Detecting machine-revised text that exhibits subtle lexical differences from the original human-generated text remains a challenge.Recent detection methods, including watermarking-based, logit-based, and trainingbased models, struggle to capture the finegrained semantic differences, especially for short texts.To address this issue, we propose Length-aware Momentum Contrastive Learning (LAMCL), a novel framework for multiscale machine-revised text detection that integrates two core modules.To enhance the discriminative semantic features, the Enhance Before Detection (EBD) module first fuses the original detected text with the counterpart processed by a Large Language Model (LLM), and then measures semantic consistency to distinguish between machine-revised and human-generated text.Meanwhile, based on the Momentum Contrastive Learning (MCL) framework, the Length-aware Weighting (LW) module leverages text length and label information for hard negative sampling, mitigating the ambiguity of short text attribution and boosting the robustness of representation learning.Experimental results demonstrate that our method outperforms the existing detectors in identifying multiscale machine-revised text across diverse practical scenarios, tasks, and LLMs.The code is available at https: //github.com/hangtze/LAMCL.

To meet the multi-dimensional modulation requirements of enabling robots to simultaneously achieve compliant adaptation to human time-varying motions and precise force tracking in unknown environments under physical human-robot-environment interaction (pHREI) scenarios, this paper proposed a fuzzy adaptive admittance control (FAAC) strategy that integrates disturbance observation and unified performance guarantees within a force-position dual-loop structure. In the force control outer loop, an extended state observer (ESO) is constructed to compensate for disturbances arising from uncertain human intentions and unstructured environmental geometry, while a PID-based admittance enhancement scheme is introduced to improve dynamic responsiveness. In the position control inner loop, a novel barrier function is embedded into the backstepping framework to systematically regulate the convergence rate, transient overshoot, steady-state accuracy, and global performance of the trajectory tracking error. Moreover, a fuzzy logic system (FLS) is employed to approximate the lumped model uncertainty, which in turn strengthens the robustness of the control algorithm. The asymptotic stability of the closed-loop system is rigorously established via the Lyapunov analysis criterion. Experiments involving trajectory tracking, planar cutting, and curved-surface carving conducted on an actual robotic platform demonstrate that the proposed method not only realizes superior force-position coordinated control, but also accommodates flexibility in physical human-robot interaction (pHRI) and smoothness in physical robot-environment interaction (pREI).

Consensus learning has been widely adopted in clustering tasks due to its robustness to noise and outliers, as well as its ability to aggregate diverse base results from multiple models. However, existing methods are often limited by feature alignment issues arising from heterogeneous feature dimensionalities and label permutation inconsistencies across models. To address these limitations, this paper introduces a novel Consensus Fuzzy Representation Learning (CFRL) framework. The CFRL framework initially employs various fuzzy clustering methods to generate diverse membership matrices, which are then transformed into affinity matrices to serve as base fuzzy representations. This transformation strategy not only effectively resolves feature alignment issues but also provides a unified processing mechanism for both single-view and multi-view data scenarios. To derive robust consensus features, the tensor Schatten$p$-norm encourages low-rank structure in the tensorized fuzzy representations, whereas an$l_{1}$-norm regularized error term captures and suppresses sparse noise. Moreover, a block diagonal regularizer is incorporated into the objective function, which guides the consensus feature matrix toward an optimal block diagonal structure. This structural constraint enhances cluster discriminability and enables reliable final cluster assignments. Comprehensive experimental evaluations validate that the proposed CFRL method achieves superior performance compared to state-of-the-art approaches.

This paper presents a dynamic event-triggered unified performance adaptive control method for nonparametric strict-feedback nonlinear systems. In contrast to most existing static/dynamic event-driven controllers that only guarantee uniformly ultimately bounded tracking results, here by introducing an external auxiliary variable into the dynamic threshold strategy and using the robust technique with the integral function, not only the communication overhead is reduced and the Zeno phenomenon is precluded, but also the asymptotic zero-error tracking is achieved. The control design and stability analysis become quite complicated and challenging when the performance constraint is taken into account. By constructing a series of functional transformations in conjunction with the core information technique to handle the nonparametric uncertainty, the proposed controller is able to guarantee multiple prescribed performance characteristics by appropriately adjusting the key design parameter, eliminating the need to redesign the controller and reanalyze the stability. Finally, simulation results are conducted to demonstrate the effectiveness of the theoretical discussion.

Most existing prescribed performance formation control methods impose performance requirements on the consensus error rather than directly on the relative states between agents, which limits the physical interpretability of their solutions. This article proposes a novel adaptive prescribed performance formation control strategy that ensures prescribed performance of relative errors in uncertain high-order multiagent systems under both directed and undirected graphs. Since performance constraints are considered for relative errors, the error dynamics involve a coupled nonlinear interaction term that contains global graphical information among agents, making the design of a fully distributed control strategy more challenging. By proposing a series of nonlinear mappings and utilizing the edge Laplacian along with Lyapunov stability theory, the presented formation control scheme offers several advantages over existing approaches. Different performance requirements can be accommodated in a unified manner by solely tuning the design parameters a priori, eliminating the need for control redesign and stability reanalysis under the proposed fixed control protocol. This enhances user-friendliness and reduces implementation complexity. Furthermore, the verification process for the initial constraint, which is often complex and burdensome in existing prescribed performance control methods, is entirely avoided when the performance requirements are global. Additionally, the proposed approach fully decouples nonlinear interactions and ensures the asymptotic stability of the formation manifold through an adaptive parameter estimation technique. The effectiveness of the theoretical results is demonstrated through simulations.

Human-robot interaction (HRI) is a crucial component in the field of robotics, and enabling faster response, higher accuracy, as well as smaller human effort, is essential to improve the efficiency, robustness, and applicability of HRI-driven tasks. In this article, we develop a novel neuroadaptive admittance control with human motion intention (HMI) estimation and output error constraint for natural and stable interaction. First, the interaction force information of the robot is utilized to predict the HMI and the stiffness in the admittance model is dynamically updated based on surface electromyography (sEMG) signals of the human upper limb to achieve human-like compliance. Then, based on the designed error transformation mechanism, an innovative prescribed performance control (PPC) is proposed that allows the trajectory error to converge to the given constraint range within a predefined time for any bounded initial conditions, thus enabling the robot to maintain a comprehensive performance of moving in the desired direction as guided by the human. Also, an adaptive neural network (NN) is employed to compensate for the uncertainty of robotics systems to improve the tracking accuracy further. According to the Lyapunov stability analysis criterion, our approach ensures that all states of the closed-loop system remain globally uniformly ultimately bounded. Finally, a series of real-world robot experiments demonstrate the effectiveness of the proposed framework.

Real-world data tends to exhibit an inherent hierarchical structure, providing a natural multiview perspective where features at different scales can be treated as distinct views. However, most existing multiview clustering algorithms primarily focus on the inter-sample relationships at a single level. These methods overlook the hierarchical structures present in the data and are specifically designed for native multiview data. This article introduces a comprehensive multiview clustering framework that transforms both typical data and images into a unified multiview feature representation. The framework allows for extracting multiscale features from the raw data and clustering different types of data with the same algorithm. A novel scale-driven pre-processing approach unifies the feature structure across various data types and explores local relationships among samples at multiple scales. Features at larger scales delineate the global cluster contours, while features at smaller scales reveal fine-grained local details. Subsequently, the proposed method learns the view-specific partitions from different scales of views and derives consensus features through tensor low-rank representation. By optimizing these consensus features, the approach effectively captures the precise cluster shapes from coarse to fine-grained levels. The final label indicator matrix is directly obtained from these consensus features. To demonstrate the effectiveness and versatility of the proposed method, we conducted experimental comparisons with state-of-the-art (SOTA) algorithms in both multiview clustering and image segmentation across diverse datasets. The source code and datasets are released at https://github.com/ChuanbinZhang/SDTR.

The assurance of faster transient response rate, higher steady-state tracking accuracy, and global adaptability are crucial for enhancing the efficiency and robustness of manipulators during operation. This article explores a novel fuzzy adaptive predefined-time controller based on global prescribed performance for robotic systems with unknown dynamics and bounded disturbances. First, a predefined time error transformation function (PTETF) is developed and combined with a barrier function based on constant value constraints for control design, which not only significantly simplifies the derivation process of the proposed predefined time prescribed performance control (PTPPC), but also equips it with the ability of global constraints on the trajectory tracking error. Then, we utilize the fuzzy logic system (FLS) with a single-parameter update mechanism to compensate for the dynamic uncertainty of manipulators, thereby reducing computational complexity and cost. In addition, a fixed time disturbance observer (FxTDOB) is introduced to alleviate the effect of nonparametric disturbances on the tracking performance. Further, by integrating the predefined time theory with Lyapunov method to ensure that all state signals of the controlled system converge in a predefined time. Finally, numerical simulations and practical experiments are carried out to demonstrate the effectiveness of the proposed framework.

In this article, we propose an event-triggered unified fault-tolerant control strategy for continuum robots with prescribed performance, considering multiple levels of actuator failures. First, by integrating a Kelvin–Voigt model, this article introduces an improved constant curvature model for continuum robots accounting for dissipative effects. Second, a comprehensive fault-handling framework is established, which combines extreme fault detection, dynamic actuator redundancy, and asymmetric performance recovery within the context of a unified prescribed performance. Third, by taking advantage of the dynamic characteristic of auxiliary variable and designing suitable event-triggering conditions, a dual-channel event-triggering mechanism is introduced to effectively reduce the number of triggers and optimize the utilization of communication resources. The proposed unified fault-tolerant control strategy ensures the boundedness of all signals in the closed-loop system and multiple kinds of prescribed performance behaviors under actuator failures. Simulation results validate the effectiveness of the proposed control strategy.

The piecewise and discontinuous output constraints are prevalent in practical engineering systems but remain insufficiently explored in the literature. Unlike existing results, the discontinuous constraints considered here exhibit two salient characteristics: 1) the bounded constraint boundary function undergoes a jump discontinuity at a specific instant during the initial operational period and 2) the system output becomes unconstrained thereafter. These features render traditional methods inadequate because the derivatives of the constraint boundary function do not exist. To address this problem, we propose a systematic neuroadaptive control framework for nonlinear systems that integrates two novel shift functions with a new barrier Lyapunov function (BLF). The presented control scheme not only ensures good tracking performance under such piecewise and discontinuous output constraints but also can be applied to several other common yet critical constraint scenarios without involving any adjustment to the controller structure. Theoretical analysis and simulation results verify the effectiveness and practicality of the proposed approach.

Accurate estimation of the Lithium-ion batteries (LiBs) state of health (SOH) is essential to ensure the safe and reliable operation of battery-powered devices. Most of the current data-driven SOH estimation models are designed under fixed ambient temperatures, overlooking the high sensitivity of LiBs to changing ambient temperatures. To bridge this gap, a novel method is proposed with transfer learning (TL) to model and estimate the SOH at varying ambient temperatures. First, canonical variate analysis is employed to capture the temporal dynamics in the time-series data of LiBs and extract temporal features. Second, at the reference temperature, an interpretable SOH estimation model with a long short-term memory network is utilized to learn the regression relationship between the temporal features and the labeled capacities. Third, at the new temperature, a small amount of data is projected into the key feature domain at the reference temperature to obtain the common temporal features. Afterward, TL is employed to take advantage of the existing process knowledge through keeping all functional layers of the previous well-trained SOH estimation model. Finally, the efficacy of the proposed method is verified on the NASA dataset with two discrete temperatures, 24°C and 44°C. Evaluated by the index of root mean squared error, the proposed method is capable of improving prediction accuracy by 94.18% when the model is transferred from 44°C to 24°C.

In this paper, a robust adaptive control scheme, capable of guaranteeing unified prescribed performances on the output tracking error and virtual errors, is developed for a class of multiple-input multiple-output (MIMO) strict-feedback nonlinear systems in presence of input quantization, which exhibits some features. Firstly, by constructing a series of function transformations multiple performance behaviors can be ensured under a fixed control framework by properly selecting the performance parameters, without the need for control redesign. Secondly, by constructing a novel performance function for the virtual errors, the demanding constraint on the initial values of virtual errors is completely circumvented. Consequently, there is no need for the tedious offline computations for the initial verification, making the control algorithm more user-friendly in design and implementation. Thirdly, due to the considerations of prescribed performance and quantization simultaneously, some additional product terms and drift terms occur in Lyapunov function differential inequality, further complicating the control design and stability analysis. To address this issue, useful Lemmas introduced, which ensure that under the normally used assumptions the closed-loop system is stable. The numerical simulations show the advantages and effectiveness of the proposed control.

In recent times, significant advancements have been made in delving into the optimization landscape of policy gradient methods for achieving optimal control in linear time-invariant (LTI) systems. Compared with state-feedback control, output-feedback control is more prevalent since the underlying state of the system may not be fully observed in many practical settings. This article analyzes the optimization landscape inherent to policy gradient methods when applied to static output feedback (SOF) control in discrete-time LTI systems subject to quadratic cost. We begin by establishing crucial properties of the SOF cost, encompassing coercivity, L -smoothness, and M -Lipschitz continuous Hessian. Despite the absence of convexity, we leverage these properties to derive novel findings regarding convergence (and nearly dimension-free rate) to stationary points for three policy gradient methods, including the vanilla policy gradient method, the natural policy gradient method, and the Gauss-Newton method. Moreover, we provide proof that the vanilla policy gradient method exhibits linear convergence toward local minima when initialized near such minima. This article concludes by presenting numerical examples that validate our theoretical findings. These results not only characterize the performance of gradient descent for optimizing the SOF problem but also provide insights into the effectiveness of general policy gradient methods within the realm of reinforcement learning.

This article addresses the entry capture problem (ECP) of uncertain nonlinear systems under asymmetric performance constraints. We show that such ECP is commonly encountered in practice that has not been well addressed, whose tracking error is free from any performance constraints initially then is driven into the prescribed region in finite time. For better-transient performance, a unified tunnel prescribed performance (TPP) is developed to provide strict and tight allowable set. By utilizing a scaling function, together with an error scaling function (ESF), and a more general error-dependent transformation function (ETF), we propose an event-triggered tracking control strategy leading to a solution for the underlying ECP with various initial conditions and asymmetric performance constraints. This control strategy is of significant simplicity, stemming from that only 1-bit signal is needed for each data transmission between controller and actuator. We also show that the tracking error (including the initial-constraint violation) is regulated into the prescribed region in a given time globally. Finally, simulations are conducted to illustrate the above theoretical findings.

Most existing results on full-state error prescribed performance control for multiple-input multiple-output (MIMO) strict-feedback nonlinear systems typically impose demanding constraining conditions on the initial full-state errors, rendering the performance boundary nonuniform with respective to initial conditions, and consequently tedious offline computations for initial error (especially the initial virtual error) constraint verification is inevitable, which is highly undesirable or even impractical for the design and implementation of the corresponding controls. In this article, we present a novel adaptive control solution that allows the performance uniformness (with respect to the initial condition) and the transient behavior (with respect to error overshoot) to be addressed simultaneously under a unified framework. The key design steps and features include: 1) by constructing a nonlinear transformation based on the time-varying scaling function, the developed performance boundary is uniform to any initial condition; 2) the demanding condition on the initial values of full-state errors in the existing prescribed performance works is removed, allowing the designer more freedom to select design parameters and rendering the solution more user-friendly and less demanding in design and implementation; and 3) by making use of the minimum eigenvalues of the resultant diagonal matrix and imposing a critical negative feedback term in the control design, the stability of closed-loop system is ensured by the developed uniform control strategy. The effectiveness of the proposed approach is verified by simulations.

It is nontrivial to achieve exponential stability even for time-invariant nonlinear systems with matched uncertainties and persistent excitation (PE) condition. In this article, without the need for PE condition, we address the problem of global exponential stabilization of strict-feedback systems with mismatched uncertainties and unknown yet time-varying control gains. The resultant control, embedded with time-varying feedback gains, is capable of ensuring global exponential stability of parametric-strict-feedback systems in the absence of persistence of excitation. By using the enhanced Nussbaum function, the previous results are extended to more general nonlinear systems where the sign and magnitude of the time-varying control gain are unknown. In particular, the argument of the Nussbaum function is guaranteed to be always positive with the aid of nonlinear damping design, which is critical to perform a straightforward technical analysis of the boundedness of the Nussbaum function. Finally, the global exponential stability of parameter-varying strict-feedback systems, the boundedness of the control input and the update rate, and the asymptotic constancy of the parameter estimate are established. Numerical simulations are carried out to verify the effectiveness and benefits of the proposed methods.

For the existing adaptive constrained robotic control algorithms, the demanding "feasibility conditions" on virtual controller is normally inevitable and the extra limits on constraining functions have to be imposed, making the corresponding approaches more demanding and less user friendly in control development. Here, we develop a new neuroadaptive constrained control strategy for uncertain robotic manipulators in the presence of position and velocity constraints. First, a novel unified mapping function (UMF) is constructed so that the restriction on constraining boundaries is removed and more kinds of constraining forms can be handled. Second, by integrating the UMF-based coordinate transformation with the "universal" approximation characteristic of neural networks over some compact set, the developed neuroadaptive control completely obviates the complicated yet undesired "feasibility conditions." Furthermore, it is proven that all closed-loop signals are semiglobally bounded and the constraints are not violated. The effectiveness of the proposed control is validated via a two-link rigid robotic manipulator.

Many fuzzy subspace clustering methods have been proposed for high-dimensional image data with rich structural information. However, since these methods do not fully exploit the subspace information in each cluster, their performance on image clustering is still not promising. In this work, we propose to find soft partitions directly based on the construction of subspaces. For each cluster, we use a bilinear orthogonal subspace to represent it. Then, through the reconstruction error of a sample in the subspace corresponding to a cluster, a new membership measure for the sample to the cluster is established. Furthermore, the graph regularization is imposed on these bilinear subspaces to preserve the local relational or manifold information of the image data in the original space. Altogether, we get a clustering model considering not only the subspace information but also the manifold information in image data. An efficient optimization algorithm is proposed to our model, and its theoretical convergence and time complexity are presented correspondingly. The proposed method is a one-stage clustering model that does not require vectorized image data, thereby reducing the computational burden while maintaining the structural relationship between pixels in the image. Competitive experimental results on benchmark datasets show that our model can converge quickly with strong clustering performance, which confirms the efficiency and superiority of the proposed method compared to other state-of-the-art fuzzy clustering methods.

In this study, a biologically inspired echo state network (ESN)-based method is established for the asymptotic tracking control of a class of uncertain multi-input multi-output (MIMO) systems. By mimicking the characters of real biological systems, a diversified multiclustered echo state network (DMCESN) is proposed in this work and then it is applied to deal with the modeling uncertainties and coupling nonlinearities in the control systems. Different from the most existing neural network (NN)-based control methods that only ensure the uniform ultimate boundedness result, the proposed method can allow the tracking error to achieve asymptotic convergence through rigorous theoretical analysis. The effectiveness of the proposed method is also confirmed by numerical simulation by comparing with multilayer feedforward network-based control scheme and traditional ESN-based control, admitting better tracking performance of the proposed control.

In this article, the problem of constant yet deferred output constraint for uncertain strict-feedback nonlinear systems is studied. By “deferred output constraint,” we mean that the system output is free/released from any constraint in the initial interval and then preserves within a bounded region right after a finite time. Due to such a form of output constraint, the normally employed Barrier Lyapunov Function (BLF)-based results become invalid because the corresponding BLF is undefined in the initial period. The problem will be rather complicated yet challenging if computation and communication constraints are taken into account. By developing an error-based nonlinear function and constructing a prescribed-time scaling function, together with the approximate ability of neural networks, a varying threshold-based event-triggering adaptive neural control algorithm is presented such that not only the deferred output constraint can be ensured and the network resources can be saved but also the tracking error is able to converge to a pregiven region in a prescribed time. Simulations are provided to demonstrate the effectiveness of the proposed control.

For pure-feedback nonlinear systems under asymmetric output constraint, we present a low-cost neuroadaptive tracking control solution with salient features benefited from two design steps. In the first step, a novel output-dependent universal barrier function (ODUBF) is constructed such that not only the restrictive condition on constraining boundaries/functions is removed but also both constrained and unconstrained cases can be handled uniformly without the need for changing the control structure. In the second step, to reduce the computational burden caused by the neural network (NN)-based approximators, a single parameter estimator is developed so that the number of adaptive law is independent of the system order and the dimension of system parameters, making the control design inexpensive in computation. Furthermore, it is shown that all signals in the closed-loop system are semiglobally uniformly ultimately bounded, the tracking error converges to an adjustable neighborhood of the origin, and the violation of output constraint is prevented. The effectiveness of the proposed method can be validated via numerical simulation.

This paper presents a neuroadaptive tracking control approach for uncertain robotic manipulators subject to asymmetric yet time-varying full-state constraints without involving feasibility conditions. Existing control algorithms either ignore motion constraints or impose additional feasibility conditions. In this paper, by integrating a nonlinear state-dependent transformation into each step of backstepping design, we develop a control scheme that not only directly accommodates asymmetric yet time-varying motion (position and velocity) constraints but also removes the feasibility conditions on virtual controllers, simplifying design process, and making implementation less demanding. Neural network (NN) unit accounting for system uncertainties is included in the loop during the entire system operational envelope in which the precondition on the NN training inputs is always ensured. The effectiveness and benefits of the proposed control method for robotic manipulator are validated via computer simulation.

In this article, a neural network (NN)-based robust adaptive fault-tolerant control (FTC) algorithm is proposed for a class of multi-input multi-output (MIMO) strict-feedback nonlinear systems with input quantization and actuation faults as well as asymmetric yet time-varying output constraints. By introducing a key nonlinear decomposition for quantized input, the developed control scheme does not require the detailed information of quantization parameters. By imposing a reasonable condition on the gain matrix under actuation faults, together with the inherent approximation capability of NN, the difficulty of FTC design caused by anomaly actuation can be handled gracefully, and the normally used yet rigorous assumption on control gain matrix in most existing results is significantly relaxed. Furthermore, a brand new barrier function is constructed to handle the asymmetric yet time-varying output constraints such that the analysis and design are extremely simplified compared with the traditional barrier Lyapunov function (BLF)-based methods. NNs are used to approximate the unknown nonlinear continuous functions. The stability of the closed-loop system is analyzed by using the Lyapunov method and is verified through a simulation example.

This paper studies the zero-error tracking control problem of Euler-Lagrange systems subject to full-state constraints and nonparametric uncertainties. By blending an error transformation with barrier Lyapunov function, a neural adaptive tracking control scheme is developed, resulting in a solution with several salient features: 1) the control action is continuous and smooth; 2) the full-state tracking error converges to a prescribed compact set around origin within a given finite time at a controllable rate of convergence that can be uniformly prespecified; 3) with Nussbaum gain in the loop, the tracking error further shrinks to zero as ; and 4) the neural network (NN) unit can be safely included in the loop during the entire system operational envelope without the danger of violating the compact set precondition imposed on the NN training inputs. Furthermore, by using the Lyapunov analysis, it is proven that all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded. The effectiveness and benefits of the proposed control method are validated via computer simulation.

In this paper, a neuroadaptive fault-tolerant tracking control method is proposed for a class of time-delay pure-feedback systems in the presence of external disturbances and actuation faults. The proposed controller can achieve prescribed transient and steady-state performance, despite uncertain time delays and output constraints as well as actuation faults. By combining a tangent barrier Lyapunov-Krasovskii function with the dynamic surface control technique, the neural network unit in the developed control scheme is able to take its action from the very beginning and play its learning/approximating role safely during the entire system operational envelope, leading to enhanced control performance without the danger of violating compact set precondition. Furthermore, prescribed transient performance and output constraints are strictly ensured in the presence of nonaffine uncertainties, external disturbances, and undetectable actuation faults. The control strategy is also validated by numerical simulation.