Search Results (141)

This paper investigates a formation tracking problem for underactuated surface vessels (USVs) subject to collision avoidance and input saturation constraints. Many existing APF-based formation-control approaches formulate collision avoidance using a single reference point or an inter-center distance, which may provide insufficient geometric information during close-range maneuvers. To improve navigation safety, an elliptical disk-based collision avoidance mechanism is developed by introducing safety points at the bow, stern, port, and starboard sides of each USV, such that multiple characteristic-point distance constraints can be simultaneously enforced. To address unknown nonlinearities caused by model uncertainties and external disturbances, a neural network-based observer is designed to estimate unavailable velocity states and lumped disturbances. Distributed control laws are synthesized by integrating artificial potential functions (APFs), the observer, and a backstepping technique. Additional controllers are introduced to address the input saturation and underactuated issues while preserving the collision avoidance capability. Stability of the closed-loop system is rigorously established via Lyapunov theory. Simulation results demonstrate that the proposed approach achieves safer close-range maneuvering performance compared with conventional single-point methods. Full article

This paper presents an interval-observer-based actuator fault detection and isolation (FDI) method for underactuated autonomous underwater vehicles (AUVs) under bounded hydrodynamic uncertainty and time-varying ocean currents. A locally frozen linear time-invariant (LTI) representation enables deterministic set-membership analysis, and the robust adaptive propagated interval observer (RAPIO) propagates admissible center–radius state bounds within a Lyapunov framework. Adaptivity is introduced through a reinforcement learning (RL)-augmented uncertainty-bound modulation mechanism, where an offline-trained agent scales a nonnegative channel-wise slack term without modifying the scheduled observer-gain rule or the nominal center predictor. Under the stated observer and disturbance-envelope conditions, positivity, stability, and diagnostic-channel inclusion hold for any bounded learning signal. Actuator loss-of-effectiveness (LoE) faults are represented through the actuator-effectiveness channel and detected through interval-consistency violations, enabling axis-wise isolation of surge, yaw-rate, and pitch-rate actuator faults. The same schedule-blind decision layer is additionally evaluated with structurally distinct additive-bias and stuck/jam actuator models. All stuck/jam events are detected, and bias-magnitude sweeps identify channel-wise 100%-detection boundaries with zero false alarms. A structured 72-case scenario sweep shows reliable detection, strong false-alarm rejection, and acceptable detection delays compared with benchmark observers. Full article

Conventional hip exoskeletons typically employ multiple actuators to provide effective assistance to the corresponding joint, leading to an increase in the weight of the exoskeleton. Underactuated designs reduce the number of actuators, thereby lowering system weight and cost. However, existing single-motor underactuated hip exoskeletons still face challenges in achieving precise assistance and accommodating non-walking movements such as free sitting. In this work, we propose an underactuated hip exoskeleton with a series elastic actuator (SEA) and two independent cables for walking assistance. The incorporation of the SEA contributes to system safety and precise assistive force control. Furthermore, the proposed differential cable structure enables free sitting movement and allows for non-strictly symmetric hip motion. In experiments, with a target assistive force of 300 N, the proposed actuator achieves a peak force-tracking accuracy of 98.01% in walking tests, and the hip exoskeleton reduces peak muscle activation by up to 23.62% during walking. Full article

This paper presents a positioning control method for underactuated unmanned surface vehicles (USVs) subject to environmental disturbances and wave-contaminated measurements. In underactuated dynamic positioning, surge motion and yaw motion can be directly regulated by the propulsion system, whereas sway motion cannot be directly controlled by an independent lateral thrust. Therefore, the lateral environmental-force component is utilized to induce the vehicle’s dynamic response to sway. To achieve this objective, a position-keeping guidance system taking into account the desired heading and the lateral positioning error is introduced in this paper. With this guidance mechanism, the lateral environmental-force component can drive the USV to reduce the cross-track error, thereby enabling underactuated positioning. A rotated coordinate system is established around the desired position, and the positioning error is decomposed into along-track and cross-track components. To improve the transient response, an error-rate feedback term is introduced into the rotated-angle update law for yaw-heading guidance design, which enhances the damping of the cross-track dynamics. Meanwhile, a wave-filtering observer is designed to make low-frequency position and velocity estimates for feedback control. Simulation results under multiple operating conditions show that the proposed observer reduces the amplitude and high-frequency variation of the control signals compared with the existing wave-filtering observer, and the proposed positioning control method achieves smaller positioning errors than the existing nonlinear positioning control (NPC). The comparative results also indicate that the proposed method is suitable for position keeping under constant or slowly varying environmental loads, moderate model uncertainty, and wave-contaminated measurements, whereas rapidly varying load directions may degrade the positioning accuracy. Full article

Precision grasping is a fundamental capability for dexterous robotic manipulation, enabling robots to handle small objects, perform delicate tasks, and interact safely with complex environments. However, achieving stable and accurate fingertip control remains challenging due to mechanical complexity, actuation limitations, and sensing constraints. This paper presents a comprehensive review of robotic hand designs from the perspective of precision grasping. The review analyzes key aspects of mechanical architecture, including finger kinematic structures, actuation and transmission systems, structural materials and fabrication methods, mechanical intelligence, and control-oriented mechanical design. Different design strategies such as fully actuated fingers, underactuated mechanisms, tendon-driven systems, linkage-based architectures, and soft robotic structures are compared in terms of dexterity, adaptability, accuracy, and system complexity. The analysis highlights several important trends, including the transition toward compliant and bio-inspired mechanisms, the integration of lightweight materials and additive manufacturing, and the increasing role of sensor–structure integration for precise force and position control. Despite significant progress, challenges such as friction, hysteresis, transmission compliance, and limited integration space still affect grasping accuracy and reliability. Based on the reviewed literature, future research should focus on hybrid actuation strategies, bio-inspired structural design, graded material architectures with structurally integrated sensing, and modular platforms to improve precision manipulation and system robustness in next-generation robotic hands. Full article

Bio-inspired serpentine robots show strong potential for operation in unstructured environments, yet existing systems often lack reliable modular docking, adaptive grasping, and an effective balance between structural stiffness and motion dexterity. This study proposes a Modular Omnidirectional Serpentine Robot (MOSR) that integrates a DNA-inspired tendon-driven helical continuum unit, an underactuated omnidirectional spherical docking gripper, and adaptive gripper fingers within a single module. The helical continuum unit provides two-degree-of-freedom compliant bending while improving axial stiffness through interleaved helices and a central constraint structure. The spherical docking gripper adopts a linkage-spring-slider underactuated mechanism to accommodate effective-diameter variations and support stable one-to-one and one-to-many docking. Gripper kinematics are modeled using an improved Denavit–Hartenberg method, and the workspace is verified by MATLAB simulation. Equivalent torsional and bending stiffness models are established for the helical continuum unit and validated by finite element analysis, with mean relative errors of 11.57% and 17.95%, respectively. Docking-angle analysis based on the receiver polar angle (θ_rec) and engager azimuth angle (θ_eng) shows that 61.1% of the receiver surface lies within the feasible docking region at an opening distance of 5.7 mm. A 3D-printed Polyamide 1010 prototype achieves a locomotion speed of 15.3 mm/s on grass and demonstrates terrain traversal, planar steering, obstacle crossing, adaptive grasping, and stable straight and oblique docking. These results verify the feasibility of integrating locomotion, grasping, and modular reconfiguration within a single serpentine robot module. Full article

With the rapid development of offshore engineering, marine cranes are widely deployed in critical maritime operations, such as the precision installation of wind turbine blades. However, their highly coupled three-dimensional spatial dynamics, uncertain payload mass, and susceptibility to severe external sea wave disturbances pose significant challenges in achieving fast, accurate payload transportation and rapid anti-swing performance. To address these issues, this paper proposes a novel energy-based intelligent coupling control strategy utilizing fuzzy logic and adaptive gravity compensation for 5-DOF (5-Degrees of Freedom) marine cranes. Firstly, to handle the severe underactuation and facilitate natural energy dissipation, a novel set of error coupling variables is constructed, organically linking the actuated crane structure with the unactuated payload swing dynamics. Then, an adaptive gravity compensation mechanism is designed to dynamically estimate the uncertain payload mass in real time, eliminating the need for precise prior mathematical models. Subsequently, to counteract complex external environmental disturbances and unmodeled internal dynamics, a targeted fuzzy observer is developed based on the universal approximation theorem, providing robust, real-time perturbation compensation. The sufficient conditions for the asymptotic stability of the closed-loop system are rigorously proven based on the Lyapunov method and LaSalle’s invariance principle. Finally, extensive comparative simulations are conducted, demonstrating that the proposed method significantly improves the operational accuracy, anti-swing capability, and safety of marine cranes under varying load conditions and persistent wave disturbances. Full article

Underactuated and strongly coupled Quadrotor Unmanned Aerial Vehicle (QUAV) systems often face challenges in formation control due to actuator failures, external unknown disturbances, and limited communication resources. To address these issues, this paper proposes a periodic adaptive event-triggered fixed-time fault-tolerant control method based on a disturbance observer. First, a dynamic estimation and compensation scheme for actuator faults is developed by combining boundary layer theory with adaptive control techniques. Next, a fixed-time disturbance observer is designed to accurately estimate and compensate for external unknown disturbances. Furthermore, considering the communication burden imposed by real-time position updates, a Non-Monitoring Periodic Adaptive Event-Triggered Control (NM-PAETC) mechanism is proposed to reduce communication resource consumption, while ensuring that the formation system maintains the desired attitude angles under the influence of actuator faults and external disturbances. The proposed method enables fixed-time formation control under limited communication resources, and the system’s convergence time is independent of the initial state. Simulation results validate the effectiveness of the proposed method. Full article

This paper proposes a dual-loop layered control mechanism for the dynamic trajectory tracking of non-holonomic unmanned ground vehicles (UGVs). The proposed scheme enhances steady-state precision while guaranteeing parameter convergence under specified trajectory constraints. To tackle the underactuated constraints of Unmanned Ground Vehicles, the control mechanism is structured into kinematic and dynamic loops. Specifically, a kinematic controller is first synthesized to serve as a virtual control law, generating desired velocity commands. Subsequently, a layered adaptive control strategy based on the Immersion and Invariance technique is developed for the dynamic loop. This strategy integrates a parameter estimation layer, which utilizes tailored tuning functions to ensure the exponential convergence of estimation errors under the condition that the reference trajectory is not persistently vertical. A controller design layer is then responsible for uncertainty compensation. By decoupling parameter adaptation from control law synthesis, the proposed mechanism circumvents the structural limitations of the certainty equivalence principle. Theoretical analysis confirms that the proposed design achieves almost-global asymptotic tracking. Simulation results demonstrate that the mechanism resolves the imprecise parameter convergence inherent in traditional adaptive schemes, eliminates steady-state pose fluctuations during time-varying trajectory tracking, and achieves asymptotic convergence of tracking errors. Full article

To improve the path-following performance of an underactuated autonomous underwater vehicle (AUV) under varying path geometries and desired velocities, this study proposes a direct X-rudder control method based on Task-Informed Inductive-Bias Conservative Soft Actor–Critic (TIB-CSAC). The proposed method directly learns the X-rudder control policy from the path-following information of the current and subsequent path segments in a data-driven way, thereby avoiding the complex design and manual tuning of guidance laws and attitude controllers for rudder command generation. To support such two-segment policy learning, a task-informed inductive-bias encoder is proposed to construct structured and conditioned state representations, thereby improving sample efficiency and overall training quality. In addition, given the long-tail characteristics of task difficulty in agent training, a multi-head conservative value evaluation mechanism is incorporated to mitigate return drawdowns induced by challenging tasks in the tail stage of training and to enhance tail-stage convergence stability. The path-following performance is validated in three representative scenarios with different path pitch, path heading variations, and desired surge velocity conditions. The results show that, compared with the baseline soft actor–critic (SAC) method, TIB-CSAC improves multiple vertical and horizontal error metrics, including maximum absolute error, mean absolute error, tail error, and error threshold exceedance ratio. These results indicate that TIB-CSAC not only improves overall adherence to the reference path, but also more effectively suppresses extreme errors and tail errors, thereby demonstrating stronger path-following robustness and reliability. Full article

The problem of path following control for underactuated Unmanned Surface Vehicles (USVs) is tackled in this work, and a scheme based on Predicted Adaptive Line-of-Sight (PALOS) is put forward. At the guidance level, prediction techniques and adaptive mechanisms are incorporated to eliminate the inherent assumption of small sideslip angle in the conventional LOS methods, enabling online estimation and dynamic feedforward compensation of time-varying sideslip angles. On the control side, radial basis function neural networks are combined with virtual parameter learning techniques to achieve online approximation of the lumped uncertainties, which include modeling inaccuracies and external disturbances. An adaptive control scheme based on lifelong learning mechanisms is developed, wherein the historical knowledge is constructed and preserved through feedback terms to achieve knowledge retention and on-demand reuse, thereby enhancing control efficiency and mitigating catastrophic forgetting. Additionally, a self-triggered mechanism acts as a knowledge transfer instrument, reducing communication overhead, relaxing transmission conditions, and rigorously precluding Zeno behavior. Through theoretical derivations, one can prove that all closed-loop signals are uniformly ultimately bounded. Comprehensive numerical simulations based on the 1:70 CyberShip II scale-model ship dynamics under complex sea conditions verify the proposed approach to be both effective and practical. Full article

Overhead cranes are underactuated systems with significant model uncertainties that pose major challenges for precise anti-swing control. These uncertainties, including unknown parameters and varying dynamics, severely limit the performance of conventional controllers. To address the control challenge of 7-degree-of-freedom (7-DOF) overhead cranes with variable cable length and double spherical pendulum dynamics, this paper proposes an adaptive sliding mode control method integrated with time-delay estimation. First, a comprehensive dynamic model that accounts for bridge movement, trolley travel, hoisting motion, and spherical swings of both the hook and the payload is established. Then, a sliding surface is constructed based on the coupling analysis between actuated and unactuated dynamics. The core innovation lies in the integration of time-delay estimation with adaptive sliding mode control, where the time-delay estimator provides accurate approximation of unknown system dynamics, while the adaptive mechanism compensates for estimation errors and parameter variations. This dual approach ensures robust performance despite model inaccuracies. Lyapunov stability analysis rigorously confirms the uniform ultimate boundedness of all closed-loop signals under model uncertainties. Experimental tests further show that the designed controller achieves accurate positioning and robust swing suppression, outperforming conventional controllers in challenging working conditions. Full article

To address the actuator fault problem faced by underactuated surface vessels (USVs), this study develops an active fault-tolerant control scheme based on finite-time output feedback. First, a finite-time neural terminal homogeneous state observer with a portional-integral structure is established. High-precision pose reconstruction enables finite-time synchronous reconstruction of unmeasured states. This allows unknown nonlinearities to be explicitly expressed online and incorporated into the compensation channel, significantly reducing the sensitivity of modeling errors to control performance. A neural damping mechanism is used to structurally reconstruct uncertain dynamics and loss-of-effectiveness (LOE) fault factors within the system, thereby constructing an online approximator to achieve real-time identification and compensation of composite uncertainties. This integrates the unknown nonlinearities and fault effects of the original system into an online-updatable estimation channel. Adopting a backstepping-based design methodology, a finite-time hybrid event-triggered control (ETC) architecture is further constructed. By introducing an event-triggered update mechanism at the control layer, the real-time continuous control signal is transformed into a discrete update. Based on Lyapunov stability theory, a comprehensive analysis is carried out to verify the stability of the proposed control scheme. Numerical simulations are finally carried out to validate the effectiveness of the scheme. Simulation results show that the tracking error is reduced by about $93\%$ and $60\%$ compared to the comparison scheme. Full article

Foldable quadcopters represent a new frontier in aerial robotics technology. The ability of a foldable quadcopter to reconfigure its geometry in flight and adapt to various flight scenarios enhances agility, maneuverability, aerodynamic efficiency, and mission versatility compared to a traditional quadcopter. However, the morphing function introduces significant variations in parameters such as center of gravity, inertia, and nonlinear dynamics, in addition to inherent underactuation, coupling dynamics, and external disturbances. Thus, the folding mechanism presents significant challenges to conventional control approaches. To solve the drawbacks of the conventional control approach, nonlinear control methods have been investigated. This article proposed the development of a prescribed performance-adaptive sliding-mode control for a foldable quadcopter UAV. It models the morphing quadcopter as a rigid body system with five morphing formations (X, Y, H, O, and T). The prescribed performance sliding mode control approach systematically addresses the time-varying parameter and aerodynamic properties impact resulting from the morphing formation. Using Lyapunov theory, a sliding mode controller is designed that ensures the error evolution remains within prescribed performance bounds, maintains closed-loop stability, and tracks the trajectory under uncertainties. The effectiveness of the proposed control algorithm is evaluated and benchmarked in structured and unstructured trajectories against conventional nonlinear sliding mode control (SMC), PID, and LQR control methods. The simulation results indicate that the prescribed performance adaptive SMC achieves better performance and improved robustness compared to benchmarked control methods. The simulation results demonstrated that the adaptive control approach is a viable and effective solution for managing the complex dynamics of foldable quadcopters UAV. Full article

This paper focuses on the dynamics modeling and control methods for an underactuated Dual-Unmanned Aerial Helicopter Slung Load System (DUH-SLS), which consists of two Unmanned Aerial Helicopters (UAHs) connected to the suspended load via two sling cables. The DUH-SLS is a multi-body coupled system with internal ideal constraint forces and has seven motion degrees of freedom (DOFs) in the longitudinal plane. In this paper, a set of independent and complete generalized coordinates is selected to describe the system’s motion. The dynamics model of DUH-SLS is established using Lagrange analytical mechanics. This approach, which avoids system internal forces, greatly improves modeling efficiency. Finally, the correctness of this dynamics model is validated using a virtual prototype of the DUH-SLS developed in the multi-body dynamics simulation software ADAMS. The DUH-SLS is a complex nonlinear controlled object, and the iterative Linear Quadratic Regulator (iLQR) method is introduced to design an integrated optimal controller to achieve trajectory tracking and swing suppression for the DUH-SLS. This method transforms the quadratic optimal control problem of nonlinear systems into a series of linear quadratic optimal control (LQR) problems through iterative optimization in function space, thus obtaining an optimal solution. The iLQR optimal controller requires offline iterative computation, but the optimal control obtained has a state feedback closed-loop form, which ensures robustness during online control. Numerical simulation results demonstrate that the proposed iLQR optimal controller exhibits excellent control performance in complex multi-task scenarios. Particularly in trajectory tracking tasks, the maximum average position tracking error of the iLQR controller is only 0.14 m, compared to 3.57 m and 3.11 m for the LQR and LMC (Lyapunov Method Controller) controllers, respectively. Furthermore, the controller demonstrates strong robustness against internal parameter perturbations and external complex wind disturbances, fully validating the effectiveness and superiority of the proposed approach. Full article