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17 pages, 6541 KB

Open AccessArticle

Active-Assistive Control Based on Dynamic Moving Window for Trajectory Tracking of an Upper Limb Exoskeleton in Assisted Rehabilitation

by Yuseop Sim, Jaehwan Kong, Seong-Sig Choi and Hak Yi

Sensors 2026, 26(7), 2160; https://doi.org/10.3390/s26072160 - 31 Mar 2026

Viewed by 501

Abstract

Rehabilitation robotics faces the challenges of aligning engineering design with patient-specific needs. Most existing controllers in rehabilitation robots often constrain motion to fixed paths or provide only passive guidance, limiting user engagement and adaptability. This study proposes a novel active-assistive mode controller that [...] Read more.

Rehabilitation robotics faces the challenges of aligning engineering design with patient-specific needs. Most existing controllers in rehabilitation robots often constrain motion to fixed paths or provide only passive guidance, limiting user engagement and adaptability. This study proposes a novel active-assistive mode controller that integrates a virtual tunnel-based force generation mechanism with a dynamic moving-window technique for tracking activities of daily living (ADL) trajectories. Unlike conventional impedance controllers, the proposed method dynamically adjusts the virtual tunnel in real time, permitting voluntary upper-limb movement within a safe operational range while preventing excessive deviation. The system was implemented on a wearable two-degree-of-freedom (DOF) upper-limb exoskeleton equipped with drive and integrated sensor units. Experimental results demonstrated that decreasing the guidance force (

F_{g f}

) increased tracking errors, from

1 °

at 100%

F_{g f}

to

5 °

at 30%

F_{g f}

, indicating greater voluntary participant motion. Peak actuator torques correspondingly decreased from 14.75 to 13.43 Nm (elbow) and from 4.14 to 2.48 Nm (wrist), confirming the controller’s capability to modulate robotic assistance according to user effort. Tests with 30 healthy participants demonstrated the effectiveness of guidance along predefined ADL trajectories, validating the controller’s potential for patient-centered rehabilitation. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

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14 pages, 6979 KB

Open AccessArticle

Compact Dual-Quaternion-Based Visual Servoing for Perpendicular Alignment with Surface Normal Constraints

by Sheng Li, Chao Ye, Chenlu Liu and Weiyang Lin

Sensors 2026, 26(6), 1889; https://doi.org/10.3390/s26061889 - 17 Mar 2026

Viewed by 365

Abstract

The ability to reliably press physical buttons is a common requirement in robotics. Conventional vision-based methods often suffer from positional errors during execution if the end-effector’s approach is not perpendicular to the target surface. This paper proposes a novel dual-quaternion-based visual servoing method [...] Read more.

The ability to reliably press physical buttons is a common requirement in robotics. Conventional vision-based methods often suffer from positional errors during execution if the end-effector’s approach is not perpendicular to the target surface. This paper proposes a novel dual-quaternion-based visual servoing method that enables robots to reach desired poses and enhances accuracy in robotic button-pressing. Our method acquires target pose information (position, depth and surface normal direction) from the RGB-D camera and converts it into dual quaternion representation to construct the visual servoing control system. The image Jacobian matrix for the dual quaternion pose is then computed. The dual-quaternion-based visual servoing ensures that the pressing direction and the optical axis of the coaxially mounted camera remain perpendicular throughout the pressing motion, thereby eliminating misalignment between the actual contact point and the visually identified target. By representing spatial displacements in SE(3) with dual quaternions, our method enables more compact, concise, and efficient pose representation and computation throughout the visual servoing process. Experimental results demonstrate that, compared to conventional methods, our technique achieves more efficient visual servoing control, significantly improving both positioning accuracy and computational efficiency. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

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21 pages, 1631 KB

Open AccessArticle

Predefined-Time Super-Twisting Sliding Mode Control for Construction Robot with Arbitrary Initial Values

by Hong-Bo Ai, Xin-Rong He and Chun-Wu Yin

Sensors 2026, 26(5), 1654; https://doi.org/10.3390/s26051654 - 5 Mar 2026

Viewed by 447

Abstract

To tackle the practical engineering challenge that construction robots are required to track the reference trajectory completely and precisely, this study puts forward a control scheme based on the extended reference trajectory and develops a novel super-twisting sliding mode controller with predefined-time convergence [...] Read more.

To tackle the practical engineering challenge that construction robots are required to track the reference trajectory completely and precisely, this study puts forward a control scheme based on the extended reference trajectory and develops a novel super-twisting sliding mode controller with predefined-time convergence capability. First, the influence mechanism of fluid materials on construction robots and their trajectory tracking control features are explored, and the design approach for the extended reference trajectory is elaborated. Subsequently, a nonsingular sliding surface with predefined-time convergence is constructed, and a RBF neural network with convergent weight vectors is established to approximate the composite disturbances existing in the robot system. On the basis of the proposed predefined-time convergent super-twisting control theory, a super-twisting sliding mode controller tailored for construction robots is devised, and the predefined-time convergence performance of the closed-loop system is theoretically validated. Numerical simulation results indicate that the proposed algorithm can guarantee that the construction robot’s angles move accurately along the actual reference trajectory, with the angular tracking error achieving a precision of 3 × 10⁻⁶ rad, thereby confirming the feasibility and effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

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22 pages, 2447 KB

Open AccessArticle

Adaptive Predefined-Time Tracking Control for Robotic Manipulator Based on Actor-Critic Reinforcement Learning

by Yong Qin, Yuan Sun, Jun Huang and Yankai Li

Sensors 2026, 26(5), 1529; https://doi.org/10.3390/s26051529 - 28 Feb 2026

Cited by 2 | Viewed by 466

Abstract

This paper proposes a novel predefined-time adaptive neural tracking control method for uncertain manipulator systems based on Actor-Critic reinforcement learning framework. The proposed control scheme integrates the advantages of predefined-time stability theory and reinforcement learning to achieve fast convergence with guaranteed settling time [...] Read more.

This paper proposes a novel predefined-time adaptive neural tracking control method for uncertain manipulator systems based on Actor-Critic reinforcement learning framework. The proposed control scheme integrates the advantages of predefined-time stability theory and reinforcement learning to achieve fast convergence with guaranteed settling time bounds while handling unknown system dynamics. An Actor neural network is designed to approximate the unknown nonlinear functions and generate control inputs, while a Critic neural network evaluates the cost-to-go function to guide the learning process. The predefined-time convergence is ensured by incorporating specially designed terms into both the control law and the neural network weight update laws. The upper bound of the settling time can be explicitly preset by a single design parameter, independent of initial conditions and system parameters. Rigorous stability analysis based on Lyapunov theory proves that all closed-loop signals are bounded and the tracking error converges to a small neighborhood of the origin within the predefined time. Simulation results on a single-link manipulator system demonstrate the effectiveness and superiority of the proposed control scheme compared with conventional PID control. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

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22 pages, 2020 KB

Open AccessArticle

ADOB: A Field-Friendly Control Framework for Reliable Robotic Systems via Complementary Integration of Robust and Adaptive Control

by Jangyeon Park, Kwanho Yu and Jungsu Choi

Sensors 2026, 26(5), 1443; https://doi.org/10.3390/s26051443 - 25 Feb 2026

Viewed by 533

Abstract

Practical robotic systems require control methods that remain reliable under limited computational resources, uncertain environments, and frequent changes in operating conditions. Although model-based control forms the foundation of high-performance robotics, real-world deployment is often hindered by model uncertainty, time-varying dynamics, and costly identification. [...] Read more.

Practical robotic systems require control methods that remain reliable under limited computational resources, uncertain environments, and frequent changes in operating conditions. Although model-based control forms the foundation of high-performance robotics, real-world deployment is often hindered by model uncertainty, time-varying dynamics, and costly identification. As a result, low-order and intuitive control schemes remain dominant, yet such approaches often fail to sustain consistent performance under disturbances and parameter variations. Robust and adaptive control provide representative paradigms to address this gap, where a Disturbance Observer (DOB) suppresses uncertainty through disturbance rejection and a Parameter Adaptation Algorithm (PAA) improves model fidelity through online identification. However, direct integration of a DOB and a PAA often introduces functional interference, including mutual masking between disturbance compensation and parameter estimation, which compromises closed-loop stability. This paper proposes an Adaptive Disturbance Observer (ADOB) that integrates a DOB with online parameter adaptation. The ADOB updates the nominal model of the DOB in real time using a Recursive Least Squares (RLS)-based PAA, while a dual-filtering structure separates disturbance rejection and parameter identification. Stability is analyzed using hyperstability theory, where a smoothing mechanism enforces the slowly varying parameter assumption. Experiments on a one-Degree-of-Freedom (DOF) electromagnetic actuator and a three-DOF robotic manipulator demonstrate reductions in model uncertainty and tracking error compared with a conventional DOB. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

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29 pages, 5664 KB

Open AccessArticle

Dynamic Event-Triggered Control for Unmanned Aerial Vehicle Swarm Adaptive Target Enclosing Mission

by Wanjing Zhang and Xinli Xu

Sensors 2026, 26(2), 655; https://doi.org/10.3390/s26020655 - 18 Jan 2026

Cited by 1 | Viewed by 614

Abstract

Multi-UAV (unmanned aerial vehicle) target enclosing control is one of the key technologies for achieving cooperative tasks. It faces limitations in communication resources and task framework separation. To address this, a distributed cooperative control strategy is proposed based on dynamic time-varying formation description [...] Read more.

Multi-UAV (unmanned aerial vehicle) target enclosing control is one of the key technologies for achieving cooperative tasks. It faces limitations in communication resources and task framework separation. To address this, a distributed cooperative control strategy is proposed based on dynamic time-varying formation description and event-triggering mechanism. Firstly, a formation description method based on a geometric transformation parameter set is established to uniformly describe the translation, rotation, and scaling movements of the formation, providing a foundation for time-varying formation control. Secondly, a cooperative architecture for adaptive target enclosing tasks is designed. This architecture achieves an organic combination of formation control and target enclosing in a unified framework, thereby meeting flexible transitions between multiple formation patterns such as equidistant surrounding and variable-distance enclosing. Thirdly, a distributed dynamic event-triggered cooperative enclosing controller is developed. This strategy achieves online adjustment of communication thresholds through internal dynamic variables, significantly reducing communication while strictly ensuring system performance. By constructing a Lyapunov function, the stability and Zeno free behavior of the closed-loop system are proven. The simulation results verify this strategy, showing that this strategy can significantly reduce communication frequency while ensuring enclosing accuracy and formation consistency and effectively adapt to uniform and maneuvering target scenarios. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

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18 pages, 3170 KB

Open AccessArticle

A Terrain Perception Method for Quadruped Robots Based on Acoustic Signal Fusion

by Meng Hong, Nian Wang, Xingyu Liu, Chao Huang, Ganchang Li, Zijian Li, Shuai Shu, Ruixuan Chen, Jincheng Sheng, Zhongren Wang, Sijia Guan and Min Guo

Sensors 2026, 26(2), 594; https://doi.org/10.3390/s26020594 - 15 Jan 2026

Viewed by 882

Abstract

In unstructured environments, terrain perception is essential for stability and environmental awareness of Quadruped robot locomotion. Existing approaches primarily rely on visual or proprioceptive signals, but their effectiveness is limited under conditions of visual occlusion or ambiguous terrain features. To address this, this [...] Read more.

In unstructured environments, terrain perception is essential for stability and environmental awareness of Quadruped robot locomotion. Existing approaches primarily rely on visual or proprioceptive signals, but their effectiveness is limited under conditions of visual occlusion or ambiguous terrain features. To address this, this study proposes a multimodal terrain perception method that integrates acoustic features with proprioceptive signals. This terrain perception method collects environmental acoustic information through an externally mounted sound sensor, and combines the sound signal with proprioceptive sensor data from IMU and joint encoder of the quadruped robot. The method was deployed on the quadruped robot Lite2 platform developed by Deep Robotics, and experiments were conducted on four representative terrain types: concrete, gravel, sand, and carpet. Mel-spectrogram features are extracted from the acoustic signals and concatenated with the IMU and joint encoder to form feature vectors, which are subsequently fed into a support vector machine for terrain classification. For each terrain type, 400 s of data were collected. Experimental results show that the terrain classification accuracy reaches 78.28% without using acoustic signals, while increasing to 82.52% when acoustic features are incorporated. To further enhance the classification performance, this study performs a combined exploration of the SVM hyperparameters

C

and

γ

as well as the time-window length

w_{i n}

. The final results demonstrate that the classification accuracy can be improved to as high as 99.53% across all four terrains. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

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24 pages, 8304 KB

Open AccessArticle

STAIR-DETR: A Synergistic Transformer Integrating Statistical Attention and Multi-Scale Dynamics for UAV Small Object Detection

by Linna Hu, Penghao Xue, Bin Guo, Yiwen Chen, Weixian Zha and Jiya Tian

Sensors 2025, 25(24), 7681; https://doi.org/10.3390/s25247681 - 18 Dec 2025

Cited by 1 | Viewed by 1049

Abstract

Detecting small objects in unmanned aerial vehicle (UAV) imagery remains a challenging task due to the limited target scale, cluttered backgrounds, severe occlusion, and motion blur commonly observed in dynamic aerial environments. This study presents STAIR-DETR, a real-time synergistic detection framework derived from [...] Read more.

Detecting small objects in unmanned aerial vehicle (UAV) imagery remains a challenging task due to the limited target scale, cluttered backgrounds, severe occlusion, and motion blur commonly observed in dynamic aerial environments. This study presents STAIR-DETR, a real-time synergistic detection framework derived from RT-DETR, featuring comprehensive enhancements in feature extraction, resolution transformation, and detection head design. A Statistical Feature Attention (SFA) module is incorporated into the neck to replace the original AIFI, enabling token-level statistical modeling that strengthens fine-grained feature representation while effectively suppressing background interference. The backbone is reinforced with a Diverse Semantic Enhancement Block (DSEB), which employs multi-branch pathways and dynamic convolution to enrich semantic expressiveness without sacrificing spatial precision. To mitigate information loss during scale transformation, an Adaptive Scale Transformation Operator (ASTO) is proposed by integrating Context-Guided Downsampling (CGD) and Dynamic Sampling (DySample), achieving context-aware compression and content-adaptive reconstruction across resolutions. In addition, a high-resolution P2 detection head is introduced to leverage shallow-layer features for accurate classification and localization of extremely small targets. Extensive experiments conducted on the VisDrone2019 dataset demonstrate that STAIR-DETR attains 41.7% mAP@50 and 23.4% mAP@50:95, outperforming contemporary state-of-the-art (SOTA) detectors while maintaining real-time inference efficiency. These results confirm the effectiveness and robustness of STAIR-DETR for precise small object detection in complex UAV-based imaging scenarios. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

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30 pages, 9331 KB

Open AccessArticle

Extended Dynamic Model for the UR16e 6-Degree-of-Freedom Robotic Manipulator

by John Kern, Luis Donoso, Claudio Urrea and Guillermo González

Sensors 2025, 25(24), 7532; https://doi.org/10.3390/s25247532 - 11 Dec 2025

Viewed by 945

Abstract

This study develops and validates an Extended Analytical Dynamic Model (EADM) of the UR16e 6-Degree-of-Freedom (DoF) industrial robot, incorporating actuator dynamics and a friction model to address the lack of dynamic information provided by the manufacturer. A two-stage validation methodology is proposed using [...] Read more.

This study develops and validates an Extended Analytical Dynamic Model (EADM) of the UR16e 6-Degree-of-Freedom (DoF) industrial robot, incorporating actuator dynamics and a friction model to address the lack of dynamic information provided by the manufacturer. A two-stage validation methodology is proposed using a Multibody Physical Model (MPM) developed in MATLAB^® R2024b/Simscape Multibody^TM as a reference. In the first stage, the Analytical Dynamic Model (ADM) without actuators or friction is evaluated by comparing its inverse dynamics torque with the torque required by the MPM under identical joint references. In the second stage, the EADM and the MPM are tested under a Proportional-Derivative Computed Torque Control (PD-CTC) scheme using Cartesian trajectories, comparing joint torques and positions. The methodology incorporates torque-level validation, a demanding criterion since torque is determined by the dynamic formulation, whereas position may be influenced by closed-loop control. The results show small torque errors in the first stage (

e_{τ}

in the range of

10^{- 17}

to

10^{- 13}

Nm) and bounded position and torque errors in the second stage (

e_{q} \leq 4 \times 10^{- 4}

rad;

e_{τ} \leq 0.4

Nm in

q_{1}

–

q_{3}

and

e_{τ} \leq 0.05

Nm in

q_{4}

–

q_{6}

). The methodology provides a systematic validation framework and demonstrates that the EADM accurately matches the MPM’s dynamic behavior. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

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16 pages, 3926 KB

Open AccessArticle

A Magnetically Controlled Capsule Robot with Biopsy Capability for Intestinal Applications

by Lingling Zheng, Jie Sun, Zhengdong Qi, Le Yan, Fen Zhao, Haifei Zhang, Le Zhang, Zixu Wang and Shuxiang Guo

Sensors 2025, 25(23), 7158; https://doi.org/10.3390/s25237158 - 24 Nov 2025

Cited by 2 | Viewed by 2041

Abstract

Magnetically controlled capsule robots offer unique advantages for performing intestinal biopsies. In this paper, we propose a novel Magnetically Controlled Capsule Robot for Intestinal Biopsy (MCCR-IB), capable of both navigation and biopsy within the intestine. To address the coupling issue between locomotion control [...] Read more.

Magnetically controlled capsule robots offer unique advantages for performing intestinal biopsies. In this paper, we propose a novel Magnetically Controlled Capsule Robot for Intestinal Biopsy (MCCR-IB), capable of both navigation and biopsy within the intestine. To address the coupling issue between locomotion control and biopsy control, a magnetic field locking method is also introduced. The locomotion performance, curved-passage capability, and biopsy ability were experimentally evaluated. Under a 7 Hz rotating magnetic field, the MCCR-IB achieved forward and backward velocities of 20.22 mm/s and 18.27 mm/s, respectively, with a biopsy needle puncture force of 1.99 N. Furthermore, ex vivo experiments were conducted to preliminarily verify the feasibility of the robot’s motion and biopsy functions. The experimental results demonstrate that the proposed MCCR-IB exhibits good performance and shows promising potential for future clinical applications. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

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17 pages, 4425 KB

Open AccessArticle

A Unified Control Framework for Self-Balancing Robots: Addressing Model Variations in Wheel-Legged Platforms and Human-Carrying Wheelchairs

by Guiyang Xin, Boyu Jin, Chen Liu and Mian Jiang

Sensors 2025, 25(23), 7144; https://doi.org/10.3390/s25237144 - 22 Nov 2025

Cited by 2 | Viewed by 1599

Abstract

Self-balancing robots, with their compact size, are capable of achieving high agility. Small wheel-legged self-balancing robots have demonstrated significant potential across various applications. However, expanding small self-balancing robots to larger sizes to serve as personal transport tools is a more attractive and impactful [...] Read more.

Self-balancing robots, with their compact size, are capable of achieving high agility. Small wheel-legged self-balancing robots have demonstrated significant potential across various applications. However, expanding small self-balancing robots to larger sizes to serve as personal transport tools is a more attractive and impactful direction than further miniaturization or confinement to niche laboratory demonstrations. This paper presents the development of a small self-balancing robot, which is then scaled up to a larger version designed to carry human passengers as a self-balancing wheelchair. A unified control framework, built around a shared core of online model-updating LQR for balance and PD for steering, is applied to both robots. This core is supplemented with platform-specific modules, such as a dedicated leg controller for the wheel-legged robot, to handle distinct dynamic maneuvers. The LQR controller is implemented for balance control in both robots. Additionally, a dedicated leg controller is applied exclusively to the small wheel-legged robot to enable dynamic maneuvers, such as jumping. A series of experiments conducted with the final prototypes validate the effectiveness of the control systems and highlight the robots’ application potential. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

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18 pages, 2243 KB

Open AccessArticle

A Novel Fixed-Time Super-Twisting Control with I&I Disturbance Observer for Uncertain Manipulators

by Lin Xu, Jiahao Zhang, Chunwu Yin and Rui Dai

Sensors 2025, 25(21), 6723; https://doi.org/10.3390/s25216723 - 3 Nov 2025

Viewed by 986

Abstract

This paper proposes a novel fixed-time super-twisting sliding mode control (ST-SMC) strategy for uncertain robotic arm systems, aiming to address the issues of control chattering and the uncontrollable upper bound of convergence time in traditional sliding mode control algorithms. The proposed approach enhances [...] Read more.

This paper proposes a novel fixed-time super-twisting sliding mode control (ST-SMC) strategy for uncertain robotic arm systems, aiming to address the issues of control chattering and the uncontrollable upper bound of convergence time in traditional sliding mode control algorithms. The proposed approach enhances system robustness, suppresses chattering, and ensures that the convergence time of the robotic arm can be explicitly bounded. First, a sliding surface with fixed-time convergence characteristics is constructed to guarantee that the tracking errors on this surface converge to the origin within a prescribed time. Then, an immersion and invariance (I&I) disturbance observer with exponential convergence properties is designed to estimate large, time-varying disturbances in real time, thereby compensating for system uncertainties. Based on this observer, a new super-twisting sliding mode controller is developed to drive the trajectory tracking errors toward the sliding surface within fixed time, achieving global fixed-time convergence of the tracking errors. Simulation results demonstrate that, regardless of the initial conditions, the proposed controller ensures fixed-time convergence of the tracking errors, effectively eliminates control torque chattering, and achieves a tracking error accuracy as low as 2 × 10⁻⁹. These results validate the proposed method’s applicability and robustness for high-precision robotic systems. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

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15 pages, 3559 KB

Open AccessArticle

An Adaptive External Torque Estimation Algorithm for Collision Detection in Robotic Arms

by Cheng Yan, Ming Lyu, Yaowei Chen and Jie Zhang

Sensors 2025, 25(20), 6315; https://doi.org/10.3390/s25206315 - 13 Oct 2025

Cited by 1 | Viewed by 1572

Abstract

As robotic applications rapidly expand into increasingly complex and dynamic environments, greater emphasis is being placed on the intelligence and safety of human–robot collaboration at the task execution level. In shared human–robot workspaces, even the most precise motion planning cannot fully prevent collisions. [...] Read more.

As robotic applications rapidly expand into increasingly complex and dynamic environments, greater emphasis is being placed on the intelligence and safety of human–robot collaboration at the task execution level. In shared human–robot workspaces, even the most precise motion planning cannot fully prevent collisions. To address this critical safety concern, we propose a variational Bayesian Kalman filtering-based external torque estimation algorithm that integrates the robot’s dynamic model while avoiding additional system complexity. We begin by reviewing the robot dynamics framework and the classical external torque estimation method based on generalized momentum. We then derive a Kalman filter-based approach for external torque estimation in robotic manipulators and analyze the adverse effects arising from mismatches in process noise covariance. Finally, we introduce a sliding window-based variational Bayesian Kalman filter, which dynamically estimates the current process noise covariance while simultaneously mitigating the accumulation of recursive errors. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

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28 pages, 2725 KB

Open AccessArticle

Intelligent Counter-UAV Threat Detection Using Hierarchical Fuzzy Decision-Making and Sensor Fusion

by Fani Arapoglou, Paraskevi Zacharia and Michail Papoutsidakis

Sensors 2025, 25(19), 6091; https://doi.org/10.3390/s25196091 - 2 Oct 2025

Cited by 2 | Viewed by 3276

Abstract

This paper proposes an intelligent hierarchical fuzzy decision-making framework for threat detection and identification in Counter-Unmanned Aerial Vehicle (Counter-UAV) systems, based on the fusion of heterogeneous sensor data. To address the increasing complexity and ambiguity in modern UAV threats, this study introduces a [...] Read more.

This paper proposes an intelligent hierarchical fuzzy decision-making framework for threat detection and identification in Counter-Unmanned Aerial Vehicle (Counter-UAV) systems, based on the fusion of heterogeneous sensor data. To address the increasing complexity and ambiguity in modern UAV threats, this study introduces a novel three-stage fuzzy inference architecture that supports adaptive sensor evaluation and optimal pairing. The proposed methodology consists of three-layered Fuzzy Inference Systems (FIS): FIS-A quantifies sensor effectiveness based on UAV flight altitude and detection probability; FIS-B assesses operational suitability using sensor range and cost; and FIS-C synthesizes both outputs, along with sensor capability overlap, to determine the composite suitability of sensor pairs. This hierarchical structure enables detailed analysis and system-level optimization, reflecting real-world constraints and performance trade-offs. Simulation-based evaluation using diverse sensor modalities (EO/IR, Radar, Acoustic, RF), supported by empirical data and literature, demonstrates the framework’s ability to handle uncertainty, enhance detection reliability, and support cost-effective sensor deployment in Counter-UAV operations. The framework’s modularity, scalability, and interpretability represent significant advancements in intelligent Counter-UAV system design, offering a transferable methodology for dynamic threat environments. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)

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