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The study delivers a cohesive system that combines structural stress investigation, navigational planning evaluation, and adaptive joint control to optimize hexapod effectiveness on hills, stairs, and uneven surfaces. The robot was developed through the iterative drafting technique and designed by assigning in PLA material. Structural examination with Finite Element Analysis (FEA) under 10 N and 20 N forces demonstrated a positive stress allocation and a safety factor of 2.8, combining compact development with durability. In the ROS/Gazebo exploration investigations utilizing global planners like A*, Dijkstra, RRT, and Artificial Potential Field (APF) in combination with a PID-driven local planner, A* as well as Dijkstra developed nearly the best pathways with 100% accuracy. This cut down on route variation by about 17% in comparison to RRT. RRT established confident that the exploration was always the same, but it established paths that were more lengthy and less smooth. APF, on the contrary, made paths that were smooth but less reliable due to the local minima. Adaptive synchronization for joint control quantitatively provided an improvement in joint angle stability, reducing oscillatory deviations by 12% and displacement errors by 15% relative to baseline controllers. The core novelty within this approach is the integrative methodology that will inherently synergize finite element structural analysis, comparative path planning, and Adaptive joint synchronization: presenting a comprehensive optimization strategy, new to hexapod robotics. Together, these advances allow for robust and efficient real-world deployment of hexapods. Future work will extend to hybrid learning-based planning, and sensor-driven dynamic adaptation.

In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion.

The problem of finite-time tracking control for n-link flexible-joint robot manipulators is addressed. An adaptive fuzzy finite-time command-filtered backstepping control scheme is presented to solve the following problems: “explosion of terms” problem, finite-time stabilization of the closed-loop system, and the reduction of computational cost. To this end, new virtual adaptive control signals and new finite-time error compensation mechanism are constructed using inherent properties of robot manipulator systems. Based on the Lyapunov theory, the finite-time stabilization of the closed-loop system is proved. Simulation studies show the effectiveness of the proposed method.

In this paper, a fixed-time prescribed performance fault-tolerant control scheme is presented for the n-link flexible joint robot with actuator failures. Firstly, a modified prescribed performance control method is proposed to enhance the robustness of the system against input perturbations and to ensure that the tracking error converges in a predetermined time, and the constrained system is transferred into an unconstrained system. Secondly, an adaptive-based passive fault-tolerant controller is constructed to counteract the actuator failures in the system. Then, the uncertainty problem in the flexible-joint robot system is solved by incorporating the radial basis function neural networks and adaptive techniques into the fixed-time backstepping framework. After that, the “complexity explosion” issue is well handled by creating the fixed-time second-order filter, in which the filtering errors are eliminated by the devised compensation mechanism. The stability analysis proves that the closed-loop system is fixed-time stable, and the tracking error is limited to the predefined range. Finally, simulations have been performed on a two-link FJR and a three-link flexible joint robot, respectively. Via the three conditions: actuators operating normally, actuators losing 50% of effectiveness instantaneously, and actuators losing 50% of effectiveness gradually, the results show that the tracking error of each joint of the system is less than 0.2 rad, and the tracking time is limited to the specified time (0.8 s), which proves the effectiveness of the proposed control scheme.

The evidence from epidemiological studies concerning the relationship between serum vitamin D concentrations and rheumatoid arthritis (RA) is inconsistent. This meta-analysis is aimed at determining the magnitude of the correlation between this common autoimmune disease and vitamin D, an important nutrient known to dampen adaptive immune responses. Through multiple search strategies, relevant literature was identified and evaluated for quality before May 16 2015. Data extracted from eligible studies was synthesized to calculate pooled correlation coefficient (r), mean difference (MD) and odds ratio (OR). The Venice criteria were applied to assess the credibility of the evidence for each statistically significant association. A total of 24 reports involving 3489 patients were selected for analysis. RA patients had lower vitamin D levels than healthy controls (MD:-16.52 nmol/L, 95% confidence intervals [CI]:-18.85 to -14.19 nmol/L). There existed a negative relationship between serum 25-hydroxyvitamin D (25OHD) level and disease activity index, e.g. 25OHD vs. Disease Activity Score in 28 joints (DAS28): r = -0.13, 95% CI -0.16 to -0.09; 25OHD vs. C-reactive protein: r = -0.12, 95% CI -0.23 to -0.00. Additionally, latitude-stratified subgroup analysis yielded a relatively stronger negative correlation between 25OHD and DAS28 in low-latitude areas. This inverse relationship also appeared more significant in developing countries than in developed countries. No publication bias was detected. RA patients had lower vitamin D values than healthy controls. There was a negative association between serum vitamin D and RA disease activity. However, more strictly controlled studies are needed to validate these findings.

In this studies, adaptive boundary control for a flexible two-link manipulator with a changeable payload at the free-end. Taking into account the infinite-dimensionality of the flexural dynamics, this study proposes a partial differential equation (PDE) model, so that the problem of possible spillover instability caused by the neglect of flexible modes can be avoided. Based on the PDE model, an adaptive boundary control scheme is designed to regulate joint position and suppress elastic vibration while compensating for parametric uncertainties. The asymptotic stability of the closed-loop system is validated theoretically. The effectiveness of the control scheme is also verified by the numerical simulations.

In this paper, an adaptive backstepping controller based on interval type-2 fuzzy neural network (IT2FNN) approximator is proposed for flexible-joint manipulator with mismatched uncertainties. Backstepping control has the ability to deal with the mismatched problem, and IT2FNN approximator can be utilized to approximate unknown nonlinear functions. Through the Lyapunov stability analysis, all the signals in the closed-loop system are guaranteed to be ultimately bounded. Simulation results show that the tracking error of the proposed controller can be reduced to arbitrarily small values, and the tracking performance is better than the adaptive backstepping controllers based on type-1 fuzzy neural network approximator and neural network approximator.

The hydraulic joint is the key driving component of a robot. To reduce the joint size of the hydraulic robot, and improve the control accuracy and dynamic response performance, this paper proposes a novel joint structure and control method of a ball double-screw hydraulic robot. Using ball and circular arc spiral groove transmission, the hydraulic joint has a small transmission friction coefficient, compact overall structure and higher transmission accuracy. Aiming to resolve the problems of low control accuracy and motion instability caused by temperature drift in valve-controlled hydraulic systems, the high-precision joint control method based on adaptive fuzzy control compensation is used to improve the control accuracy and stability. The static and dynamic characteristics of the designed hydraulic joint are analyzed by simulation. A test platform was built, and the physical prototype of the hydraulic joint underwent static testing, dynamic control, amplitude frequency response and trajectory tracking tests. The experimental results were similar to the simulation results. The ball double-screw hydraulic robot joint has the characteristics of low starting pressure, high energy density, fast dynamic response, small amplitude frequency attenuation and high control accuracy. The starting pressure is 0.5 MPa, maximum swing frequency is 3 Hz, positioning accuracy is ± 0.03°, tracking accuracy is ± 3.9° and maximum angular velocity at 10 MPa is about 7.6 rad/s, which is close to the angular velocity of the actual human joint.

Robust tracking control of electrically flexible-joint robots is addressed in this paper. Two important practical situations are considered. The fact that robot actuators have limited voltage and that current measurement is subjected to noise. Let us notice that a few solutions for the voltage-bounded robust tracking control have been proposed. In this paper, we contribute to this subject by presenting a new form of voltage-based control strategy. It proves that the closed loop system is BIBO stable, while actuator/link position errors are uniformly–ultimately bounded stable in agreement with Lyapunov’s direct method in any finite region of the state space. As a second contribution of this paper, we present a robust adaptive control scheme without the need for computation of regressor matrix and current measurement, with the same result on the closed loop system stability. This novelty gives a simple robust tracking control scheme for both structured and unstructured uncertainties based on the function approximation technique. The analytical studies as well as experimental results produced using MATLAB/Simulink external mode control on a flexible-joint electrically driven robot demonstrate high performance of the proposed control scheme.

Here, an adaptive radial basis function (RBF) neural network (NN) backstepping controller is proposed for a class of input‐constrained flexible joint robotic manipulators represented by strict‐feedback form with unknown terms, external stochastic disturbance, and output disturbance. The proposed approach is robust against both deterministic and stochastic uncertainties and disturbances and copes with the control input amplitude saturation. Moreover, by deploying the minimal learning parameter method and command filter technique, the computational burden of derivative terms and adaptive terms greatly decreases. Considering the mean‐value theorem assists us to avoid the need for having the input saturation bounds in prior. The suggested tracking control scheme mandates the closed‐loop system states to be semi‐globally bounded‐in‐probability. Also, a quartic Barrier Lyapunov function is utilized to force the tracking error to be confined within a pre‐chosen small region around the origin. Eventually, a numerical simulation of a flexible joint robot manipulator with a single link is performed to show the effectiveness and performance of the developed control method. An adaptive neural network backstepping controller is proposed for input‐constrained flexible joint robotic manipulators with unknown terms, external stochastic disturbance, and output disturbance. Deploying the minimal learning parameter and command filter techniques decreases the computational burden. Considering the mean‐value theorem avoids the pre‐need for input saturation bounds. The quartic Barrier Lyapunov function confines the tracking error and mandates semi‐globally bounded‐in‐probability.