Finite time control of intelligent ship mooring robotic arm considering faults and input saturation

ABSTRACT The incessant pursuit of the large-scale, complex-geometry components catalyses the emergence of the new additive manufacturing process. The robotic-arm assisted screw-extrusion additive manufacturing (SEAM) process has been considered an ideal solution for meeting those ever-increasing demands. However, due to the lack of an integral controller, such a process faces a unique challenge to synchronise the extrusion flow with the motion. To tackle this challenge, this study proposes a velocity-dependent feedforward extrusion controller to tune the extrusion volume in real-time. This study shows that the extrusion volume of the custom-built screw-extrusion system is linearly dependent on the rotation velocity of the screw, which can be tuned by the external DC voltage signal. By extracting the velocity signal from the robotic system, the proposed extrusion controller can effectively tune the extrusion volume without accessing the robotics’ motion controller. Further micro-scale characterisation reveals that the proposed controller can significantly reduce the undesired overflow and underflow, leading to more refined surface finish and consistent wall thickness. The findings of this study not only provide a systematic and practical guideline for flow manipulation in robotic SEAM systems but also contribute to the broader field of motion-synchronised extrusion control for six-axis robotic arms.

Robotic arms have evolved rapidly over the past decade, integrating advanced control algorithms, lightweight materials, and artificial intelligence to perform diverse tasks across industries. This review paper provides a comprehensive overview of robotic arm development, including design structures, kinematic modeling, control methodologies, and multi-domain applications. By synthesizing findings from recent studies, this paper highlights innovations such as compliant joints, twin actuation systems, hybrid control mechanisms, and modular collaborative designs. Applications in industrial automation, medical surgery, space exploration, and precision agriculture are critically analyzed. Finally, challenges and research directions are discussed to promote further development in efficiency, adaptability, and intelligence of robotic arms.

Ensuring precise and stable motion synchronization in game engine-driven digital twin robotic arms is challenging due to real-time data transmission delays. Various strategies—such as predictive modeling, network optimization, and robotic arm control techniques—have been proposed to address latency-aware motion synchronization. However, the absence of recent review papers in this area limits researchers’ ability to identify the most effective solutions for minimizing latency and enhancing system reliability. To fill this gap, we conducted a systematic literature review (SLR) to identify, analyze, classify, and summarize existing latency reduction techniques. A total of 125 research studies from reputable sources were reviewed to uncover recent trends in the field. We developed a taxonomy to group the identified methods based on common characteristics and provided concise summaries of each approach. Furthermore, this study outlines key research challenges and suggests future directions for improving synchronization accuracy. The findings offer a comprehensive and structured overview of existing solutions and serve as a valuable reference for researchers and practitioners aiming to advance real-time digital twin applications through game engine-based visualization.

A brain-computer interface (BCI) establishes a pathway for information transmission between a human (or animal) and an external device. It can be used to control devices such as prosthetic limbs and robotic arms, which in turn assist, rehabilitate, and enhance human limb function. At present, although most studies focus on brain signal acquisition, feature extraction and recognition, and further explore the use of brain signals to control external devices, the features obtained via noninvasive approaches are fewer and less robust, which makes it difficult to directly control devices with more degrees of freedom such as robotic arms. To address these issues, we propose an extended instruction set based on motor imagery that fuses eye-movement signals and electroencephalogram (EEG) signals for motion control of a dual collaborative robotic arm. The method incorporates spatio-temporal convolution and attention mechanisms for brain-signal classification. Starting from a small base of control commands, the hybrid BCI combining eye-movement signals and EEG expands the command set, enabling motion control of the dual cooperative manipulator. On the Webots simulation platform, we carried out kinematic control and three-dimensional motion simulation of a dual 6-degree-of-freedom collaborative robotic arm (UR3e). The experimental results demonstrate the feasibility of the proposed method. Our algorithm achieves an average accuracy of 83.8% with only 8.8k parameters, and the simulation results are within the expected range. The results demonstrate that the proposed extended instruction set based on motor imagery is effective not only for controlling dual collaborative robotic arms to perform grasping tasks in complex scenarios, but also for operating other multi-degree-of-freedom peripheral devices.

This study presents a comparative analysis of Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC) methods applied to a custom-designed and 3D-printed 3-Degree-of-Freedom (3-DoF) Revolute-Revolute-Revolute (RRR) robotic manipulator. A central contribution of this work is the development of a dual-environment validation framework that integrates ROS Noetic with the Gazebo simulation platform, enabling seamless testing of controllers in both virtual and physical settings. This framework provides a practical pathway for bridging the gap between simulation-based evaluations and real-world experimentation, an aspect that remains underexplored in existing studies. The performance of both controllers is assessed through joint position errors, trajectory tracking accuracy, and torque demands for a cubic trajectory application. Experimental results show that while both controllers achieve satisfactory performance, SMC demonstrates superior trajectory tracking, with consistently lower Root Mean Square (RMS) errors across all joints. This improvement, however, is accompanied by slightly higher torque requirements compared to PID, highlighting the trade-off between enhanced accuracy and increased actuator effort. By combining a low-cost robotic platform with a reproducible dual-environment methodology, this study not only offers insights into the practical strengths and limitations of model-free PID and SMC but also establishes a framework that can inform future research and industrial applications.

ABSTRACT Conductive hydrogels have great potential as flexible sensors in wearable devices. However, it remains a challenge to integrate excellent mechanical properties, high conductivity, and sensitivity into hydrogels using simple green methods. Here, cellulose nanofibers (CNFs) and MXene were introduced into a polyvinyl alcohol (PVA) hydrogel system based on the multi‐scale synergistic enhancement mechanism of nanocomposites. By forming interfacial hydrogen bonds and entanglement of molecular chains, a nanofiber‐reinforced PVA/CNF/MXene (PCM) hydrogel was developed. MXene enhances the electrical conductivity of the hydrogel, reaching 0.175 S/m, which represents a 191.7% increase compared to the hydrogel without MXene. The synergistic enhancement between CNF and MXene confers the PCM hydrogel with high mechanical strength (334 kPa) and high stretchability (339%). Moreover, PCM hydrogel serves as a strain sensor with outstanding strain sensitivity (GF = 3.25), wide dynamic detection range (0%–220%), and low detection threshold (0.3%), demonstrating excellent stability and durability in response to stimulus signals. The hydrogel sensor enables accurate detection of subtle human movements and synchronized control of robotic arms, demonstrating its broad applicability in motion monitoring and human–machine interaction (HMI).

Due to the rapid development of industrial automation and intelligent manufacturing, robotic arms have been widely used in many fields. This study analyzes the limitations of traditional control methods in robot arm control and introduces the concept of multi-agent reinforcement learning (MARL), which treats each joint or functional module of the robot arm as an independent agent that collaborates to complete complex tasks. The research results indicate that, compared with traditional recurrent neural network (RNN) and deep neural network (DNN) control methods, the method based on MARL exhibits significant advantages in multitask control. In terms of task completion rate, this method has improved by an average of about 10%, especially in complex tasks where it performs even better. This method also effectively reduces the fluctuation of task completion rate, demonstrating better stability and consistency. The method based on MARL has significant advantages in promoting effective collaboration among agents and improving task execution efficiency.

This paper presents the dynamic modeling and control evaluation of a six degrees-of-freedom (6-DOF) robotic manipulator. The manipulator was developed in the Robot Operating System (ROS) and Gazebo using a detailed URDF model with complete geometric and inertia parameters. Proportional–Integral–Derivative (PID) controllers were tuned through ROS dynamic reconfiguration and tested under four payloads: 0; 0,19; 0,39; and 0,50 kg. Controller performance was assessed using rise time, settling time, overshoot, and steady-state error. The results show stable responses across all conditions, with no overshoot and near-zero steady-state errors. Increasing payloads generally led to longer rise and settling times, while joints aligned with gravity exhibited faster responses under heavier loads. These findings confirm that properly tuned PID controllers can maintain robust and accurate manipulator performance and demonstrate the effectiveness of ROS–Gazebo as an open-source platform for robotic control experimentation and future integration of adaptive or AI-based methods.

This study presents a hybrid control system that combines Iterative Learning Control (ILC) and Proportional-Integral-Derivative (PID) control to improve the joint control of the SCORBOT ER4u robotic arm. The aim is to improve the accuracy, stability, and efficiency of repeated motion tasks in low-cost instructional robots. PID offers fast response and inherent stability, and ILC progressively refines control performance through repetition. By integrating PID and ILC, the system utilizes their complementary strengths. The controller's performance was evaluated using multiple metrics, including Root Mean Square Error (RMSE), Root Mean Square Percentage Error (RMSPE), Integral of Time-Weighted Absolute Error (ITAE), and Integral of Time-Weighted Squared Error (ITSE), to capture both transient and long-term error characteristics. The hybrid PID-ILC approach reduced steady-state errors by 35% under no-load conditions and by 71% with a 1 kg payload. The system exhibited exceptional stability, with no oscillations or divergence, demonstrating apparent convergence and a bounded response across iterations. These results confirm that the proposed method significantly improves motion precision, reliability, and robustness, making it well-suited for tasks requiring accurate and repetitive control in educational and industrial contexts.

Physically feasible dynamic model identification and constrained control of robotic arms: A case study on the ViperX-300 6-DoF robotic manipulator

Rapid and precise intervention in disaster and medical-aid scenarios demands aerial platforms that can both survey and physically interact with their environment. This study presents the design, fabrication, modeling, and experimental validation of a one-piece, 3D-printed quadcopter with an integrated six-degree-of-freedom aerial manipulator robotic arm tailored for emergency response. First, we introduce an ‘X’-configured multi-rotor frame printed in PLA+ and optimized via variable infill densities and lattice cutouts to achieve a high strength-to-weight ratio and monolithic structural integrity. The robotic arm, driven by high-torque servos and controlled through an Arduino-Pixhawk interface, enables precise grasping and release of payloads up to 500 g. Next, we derive a comprehensive nonlinear dynamic model and implement an Extended Kalman Filter-based sensor-fusion scheme that merges Inertial Measurement Unit, barometer, magnetometer, and Global Positioning System data to ensure robust state estimation under real-world disturbances. Control algorithms, including PID loops for attitude control and admittance control for compliant arm interaction, were tuned through hardware-in-the-loop simulations. Finally, we conducted a battery of outdoor flight tests across spatially distributed way-points at varying altitudes and times of day, followed by a proof-of-concept medical-kit delivery. The system consistently maintained position accuracy within 0.2 m, achieved stable flight for 15 min under 5 m/s wind gusts, and executed payload pick-and-place with a 98% success rate. Our results demonstrate that integrating a lightweight, monolithic frame with advanced sensor fusion and control enables reliable, mission-capable aerial manipulation. This platform offers a scalable blueprint for next-generation emergency drones, bridging the gap between remote sensing and direct physical intervention.

With the continuous development of Human–Robot Collaboration (HRC), an increasing demand exists for robotic arm systems that not only understand human behavioral intentions but also ensure safe and efficient coexistence with humans. This study introduces an innovative approach to robotic arm trajectory planning and optimization, integrating pattern recognition, deep learning, and computer vision technologies to address two critical issues in complex HRC tasks. First, we propose an improved Informer-based method that combines machine vision and deep learning to precisely predict human hand trajectories, providing accurate data for subsequent robotic arm trajectory planning. Second, we present an optimization strategy that combines Rapidly-exploring Random Trees (RRT*) for initial path planning with Dynamic Movement Primitives (DMP), integrating torque feedback from a robotic arm’s internal sensors to enhance trajectory smoothness and adaptability. This method not only enables automatic obstacle avoidance and optimizes trajectory smoothness but also significantly reduces a robotic arm’s power consumption by approximately 22.68% during task execution. These technological improvements facilitate more fluid and natural robotic arm movements, enhancing both efficiency and flexibility in complex HRC scenarios. Our integrated system aims to achieve precise and efficient control of robotic arms across various interaction scenarios, ensuring high sensitivity and responsiveness to human operators’ intentions while improving movement smoothness and naturalness.

تتناول هذه الورقة البحثية تطبيق الخوارزمية الجينية (GA) لتحسين معاملات متحكم PID وهي Kp, Ki, Kd لذراع روبوتي أحادي الوصلة. يهدف البحث إلى تقليل مؤشرات الأداء الأساسية مثل زمن الاستقرار ونسبة التجاوز (الزيادة القصوى) والخطأ في الحالة المستقرة. تم استخدام نموذج ديناميكي مبسط للذراع الروبوتي لغرض المحاكاة. وقد تم تنفيذ الخوارزمية الجينية باستخدام اختيار عجلة الروليت (Roulette Wheel Selection) والعبور أحادي النقطة (Single-Point Crossover) والطفرة الغاوسية (Gaussian Mutation). أظهرت نتائج المحاكاة فعالية الخوارزمية الجينية في الضبط التلقائي لمعاملات متحكم PID، مما أدى إلى تحسينات كبيرة في الاستجابة الانتقالية وفي الحالة المستقرة.

Abstract The primary objective of this study is to develop an automatic spray coating system. The overall system architecture consists of a production line control system and a 7-axis robotic arm control system, each operating independently. A servo control board based on FPGA serves as the core for motion control. When a workpiece is suspended on a fixture and enters the spraying area, sensors detect whether any positional deviation has occurred. If no deviation is detected, the 7-axis suspended robotic arm executes the spraying process following a pre-defined trajectory. In the event of a deviation, the system immediately transmits the data to the robotic arm control computer, which recalculates a corrected path to compensate and ensure spraying precision. Experimental validation was conducted using a motorcycle shell sample provided by the manufacturer. Spray trajectory tracking tests were performed under both no deviation and 10° deviation conditions. The spraying motion of the robotic arm is derived through inverse kinematics, while the MDDS control method is applied in conjunction with forward kinematics to calculate positional errors along the X, Y, and Z axes. Results show that the maximum positional error in all three axes is less than 0.5 mm, and the final coating thickness ranged from 36 to 38 μm, meeting the manufacturer’s specification of 35-40 μm. The successfully developed automatic spray coating system significantly improves coating quality, operational efficiency, and system stability. It offers high scalability and serves as a practical and forward-looking automated solution for the advanced manufacturing industry.

The development of robotics applications heavily relies on artificial intelligence and machine learning, which are prerequisites for creating ‎intelligent robotic systems. Furthermore, the development of intelligent robots requires feature selection, classification, and fuzzy rule-based ‎decision making. Because they immediately aid the elderly in receiving medical care, allowing them to go about their daily lives more ‎quickly and effectively, medical assistive robots are beneficial to society. By removing characteristics that don't contribute, such as noise, ‎null values in databases, and properties unrelated to classification problems, feature selection lowers the dimension of the data. Natural ‎language processing can be utilized in medical applications, such as providing medical assistance through robot arm control, to handle the ‎challenging task of directing the robot arm using elderly instructions or orders. To effectively convert speech to text and conduct ‎morphological, syntactic, and semantic analysis on the converted text to more precisely detect the commands issued to robots by older ‎adults, the Fuzzy Temporal Rule-based Semantic Analysis Algorithm (FTRSAA) is suggested in this study. Additionally, the efficient ‎design and execution of the system that helps the elderly are made possible by this voice-activated robot control system, which utilizes the ‎latest intelligent robot technology. By using these recently suggested algorithms to comprehend natural language texts generated from ‎discussions, it communicates with older people.

Adaptive Robotic Arm Control: Leveraging Curriculum Learning and Deep Reinforcement Learning for Efficient Pick-and-Lift Operations Through Simulation

ABSTRACT With the growing demand for precise and efficient control of robotic arms in the era of Industry 4.0, traditional methods for solving the inverse kinematics problem face significant limitations in terms of accuracy, computational speed, and adaptability to complex robotic configurations. This paper proposes a novel approach for solving the inverse kinematics problem by integrating the Improved Dung Beetle Optimization (IDBO) algorithm with Backpropagation Neural Networks (BPNN). This hybrid method is applied to Newton‐Raphson (NR) iterative algorithms for computing the kinematic solutions of robotic arms, effectively enhancing both optimization efficiency and solution accuracy. The IDBO algorithm, an advanced version of the traditional Dung Beetle Optimization (DBO), incorporates innovative strategies that improve convergence speed and balance local and global search capabilities, making it an effective tool for optimizing the weights and biases of the neural network. As a case study, the UR5e robotic arm is modeled using the Denavit‐Hartenberg convention. The proposed IDBO‐BPNN method is benchmarked against traditional and other optimization algorithms through simulations, demonstrating superior performance in terms of convergence speed, solution accuracy, and computational stability. Notably, the IDBO‐BPNN‐NR approach significantly reduces computation time, achieving an 80.6% reduction compared to the Random Iteration Point‐NR method and a 66.6% reduction compared to the Fixed Starting Point‐NR method. By comparing the solution parameters obtained using the IDBO‐BPNN‐NR algorithm and the Fixed Starting Point‐NR algorithm across robotic arms with varying degrees of freedom and structural configurations, the robust generalization capability of the proposed method is further validated. The results indicate that this hybrid approach is highly suitable for real‐time robotic applications, offering a scalable, efficient, and accurate solution to the inverse kinematics problem.

Various robotic arm control systems have been proposed to support the daily lives of patients with severe motor impairments; however, existing robotic arm control devices typically require physical input devices, such as joysticks, which are often difficult for patients with motor impairments to use. To overcome these limitations, this paper proposes a new method for controlling a robotic arm using augmented reality and object detection. The system automatically configures the path from the robotic arm to the object, allowing all operations to be performed using only eye tracking. With precise object localization and an intuitive gaze-based interface, the proposed robotic arm control system offers a significant advantage for patients with motor impairments, providing a more accessible and user-friendly alternative to traditional control methods.

Abstract The problem of precise joint position tracking has remained as a core challenge for a 6-DoF Cobot arm, especially due to often scenario of an arbitrary waypoint reference trajectory generated due to human interactions. To tackle this problem, we propose a novel cascade training based Deep Reinforcement Learning (DRL) algorithm that tunes the PID controller gains for each joint simultaneously, ensuring accurate positional tracking for all joints. This also addresses the problem of overestimation of control parameters by ensuring that performance criteria are met in a phased manner during the training process. The tuned DRL based PID clearly outperforms the conventional PID control by accurately tracking the arbitrary waypoints given to each joints of the Cobot arm. We show the efficacy of the proposed method through exhaustive simulations and performing quantitative analysis of various key performance criteria like- Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Average Control Effort (ACE) error for the Cobot. The obtained results of DRL-PID control, when compared with its conventional PID counterpart, clearly depict the superiority of the proposed DRL-PID scheme via a cascade training approach. We have also remarked on some trade off and implementation aspects of the proposed control policy for the Cobot based applications. This method has the potential to be applicable to similar complex dynamical systems like a Cobot, where arbitrary reference and human interactions are prime concerns.