Haitham El-Hussieny

Continuum robot arms offer compliance and confined-space dexterity that rigid-link manipulators cannot match, but each published design is built around a fixed structural configuration serving a single motion type, so that finger-like, planar, and spatial motion each demand a separate design and model. This work introduces a tendon-driven, multi-bladed continuum robot arm in which the blade count is a design parameter, together with a procedure that assembles the corresponding multibody model automatically for any blade count. Static response is modeled in the absolute nodal coordinate formulation using the fully parameterized BE24 beam element with the strain-split method, which alleviates the Poisson locking the element otherwise exhibits for the near-incompressible material considered. The static model characterizes force-displacement response, stiffness modulation by axial preload, and the reachable set of each variant. Blade count governs both the compliance of the arm and the dimensionality of the reachable set—a curve, a planar region, and a spatial surface for one, two, and three blades—and the three-blade set is trilobed rather than circular, reflecting pull-only tendon actuation. One construction and one modeling procedure thus span finger-like, planar, and spatial manipulators, and form the parametric model base for subsequent optimal design, targeted experimental validation, and inverse kinematics.

Soft continuum robots exhibit highly nonlinear and configuration-dependent dynamics, making accurate trajectory tracking challenging under model uncertainty and external disturbances. This paper presents an adaptive Koopman-based model predictive control (MPC) framework for a tendon-driven soft continuum robot and evaluates its performance through comprehensive closed-loop simulations. A lifted linear Koopman model is identified from experimentally collected robot data and incorporated into an MPC formulation to provide computationally efficient prediction while capturing dominant nonlinear behavior. To compensate for plant–model mismatch and time-varying uncertainties, an online Recursive Least Squares (RLS) adaptation mechanism is integrated into the Koopman–MPC framework, enabling continuous model refinement during closed-loop operation without repeated offline retraining. The proposed controller is evaluated through comprehensive closed-loop simulations on circular, triangular, helical, and figure-eight trajectories under stochastic disturbances and structured parametric bias conditions using a Koopman model identified from experimentally collected robot data. Results demonstrate consistent improvements in tracking performance compared with fixed Koopman MPC while maintaining real-time computational feasibility. Under structured parametric bias, the proposed controller reduces the mean tracking error from 9.30 mm to 1.36 mm during circular trajectory tracking and achieves sub-millimeter accuracy in several operating conditions. These findings highlight the potential of online Koopman model adaptation for predictive control of soft continuum robots operating under uncertainty.

The growing demand for elderly and bedridden patient care in hospitals, nursing homes, and long-term care facilities has increased the need for safe and efficient repositioning methods. Repositioning immobile patients is essential for preventing pressure injuries and other complications associated with prolonged immobility. However, this task is still commonly performed manually using bed sheets, pillows, and similar support aids, making it physically demanding and increasing the risk of musculoskeletal injury among caregivers. This paper presents a two-stage soft growing robot for safe and assisted patient repositioning from a supine posture to a side-lying position. The proposed mechanism consists of two soft pneumatic chambers with distinct roles. The first chamber enables pressure-driven eversion, allowing the robot to deploy smoothly beneath the patient with minimal friction. The second chamber is then pressurized to generate the lifting and rolling motion required for repositioning. A first-principles dynamic model of the pressure-driven vine robot is developed by integrating pneumatic supply dynamics, internal pressure evolution, and tip-extension mechanics within a Lagrangian framework. Based on this model, a robust multi-stage nonlinear model predictive control strategy is formulated to regulate deployment beneath the patient under parameter uncertainty. The rolling dynamics of the second stage are also analyzed to determine the minimum pressure required for repositioning as a function of patient weight and roll angle. Simulation results show that the proposed controller achieves smooth and accurate deployment while satisfying input and state constraints under uncertainty. The rolling analysis further indicates that the required pressure increases with patient weight and decreases with roll angle. These findings demonstrate the potential of the proposed mechanism to reduce caregiver effort and enable safe, controlled patient repositioning.

Purpose: This study aims to develop a data-driven Deep Koopman-based model predictive control (DK-MPC) framework for real-time trajectory tracking and stabilization of quadrotor systems. The objective is to overcome the computational limitations of conventional nonlinear model predictive control (NMPC) while preserving high-accuracy control performance. Design/methodology/approach: A deep neural network-based Koopman operator is employed to map the nonlinear quadrotor dynamics into a globally linear latent space, enabling the formulation of a computationally efficient linear model predictive control (MPC) problem. The Koopman embeddings are trained using the publicly available WaveLab Pelican dataset to capture the coupled and nonlinear dynamics of the quadrotor. The proposed DK-MPC framework is evaluated through numerical simulations involving point stabilization and trajectory tracking tasks, including previously unseen helical trajectories. Performance is assessed in terms of tracking accuracy, computational efficiency and real-time feasibility, with comparisons made against conventional NMPC. Findings: Simulation results demonstrate that the proposed DK-MPC framework achieves high-precision control with a coefficient of determination (R2) of 99%. The method requires approximately 10% of the computation time per control step compared to NMPC. For a prediction horizon of H = 50, DK-MPC maintains real-time feasibility with an average computation time of 15 ms per control step, while NMPC frequently fails to converge within the required time constraints. These findings confirm the effectiveness of the Koopman-based linear embedding in reducing computational burden without compromising control accuracy. Originality/value: The study presents a scalable and computationally efficient integration of deep Koopman operator learning with MPC for quadrotor systems. By combining data-driven nonlinear system representation with linear MPC optimization, the proposed framework bridges the gap between advanced learning-based modeling and real-time predictive control implementation.

Robotic-assisted rehabilitation is a promising method for improving motor function in individuals with upper limb impairments. However, generation of personalized and adaptive assistance patterns remains a challenge. Our study introduces a Learn-from-Therapist Demonstration (LfTD) framework, which employs Dynamic Movement Primitives (DMP) to train a robot arm to learn from therapist skills. Initially, therapist movements were captured via visual tracking, and the DMP accurately learned and replicated these motions via a robotic arm to assist patients. These movements were then effectively generalized to new goals while maintaining the original motion patterns. Meanwhile, a Model Reference Adaptive Controller (MRAC) has been utilized to refine the robot's adaptive performance while assuring demonstration tracking. We assessed the LfTD's efficacy with a simulated two-link robot, which showed excellent learning, adaptation, and ability to perform complex rehabilitation tasks with precise trajectory tracking. Further tests evaluated the MRAC's robustness against introduced human deviations, demonstrating its resilience and adaptability. These findings highlight LfTD’s potential to improve upper limb robotic-assisted rehabilitation through precise, adaptable motion replication, setting the stage for clinical trials with actual robots.

This paper presents an experimental validation of a decentralized Model Predictive Control (MPC) framework for cooperative object transportation utilizing a multi-robot system consisting of two mobile robots. Each robot is a differential-drive robot that independently solves local constrained optimization problems while ensuring global coordination through joint-space coupling. The formulation explicitly captures nonlinear kinematics, revolute-prismatic joint dynamics, inter-robot constraints, and dynamic obstacle avoidance within a real-time optimization setting. Adaptive weighting of cost terms is employed to balance trajectory tracking and formation objectives under varying task demands. The framework is deployed on a physical testbed integrating vision-based pose estimation, sensor fusion via a Kalman filter, and a ROS 2 control infrastructure. Experiments across point-to-point, curvilinear, and obstacle-rich scenarios show accurate trajectory tracking, strict constraint satisfaction, and robustness to environmental uncertainties. These results substantiate decentralized constrained MPC with adaptive weights as a practical and scalable solution for real-time multi-robot cooperative transport along arbitrary reference paths.

This study presents U-Net deep learning of total electron content (TEC) obtained from Global Ionosphere Maps (GIMs) to forecast ionospheric TEC over the African 0–40° N latitude sector during geomagnetic storms which have occurred between 2011 and 2024. Before being utilized in the deep learning procedure, the GIM-TEC data were improved by assimilating ground-based vertical TEC (VTEC) observations from available Global Navigation Satellite System (GNSS) receiver stations. The U-Net one-hour-ahead prediction of TEC was examined during the intense geomagnetic storm of May 2024. Additionally, the model’s accuracy and reliability were evaluated through quantitative comparison with established climatological models, including IRI-2020 and AfriTEC storm time models. The results indicate that the integration of data assimilation with the deep learning framework yields TEC estimates that closely agree with observations, achieving a RMSE of approximately 5 TECU. On the other hand, the IRI-2020 model exhibits substantially larger errors, with RMSE ~10–17 TECU, while the AfriTEC model shows the poorest performance, with RMSE reaching approximately 15–22 TECU. Further, the U-Net was validated using two equatorial and mid-latitude GNSS stations whose data were excluded from the assimilation process, achieving RMSE values of 4.44 and 6.75 TECU and correlation coefficients of 0.93 and 0.97, confirming the model forecasting capability for reproducing ionospheric TEC variability. These results establish the model as a precise, robust tool for TEC prediction in regions with sparse GPS coverage that is crucial for ionospheric monitoring and space weather applications.

Parameter estimation plays an important role in improving the accuracy, control, and diagnostic performance of mechanisms, particularly in parallel mechanisms such as the Stewart platform, which are increasingly used in high-precision automation, advanced manufacturing, and machine-centric applications. This paper presents a multibody–based framework for generalized dynamic modeling and inertial parameter estimation of parallel robotic manipulators, demonstrated on the DeltaLab-SMT EX800 Stewart platform. A systematic constrained multibody dynamic formulation is developed using an iterative kinematic–dynamic coupling scheme to compute generalized coordinates and their time derivatives under prescribed motion trajectories. The proposed identification manifold is experimentally validated on the physical test rig, in which the platform motion is executed via the control/DAQ system, while inertial measurements are acquired using an external 6-axis motion sensor to obtain direct acceleration data from the moving platform. Platform acceleration measurements are mapped through the inverse dynamics of the multibody model to derive the corresponding generalized forces, providing a practical and cost-effective alternative to direct force measurement with transducers. A Kalman filter is subsequently employed to combine the measured and the model-predicted data, yielding optimally filtered estimates of the inertial coordinates for accurate parameter identification. Inertial parameters are estimated using particle swarm optimization and bench marked against a gradient-based Levenberg–Marquardt approach, with comparison in terms of convergence behavior, robustness, and estimation accuracy. The results support the proposed framework as a measurement-informed benchmark methodology for parameter estimation of parallel manipulators.