Post-stroke gait abnormalities significantly reduce mobility, independence, and quality of life. This study presents a simulation-based evaluation of a unilateral thigh-and-shank knee exoskeleton modeled in OpenSim to assist gait correction in stroke survivors. Using subject-specific scaling, inverse kinematics, and dynamic simulations, joint kinematics of able-bodied, post-stroke, and exoskeleton-assisted individuals are analyzed. A proportional controller applied assistive torque at the knee joint to enhance gait alignment. Quantitative metrics are employed to assess performance, including range of motion analysis, RMS deviation, dynamic time warping, and Spearman’s correlation. The exoskeleton achieved a notable 87.1% similarity in knee joint trajectories to healthy gait, improved Spearman correlation at the hip from 0.59 to 0.96, and enhanced joint coordination patterns. While knee correction was most effective, moderate improvements were observed at the ankle and partial correction at the hip. These results highlight the potential of targeted exoskeleton assistance and musculoskeletal simulation for advancing personalized stroke rehabilitation strategies.

This paper proposes a novel Puma Optimizer-based PID control (PO-PID) strategy for quadruped robots and presents a comprehensive comparison against two established approaches: Harris Hawks Optimization-PID (HHO-PID) and Slime Mould Algorithm-PID (SMA-PID). While conventional PID tuning methods struggle with high-dimensional search spaces and local optima, the PO algorithm leverages adaptive exploration-exploitation mechanisms inspired by puma hunting behavior to achieve globally optimal PID gains. Detailed simulations evaluate the three controllers in terms of trajectory accuracy and control effort. The PO-PID controller achieved the lowest joint trajectory error and significantly outperformed HHO-PID and SMA-PID in Cartesian accuracy while maintaining a lower or comparable torque demand. Additionally, PO-PID demonstrated faster convergence and more consistent optimization, indicating its robustness and reliability. These findings validate the PO-PID controller as a powerful and efficient solution for enhancing quadruped robot locomotion.

Predicting future gait movements in children can provide crucial input for controlling lower limb robotic exoskeletons. Unlike previous research that primarily focused on predicting gait patterns of typically developing individuals, this study targets pathological gait forecasting, which is more complex due to high intra- and inter-subject variability. In this study, a deep temporal model, named as PathoGaitNet, is developed by combining a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network to predict joint angle trajectories at the hip, knee, and ankle in pitch and roll directions. The open-access dataset consists of motion capture recordings of children aged 4–19 years with various neurological disorders, primarily cerebral palsy, collected using a VICON system at 120 Hz. Three different input window sizes (200 ms, 400 ms, and 600 ms, corresponding to 24, 48, and 72 timesteps) were tested while keeping the output window size fixed at 8.33 ms (single timestep). Results show that the CNN-LSTM consistently outperformed the standalone CNN and LSTM models in terms of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Pearson Correlation Coefficient (PCC), achieving the best performance with a 72-timestep input window (MAE = 0.631, PCC = 0.996). Joint-wise analysis revealed that the model achieved higher prediction accuracy for knee and hip joints than the ankle, likely due to the lower variability and smoother dynamics in proximal joints. These findings suggest the feasibility of deploying deep learning-based single-step gait forecasting in real-time control systems for rehabilitation robotics.

This paper presents the design, modeling, control, and experimental validation of a biomimetic robotic fish that emulates thunniform locomotion. Motivated by the challenges of traditional underwater vehicles in terms of maneuverability and adaptability, the study aims to leverage bio-inspired propulsion strategies to enhance aquatic navigation in constrained environments. The fish robot is designed and developed with a hydrodynamic structure, incorporating active and passive fins and flexible joints to mimic natural fish movements. The dynamic and kinematic models are derived using Lagrangian mechanics, and a Central Pattern Generator (CPG)-based control scheme is implemented to generate rhythmic joint actuation without requiring precise trajectory planning. Simulation studies conducted in MATLAB/Simulink demonstrate smooth transitions from static to straight-line and turning maneuvers with a maximum lateral deviation of under 2 cm and a root mean square trajectory error of 0.0143 m. Experimental validations, both mid-air and in a water tank, confirm the sinusoidal motion patterns and verify the effectiveness of the control strategy. The results showcase the robot’s capability for stable and lifelike planar swimming, offering a promising platform for further developments in autonomous underwater systems.

The increasing adoption of legged robots for applications such as search and rescue, environmental monitoring, and planetary exploration presents unique challenges in navigating diverse and complex terrains. Accurate terrain classification is crucial for adaptive locomotion and reliable performance, yet existing approaches often suffer from limited generalizability and accuracy due to the inherent variability of terrains and sensor noise. This study proposes a multi-terrain classification framework leveraging the HistGradient Boosting (HGB) machine learning technique to address these challenges. The system utilizes a force sensor and IMU data from a quadruped robot to extract meaningful features for robust classification. The HistGradient Boosting model achieved the highest accuracy of 0.9931, highlighting its superior classification performance compared to k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) with accuracy of 0.9410, 0.9878, and 0.9729, demonstrating its effectiveness in handling the complexities of multi-terrain environments.

This paper introduces a novel robust control strategy specifically designed for a one-degree-of-freedom (1-DoF) knee rehabilitation exoskeleton robot, focusing on position control. Our approach addresses several key challenges, including state constraints, parameter uncertainties, solid and viscous frictions, and external disturbances. We suggest a composite controller that combines a nonlinear controller with a linear state feedback controller in order to successfully address these issues. By utilizing a quadratic Lyapunov function, we derive the expression of the nonlinear control law and establish the Linear Matrix Inequality (LMI) conditions for computing the matrix gain of the linear controller, ensuring robust stabilization of the exoskeleton robot to the desired position. These conditions are derived through the application of advanced mathematical techniques such as the matrix inversion lemma, the Young inequality, the Schur complement, and the S-procedure lemma. Numerical results show the effectiveness of our suggested control law, demonstrating the continuous convergence of the intended angular position to the real one, even when external disturbances and uncertainties are present. Overall, our research not only presents a novel methodology for robust control but also delivers promising results that underscore the efficacy of our approach in achieving stable and reliable performance for 1-DoF knee exoskeleton robots.

This study introduces adaptive control for training pediatric cerebral palsy (CP) patients with an exoskeleton to improve gait. The patient-exoskeleton dynamic model, complex due to uncertainties, poses challenges for effective gait-tracking control. To address this, we propose radial basis function-based adaptive non-singular fast terminal sliding mode (RBF-ANFTSM) control for passive-assist exoskeleton systems. The proposed control mitigates uncertainties by leveraging radial basis function neural networks, while non-singular fast terminal sliding mode control ensures finite-time convergence and tackles singular issues. The Lyapunov approach, with adaptive weight laws, ensures system stability and robustness. Effectiveness is demonstrated with a 12-year-old CP patient coupled with the exoskeleton. Comparative analysis reveals that the proposed adaptive control outperforms existing ANFTSM control and RBF-computed torque control by more than 60% and 50%, respectively, in terms of gait tracking efficiency. Additionally, the proposed control exhibits converging sliding surfaces with lesser chattering (|s|hipkneeankle< 0.05) and offers localized convergence behavior over each gait cycle in estimating lumped uncertainties due to the influence of sliding surfaces in the adaptive weight law, underscoring its effectiveness.

This study addresses the challenge of hand rehabilitation, especially for those with neuromuscular impairments, by proposing a tendon-driven soft hand exoskeleton with a robust control scheme. We introduce a novel non-singular terminal sliding mode (NSTSM) control approach tailored for tendon-driven hand exoskeletons. This control strategy enhances trajectory tracking during rehabilitation exercises by addressing uncertainties and external disturbances, ensuring robust performance while avoiding singularities. Through numerical simulations, we evaluate the efficacy of the NSTSM control in accurately following desired trajectories across multiple finger joints, emphasizing singularity-free control and stability analysis based on Lyapunov theory. Our findings reveal that the NSTSM control achieves superior trajectory adherence compared to conventional PID controls, with reduced RMS error values averaging 0.005 [rad] for MCP and PIP joints, and 0.02 [rad] for DIP joint. The sliding surface analysis confirms the NSTSM control’s robustness, highlighting the NSTSM control’s consistent performance, and offering a promising approach to enhance rehabilitation outcomes in stroke survivors with hand impairments.

Upper limb paralysis affects the quality of life. Functional Electrical Stimulation (FES) offers a solution to restore lost motor functions. Yet, there remain challenges in controlling FES to induce arbitrary arm movements. Reinforcement learning (RL) emerges as a promising method for controlling arm movement with success in simulation. However, challenges remain in translating the successes into real-world settings. One dominant challenge is the sample efficiency of RL. This study presents a practical RL setup to control FES for arm movements. We also present a flexible method, called co-kriging adjustment (CKA), which combines a biomechanical simulator and real data to build an accurate model of the real system. We demonstrate our RL-based control on a 2-DoF planar setting where the subject’s arm, placed on a frictionless supporter, is stimulated to perform point-to-point reaching. By using 90 seconds of real interaction data, our RL-based control can perform the reaching with the average error over the workspace of 5.5 cm. Beyond the application of FES, our method can be extended to other control systems, propelling RL towards general uses in the real world.

This study proposes an adaptive non-singular fast terminal sliding mode (ANSFTSM) control for an exoskeleton system in passive-assist mode. The exoskeleton serves the purpose of motion assistance and gait restoration for the pediatric group aged 8–12 years. The complexity of the dynamic model, characterized by uncertain dynamics and external perturbations, poses a challenge in developing a gait tracking control for a pediatric exoskeleton. The experimental setup and real-time control architecture are introduced, followed by the dynamic formulation of the subject-exoskeleton system. Thereafter, a non-singular fast terminal sliding surface (NSFTSM) is considered to design the proposed equivalent control scheme. The adaptive laws are introduced in the reaching control scheme. Lyapunov's theory is employed to verify the rapid convergence of the tracking error in a finite time. The proposed ANSFTSM control is implemented for a pediatric subject (12 years) coupled with the exoskeleton in passive-assist gait tracking. The robust control provides efficient gait tracking (RMSEhip: 2.06°, RMSEknee: 3.81°, RMSEankle: 1.26°) with converging sliding surfaces (|s|hipkneeankle< 0.04) and negligible chattering in finite-time. This study showcases the potential to apply the proposed control to a broader range of pediatric subjects.