This paper investigates an adaptive fault-tolerant control problem for a class of strict-feedback incommensurate fractional-order multi-input multi-output (MIMO) nonlinear systems with intermittent actuator faults. To avoid the complexity explosion inherent in conventional backstepping methods, a control strategy without backstepping is developed. Firstly, via state transformation, the original system is reformulated into a structure suitable for the proposed framework, and an error observer is designed to estimate the unmeasurable states caused by the aforementioned transformation. To effectively compensate for intermittent actuator faults, a fault-tolerant controller incorporating the hyperbolic tangent function is constructed. Furthermore, fuzzy logic systems (FLSs) are employed to approximate the unknown nonlinear functions arising in the reconstructed system, thereby supporting the controller design. To facilitate stability analysis without resorting to frequency-distributed models, a sum-separable Lyapunov function is established. Due to the multi-order dynamic coupling in incommensurate fractional-order systems, a convex optimization problem arises. Therefore, a novel feasibility search algorithm for Riccati-type LMI constraints is put forward to systematically obtain feasible solutions. Subsequently, stability analysis demonstrates that all signals in the closed-loop system are uniformly bounded. Meanwhile, the prespecified control objectives are successfully achieved. Finally, a simulation example of the fractional-order Chua-Hartley system is provided to verify the effectiveness of the proposed fault-tolerant control scheme.

In complex indoor environments, mobile robots frequently encounter non-convex obstacles such as L-shaped and star-shaped contours. Conventional convex envelopes (ellipses, superellipses) represent these obstacles conservatively, introducing unnecessary path detours and compressing feasible space. This paper proposes a real-time obstacle avoidance method that integrates radial basis function (RBF)-based implicit modeling with velocity field shaping (VFS). First, obstacle boundaries are recovered from raw point clouds using Alpha Shape, followed by bilateral normal-direction sampling and regularized RBF fitting to obtain a continuously differentiable implicit function that accurately captures concave structures. A ray-casting-based safety distance function with an adjustable safety margin is then defined, enabling the VFS modulation matrix to suppress the normal velocity component and enhance the tangential component near the boundary, thereby generating smooth, collision-free trajectories. For multi-obstacle scenarios, a weighted product fusion strategy and an intersection detection/re-modeling mechanism are presented. Theoretical analysis proves non-penetration and asymptotic stability. Simulation and real-robot experiments on L-shaped and star-shaped obstacles demonstrate that the proposed method reduces path length by 15% and maximum angular jerk by 30% compared to elliptical envelope baselines, maintains real-time performance (8.0 ms for RBF fitting, ¡0.5 ms for VFS), and achieves 100% success rate over 20 trials, significantly outperforming baseline methods.

This work investigates fuzzy adaptive output-feedback control issue for incommensurate fractional-order nonlinear systems with input and output quantization. The input and output are quantized via a sector bounded quantizer. Due to the case that system states are partially measurable, a fuzzy state observer with quantized signals is constructed. Fuzzy logic systems are employed to model the nonlinear dynamics. Further, the fractional-order dynamic surface control strategy is developed to overcome the complexity brought by adaptive backstepping technique. Then, for the purpose of ensuring the boundability of a series of errors caused by continuous original state in stability analysis and discontinuous quantization state in control, a novel fractional-order projection operator with smooth property is proposed. Finally, by means of the frequency distribution model and indirect Lyapunov method, it is proved that all closed-loop signals are bounded. The advantages of the proposed control method are illustrated by a numerical example.

Automating contact-rich manipulation of viscoelastic objects with rigid robots faces challenges including dynamic parameter mismatches, unstable contact oscillations, and spatiotemporal force-deformation coupling. In our prior work, a Compliance-Aware Tactile Control and Hybrid Deformation Regulation (CATCH-FORM-3D) strategy fulfills robust and effective manipulations of 3D viscoelastic objects, which combines a contact force-driven admittance outer loop and a PDE-stabilized inner loop, achieving sub-millimeter surface deformation accuracy and +/- 5% force tracking. However, this strategy requires fine-tuning of object-specific parameters and task-specific calibrations, to bridge this gap, a CATCH-FORM-ACTer is proposed, by enhancing CATCH-FORM-3D with a framework of Action Chunking with Transformer (ACT). An intuitive teleoperation system performs Learning from Demonstration (LfD) to build up a long-horizon sensing, decision-making and execution sequences. Unlike conventional ACT methods focused solely on trajectory planning, our approach dynamically adjusts stiffness, damping, and diffusion parameters in real time during multi-phase manipulations, effectively imitating human-like force-deformation modulation. Experiments on single arm/bimanual robots in three tasks show better force fields patterns and thus $10\%-20\%$ higher success rates versus conventional methods, enabling precise, safe interactions for industrial, medical or household scenarios.

Under the demands of intelligent manufacturing and high-precision operations, robotic arm trajectory planning must ensure both motion smoothness and obstacle avoidance. Although search-based methods can quickly generate feasible paths, their trajectory smoothness is relatively low. In contrast, improved methods based on Bezier curves and optimization frameworks enhance smoothness, but often struggle with obstacle avoidance or computational inefficiency in complex environments. To address these issues, we propose a novel trajectory planning framework based on Velocity Adjustment Planning (VAP) in safe corridors. First, a collision-free corridor with geometric constraints is constructed by connecting key path points. Then, VAP introduces a dynamic modulation matrix to shape the velocity field in ellipsoidal regions, to enforce reachability constraints between arbitrary start and end. The global trajectory is constructed by progressively stitching together position and velocity segments. Simulation and real-world experiments are conducted on a 6-DOF RealMan robotic arm. The results demonstrate that the proposed method generates smooth and collision-free trajectories with strong robustness in complex environments.

Achieving human-like dexterity in robotic manipulation requires seamless integration of tactile perception and adaptive control strategies. This work presents a novel control framework integrating data-driven slip detection with constrained force optimization for four-fingered robotic manipulation using the Paxini Dexhand platform. We develop a comprehensive force balance constraint algorithm employing friction cone projections and optimization objective V(f) for real-time grasp force regulation. A compact slip detection network utilizing dual-domain feature extraction and channel attention mechanisms achieves high-precision slip identification, providing reliable tactile feedback for force control. The systematic integration strategy translates slip detection results into concrete force regulation actions, realizing a closed-loop system from reactive detection to proactive control. Experimental validation demonstrates detection latencies under 30ms with slip detection accuracy exceeding 95%, adaptively adjusting contact forces in real-time during dynamic manipulation tasks, reducing average gripping forces by 35-45% compared to conservative approaches while handling objects from delicate items to heavy industrial components.

Active Simultaneous Localization and Mapping (Active SLAM) involves the strategic planning and precise control of a robotic system's movement in order to construct a highly accurate and comprehensive representation of its surrounding environment, which has garnered significant attention within the research community. While the current methods demonstrate efficacy in small and controlled settings, they face challenges when applied to large-scale and diverse environments, marked by extended periods of exploration and suboptimal paths of discovery. In this paper, we propose MA-SLAM, a Map-Aware Active SLAM system based on Deep Reinforcement Learning (DRL), designed to address the challenge of efficient exploration in large-scale environments. In pursuit of this objective, we put forward a novel structured map representation. By discretizing the spatial data and integrating the boundary points and the historical trajectory, the structured map succinctly and effectively encapsulates the visited regions, thereby serving as input for the deep reinforcement learning based decision module. Instead of sequentially predicting the next action step within the decision module, we have implemented an advanced global planner to optimize the exploration path by leveraging long-range target points. We conducted experiments in three simulation environments and deployed in a real unmanned ground vehicle (UGV), the results demonstrate that our approach significantly reduces both the duration and distance of exploration compared with state-of-the-art methods.

A fixed-time tracking performance control strategy is devised for stochastic nonlinear systems with tracking error constraints to come true the tracking control under the uncertain desired trajectory. Primarily, bonding the Fourier series and the radial basis function neural network (RBFNN), the unknown ideal tracking target is reconstructed, and the controller is designed on this basis. Subsequently, a modified error transformation technique with the fixed-time performance function (FPF) is formulated to dispose of the tracking error constraint. Then, by introducing an improved first-order filter, the problem of differential explosion is avoided. Moreover, the Lyapunov stability theorem proves that the whole system states remain bounded and the tracking error is kept in the preset limits in fixed time despite of the ideal trajectory is unknown. Definitively, the simulation example is designed to confirm the feasibility.

Dual-arm manipulation has gained significant attention in robotics, yet the safety of such systems remains an underexplored area of research. A key challenge is ensuring collisionfree operation while preserving effective coordination between the two arms. To address this challenge, we propose a novel framework for safe and efficient trajectory planning in dual-arm manipulation. The framework decomposes the dual-arm collaboration task into a low-dimensional quadratic programming (QP) problem. By solving this QP problem, the system ensures both physical constraints and collision avoidance, enabling successful dual-arm coordination. The proposed approach is evaluated through extensive simulations and physical experiments, demonstrating its robustness, safety, and effectiveness in real-world scenarios.

Effective vehicle control contributes to the safety and efficiency of connected automated vehicles (CAVs). Many existing solutions do not consider the maximum effective communication distance and bearing angle constraints between vehicles. This article proposes a novel prescribed performance method to handle distance and angle constraints to achieve vehicle stability under deception attacks. A key aspect is that the above two constraints are successfully transformed from inequality-constrained form to equivalent equation unconstrained form through introducing error transformations, and we prove that the errors of distance and angle are strictly contained within the boundary of the performance function. Another key aspect is to use adaptive bias radial basis function neural network (RBFNN) to approximate unknown nonlinear functions and deception attacks in the system and integrate the approximated results into recursive construction to design adaptive laws and multilane merging control laws. Analysis shows that all signals in a closed-loop system are practical prescribed time stable. Simulations validate that our control method has a faster convergence time than the existing advanced two-dimensional (2-D) vehicle approach and can adaptively adjust convergence to predefined sets under different attack intensities.

This paper studies the leader-following consensus problem of nonlinear multi-agent systems (MAS) with one-sided Lipschitz conditions under the directed graph, and a novel observer-based distributed tracking protocol that only utilizes the relative output information of adjacent agents is proposed.To avoid the disadvantage of solving Laplace matrix eigenvalues, a fully distributed adaptive consensus scheme is constructed.The sufficient conditions for linear matrix inequalities (LMIs) for the consensus of MAS systems are derived using the Lyapunov function and the properties of one-sided Lipschitz nonlinear systems.Finally, the effectiveness of the proposed control method can be verified by two examples-based simulations with a leader and five followers.

In complex map environments, the Rapidlyexploring Random Tree (RRT) algorithm is recognized as an efficient initial path planning method for unmanned aerial vehicles (UAVs). Relying solely on centralized computation or planning capabilities of a single node often faces challenges such as limited device resources, vulnerability of navigation points to interception, and insufficient real-time adaptability. This paper proposes a distributed path optimization framework based on federated learning (FL) and edge computing (EC), referred to as DPOFEC, which formulates the path optimization problem as a global optimization task within the framework of FL. First, edge nodes of the server execute initial path planning using the RRT algorithm to generate local path segments. Then, the FL framework aggregates the weights uploaded by each edge node and optimizes the path points. Finally, the enclosed and safe sphereshaped corridors are designed around the optimized global path points, with the size of these corridors dynamically adjusted to accommodate obstacle distributions and the UAV's flight state. Experiments demonstrate that in a simple scenario (Case 1), the proposed method improves the path generation processing time by approximately 38 % and 43 %, compared to the traditional RRT algorithm attempts 1 and 2, respectively. In a complex scenario (Case 2), the improvements are approximately 51 % and 40 %, respectively. Leveraging distributed collaboration, the algorithm enhances the performance and robustness of path planning while effectively protecting the privacy of path-point data.

This paper investigates the adaptive finite-time containment control problem for a class of nonlinear multiagent systems (MASs) under false data injection (FDI) attacks. On account of the fact that instability is inevitable when the false datas are injected into the researched system, the backstepping technique based on the modified coordinate transformation is applied to eliminate the impact of the FDI attacks. Furthermore, the "dynamic surface control"(DSC) approach is utilized to overcome the issue of "explosion of complexity" caused by repeatedly taking derivatives for the virtual control laws. Then, by designing an observer to refactor the immeasurable states of the MASs, a finite-time adaptive output-feedback tracking containment controller is constructed. It is shown that all the signals in the closed-loop systems are semi-globally practical finite-time stability(SGPFS), and the outputs of all the followers converge to the convex hull spanned by the multiple leaders's outputs. Besides, the observer errors and the containment errors can converge to a small neighborhood of the origin in finite time. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed containment control protocol. Note to Practitioners-The containment control problem is a hot topic in the field of control, which plays an important role in practical engineering. Especially for this problem of nonlinear MASs, the mathematical models are difficult to be obtained accurately. This paper investigates the adaptive finite-time containment control problem for the nonlinear MASs, whose model can be extended to more complex engineering applications, such as UAV formations and intelligent traffic management. It is worth noting that network security and unmeasured state problems often exist in practical applications. Therefore, by designing an observer to refactor the immeasurable states of the MASs, it is ensured that all the signals in the closed-loop systems are SGPFS. To sum up, the paper proposes an adaptive finite-time containment control strategy, which contributes to the development of containment control for MASs in practical applications.

Most feature-based stereo visual odometry (SVO) approaches estimate the motion of mobile robots by matching and tracking point features along a sequence of stereo images. However, in dynamic scenes mainly comprising moving pedestrians, vehicles, etc., there are insufficient robust static point features to enable accurate motion estimation, causing failures when reconstructing robotic motion. In this paper, we proposed DynPL-SVO, a complete dynamic SVO method that integrated united cost functions containing information between matched point features and re-projection errors perpendicular and parallel to the direction of the line features. Additionally, we introduced a \textit{dynamic} \textit{grid} algorithm to enhance its performance in dynamic scenes. The stereo camera motion was estimated through Levenberg-Marquard minimization of the re-projection errors of both point and line features. Comprehensive experimental results on KITTI and EuRoC MAV datasets showed that accuracy of the DynPL-SVO was improved by over 20\% on average compared to other state-of-the-art SVO systems, especially in dynamic scenes.

In this article, we analyzed the principle of the air- induced passive intermodulation (PIM) signals generated by passive devices in communication systems and modeled the air-induced PIM. Air-induced PIM is the interference caused by nonlinear transformations of the transmitted signals and can severely degrade the quality of the received signals. Therefore, in this article, we designed a neural network model, CNN-LSTM-FIR, to model and compensate for the air-induced PIM signals generated by the system. Firstly, we follow the actual physics process generated by air-induced PIM, and add the leading and lagging terms of the signal as inputs. The memory of the signal is extracted by using the CNN layer, and the nonlinearity of the signal is extracted by the nonlinear activation function. In addition, the fully connected layer is used to distribute the weight of the signal, and then the deeper memory of the signal is extracted through the LSTM layer, and finally the signal is effectively sorted out and output through the FIR filter layer. Experiments show that the above model has excellent modeling and cancellation capabilities, and achieves a cancellation performance of more than 18dB.

In this paper, an adaptive control tactic is designed for a class of nonlinear systems with nonconvex parameterization by using the backstepping method. During the backstepping control process, an improved nonlinear filter with a compensation term is introduced to deal with the repetitive differentiation problem of the designed virtual controllers, which removes the boundary layer error simultaneously. The nonconvex pa-rameterized terms are discussed in light of the extensions of explicit realizability assumptions. Under the constructed control algorithms, the bound ness of all signals in the closed-loop system is proved, and the tracking error can reach 0 as time approaches to infinity. Finally, the effectiveness of the proposed method is demonstrated through a simulation example.

Small object detection in aerial images is a challenge in remote sensing. Recently, convolutional neural networks (CNNs) have succeeded by learning localized filters that embed relative spatial information but fail to detect the small objects in the aerial images for the uneven padding. Even though the padding mechanism in CNNs allows for the capture of absolute position information and ensures consistent input–output resolution, it leads to a diminishing extraction of absolute position context from the edges to the center. This results in asymmetry bias, which negatively impacts position-dependent visual tasks like small object detection, causing blind spots and misdetection. In this study, we uncover that complex-valued CNNs, capable of explicitly encoding absolute position information, can significantly enhance conventional object detection techniques. To accomplish this, we introduce the position information encoding feature pyramid network (PieFPN), which consists of a complex-valued encoder and a real-valued decoder for explicit position information encoding. Additionally, we present the general Gaussian normalization and Gaussian error linear unit for multi-variables, incorporating them into end-to-end training schemes. To utilize the ImageNet pre-trained weights, we merge PieF with traditional feature pyramid networks, allowing for seamless integration into existing object detection pipelines. Our complex-valued designs outperform their real-valued counterparts, achieving state-of-the-art results on the DOTA-v2.0 oriented object detection in aerial images dataset.

This paper proposes a novel hierarchical methodology to planning safe UAV trajectories in complex environments. We start by improving a canonical hybrid A* in relation to high memory requirements, performance degradation, and the low efficiency customarily observed in the initial global trajectory suggested by the planner. Then, the Marden theorem is applied -for the first time in local path planning -to generate continuous, non-intersecting, enclosed, and safe flight corridors, termed homotopic enclosed safe motion corridors (HESMCs) hereafter. This is efficiently realized through a series of unique ellipsoids along the initial route. Meanwhile, the optimized motion trajectory along the corridors is built by considering two waypoints and prescribed performance functions. The resolved path is safe and complete, with a comprehensive Lyapunov stability analysis included to ensure accurate and efficient trajectory tracking. The simulation and physical tests demonstrate the superiority of our proposed planner over existing state-of-the-art methods, with consistent and significant improvements in processing time and guaranteed completeness. Note to Practitioners-The authors perceived the contribution of the manuscript of particular relevance to users of UAVs seeking advanced safety in their guidance and navigational solutions, offering a blend of theoretical innovation and practical applicability. The work introduces a distinct hierarchical motion planner specifically designed to enhance safety and reliability in UAV navigation. Key to this is the development of an improved hybrid A* algorithm for global planning, which effectively tackles practical issues such as high memory consumption and performance degradation. A significant theoretical contribution is the application of the Marden theorem in local optimization. This facilitates the generation of homotopic enclosed motion corridors using unique safe boundary ellipsoids, thus reducing navigation complexity and the risk of failure during task execution. Additionally, the proposed scheme emphasizes the generation of motion trajectories considering position errors and prescribed performance functions, supplemented by a thorough Lyapunov stability analysis. Looking ahead, we aim to extend the proposed scheme in the context of UAV swarms for more efficient navigation in complex environments.

The problem of integral sliding mode control with arbitrary convergence time for nonlinear uncertain systems with matched disturbances is studied. In this paper, the prescribed performance arbitrary convergence time integral sliding mode controller based on the super twisting disturbance observer is designed to overcome the overshoot problem of the system state response curve at the prescribed time. Compared with the existing similar results, this paper introduces the super twisting disturbance observer into the integral sliding mode controller, which can effectively compensate the matching disturbance while avoiding the high-frequency chattering problem and ensure the free-will arbitrary time stable (FATS) of the closed-loop system. In addition, this paper uses the prescribed performance control (PPC) technology to obtain better transient performance. Finally, the effectiveness of the proposed control method is verified through MATLAB simulations.

With the rapid rise of smart devices, such as smartphones and smart home devices, intelligent routing through Software Defined Network(SDN) has become a research focus. Recently, the concept of Knowledge-Defined Networking (KDN) has been proposed. In this paper, we propose Knowledge-Defined Networking oriented SDN multi-domain routing, and a DDQN routing algorithm based on graph neural network at the knowledge layer. The communication of SDN multi-domain architecture for Knowledge-Defined Networking and intelligent routing decision are realized. The network performance such as delay jitter, packet loss rate and throughput is improved compared to existing approaches in simulation.