Electrical and Electronic Engineering, Control and Systems Engineering
32
Scopus Publications
639
Scholar Citations
15
Scholar h-index
16
Scholar i10-index
Scopus Publications
Advancements and theoretical foundations of cable-driven parallel robots: A comprehensive overview Kamran Joyo, Han Yuan, Jun Wu, Wenfu Xu, Honghao Yue Chinese Journal of Mechanical Engineering English Edition, 2026 Parallel robotic mechanisms using cables instead of rigid limbs are termed cable-driven parallel robots (CDPRs). Electric motors and pulley mechanisms actuate cables to provide motion for an end-effector in a cable robot. Consequently, CDPRs have emerged as indispensable tools across a spectrum of industrial and technological domains, including astronomy, aerospace, logistics, simulators, and rehabilitation. Their inherent compatibility with the evolving concept of rigid-flexible fusion places CDPRs at the forefront of cutting-edge robotics research. This comprehensive paper aims to consolidate the core theories and advancements underpinning CDPRs, encompassing key aspects such as configuration design, cable-force distribution, workspace and stiffness analysis, performance evaluation, optimisation techniques, and motion control. We provide in-depth insights into kinematic modelling, workspace exploration, and cable-force solutions. Furthermore, the paper delves into the intricacies of stiffness and dynamic modelling, presenting a range of analytical methods to elucidate their effects on CDPR performance. Addressing reliability concerns and developing a unified control framework are identified as essential in ensuring the practical deployment of CDPRs in real-world scenarios. This research paper offers a comprehensive overview of the theories and advancements in CDPRs, identifying critical areas for further research and development to unlock the full potential of these versatile and high-performance robotic systems.
A Comprehensive Framework of Ant Colony Optimization for Optimal Control of Upper Extremity Rehabilitation Robot Muhammad Kamran Joyo, Izanoordina Ahmad, Kushsairy Abdul Kadir, Talha Ahmed Khan, Noaman M Noaman Iium Engineering Journal, 2026 Passive rehabilitation of the upper limb requires precise joint positioning to facilitate motor recovery and ensure patient safety. For a two-degree-of-freedom (DOF) elbow robot, the primary challenge is unwanted disturbances arising from human coupling and voluntary and involuntary forces applied by the subject. This study addresses this issue by proposing a comprehensive control framework based on a nature-inspired meta-heuristic approach. The robot dynamics were obtained using the Lagrangian formulation, which was then combined with a realistic actuator model, yielding a closed-loop transfer function that describes system behavior. A two-degree-of-freedom (2-DOF) PID controller was used as the position controller. To determine the optimal PID parameters, an ant colony optimization (ACO) algorithm is used with an appropriate performance index to select the PID parameter values. The resulting ACO-based PID controller showed significant improvement over the conventional Ziegler–Nichols (Z-N) approach, as it could reject significant patient-induced forces, maintain the joint trajectory with the reference, and remain stable even under perturbations. Simulations were performed, confirming that the optimized controller delivers smooth, precise movements, reduces tracking error, and enhances the safety of passive rehabilitation procedures. ABSTRAK: Pemulihan secara pasif di bahagian anggota atas pesakit memerlukan kedudukan sendi yang tepat bagi memudahkan pemulihan motor pesakit daripada bahaya atau cedera. Manakala pada robot siku dua darjah kebebasan (DOF), cabaran utama ialah mengelakkan gangguan yang tidak diingini oleh manusia dan daya sukarela atau tidak sukarela yang dikenakan oleh subjek. Kajian ini mencadangkan rangka kerja kawalan komprehensif berdasarkan pendekatan meta-heuristik yang diilham oleh alam semula jadi. Dinamik robot diwujudkan dengan menggunakan rumusan Lagrangian, diikuti dengan gabungan model penggerak realistik, agar dapat menghasilkan fungsi pemindahan gelung tertutup yang boleh menggambarkan tingkah laku sistem. Pengawal PID 2-DOF pula digunakan sebagai pengawal kedudukan. Bagi mencapai parameter optimum PID, algoritma Pengoptimuman Koloni-Semut (ACO) digunakan dengan indeks prestasi yang sesuai agar dapat memilih PID paling optimum. Pengawal PID berasaskan ACO yang terhasil mengatasi pendekatan Ziegler Nichols (Z-N) konvensional, kerana ia mempunyai keupayaan menolak daya teraruh pesakit yang ketara sambil mengekalkan trajektori bersama rujukan dan kekal stabil walaupun ketika gangguan. Simulasi yang dijalankan dalam kajian ini mengesahkan bahawa pengawal yang dioptimumkan berjaya mencapai pergerakan yang lebih lancar, tepat, seterusnya mengurangkan ralat pengesanan serta mempromosi keselamatan prosedur pemulihan pasif.
Optimal PID controller with evolutionary optimization algorithms for rehabilitation robots Kamran Joyo, Talha Ahmed Khan, Kushsairy Abdul Kadir, Mohd Nizam Bin Husen, Haidawati Mohamad Nasir, Munaza Azhar Discover Applied Sciences, 2025 Precision and smoothness in controlling mechanism is a mandatory requirement for those robotic applications, which are associated with upper limb rehabilitation and comprise of five degree of freedom. This study comprises of analysis of a vast range of techniques of optimization, for determining ideal parameters of a Proportional Integral Derivative (PID) controller, which is managing a dynamic model of the upper limb rehabilitation system. The control technique which has been proposed was carried out on a five-degree of freedom (DOF) hardware, and priory to any trials under clinics, more than five healthy subjects underwent trials following strict protocols. There exist evidences of rejection to the external disturbances by the control technique, alongside keeping the system stable during the course of harsh and extremely uneven circumstances. This research study validates the mathematical models for finding optimizing parameters of the PID, including a comparative study comprising of four diverse cost functions. The objective functions include Integral Absolute Error (IAE) and Integral Squared Error (ISE). A variety of PID tuning methods were assessed based on core performance indicators such as Integral Square Error (ISE), Integral Absolute Error (IAE), overshoot, rise time, and settling time. The PID controller tuned using Particle Swarm Optimization (PSO) struck a good balance, with relatively low IAE, moderate overshoot (4.51%), and a decent settling time of 8.28 s. The Firefly algorithm also delivered promising results, achieving the shortest rise time (0.689 s) and a quick settling time (1.08 s), while keeping the overshoot (4.45%) nearly on par with PSO. In comparison, Ant Colony Optimization (ACO) produced a significantly higher overshoot of 29.6% and longer settling behavior. The Artificial Bee Colony (ABC) method stood out with the lowest settling time (0.84 s) and minimal overshoot (4.29%). The classical Ziegler-Nichols approach, however, showed poor performance, with a high overshoot of 55.3% and slower system response. Overall, these outcomes suggest that nature-inspired techniques, especially Firefly and ABC, can offer more efficient and stable control than conventional tuning methods.
Controlling Cable Driven Parallel Robots Operations—Deep Reinforcement Learning Approach Muhammad Kamran Joyo, Abdulmajeed M. Alenezi, Wenfu Xu, Mohamad A. Alawad, Muhmmad Tayyab Yaqoob, Noor Maricar, Sheroz Khan IEEE Access, 2025 Deep Reinforcement Learning (DRL) is a powerful approach for generating control strategies for a variety of complex systems, representing an emerging paradigm in control applications. An important feature of Deep RL is that it does not explicitly model the process, but instead it relies on optimization-driven techniques to devise effective control policies. Despite its remarkable success in simulated environments, RL holds great potential in real-world applications. This article explores the complex challenges involved in implementing Deep Reinforcement Learning (DRL) algorithms on a cable-driven parallel robot. A key contribution of this work as specific advancement is the integration of a Proportional-Integral-Derivative (PID) controller within the RL framework, establishing a unique approach to CDPR control that leverages adaptive learning capabilities. A Reinforcement Learning (RL) agent for reference tracking is trained using the novel application of the adaptive-featured Twin Delayed Deep Deterministic (TD3) policy gradient algorithm, tailored to enhance CDPR adaptability and precision in dynamic environments. The first step is to test the performance of the trained agent on point-to-point robotic application tasks. As a result of such tasks, it is possible to evaluate the level of adaptability and performance of the RL agent. Multiple experiments are conducted to assess the versatility of the RL agent involving linear and circular scenarios. This research significantly advances the field by demonstrating the applicability of RL for complex robotic structures like CDPRs, showcasing promising results that underline the robustness and adaptability of the proposed approach. As a result of the TD3 adaptive learning process, the trained agent is able to perform the designated action in order to determine which policy stands out as the most rewarding.
Optimal Distributed Generation Placement in Radial Distribution System Using Particle Swarm Optimization Muhammad Shahab, Abdul Wahab Usman Ullah, Muhammad Shehzad, Syed Bilal Shah, M. Kamran Joyo, Sheroz Khan Icetas 2024 9th IEEE International Conference on Engineering Technologies and Applied Sciences, 2024 This paper addresses losses of power in Radial Distribution Systems (RDS), which significantly affect voltage levels and operational costs. The main aim is to optimize the positioning and size of Distributed Generation (DG) units, including Photovoltaic (PV) cells, and Wind Turbines (WT), to analyze the effect of DG placement in reducing the losses in power and enhancing the voltage profiles in radial distribution system (IEEE 33 bus system). This research employs Particle Swarm Optimization (PSO), a robust algorithm well-suited for tackling non-linear optimization issues in order to identify the appropriate placement and size for DG units. A number of scenarios with varying numbers of DG units are simulated, indicating significant reductions in active as well as reactive power losses. Likewise, the consistency and reliability requirements of modern distribution systems are improved as the voltage profile is improved. The key findings demonstrate that optimal DG integration enhances system efficiency, contributes to operational cost savings, and improves grid stability. PSO was chosen for its ability to effectively balance computational effort while achieving high accuracy in minimizing the losses associated with power and enhancing voltage profiles. In contrast to traditional optimization techniques, PSO offers superior accuracy and efficiency in addressing the challenges of non-linear optimization in RDS.
Comparative Analysis for Machine-Learning-Based Optimal Control of Upper Extremity Rehabilitation Robots † Muhammad Kamran, Talha Ahmed Khan, Umar Iftikhar, Safdar A. Rizvi, Irfan Tanoli, Kushsairy Kadir Engineering Proceedings, 2023 It has been observed from many pieces of research and through applications that robotic movements using human interaction are considered dangerous, tiresome and require extraordinary precision and smooth control.
Anticipating in Vehicle Accident using Recurrent Neural Network Waqar Riaz, Saifullah, Junaid Tahir, Waqas Khalid, Kamran Joyo, Shabeer Ahmad, Abdullah Azeem, Mudassar Laghari 2022 IEEE 5th International Conference on Information Systems and Computer Aided Education Iciscae 2022, 2022 This paper proposes an accident anticipation framework based on combination of Resnet, Gated Recurrent Unit (GRU) with Attention Mechanism (AM), by which model is capable of predicting most accidents earlier than it actual time, thus preventing casualties. Therefore, this work combines GRU with the advantages of Attention module and optimize the model structure to ensure more good learning efficiency, hence, achieving better prediction performance. For this framework, the input frames are initially passed by Faster RCNN network for detection of meaningful objects within the given number of frames. Second, the resnet is used to extract feature dependencies so that spatial information can be effectively used and input data dimensions reduced. To capture the temporal dependencies, the GRU is employed in the later phase. As a last step, we introduce the attention layer to emphasize its key features for better prediction accuracy. According to the results, the proposed method achieves 72% AP and 3.7% of ATTA for accident prediction.
Muscle Fatigue Detection and Analysis Using EMG Sensor Syed Faiz Ahmed, M. Kamran Joyo, Hussain Falih Mahdi, Isho James Kiwarkis 7th IEEE International Conference on Engineering Technologies and Applied Sciences Icetas 2020, 2020 Electromyography (EMG) is a sensor to diagnose the health of muscle with the placement of electrodes through the surface of the skin. Nowadays, muscular disorder occurs where the problem is the cause of muscle weakness, pain, fatigue, and also due to paralysis. Biomedical applications where the electromyography (EMG) sensor is used to collect data of a normal muscles contradiction, and there are two classifiers which are linear discriminant analysis (LDA) and support vector machines (SVM) that are prompt to undergo with the process and also mean absolute value feature (MAV) traces are used to extract from the electromyography (EMG). Ascertain part of body muscle targeted with the different subject with certain body mass index (BMI), the movement contradiction of the muscles through the sensor pad electrodes that are attached to the human body where the point of muscles that wanted to observe and the data collected or being used. The study provides guidelines for the prediction of muscle fatigue using electromyography (EMG) sensors with classification methods that are approved.
Study of Parametric Effects due to Mutual Coupling using High Permittivity Dielectric-Director in Planar Array Configuration Faraz Ahmed Shaikh, Sheroz Khan, Bilal Ahmad Alvi, AHM Zahirul Alam, Dominique Baillargeat, M. Kamran Joyo 7th IEEE International Conference on Engineering Technologies and Applied Sciences Icetas 2020, 2020 This paper evaluate the parametric effects due to mutual coupling between inter-elements using balance antipodal Vivaldi antenna (BAVA) with high permittivity dielectric director in an array configuration. An elliptical shaped dielectric-director made by the substrate of Rogers RO-3010 with high permittivity of εr=10.2 is used with BAVA in order to enhance the radiation characteristics of the antenna in focus. An antenna (66mm × 60.75mm) in dimension designed on FR4 substrate, which covers ultra-wide frequency band of 114.28% from 3GHz to 8GHz. The performance of an antenna with dielectric director is evaluated in a capacity of gain enhancement, directivity, regular radiation pattern with low side lobe level and beam width. Further, the use of dielectric directors in planner antenna array based configuration can provide significantly change in radiation characteristic and also provide help in the reduction of mutual coupling inter-elements. The same results can be obtained by using four elements with high permittivity dielectric directors in the planner antenna array based arrangement instead of eight or more elements without directors. This is an alternative way to reduce the number of elements in an antenna array based system. The design and optimization progression is realized out using CST simulation software.
Review on Sliding Mode Controller and Its Modified Types for Rehabilitation Robots Syed Faiz Ahmed, Yarooq Raza, Hussain F. Mahdi, W. M Wan Muhamad, M. Kamran Joyo, Asadullah Shah, M.Y. Koondhar Icetas 2019 2019 6th IEEE International Conference on Engineering Technologies and Applied Sciences, 2019 Recent advancements in the field of rehabilitation robotics have led to the development of variety of exoskeletons that are used extensively during therapeutic exercises for the poststroke patients. These robotic devices are meant to provide ease and comfort to the patient undergoing therapy, so controlling these robots effectively is very crucial. Widely used control algorithms for controlling these devices are linear controllers which are not much robust and efficient as compared to non-linear controllers. Since rehabilitation robots are highly nonlinear in nature, therefore nonlinear controllers are required that can perform well under uncertainties and resistant to parametric changes. One of the most extensively used non-linear control method is Sliding Mode Controller (SMC). SMC has proved its robustness in terms of trajectory tracking, stability and insensitiveness to disturbances and parametric uncertainties. One drawback of using classical SMC is chattering, which is high frequency oscillations. This paper presents sliding mode controller and its different types along with the method of chattering removal, for controlling rehabilitation robotic devices.
Hardware implementation of fast-sequency ordered complex hadamard transform Syed Safi Uddin Qadri, Choudhry Fahad Azim, D. Hazry, S. Faiz Ahmed, M. Kamran Joyo, M. Hassan Taveer, F. A. Warsi Proceedings 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications Cspa 2014, 2014
A Comprehensive Framework of Ant Colony Optimisation for Optimal Control of Upper Extremity Rehabilitation Robot MK Joyo, I Ahmad, KA Kadir, TA Khan, NM Noaman IIUM Engineering Journal 27 (2), 529-544 , 2026 2026
Advancements and Theoretical Foundations of Cable-Driven Parallel Robots: A Comprehensive Overview K Joyo, H Yuan, J Wu, W Xu, H Yue Chinese Journal of Mechanical Engineering, 100136 , 2025 2025
Controlling cable driven parallel robots operations—deep reinforcement learning approach MK Joyo, AM Alenezi, W Xu, MA Alawad, MT Yaqoob, N Maricar, S Khan IEEE Access 13, 36212-36223 , 2025 2025 Citations: 3
Optimal PID controller with evolutionary optimization algorithms for rehabilitation robots HMNMA Kamran Joyo, Talha Ahmed Khan, Kushsairy Abdul Kadir, Mohd Nizam Bin Husen Discover Applied Sciences , 2025 2025 Citations: 6
Implementing Ant Colony Optimization for Multi-Robot Coordination in Unmanned Ground Robots M Shafiq, SR Talpur, F Jafri, MK Joyo, U Laila Sir Syed University Research Journal of Engineering & Technology 14 (2), 108-113 , 2024 2024
Optimal distributed generation placement in radial distribution system using particle swarm optimization M Shahab, AWU Ullah, M Shehzad, SB Shah, MK Joyo, S Khan 2024 IEEE 9th International Conference on Engineering Technologies and … , 2024 2024
Proficient Optimization for Precise control of Rehabilitation Robots using Machine Learning Algorithms: PSO and Firefly Comparison Muhammad Kamran, Talha Ahmed Khan, Umar Iftikhar, Safdar A. Rizvi, Irfan ... 8th International Electrical Engineering Conference (Eng. Proc. 2023, 46(1), 34) , 2023 2023
Comparative Analysis for Machine-Learning-Based Optimal Control of Upper Extremity Rehabilitation Robots Muhammad Kamran, Talha Ahmed Khan, Umar Iftikhar, Safdar A. Rizvi, Irfan ... 8th International Electrical Engineering Conference (Eng. Proc. 2023, 46(1), 34) , 2023 2023
Muscle fatigue detection and analysis using EMG sensor SF Ahmed, MK Joyo, HF Mahdi, IJ Kiwarkis 2020 IEEE 7th International Conference on Engineering Technologies and … , 2020 2020 Citations: 8
Study of parametric effects due to mutual coupling using high permittivity dielectric-director in planar array configuration FA Shaikh, S Khan, BA Alvi, AHMZ Alam, D Baillargeat, MK Joyo 2020 IEEE 7th International Conference on Engineering Technologies and … , 2020 2020 Citations: 1
Controller for Upper Limb Rehabilitation Robot MK Joyo, Y Raza, SF Ahmed, MM Billah, K Kadir Cognitive Robotics & Control, 61 , 2020 2020
Review on sliding mode controller and its modified types for rehabilitation robots SF Ahmed, Y Raza, HF Mahdi, WMW Muhamad, MK Joyo, A Shah, ... 2019 IEEE 6th International Conference on Engineering Technologies and … , 2019 2019 Citations: 21
Firefly optimised pid control for upper extremity rehabilitation robot MK Joyo, Y Raza, K Kadir, K Naidu, SF Ahmed, S Khan 2019 IEEE International Conference on Smart Instrumentation, Measurement and … , 2019 2019 Citations: 5
Model predictive control for upper limb rehabilitation robotic system under disturbed condition SF Ahmed, A Ali, SY Raza, KA Kadir, MK Joyo, K Naidu AIP Conference Proceedings 2129 (1), 020123 , 2019 2019 Citations: 6
Optimized proportional-integral-derivative controller for upper limb rehabilitation robot MK Joyo, Y Raza, SF Ahmed, MM Billah, K Kadir, K Naidu, A Ali, ... Electronics 8 (8), 826 , 2019 2019 Citations: 68
Optimized Proportional-Integral-Derivative Controller for Upper Limb Rehabilitation Robot. Electronics MK Joyo, Y Raza, SF Ahmed, MM Billah, K Kadir, K Naidu, A Ali, ZM Yusof 2019 Citations: 6
Model predictive control for upper limb rehabilitation robotic system under noisy condition SY Raza, SF Ahmed, A Ali, KA Kadir, MK Joyo, S Khan, Z Janin 2018 IEEE 5th international conference on smart instrumentation, measurement … , 2018 2018 Citations: 12
Optimization of PID using PSO for upper limb rehabilitation robot Y Raza, SF Ahmed, A Ali, MK Joyo, KA Kadir 2018 IEEE 5th International Conference on Engineering Technologies and … , 2018 2018 Citations: 20
Energy and Environmentally Efficient Architectural Model Using Native and Adopted Strategies for Lyari Hospital MJ SHAIKH, YF AZEEM, MK JOYO, SF AHMED Sindh University Research Journal (Science Series) 50 (3D), 101-109 , 2018 2018
Efficient Maneuvering and Control Designing of Unmanned Underwater Vehicle (UUV) A Ali, SF Ahmed, SYR Naqvi, MK Joyo Sindh University Research Journal (Science Series) 50 (3D), 95-100 , 2018 2018 Citations: 3
MOST CITED SCHOLAR PUBLICATIONS
Stabilized controller design for attitude and altitude controlling of quad-rotor under disturbance and noisy conditions MH Tanveer, SF Ahmed, D Hazry, FA Warsi, MK Joyo American Journal of Applied Sciences 10 (8), 819 , 2013 2013 Citations: 74
Optimized proportional-integral-derivative controller for upper limb rehabilitation robot MK Joyo, Y Raza, SF Ahmed, MM Billah, K Kadir, K Naidu, A Ali, ... Electronics 8 (8), 826 , 2019 2019 Citations: 68
Yaw, Pitch and Roll controller design for fixed-wing UAV under uncertainty and perturbed condition FA Warsi, D Hazry, SF Ahmed, MK Joyo, MH Tanveer, H Kamarudin, ... 2014 IEEE 10th International Colloquium on Signal Processing and its … , 2014 2014 Citations: 51
Altitude and horizontal motion control of quadrotor UAV in the presence of air turbulence MK Joyo, D Hazry, SF Ahmed, MH Tanveer, FA Warsi, AT Hussain 2013 IEEE Conference on Systems, Process & Control (ICSPC), 16-20 , 2013 2013 Citations: 49
Attitude stabilization of Quad-rotor (UAV) system using Fuzzy PID controller (an experimental test) SF Ahmed, K Kushsairy, MIA Bakar, D Hazry, MK Joyo 2015 Second International Conference on Computing Technology and Information … , 2015 2015 Citations: 42
Fuzzy PID controller for upper limb rehabilitation robotic system A Ali, SF Ahmed, KA Kadir, MK Joyo, RNS Yarooq 2018 IEEE international conference on innovative research and development … , 2018 2018 Citations: 39
Mobility assistance robot for disabled persons using electromyography (EMG) sensor SF Ahmed, A Ali, MK Joyo, M Rehan, FA Siddiqui, JA Bhatti, A Liaquat, ... 2018 IEEE International Conference on Innovative Research and Development … , 2018 2018 Citations: 32
NMPC-PID based control structure design for avoiding uncertainties in attitude and altitude tracking control of quad-rotor (UAV) MH Tanveer, D Hazry, SF Ahmed, MK Joyo, FA Warsi, H Kamaruddin, ... 2014 IEEE 10th International Colloquium on Signal Processing and its … , 2014 2014 Citations: 31
MPC-PID comparison for controlling therapeutic upper limb rehabilitation robot under perturbed conditions A Ali, SF Ahmed, MK Joyo, K Kushsairy 2017 IEEE 3rd International Conference on Engineering Technologies and … , 2017 2017 Citations: 24
Review on sliding mode controller and its modified types for rehabilitation robots SF Ahmed, Y Raza, HF Mahdi, WMW Muhamad, MK Joyo, A Shah, ... 2019 IEEE 6th International Conference on Engineering Technologies and … , 2019 2019 Citations: 21
Position controller design for quad-rotor under perturbed condition MK Joyo, SF Ahmed, D Hazry, MH Tanveer, FA Warsi Wulfenia Journal 20 (7), 178-189 , 2013 2013 Citations: 21
Optimization of PID using PSO for upper limb rehabilitation robot Y Raza, SF Ahmed, A Ali, MK Joyo, KA Kadir 2018 IEEE 5th International Conference on Engineering Technologies and … , 2018 2018 Citations: 20
Augmented reality with Haptic technology based online experimental based distance learning education technique SF Ahmed, G Banky, A Blicblau, MK Joyo AIP Conference Proceedings 1775 (1), 030068 , 2016 2016 Citations: 19
Energy conservation and management system using efficient building automation SF Ahmed, D Hazry, MH Tanveer, MK Joyo, FA Warsi, H Kamarudin, ... AIP Conference Proceedings 1660 (1), 090019 , 2015 2015 Citations: 19
Disturbance and noise rejection controller design for smooth takeoff/landing and altitude stabilization of quad-rotor MH Tanveer, SF Ahmed, D Hazry, MK Joyo, FA Warsi Journal of Applied Sciences Research 9 (5), 3316-3327 , 2013 2013 Citations: 15
Model predictive control for upper limb rehabilitation robotic system under noisy condition SY Raza, SF Ahmed, A Ali, KA Kadir, MK Joyo, S Khan, Z Janin 2018 IEEE 5th international conference on smart instrumentation, measurement … , 2018 2018 Citations: 12
Robotic exoskeleton control for lower limb rehabilitation of knee joint SF Ahmed, MK Joyo, A Ali, AMM Ali, KA Kadir, YR Naqvi, BA Bakar, ... International Journal of Engineering & Technology 7 (2.34), 56-59 , 2018 2018 Citations: 9
LQR based controller design for altitude and longitudinal movement of quad-rotor SF Ahmed, K Kadir, MK Joyo 2017 Citations: 9
Muscle fatigue detection and analysis using EMG sensor SF Ahmed, MK Joyo, HF Mahdi, IJ Kiwarkis 2020 IEEE 7th International Conference on Engineering Technologies and … , 2020 2020 Citations: 8
Control Strategies for Robot Therapy A Ali, SF Ahmed, MK Joyo, A Malik, M Ali, K Kadir, ZM YUSOF 2017 Citations: 8
