work introduces a fixed-time adaptive fault-tolerant control approach to tackle the trajectory tracking issues in wheeled mobile robots by accounting for unknown dead zones and actuator faults. Firstly, by introducing smoothing functions, bounded estimates, and adaptive parameters, combined with a fixed-time control strategy, actuator faults and the impacts of unknown dead zones are effectively eliminated. Second, an adaptive fault-tolerant control strategy for nonlinear wheeled mobile robot systems is developed. Subsequently, a fixed-time trajectory tracking controller is designed to guarantee system stability and achieve high-precision control for wheeled mobile robots. Furthermore, leveraging the principles of Lyapunov stability theory, this work rigorously establishes the convergence properties of the controller, offering a robust theoretical foundation for ensuring the precision and reliability of trajectory tracking. Finally, the proposed control strategy’s effectiveness and feasibility are validated through simulation outcomes.

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.

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 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.

This article investigates the bipartite time-varying output formation tracking problem of heterogeneous linear multiagent systems with disturbances by adaptive dynamic event-triggered control. The goal is to make the outputs of all followers complete the preset formation configuration, and track the convex combination composed of the leaders’ outputs at the same time. Distributed dynamic event-triggered compensator is first designed to estimate the convex combination of the leaders’ states, where the internal variable is introduced into the dynamic event-triggered mechanism to dynamically adjust the threshold of each follower. Compared with the traditional static threshold, the time-varying threshold ensures larger adjacent triggering time intervals. Then, the output formation control law based on distributed compensator, compensation input and time-varying formation signal is proposed, where the compensation input provides a degree of freedom for the feasible formation set. Finally, two examples are introduced to verify the main results.

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 time-varying formation (TVF) tracking problem is studied for linear multiagent systems (MASs), where followers reach a preset TVF when tracking the leader's state. Followers are divided into the informed ones, which directly receive the leader's information, and uninformed ones. To alleviate communication requirements, trigger mechanisms are designed for the leader and all edges. Note that the designed trigger mechanisms enable the leader to send information intermittently and each follower to transmit information asynchronously when the corresponding trigger mechanism is satisfied. To address the TVF tracking problem, the node-event (for the leader) and (dynamic) edge-event triggered adaptive control strategy is proposed, which is fully distributed and has no relation to the system network's scale. Moreover, the MASs do not exhibit the Zeno behavior. Finally, a practice example is introduced to effectively illustrate the theoretical results.

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 study focuses on dissipativity-based fault detection for multiple delayed uncertain switched Takagi–Sugeno fuzzy stochastic systems with intermittent faults and unmeasurable premise variables. Nonlinear dynamics, exogenous disturbances, and measurement noise are also considered. In contrast to the existing study works, there is a wider range of applications. An observer is explored to detect faults. A controller is studied to stabilize the considered system. A piecewise fuzzy Lyapunov function is collected to obtain delay-dependent sufficient conditions by means of linear matrix inequalities. The designed observer has less conservatism. In addition, the strict$(\mathfrak {Q},\mathfrak {S},\mathfrak {R})-{\epsilon }-$dissipativity performance is achieved in the residual dynamic. Besides, the elaborate$H_{\infty }$performance and the elaborate$H\_{}$performance are also acquired. Finally, the availability of the method in this study is verified through two simulation examples.

This article devotes to solve the fault-tolerant control problem based on interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy stochastic switched uncertain time-delayed systems with signal quantization. Stochastic switched systems can model a dynamic structure susceptible to abrupt faults, making it more practically significant in power systems or economic systems. The core design is an observer-based fault-tolerant control scheme that can estimate incomplete measurable variables and eliminate the influence of fault dynamically well and enhancing the robust stability of the systems subject to quantization effects. A novel method in seeking the upper bound solution of time-varying delay efficiently decreases conservativeness, especially for the proposed time-delayed system. The simulated analysis is specified to verify the availability and validity of the obtained design method.

Considering that there are many systems with limited network bandwidth in practice, this article studies the leader-following consensus problem for a class of nonlinear multiagent systems (MASs). The purpose of this article is to reduce unnecessary information transmission between any pair of adjacent agents including the leader in the MASs through intermittent communication. The novel event-triggered and asynchronous edge-event triggered mechanisms are designed for the leader and all edges, respectively. The static and dynamic consensus protocols under these mechanisms are proposed to address the leader-following consensus problem for MASs with Lipschitz dynamics, and the systems will not exhibit Zeno behavior under these two control schemes. Note that the dynamic consensus protocol does not rely on any global values of MASs, it is a fully distributed way. Finally, a practice simulation example is introduced to illustrate the theoretical results obtained.

This article investigates multiple delay-dependent robust$H_\infty$finite-time filtering for uncertain Itô stochastic Takagi–Sugeno (T–S) fuzzy semi-Markovian jump systems with state constraints. Few studies exist for Itô stochastic T–S fuzzy semi-Markovian jump systems with state constraints. First, a T–S fuzzy semi-Markovian jump filter is explored and a controller is designed. In terms of linear matrix inequalities, delay-dependent sufficient conditions as well as bounded real lemma are gathered by utilizing stochastic Lyapunov function. Robust finite-time boundedness and input–output finite-time mean square stabilization are achieved and gain matrices of the filter and controller are obtained at the same time. A numerical example is presented to validate the feasibility of the proposed results in this article. And the admissible maximal delay bounds are calculated.

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.

This article is concerned with the issue of dissipativity-based finite-time multiple delay-dependent filtering for uncertain semi-Markovian jump random nonlinear systems with state constraints. There are multiple time-varying delays, nonlinear functions, and intermittent faults (IFs) in the systems. This is one of the few attempts for the issue studied in this article. First, a filter is designed for the uncertain semi-Markovian jump random nonlinear systems. An augmented system with regard to the resulting filtering error is acquired. Then, sufficient conditions of the augmented system are generated by the stochastic Lyapunov function. Finite-time boundedness (FTB) and input-output finite-time mean square stabilization (IO-FTMSS) are both realized. The effectiveness and feasibility of the method are rendered via three examples.

For the nonlinear system with partially unknown dynamic, the problems of fault estimation and fault-tolerant control are considered. A novel adaptive observer is designed to reconstruct the system state, process fault, sensor fault, and the measurement disturbance, simultaneously. The designed observer can be treated as a dynamic proportional-integral observer, where both the output information and the derivative of the output are used, while the output derivative can be unmeasurable. Utilizing the estimation information obtained by the designed observer, a nonlinear fault-tolerant control technique is proposed to stabilize the system. At the end of this article, three examples are listed to verify the proposed method.

The issue of fault detection (FD) and fault-tolerant control is developed for a switched random nonlinear system against multiple delays in this work. There also exist parameter uncertainties, exogenous disturbances, nonlinear functions as well as measurement noise in this model. This is one of the few tries to investigate the dissipativity issue and an design FD-based observer for the switched random system. Based on the dimensionally adjustable observer, the controller is designed to make this system noise-to-state exponentially mean-square stable. Then, these sufficient stability conditions can be earned by the piecewise Lyapunov function. Concurrently, this strict$\left({\mathsf {Q}, \mathsf {S}, \mathsf {R}}\right)-{\upsilon }-$dissipativity performance, this elaborate$H_{\infty }$performance as well as this elaborate$H\_{}$performance can be gained, separately. In addition, the merits and feasibility are presented via a numerical simulation and a practical example.

Compared with stochastic differential equations (SDEs) driven by white noise, random differential equations (RDEs) generated by colored noise are claimed to be more practical. This article considers reduced-order high-gain observer (ROHGO)-based neural tracking control on random nonlinear systems having output delay. In order to foster the design and analysis, the estimated states and the estimation errors are scaled by the high gain of the observer. Based on neural network (NN) approximation and state observation, an adaptive controller is designed for the overall system using the backstepping method. It is proved that all the closed-loop signals are bounded almost surely, letting alone the tracking error. By tuning the related design parameters, the asymptotic tracking error could be regulated arbitrarily small. Within the best of our knowledge, this article serves as the first attempt for NN-based control on RDE systems. Finally, the validity of main results is confirmed by a simulation example.

This article is concerned with observer-based fault estimation (FE) and tolerant controller design for a series of discrete-time Takagi-Sugeno (T-S) fuzzy stochastic systems. There exist multiple time-varying state delays, intermittent sensor and actuator faults, nonlinear dynamics, and exogenous disturbances in the systems. Compared with the results of the existence, this approach suggested in this article is more flexible and feasible. By means of the FE information, a novel fuzzy adaptive descriptor observer is developed to obtain the error dynamics. Then, an active observer-based fault-tolerant controller is designed to stabilize the closed-loop fuzzy system. Furthermore, a set of delay-dependent sufficient conditions are provided by the fuzzy Lyapunov function with the way of linear matrix inequalities (LMIs), which has less conservatism compared with the ones of the existing observers and fault-tolerant controllers. Finally, a simulation example is shown to illustrate the advantages and effectiveness of this approach depicted in this article.

This article addresses an adaptive fuzzy control for the nonstrict-feedback nonlinear systems with asymmetric time-varying full state constraints. To prevent the state constraints being violated, the nonlinear state-dependent function (NSDF) is introduced instead of the tradition barrier lyapunov function (BLF). The constrained systems are transformed into a novel free-constrained systems based on the NSDF. A direct approach is given to cope with the state constraints. Fuzzy logic system is utilized to approximate the uncertain nonlinear system functions. The new affine variables are constructed for the transformed systems, and the prior knowledge of the control gains is not required necessarily. Under the designed control scheme, the boundedness of the signals in the closed-loop systems is guaranteed certainly, and the asymmetric time-varying constraints are maintained. This proposed method removes the feasibility condition on the virtual controller derived from the method of BLF. The effectiveness of the proposed control strategy is verified by two simulation examples.

This article deals with delay-dependent H∞guaranteed cost control for uncertain switched Takagi-Sugeno (T-S) fuzzy systems with multiple interval time-varying delays. It is the first time that the fault tolerant control (FTC) of nonlinear systems subject to measurement noise, external disturbances, nonlinear functions, and intermittent faults (IFs) in sensors and actuators is investigated. A passive dynamic full-order output feedback controller is first addressed for the system to guarantee the exponential stability. Compared with the existing results, the restrictions on the multiple time-varying delays are relaxed in the derivation of this work. The suggested controller has a wider application. Moreover, as the piecewise fuzzy Lyapunov function is introduced with some slack matrices, the delay-dependent sufficient conditions are provided in the way of a cluster of linear matrix inequalities (LMIs) and then less conservatism is attained by our design contrast to the existing fault tolerant controllers. At last, two simulation examples and some comparisons are achieved to illustrate the novelty and feasibility of the method gathered in the article.

In this paper, fault estimation (FE) and fault-tolerant control (FTC) are investigated for Takagi-Sugeno (T-S) fuzzy systems with multiple time-varying delays as well as actuator and sensor faults. Local nonlinear models and external disturbances are also considered. Inspired by the information from the (k-1)th induction FE, a novel observer is addressed to establish the kth error dynamics. The k-step induction FE observer can weaken the effect from input disturbances caused by the derivatives of actuator faults and can perform better FE subject to sensor and actuator faults and multiple time delays simultaneously. Compared with the existing results, the proposed observer can realistically better show the sizes and shapes of the actuator and sensor faults. In addition, according to online information from the k-step FE, an active dynamic output feedback fault-tolerant controller is proposed to make the closed-loop fuzzy system asymptotically stable. Moreover, the fuzzy Lyapunov-Krasovskii functional is developed by introducing some free-weighting matrices such that the delay dependent sufficient conditions are given in the form of a set of linear matrix inequalities (LMIs) with less conservatism for the existence of observer and fault-tolerant controller. At last, two simulation examples are given to prove the advantages and effectiveness of the approach given in this paper.