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Articles
Publication Date: 2026
Journal of the Franklin Institute (00160032)363(2)
This paper explores the challenges of synchronizing Euler-Lagrange networks that include a dynamic leader, utilizing a disturbance observer method. In this network, each agent is characterized by Euler-Lagrange equations of motion with dynamics that are not fully known. Unlike most existing approaches for handling uncertainties in Euler-Lagrange networks, the structure of the dynamic equations is assumed to be unavailable, making existing adaptive control schemes inapplicable. Therefore, a new formulation is proposed for Euler-Lagrange networks to lump the uncertain dynamical terms, estimate them, and suppress their effects accordingly. The closed-loop stability of the overall system is analyzed using the Lyapunov stability theorem. Simulation results show that the proposed approach outperforms two existing benchmark control methods. © 2025 The Franklin Institute. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Publication Date: 2026
Scientific Reports (20452322)16(1)
Deep vitrectomy, a complex ophthalmic procedure, demands precise instrument control and remains challenging to master. This study evaluated the ARASH:ASiST robotic system as a platform for real-time, quantitative assessment of deep vitrectomy in a pre-clinical setting using cadaveric human eyes. The system records force and motion data without disrupting the surgical workflow. Cadaveric eyes were prepared under strict safety protocols, and a mannequin head with a customised eye holder was designed, refined through repeated testing, and approved by surgeons to fully imitate real-world conditions. Four surgeons with different levels of experience, defined by their years of practice, performed the “drunk walk” manoeuvre while intraoperative force, positional, and temporal data were captured. Despite the inherent limitations of human cadaveric eyes, the aim was to reproduce the drunk walk method as a precise, predefined trajectory for posterior vitreous detachment induction. In total, 14 surgical trials were recorded: 2 from one expert surgeon (right-handed), 4 from two fellows (right-handed), and 8 from one intermediate surgeon, who performed four procedures with each hand. Quantitative metrics, including Normalised Jerk, Force Range, Peak Force Magnitude, and Force RMS, were used to compare individual performances. Group-level comparisons between expert, fellow, and intermediate surgeons were conducted using ANOVA and Kruskal–Wallis tests. While not statistically significant, likely due to the limited sample size, the analysis of descriptive statistics and effect sizes indicated potential trends in performance metrics across experience levels, highlighting the feasibility of the platform for capturing such variations. During data acquisition, the robotic system’s remote-centre-of-motion was accurately calibrated with laser-based technology, while encoders and force sensors were also precisely calibrated. The system’s real-time graphical interface provided immediate feedback and enabled detailed postoperative analysis. Usability testing confirmed its practicality and non-intrusiveness. Overall, the ARASH:ASiST system demonstrates feasibility as an objective platform for the evaluation of deep vitrectomy based on ex vivo results. These findings support further validation of this prototype in synthetic and clinical environments. © The Author(s) 2025.
Publication Date: 2025
Iranian Conference on Electrical Engineering, ICEE (26429527)pp. 326-331
This paper presents the design of a bilateral robotic system that integrates advanced control strategies to enhance the teleoperation performance. The system employs these strategies to enable smooth bidirectional communication of motion between the master and slave robots. The estimation of environmental interaction and human force eliminates the need for force sensors on both sides. Impedance-based control is utilized to ensure system stability and improve interaction accuracy. The proposed approach demonstrates effective human-environment interaction and precise teleoperation, as validated through simulation re-sults, showcasing its ability to maintain precise synchronization between the master and slave robots while dynamically adapting the estimation parameters. © 2025 IEEE.
Publication Date: 2025
IEEE Transactions on Medical Robotics and Bionics (25763202)
Ocular surgery demands exceptional precision due to the eye's delicate anatomy, where errors, particularly by novice surgeons, can lead to severe complications. This underscores the critical need for advanced training and skill development methodologies. The integration of versatile AI/Robotic architectures into ophthalmic surgical training is revolutionizing how surgeons acquire and refine their skills. These specialized training tools provide a safe and realistic environment, crucial for deliberate practice, skill enhancement, and the delivery of personalized feedback. This paper offers a comprehensive review of such AI/Robotic architectures specifically designed for or adapted to ophthalmic surgery training. It examines these systems from multiple viewpoints: for ophthalmologists, it details how these technologies are reshaping training paradigms, improving skill acquisition, and enabling competency-based educational models. For control and robotic engineers, it provides an in-depth technical analysis of contemporary training systems, with a focus on their control architectures, simulation environments, haptic feedback mechanisms, and varying levels of autonomy within these educational platforms. Furthermore, by identifying emerging commercial training simulators and AI-driven educational tools, this review highlights new market opportunities in the domain of surgical education. Ultimately, this comprehensive overview identifies promising directions for future research and development, offering valuable guidance for advancing the field of AI and robotics in ophthalmic surgical training. © IEEE. 2018 IEEE.
Publication Date: 2025
IFAC-PapersOnLine (24058963)59(10)pp. 697-702
This research proposes a three-channel control scheme to improve transparency and stability in nonlinear bilateral telerobotic systems. The control framework emphasizes position tracking and synchronization, with an unknown input observer estimating the external force exchanged between the environment and the slave robot. This estimated force signal is incorporated into the master robot's controller to achieve the design objectives. Specifically, the control approach addresses three primary goals: (1) position tracking and synchronization; (2) enhanced transparency through accurate force refection; and (3) stability analysis of the telerobotic system. The system architecture, structured as a three-channel bidirectional design, leverages the observer-based control law to ensure that position tracking errors remain stable. Ultimately, the performance of the proposed control scheme is validated on a nonlinear bilateral telerobotic system, demonstrating its potential to enhance both stability and transparency in real-world applications. Copyright © 2025 The Authors.
Publication Date: 2025
International Journal of Systems Science (00207721)
This study presents the design, comprehensive analysis, and implementation of a nonlinear bilateral telerobotic system operating in a master-slave configuration. It focuses on the critical importance of transparency, which is essential for achieving realistic force feedback in bilateral teleoperation. While some prior research has investigated the concept of transparency in nonlinear systems, this work represents the first attempt to implement variable impedance control, along with real-time parameter estimation through adaptive rules, to achieve transparency in a nonlinear bilateral teleoperation system. The suggested approach combines robust position control with impedance-based force regulation, while an Extended Kalman Filter (EKF) estimates environmental stiffness and damping parameters online. This synthesis enables the system to adapt to variable interaction conditions and maintain stable and high-fidelity force reflection. In addition, the presented stability analysis using the Lyapunov-based approach guarantees the theoretical robustness of the system under various operating conditions. Extensive simulation studies are presented to confirm the potential of the proposed approach, and the implementation results also validate its practical reliability and effectiveness. This makes the system well-suited for advanced teleoperation, haptic interfaces, and other domains requiring precise control and accurate force reflection capabilities. © 2025 Informa UK Limited, trading as Taylor & Francis Group.
Publication Date: 2025
European Journal of Control (09473580)85
This research addresses the challenge of effective human-robot interaction in master-slave robotic systems, particularly for applications like manufacturing and healthcare. A method is proposed for transferring desired impedance from a human operator to a slave robot. A three-term model estimates the interactive force/torque between the human hand and the master robot, with adaptive rules for updating stiffness and damping coefficients in real-time to provide accurate and responsive haptic feedback. These updated coefficients dynamically adjust the reference impedance model used to control the slave robot. This architecture, incorporating robust control techniques and estimators, ensures stability and transparency, enabling the master-side user to perceive conditions faced by the slave robot (e.g., obstacles). The slave robot responds according to the user's desired impedance, providing a seamless and intuitive interaction. Input-to-state stability analysis demonstrates robustness to disturbances and uncertainties. The proposed approach in this paper allows replicating the user impedance of the master robot to the slave robot, with the input-to-state stability of the entire closed-loop system analyzed in the presence of the proposed three-term model. The comparison of the root mean square (RMS) error measure for the tracking position and the tracking force/torque when the slave robot encounters an obstacle shows the favorable performance of the proposed approach compared to the impedance reference model approaches with fixed stiffness and damping coefficients and traditional position control approaches. Numerical simulations and experimental implementation validate the efficiency and accuracy of the proposed approach. © 2025
Publication Date: 2025
IEEE/ASME Transactions on Mechatronics (10834435)30(1)pp. 775-786
Lack of adequate skills are the most prominent contributor to the persistent problem of surgical errors throughout the early phases of a novice surgeon's training. Therefore, it is essential to thoughtfully and methodically consider the development of an effective strategy for transferring the trainers' expertise to novice surgeons, while simultaneously increasing the trainers' involvement in the training process. This article proposes a collaborative dual-user haptic-enabled surgical training system that utilizes a responsive variable impedance control structure. In this training system, a novice (trainee) and an expert (trainer) collaboratively conduct a particular task through their respective haptic devices. The desired parameters of the impedance model for the trainer's haptic device remain constant throughout the operation, while those of the trainee's haptic device are time-varying depending on his/her relative task performance level. The main purpose of this performance-based variable impedance control structure is to imitate or enhance the hands-on training experience. High-gain observers with unknown input are considered to estimate the interaction forces. The small-gain theorem and the input-to-state stability method are utilized to examine the overall nonlinear closed-loop system stability. Experiments are conducted to demonstrate the effectiveness of the proposed training system. © 1996-2012 IEEE.
Publication Date: 2024
Mechatronics (09574158)101
Collaborative haptic training systems offer numerous benefits, including enhanced safety, streamlined training processes, and improved maneuverability. These systems typically involve an expert user (the trainer) and a novice user (the trainee) engaging in collaborative operations. One of the primary challenges in designing controllers for such systems is ensuring task stability and maintaining stable interaction between the operators and the system, while also achieving satisfactory task performance. However, existing control schemes often overlook the need for supervision and intervention by the trainer during the operation, along with a comprehensive stability analysis. This article aims to address the above issues for a system in which the trainee conducts the operation and the trainer is provided with the capability to intervene and modify the incorrect actions of the trainee. This is accomplished through the implementation of impedance controllers at each haptic interface and dynamic adjustment of the target impedance on both ends based on the trainer's estimated impedance gain. The Input-to-State Stability approach and the small gain theorem are employed to analyze the stability of the closed-loop system. The effectiveness of the proposed approach is demonstrated through simulation and experimental results, showcasing the ability of the proposed scheme to enhance the collaborative training process and ensure stable interaction between the trainer and the trainee. © 2024 Elsevier Ltd
Publication Date: 2024
pp. 281-287
In this paper, the control performance of a bilateral teleoperation system is studied in the presence of a data injection attack. It is shown that even a simple data injection attack has the potential to deteriorate the system's guaranteed stability. Hence, a high-gain observer algorithm is proposed to detect data injection attacks. The stability of the closed-loop system with the passivity-based control law of robot manipulators, together with the proposed observer-based attack detection scheme, is proven using the ISS approach and small gain theorem. Finally, simulation results are presented, to demonstrate the accuracy and effectiveness of the proposed method. © 2024 IEEE.
Publication Date: 2023
IET Control Theory and Applications (17518644)17(12)pp. 1637-1647
In collaborative haptic training systems, a novice operator is interfaced with an expert operator and cooperatively performs some task on a real/virtual environment. Most control architectures for collaborative haptic training systems do not consider the switching task dominance together with investigating overall stability in the presence of nonlinear dynamics and uncertainty. In this paper, a theoretical framework is presented for switching task dominance in collaborative haptic training systems based on supervision and intervention of the expert operator. To that effect, the novice operator performs the operation with as little as possible interference haptic signals in the normal operational conditions. On the other hand, the expert operator is able to intervene the operation to guide the novice operator when it is necessary. The most challenging part of controller design for such systems is to provide the mentioned supervisory framework in a way that the stability of interaction between the operators and the system is ensured with acceptable task performance in various operational conditions. This work offers a variable-gain dual robust control scheme to address the above problem. The key idea is that the tracking gain of each controller is adjusted in real-time to switch the task authorities. It is verified that the input-to-state stability property is satisfied for each subsystem. Then, the overall stability is proved by leveraging the small gain theorem. Finally, the functionality and performance of the suggested control architecture is demonstrated through simulation and experimental studies. © 2023 The Authors. IET Control Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Publication Date: 2023
pp. 614-619
The Virtual-Reality Stationary Bicycle revolutionizes the rehabilitation experience by combining physical therapy with the immersive world of virtual reality. This innovative approach not only aids patients in regaining their strength and mobility but also transforms the challenging recovery process into an enjoyable and engaging journey. To create an environment that keeps riders motivated and fully immersed in their rehabilitation process, precise speed synchronization between the pedal and video display is necessary. This synchronization ensures that the virtual world feels smooth and real as the user pedals. In our study, we developed a miniature, portable device designed for easy attachment to any stationary bike's pedal. This device employs a Kalman filter to extract pedal kinematics from Inertial Measurement Unit (IMU) data. Its connection to the video processor through Bluetooth technology guarantees high-speed robust data transmission. The experimental results show the success of this innovative approach and its function. The Virtual-Reality Stationary Bicycle represents a significant step forward in the field of rehabilitation, offering a promising path toward better outcomes and patient-improved quality of life. © 2023 IEEE.
Publication Date: 2023
pp. 76-80
In this research, the problem of controlling a one-link robot with joint flexibility by the non-linear model predictive control method (NMPC) is considered. The issue concerning the input-to-state stability (ISS) of the NMPC has been considered. Through using the cost function of the NMPC problem in order to play the role of the Lyapunov function, the ISS stability of the system in the presence of disturbances and uncertainties, such as robot joint flexibility, is reached. Two modeling examples for a single-link robot have been investigated, which include unmeasurable variables as a portion of the system state variables. These unmeasured states are representative of the unmodeled dynamics, standing for either the flexibility at the joint or the link itself. In the first example, the control input is the actuation torque, and in the second one, the voltage to the motor. Simulation results demonstrate the effectiveness and stability of the proposed approach in the presence of disturbances and system uncertainties. © 2023 IEEE.
Publication Date: 2022
IEEE International Conference on Intelligent Robots and Systems (21530858)2022pp. 9635-9641
This paper proposes a variable impedance control architecture to facilitate eye surgery training in a dual-user haptic system. In this system, an expert surgeon (the trainer) and a novice surgeon (the trainee) collaborate on a surgical procedure using their own haptic devices. The mechanical impedance parameters of the trainer's haptic device remain constant during the operation, whereas those of the trainee vary with his/her proficiency level. The trainee's relative proficiency might be objectively quantified in real-time based on position error between the trainer and the trainee. The proposed architecture enables the trainer to intervene in the training process as needed to ensure the trainee is following the right course of action and to avoid the trainee's from potential tissue injuries. The stability of the overall nonlinear closed-loop system has been investigated using the input-to-state stability (ISS) criterion. High-gain observer with unknown inputs is considered in this work to estimate the interaction forces. Simulation and experimental results under different scenarios confirm the effectiveness of the proposed control methods. © 2022 IEEE.
Publication Date: 2022
pp. 610-615
In the present research, a synchronization control approach together with a novel disturbance observer for nonlinear passive bilateral teleoperation systems is proposed. All if the unknown dynamic terms such as external disturbances, modeling uncertainties and friction forces are lumped into one disturbance signal. Afterwards, the DOB technique is designed to obtain the estimated lump disturbance signal for both the master and the slave manipulators. The exponential convergence of lump disturbance estimation error to zero is verified. Also, based on the proposed control law, the positions of the master-slave system synchronize and the position errors are globally exponentially stable despite communication delay. Finally, the efficacy of the suggested scheme on a bilateral tele-rehabilitation system is shown by simulation results. © 2022 IEEE.
Publication Date: 2022
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025
This article investigates the dynamic parameter calibration of ARAS Haptic System for EYE Surgery Training (ARASH:ASiST). ARASH:ASiST is a 3-DOF haptic device developed for intraocular surgery training. In this paper, the linear regression form of the dynamic formulation of the system with respect to its dynamic parameters is derived. Then the dynamic parameters of ARASH:ASiST are calibrated using the least square (LS) identification scheme. The cross-validation results for different trajectories indicate that the identified model has a suitable approximate fitness percentage in both translational and rotational motions of the surgical instrument. Finally, a robust model-based controller is implemented on the real prototype by the use of the calibration outcome, and it is verified that by using the estimated dynamic model, the trajectory tracking performance is significantly improved and the tracking error is reduced 50% compared to that of using the nominal dynamic model of the robot. © 2022 IEEE.
Publication Date: 2021
pp. 586-591
This paper aims to develop the impedance control structure of a dual-user haptic training system for the application of surgical training. Through the proposed structure, the process of skill transfer from the trainer to the trainee is considered through automatic transformation of impedance coefficients, based on the evaluation of the trainee's performance and its adaptation to the trainer's behavior. The similarities between the position of the trainer and the trainee are examined in a time window to generate the base structure for varying impedance coefficients. In presence of modeling uncertainties, the proposed impedance controller is capable to enforce two reference impedance dynamics for the trainer and the trainee. In the proposed control structure, a high-gain observer is used to satisfy force demands without requiring expensive sensors. The input-to-state stability (ISS) of the dual-user haptic training system is analyzed in details. Finally, verification of the effectiveness of the presented structure is reported through computer simulations. © 2021 IEEE.
Publication Date: 2021
Frontiers in Robotics and AI (22969144)8
This paper examines how haptic technology, virtual reality, and artificial intelligence help to reduce the physical contact in medical training during the COVID-19 Pandemic. Notably, any mistake made by the trainees during the education process might lead to undesired complications for the patient. Therefore, training of the medical skills to the trainees have always been a challenging issue for the expert surgeons, and this is even more challenging in pandemics. The current method of surgery training needs the novice surgeons to attend some courses, watch some procedure, and conduct their initial operations under the direct supervision of an expert surgeon. Owing to the requirement of physical contact in this method of medical training, the involved people including the novice and expert surgeons confront a potential risk of infection to the virus. This survey paper reviews recent technological breakthroughs along with new areas in which assistive technologies might provide a viable solution to reduce the physical contact in the medical institutes during the COVID-19 pandemic and similar crises. © Copyright © 2021 Motaharifar, Norouzzadeh, Abdi, Iranfar, Lotfi, Moshiri, Lashay, Mohammadi and Taghirad.
Publication Date: 2021
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 385-390
Automatic surgical instruments detection in recorded videos is a key component of surgical skill assessment and content-based video analysis. Such analysis may be used to develop training techniques, especially in ophthalmology. This research focuses on capsulorhexis, the most fateful process in cataract surgery, which is a very delicate procedure and requires very high surgical skill. Assessment of the surgeon's skill in handling surgical instruments is one of the main parameters of surgical quality assessment, and requires the proper detection of important instruments and tissues during a surgical procedure. The traditional methods to accomplish this task are very time-consuming and effortful, and therefore, automating this process by using computer vision approaches is a stringent requirement. In order to accomplish this requirement, a proper dataset is prepared. By consulting the expert surgeons, the pupil, and the surgical tool, namely the capsulorhexis cystotome, are annotated in this dataset. Then, we created a general framework for implementing and examining different approaches, models, and techniques on the developed dataset, and reporting the comparative analysis. This study shows that the object detection task can be accurately performed for various scenarios using this dataset. The developed dataset, the developed deep learning general framework, and other developments are made public for further research. © 2021 IEEE.
Publication Date: 2021
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 59-65
This article elaborate on the kinematic and dynamic analysis of ARASH:ASiST, ARAS Haptic System for Eye Surgery Training, which is developed for vitrectomy eye surgery training. The mechanism selection of this system is reviewed first, in order to assist such a precise intraocular eye surgery training. Then the kinematics and dynamics analysis of the proposed haptic system is investigated. To verify the reported result, a prototype of ARASH:ASiST is modeled in MSC-ADAMS®, and the results of the dynamic formulation are validated. Finally, a common model-based controller is implemented on the real prototype, and it is verified that with such controller a suitable accuracy of 200 µm is attainable for the surgical instrument. © 2021 IEEE.
Publication Date: 2020
Mechanical Systems and Signal Processing (0888-3270)135
This article aims at developing a control structure with online authority adjustment for a dual-user haptic training system. In the considered system, the trainer and the trainee are given the facility to cooperatively conduct the surgical operation. The task dominance is automatically adjusted based on the task performance of the trainee with respect to the trainer. To that effect, the average norm of position error between the trainer and the trainee is calculated in a sliding window and the relative task dominance is assigned to the operators accordingly. Moreover, a robust controller is developed to satisfy the requirement of position tracking. The stability analysis based on the input-to-state stability (ISS) methodology is reported. Experimental results demonstrate the effectiveness of the proposed control approach. © 2019 Elsevier Ltd
Publication Date: 2020
IEEE Transactions on Control Systems Technology (10636536)28(6)pp. 2404-2415
The design problem for the control a dual-user haptic surgical training system is studied in this article. The system allows the trainee to perform the task on a virtual environment, while the trainer is able to interfere in the operation and correct probable mistakes made by the trainee. The proposed methodology allows the trainer to transfer the task authority to or from the trainee in real time. The robust adaptive nature of the controller ensures position tracking. The stability of the closed-loop system is analyzed using the input-to-output stability approach and the small-gain theorem. Simulation and experimental results are presented to validate the effectiveness of the proposed control scheme. © 1993-2012 IEEE.
Real-time instrument tracking is an essential element of minimally invasive surgery and has several applications in computer-assisted analysis and interventions. However, the instrument tracking is very challenging in the vitreo-retinal eye surgical procedures owing to the limited workspace of surgery, illumination variation, flexibility of the instruments, etc. In this article, as a powerful technique to detect and track surgical instruments, it is suggested to employ a convolutional neural network (CNN) alongside a newly produced ARAS-EYE dataset and OpenCV trackers. To clarify, firstly you only look once (YOLOv3) CNN is employed to detect the instruments. Thereafter, the Median-flow OpenCV tracker is utilized to track the determined objects. To modify the tracker, every 'n' frames, the CNN runs over the image and the tracker is updated. Moreover, the dataset consists of 594 images in which four 'shaft', 'center', 'laser', and 'gripper' labels are considered. Utilizing the trained CNN, experiments are conducted to verify the applicability of the proposed approach. Finally, the outcomes are discussed and a conclusion is presented. Results indicate the effectiveness of the proposed approach in detection and tracking of surgical instruments which may be used for several applications. © 2020 IEEE.
Publication Date: 2019
pp. 974-978
In this paper, an impedance control based training scheme for a dual user haptic surgery training system is introduced. The training scheme allows the novice surgeon (trainee) to perform a surgery operation while an expert surgeon (trainer) supervises the procedure. Through the proposed impedance control structure, the trainer receives trainee's position to detect his (her) wrong movements. Besides, a novel force reflection term is proposed in order to efficiently utilize trainer's skill in the training loop. Indeed, the trainer can interfere into the procedure whenever needed either to guide the trainee or suppress his (her) authority due to his (her) supposedly lack of skill to continue the operation. Each haptic device is stabilized and the closed loop stability of the nonlinear system is investigated. Simulation results show the appropriate performance of the proposed control scheme. © 2019 IEEE.
Publication Date: 2019
IEEE/ASME Transactions on Mechatronics (10834435)24(4)pp. 1553-1564
The controller design and stability analysis of a dual user training haptic system is studied. Most of the previously proposed control methodologies for this system have not simultaneously considered special requirements of surgery training and stability analysis of the nonlinear closed-loop system which is the objective of this paper. In the proposed training approach, the trainee is allowed to freely experience the task and be corrected as needed, while the trainer maintains the task dominance. A special S-shaped function is suggested to generate the corrective force according to the magnitude of motion error between the trainer and the trainee. The closed-loop stability of the system is analyzed considering the nonlinearity of the system components using the Input-to-State Stability approach. Simulation and experimental results show the effectiveness of the proposed approach. © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Publication Date: 2019
pp. 181-185
In this paper, an impedance controller with switching parameters for a dual-user haptic training system is introduced. The trainer and the trainee are connected through their haptic consoles, and the trainee performs the surgical procedure on the environment. The trainer can intervene in the procedure by pressing a mechanical pedal; thus, the control parameters are switched to transfer the authority over the task from the trainee to the trainer. The stability of each subsystem and the closed-loop stability of the overall system are investigated. The simulation results verify the performance of the proposed controller. © 2019 IEEE.
Publication Date: 2019
pp. 186-191
The aim of this paper is to develop a practical skill assessment for some designed experimental tasks, retrieved from Minimally Invasive Surgery. The skill evaluation is very important in surgery training, especially in MIS. Most of the previous studies for skill assessment methods are limited in the Hidden Markov Model and some frequency transforms, such as Discrete Fourier transform, Discrete Cosine Transform and etc. In this paper, some features have been extracted from time-frequency analysis with the Discrete Wavelet Transform and temporal signal analysis of some kinematic metrics which were computed from Geomagic Touch kinematic data. In addition, the k-nearest neighbors classifier are employed to detect skill level based on extracted features. Through cross-validation results, it is demonstrated that the proposed methodology has annrouriate accuracy in skill level detection. © 2019 IEEE.
Publication Date: 2018
pp. 422-427
The widespread use of minimally invasive surgery (MIS) demands an appropriate framework to train novice surgeons (trainees) to perform MIS. One of the effective ways to establish a cooperative training system is to use virtual fixtures. In this paper, a guiding virtual fixture is proposed to correct the movements of the trainee according to trainer hand motion performing a real MIS surgery. The proposed training framework utilizes the position signals of trainer to modify incorrect movements of the trainee which leads to shaping the trainee's muscle memory. Thus, after enough training sessions the trainee gains sufficient experience to perform the surgical task without any further help from the trainer. The passivity approach is utilized to analyze the stability of system. Simulation results are also presented to demonstrate the effectiveness of the proposed method. © 2018 IEEE.
Publication Date: 2017
pp. 192-197
This paper investigates the controller design problem for the dual user haptic surgical training system. In this system, the trainer and the trainee are interfaced together through their haptic devices and the surgical operations on the virtual environment is performed by the trainee. The trainer is able to interfere into the procedure in the case that any mistakes is made by the trainee. In the proposed structure, the force of the trainer's hands is reflected to the hands of the trainee to give necessary guidance to the trainee. The force signal is obtained from an unknown input high gain observer. The position of the trainee and the contact force with the environment are sent to the trainer to give him necessary information regarding the status of surgical operations. Stabilizing control laws are also designed for each haptic device and the stability of the closed loop nonlinear system is proven. Simulation results are presented to show the effectiveness of the proposed controller synthesis. © 2017 IEEE.
Publication Date: 2017
Mechatronics (09574158)46pp. 46-59
An iterative synthesizing strategy for robust force reflecting control of a Haptic exploration device is proposed. The proposed strategy guarantees the robust stability of the closed loop system with respect to uncertainties caused by the robot dynamics and environmental impedance as well as time-varying communication delays. In order to achieve the stability and performance objectives of the teleoperation system through a multiobjective optimization framework, a suboptimal robust controller is obtained with guaranteed global stability. Under a decentralized structure, the proposed approach provides a systematic design framework using H∞ robust approach in the presence of interconnection in the structure. Through experimental results, the improved performance of the proposed approach is demonstrated. © 2017 Elsevier Ltd
Publication Date: 2017
2018pp. 86-91
In this paper robust controller synthesis for a nonminimum phase (NMP) system in presence of actuator saturation is elaborated. The nonlinear model of a system is encapsulated with a nominal model and multiplicative uncertainties. Two robust control approaches namely mixed sensitivity H∞ and μ-synthesis are comparedfrom the robust stability and robust performance points of views. Finally, through simulation results it is demonstrated that both the robust controller approaches have superior performance compared to that of a conventional PID controller, while H∞ controller performs best. © 2017 IEEE.
Publication Date: 2017
Asian Journal of Control (19346093)19(2)pp. 625-635
In this paper, a robust output feedback control strategy is proposed for a nonlinear teleoperation system which can deal with stability as well as transparency despite the variable time-delay and uncertain dynamics. The proposed approach is composed of two steps. First, local Lyapunov based adaptive controllers are applied to both master and slave sides in order to suppress the nonlinearities in the system dynamics. Afterwards, a new observer-based controller scheme is proposed to achieve stability and performance (transparency) of the teleoperation system. Using the Lyapunov techniques, stability and performance objectives are cast as some linear matrix inequality (LMI) feasibility conditions. To evaluate the performance of the proposed controller, a set of simulations and experiments are performed. Through simulation results, it is demonstrated that the proposed approach significantly outperforms the existing methodologies reported in the literature. © 2016 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd
Publication Date: 2017
pp. 685-690
In this paper, a decentralized control structure is proposed based on H∞ robust control synthesis for teleoperated needle insertion in an iterative approach. Since the teleoperation system is subject to unknown time delays in the communication channels, the proposed methodology should be capable of dealing with nonlinearities and uncertainty in environment model and communication channel. The ideal transparency besides robust stability is achieved through a suboptimal solution in an H∞ optimization problem. The method is scrutinized in details for a reality-based model of soft tissues as environment. Simulation results reveal applicability of the proposed methodology for practical implementations. © 2017 IEEE.
Publication Date: 2016
Computers and Electrical Engineering (00457906)56pp. 700-714
The problem of designing a framework for simultaneous training and therapy in multilateral tele-rehabilitation systems is considered in this paper. The usage of robotic devices in the automation of rehabilitation procedure helps overcoming the limitation in conventional physiotherapy methods by decreasing duration of process and training sessions. The problem arises by increasing the number of robots interacting together in the tele-rehabilitation process. These robots are called “operators”. In this paper, a new approach is proposed to overcome such issues. The self-intelligence between the numbers of agents working together in the multi-agent system is the key item which is used to achieve the cooperative tele-rehabilitation system. Three scenarios are proposed for implementing practical tele-rehabilitation process to satisfy the aim of simultaneous therapy and training. A simulation was launched to verify the performance of the multi-agent network; additionally, experimental study was done on the AUTWRIST robot which was designed and implemented by the authors. The simulation results approved the superiority of the proposed method in the presence of a trainee, a patient, and a therapist all together. © 2016
Publication Date: 2016
pp. 302-307
The aim of this paper is to develop an adaptive force reflection control scheme for dual master nonlinear teleoperation systems. Having a sense of contact forces is very important in applications of dual master teleopreation systems such as surgery training. However, most of the previous studies for dual master nonlinear teleoperation systems are limited in the stability analysis of force reflection control schemes. In this paper, it is assumed that the teleopreation system consists of two masters and a single slave manipulator. In addition, all communication channels are subject to unknown time delays. First, adaptive controllers are developed for each manipulator. Next, Input-to-State Stability (ISS) approach is used to analyze the stability of the closed loop system. Through simulation results, it is demonstrated that the proposed methodology is effective in a nonlinear teleopreation system. © 2016 IEEE.
Publication Date: 2016
pp. 1318-1323
This technical note aims at proposing an adaptive control scheme for dual-master trilateral teleoperation in the presence of communication delay and dynamic uncertainty in the parameters. The majority of existing control schemes for trilateral teleoperation systems have been developed for linear systems or nonlinear systems without dynamic uncertainty or time delay. However, in the practical teleoperation applications, the dynamics equations are nonlinear and contain uncertain parameters. In addition, the time delay in the communication channel mostly exists in the real applications and can affect the stability of closed loop system. As a result, an adaptive control methodology is proposed in this paper that to guarantee the stability and performance of the system despite nonlinearity, dynamic uncertainties and time delay. Simulation results are presented to show the effectiveness of the proposed adaptive controller methodology. © 2016 IEEE.
Publication Date: 2016
pp. 136-141
This paper aims at designing a robust controller for a 2RT parallel robot for eye telesurgery. It presents two robust controllers designs and their performance in presence of actuator saturation limits. The nonlinear model of the robot is encapsulated with a linear model and multiplicative uncertainty using linear fractional transformations (LFT). Two different robust control namely, H∞ and μ-synthesis are used and implemented. Results reveal that the controllers are capable to stabilize the closed loop system and to reduce the tracking error in the presence of the actuators saturation. Simulation results are presented to show that effectiveness of the controllers compared to that of conventional controller designs. Furthermore, it is observed that μ-synthesis controller has superior robust performance. © 2016 IEEE.
Publication Date: 2015
IEEE/ASME Transactions on Mechatronics (10834435)20(4)pp. 1912-1919
In this paper, a control methodology is proposed for guiding and stabilizing flexible bevel-tip needles to an arbitrary planar slice. The proposed controller is an adaptive version of the previously proposed nonlinear output feedback controller, which was built upon the well-known nonholonomic kinematic model of needle steering. On the grounds that such a model is subject to parametric uncertainty, an adaptive controller is necessary for adjusting the model parameters. In addition, a nonlinear observer is required due to the existence of some unmeasurable state variables. Although the original form of the studied system is linearly parameterized, its canonical form is not, preventing the application of conventional adaptive control schemes. In this paper, a nonlinear adaptive control methodology is proposed and applied to the guidance problem of steerable needles. Through simulation results, it is illustrated that the proposed methodology greatly outperforms the existing feedback linearization-based approach. © 1996-2012 IEEE.
Publication Date: 2012
Proceedings of the American Control Conference (07431619)pp. 4849-4854
Flexible needles with a bevel tip (steerable needles) promise to enhance targeting accuracy and maneuver inside the human body in order to avoid collision with delicate organs. Contributing image feedback to needle insertion tasks greatly improves such objectives. An important issue in 2D motion planning tasks is stabilizing the needle in a desired plane. Any divergence from the plane leads to the inefficiency of the motion planning scheme. Hence, a control scheme is proposed in this paper which guides the needle to a desired plane. The system of such task is subject to parametric uncertainty. Although the original system is linearly parametrized, the feedback linearized form is not, which prevents the application of conventional adaptive control schemes. Moreover, all state variables of the system could not be measured and a nonlinear observer is necessary to observe the system states. In this paper, the previously proposed adaptive state feedback controller for such systems is modified to an adaptive output feedback controller and the proposed scheme is applied to the problem of needle guidance. Simulation results are presented to illustrate the enhanced performance of the proposed controller methodology as compared to previously proposed feedback linearization scheme. © 2012 AACC American Automatic Control Council).
Publication Date: 2011
pp. 970-975
In this paper, a new control methodology for single-master/multi-slave teleoperation systems is presented. First, the dynamics of slave robots and the tool are incorporated into an augmented system. Then, an adaptive sliding mode controller is proposed to transform both tool and master dynamics into desired impedances in the presence of unmodeled dynamics. Then, the traditional two channel architecture is modified in order to achieve prefect transparency. Finally, simulation results are presented to illustrate the enhanced performance of the proposed control methodology. © 2011 IEEE.
Publication Date: 2011
Proceedings of the American Control Conference (07431619)pp. 3710-3715
In this paper, the control of time-delay bilateral teleoperation systems is considered. Control complexity of such systems arises due to the nonlinear and uncertain dynamics of the system as well as the latency in data communication between the master and slave sides. Hence, a novel control scheme is proposed in this paper which improves both stability and transparency of the system despite the above mentioned limitations. The proposed controller is composed of two control loops. First, a local Lyapunov-based adaptive controller is applied (in both master and slave sides) to cancel system nonlinearities. Subsequently, a new observer-based controller scheme is proposed to achieve the stability and performance (transparency) objectives. Using the Lyapunov techniques, stability and performance objectives are cast as some Linear Matrix Inequality (LMI) feasibility conditions. Experimental results are presented to illustrate the enhanced performance of the proposed controller methodology. © 2011 AACC American Automatic Control Council.
