Adaptive Parameter Research Articles

Impact of unilateral knee restraint on symmetry adaptation and double-support phase dynamic stability during split-belt walking.

The split-belt treadmill task is an effective tool for studying walking adaptation, particularly the symmetry adaptation of spatiotemporal parameters such as step length and double support time. This study aimed to evaluate the relationship between symmetry adaptation of spatiotemporal parameters and dynamic stability during the double-support phase in split-belt walking. We hypothesized that restraining fast-side knee extension, which is necessary for step lengthening during adaptation, would decrease dynamic stability during the double-support phase. Ten able-bodied male participants performed split-belt walking tasks under three conditions: control, fast-side knee restraint, and slow-side knee restraint. Our findings revealed that slow-side knee restraint disrupted symmetry in double support time and significantly decreased stability on the fast side during the early and late adaptation phases. Contrary to our hypothesis, fast-side knee restraint did not have a statistically significant effect on dynamic stability or symmetry. These results suggest that decreased dynamic stability during the double-support phase, particularly due to limitations in the movement of the trailing leg, may hinder the adaptation process. This study highlights the importance of dynamic stability control during the double-support phase for successful walking adaptation. Future studies with larger sample sizes and varying speed conditions are recommended to generalize these findings and develop targeted interventions to improve walking adaptability and dynamic stability.

Adaptive Search Algorithms: A Comprehensive Overview and Emerging Optimization Trends

Adaptive search algorithms have emerged as pivotal tools for addressing complex, high-dimensional, and nonlinear challenges across various domains. This paper provides a detailed review of adaptive search techniques, including evolutionary algorithms, swarm intelligence methods, and cutting-edge hybrid models, with a unique contribution of a systematic comparison that showcases quantifiable improvements—up to 50% reduction in computational overhead and a 30% increase in solution accuracy across diverse benchmarks. It delves into key methodologies such as genetic algorithms, particle swarm optimization, and differential evolution, highlighting recent breakthroughs in adaptive parameter tuning and multi-objective optimization frameworks. The research emphasizes significant advancements in practical applications like machine learning, engineering design, and logistics, where these algorithms have improved the balance between exploration and exploitation for more optimal outcomes. Furthermore, emerging trends such as bio-inspired models and the integration of reinforcement learning and quantum-enhanced optimization are discussed, promising to reshape the adaptive search landscape by equipping it with sophisticated tools to manage the growing complexity of optimization challenges. This paper aims to map the current state and guide future directions of adaptive search algorithms, fostering the development of more robust, efficient, and adaptable optimization strategies essential for ongoing academic and practical innovations.

Control Error Convergence Using Lyapunov Direct Method Approach for Mixed Fractional Order Model Reference Adaptive Control

Abstract: This paper extends Lyapunov stability theory to mixed fractional order direct model reference adaptive control (FO-DMRAC), where the adaptive control parameter is of fractional order, and the control error model is of integer order. The proposed approach can also be applied to other types of model reference adaptive controllers (MRACs), provided the form of the control error dynamics and the fractional order adaptive control law are similar. This paper demonstrates that the control error will converge to zero, even if the derivative of the classical Lyapunov function is positive during a transient period, as long as tends to zero as time approaches infinity. Finally, this paper provides application examples that illustrate both the convergence of the control error to zero and the behavior of .

Multi-Unmanned Aerial Vehicle Path Planning Based on Improved Nutcracker Optimization Algorithm

For the multi-UAV path planning problem, environmental modeling and an improved swarm intelligence-based optimization algorithm are discussed in this paper. Firstly, to align with reality, specific constraints of UAVs in motions, attitudes and altitudes, real-world threats such as radars and no-fly zones, and inter-UAV collisions are considered. Thus, multi-UAV path planning is transformed into a multi-objective constrained optimization problem. Accordingly, an improved nutcracker optimization algorithm is proposed to solve this problem. Through initializing with logistic chaotic mapping and the lens imaging inverse learning strategy, a more fit elite initialization population is produced to increase the efficiency of path planning. Furthermore, by adjusting adaptive parameters and integrating an improved sine-cosine search strategy, a balance between global exploration capability and local exploitation capability during path planning is achieved. Experimental results show that the improved nutcracker optimization algorithm surpasses other algorithms with respect to both convergence speed and convergence value, making it an effective method for multi-UAV path planning.

MOEA/D with adaptive weight vector adjustment and parameter selection based on Q-learning

MOEA/D with adaptive weight vector adjustment and parameter selection based on Q-learning

A Control Strategy for Salient-Pole Permanent Magnet Axial-Gap Self-Bearing Motors Considering Stator Inductance Variation

Axial-Flux Self-Bearing Motors (AFBMs) are distinguished by considerable variations in inductance resulting from axial displacement. Conventional controllers are frequently engineered to accommodate a wide range of inductance changes. Nevertheless, this methodology is often suboptimal, particularly when inductance is not accurately determined. This paper puts forward a novel inductance approximation function for the stator, designed to minimize modeling errors. Furthermore, an advanced control strategy is presented, based on a sliding mode controller with online adaptive parameter tuning. The experimental results demonstrate the efficacy of the proposed control strategy, exhibiting enhanced performance, stability, and robustness across a range of operating conditions.

Bisphenol A induces sex-dependent alterations in the neuroendocrine response of Djungarian hamsters to photoperiod.

In nature, species synchronize reproduction and energy metabolism with seasons to optimize survival and growth. This study investigates the effect of oral exposure to bisphenol A (BPA) on phenotypic and neuroendocrine seasonal adaptations in the Djungarian hamster, which in contrast to conventional laboratory rodents, is a well-recognized seasonal model. Adult female and male hamsters were orally exposed to BPA (5, 50, or 500 μg/kg/d) or vehicle during a 10-week transition from a long (LP) to short (SP) photoperiod (winter transition) or vice versa (summer transition). Changes in body weight, food intake, and pelage color were monitored weekly and, at the end of the exposure, expression of hypophysio-hypothalamic markers of photoperiodic (TSHβ, deiodinases), reproductive (Rfrp, kisspeptin) and metabolic (somatostatin, Pomc) integration, reproductive organ activity, and glycemia were assessed. Our results revealed sex-specific effects of BPA on acquiring SP and LP phenotypes. During LP to SP transition, females exposed to 500 μg/kg/d BPA exhibited delayed body weight loss and reduced feed efficiency associated with a lower expression of somatostatin, while males exposed to 5 μg/kg/d BPA showed an accelerated acquisition of SP-induced metabolic parameters. During SP to LP transition, females exposed to 5 μg/kg/d BPA displayed a faster LP adaptation in reproductive and metabolic parameters, along with kisspeptin downregulation occurring 5 weeks earlier and Pomc upregulation delayed for up to 10 weeks. In males, BPA exposure led to decreased expression of central photoperiodic integrators, with no effect on the acquisition of the LP phenotype. This pioneering study investigating EDC impacts on mammalian seasonal physiology shows that BPA alters the dynamic of metabolic adaptation to both SP and LP transitions with marked sex dimorphism, causing temporal discordance in seasonal adaptation between males and females. These findings emphasize the importance of investigating EDCs' impact on non-conventional animal models, providing insights into wildlife physiology.

Adaptive Inert Gas Exchange Model for Improved Hypobaric Decompression Sickness Risk Estimation

INTRODUCTION: Future high-altitude military operations and spaceflight will require new procedures to protect crews from decompression sickness while limiting the operational impact. It is hypothesized that the current prediction models do not accurately reflect actual inert gas dynamics, making them unsuitable for the risk estimation of new hypobaric exposure profiles. METHODS: A biophysical gas exchange model was created, allowing modification of various physiological parameters. Predicted nitrogen (N2) volume flows were compared with an experimental study by the Swedish Aerospace Physiology Centre. Bubble growth predictions, made using the Tissue Bubble Dynamics Model, were compared with measured venous gas emboli (VGE). RESULTS: While the simulated washout curves captured the general trends, some important discrepancies were observed when using the nominal model parameters. The new biophysical gas exchange model, incorporating changes in cardiac output and individual anthropometric variations, improved the predictions and approximated the experimentally observed N2 washout. The standard bubble growth predictions did not match measured VGE. Using weighing factors based on the N2 gas flow components predicted by the new biophysical model, the bubble growth pattern agrees much better with the measured VGE scores. DISCUSSION: Traditional decompression models do not account for variations in physiological and environmental factors, leading to incorrect estimates of N2 washout and bubble growth predictions. Using an adaptive biophysical gas exchange model significantly improves the predictions for various altitude exposure profiles. We therefore strongly recommend incorporating adaptive physiological parameters in any model to be used for estimating decompression sickness risk and designing mitigation procedures. De Ridder S, Neyt X, Germonpré P. Adaptive inert gas exchange model for improved hypobaric decompression sickness risk estimation. Aerosp Med Hum Perform. 2025; 96(2):85–92.

Review of impaired immune parameters in RSV infections in the elderly.

Respiratory syncytial virus (RSV) infections in elderly individuals are associated with increased rates of severe clinical disease and mortality compared to younger adults. Age-associated declines in numerous innate and adaptive immune parameters during RSV infection contribute to infection susceptibility, impaired viral clearance, and distorted cytokine profiles in the elderly. Impaired immune responses in this age group also adversely affect longevity of RSV immunity following vaccination in experimental settings. This review summarizes the effects of aging on cellular immune responses to RSV in humans and animal models, molecular mechanisms for these impaired responses where they have been elucidated, and the clinical consequences of impaired immunity in the elderly.

Fuzzy Extended State Observer-Based Sliding Mode Control for an Agricultural Unmanned Helicopter

In the context of agricultural unmanned helicopters, the complex wind disturbances over crop fields and structural perturbations due to variations in pesticide container weights present substantial challenges to flight safety. To address these issues, this paper proposes an innovative fuzzy extended state observer-based sliding mode control (FESO-SMC) methodology aimed at enhancing the aircraft’s resilience against such disturbances. Initially, this study adopts a state expansion strategy to integrate both wind and structural disturbances into a comprehensive disturbance model applicable to the agricultural unmanned helicopter. Following this, a sliding mode control law is formulated with consideration for unknown total disturbances, employing specific sliding mode functions alongside exponential reaching laws. An extended state observer is simultaneously implemented within the sliding mode control framework to estimate and mitigate these disturbances, thereby augmenting the disturbance rejection capabilities of the flight control system. Additionally, the integration of fuzzy logic facilitates adaptive parameter adjustment for the extended state observer, leading to more accurate disturbance estimation. Consequently, a trajectory tracking control system tailored specifically for the agricultural unmanned helicopter has been developed, and its performance was evaluated through simulation experiments. The results indicate that, under certain disturbances, the attitude control error of the FESO-SMC controller is reduced to one-fifth that of traditional sliding mode controllers, while position control accuracy is enhanced more than twofold, thus demonstrating that the proposed FESO-SMC controller not only exhibits superior anti-disturbance capability and robustness but also achieves higher tracking accuracy compared to conventional sliding mode controller.

A Two-Stage Adaptive Differential Evolution Algorithm with Accompanying Populations

Stochastic simulations are often used to determine the crossover rates and step size of evolutionary algorithms to avoid the tuning process, but they cannot fully utilize information from the evolutionary process. A two-stage adaptive differential evolution algorithm (APDE) is proposed in this article based on an accompanying population, and it has unique mutation strategies and adaptive parameters that conform to the search characteristics. The global exploration capability can be enhanced by the accompanying population to achieve a balance between global exploration and local search. This algorithm has proven to be convergent with a probability of 1 using the theory of Markov chains. In numerical experiments, the APDE is statistically compared with nine comparison algorithms using the CEC2005 and CEC2017 standard set of test functions, and the results show that the APDE is statistically superior to the comparison methods.

A Novel Reinforcement Learning Algorithm-Based Control Strategy for Grid-Configured Inverters

To address the power oscillation problem due to the introduction of inertia and damping, this paper proposes a new deep reinforcement learning algorithm based on the SD3 (Softmax Deep Double Deterministic policy gradients) algorithm for grid configuration inverter control strategy to compensate for the loss of inertia and damping in the grid. Virtual synchronous generator control, as a typical grid configuration technique, inevitably brings stability problems while providing inertia and damping support to the grid. In this paper, we first analyze the nonlinear relationship between inertia and angular velocity, and find the key parameters to maintain the power stability; then, we migrate the deep reinforcement learning strategy, and design the control strategy applicable to the virtual synchronous generator; finally, through the adaption of the key parameters, we combine the control strategy with the grid-connected inverter, and solve the problem of the excessive grid-connected power oscillation of the inverter. The effectiveness and accuracy of the control method compared with other algorithms are verified by building a simulation model in MATLAB/Simulink, which realizes the purpose of reducing power oscillation.

Estimation of stationary and non-stationary moving average processes in the correlation domain.

This paper introduces a novel approach for the offline estimation of stationary moving average processes, further extending it to efficient online estimation of non-stationary processes. The novelty lies in a unique technique to solve the autocorrelation function matching problem leveraging that the autocorrelation function of a colored noise is equal to the autocorrelation function of the coefficients of the moving average process. This enables the derivation of a system of nonlinear equations to be solved for estimating the model parameters. Unlike conventional methods, this approach uses the Newton-Raphson and Levenberg-Marquardt algorithms to efficiently find the solution. A key finding is the demonstration of multiple symmetrical solutions and the provision of necessary conditions for solution feasibility. In the non-stationary case, the estimation complexity is notably reduced, resulting in a triangular system of linear equations solvable by backward substitution. For online parameter estimation of non-stationary processes, a new recursive formula is introduced to update the sample autocorrelation function, integrating exponential forgetting of older samples to enable parameter adaptation. Numerical experiments confirm the method's effectiveness and evaluate its performance compared to existing techniques.

Improvement of Aerodynamic Performance of Bilaterally Symmetrical Airfoil by Co-Flow Jet and Adaptive Morphing Technology

For a special bilaterally symmetric airfoil (BSA), this paper designs an active flow control scheme based on the Co-Flow Jet (CFJ) and adaptive morphing technology, and establishes a numerical simulation method which is suitable for simulating aerodynamic characteristics. The accuracy and effectiveness of the numerical method has been verified through benchmark cases. This study investigates the effects of jet intensity, suction slot position and angle, and deflection angles of the leading and TE flap on the aerodynamic performance parameters and flow field structure of the bilaterally symmetric airfoil. The results show that the adaptive morphing technology can significantly improve the equivalent lift coefficient and equivalent lift-to-drag ratio of the bilaterally symmetric airfoil, without obviously increasing the CFJ power consumption coefficient. Selecting an appropriate CFJ intensity can achieve a relatively high equivalent lift-to-drag ratio with a low compressor power requirement. Moving the suction slot rearward can increase the lift coefficient, and placing it on the trailing edge (TE) flap can more efficiently delay flow separation, reduce power consumption, and increase the equivalent lift-to-drag ratio. The suction slot angle has little effect on the lift coefficient, but a larger suction slot angle can enhance the equivalent lift-to-drag ratio. Increasing the TE flap deflection angle enhances both the lift coefficient and drag coefficient, as well as the power consumption coefficient at high angles of attack. But it has little effect on the maximum equivalent lift-to-drag ratio. Increasing the leading edge flap deflection angle can improve the maximum equivalent lift-to-drag ratio while increasing the angle of attack corresponding to it. Overall, choosing a CFJ and adaptive morphing parameters by considering different factors can enhance the aerodynamic performance of the bilaterally symmetric airfoil.

Ultraprecision multi-axis CARIC control strategy with application to a nano-accuracy air-bearing motion stage.

Multi-axis contouring control is crucial for ultraprecision manufacturing industries, contributing to meeting the ever-increasingly stringent performance requirements. In this article, a novel contouring adaptive real-time iterative compensation (CARIC) method is proposed to achieve extreme multi-axis contouring accuracy, remarkable trajectory generalization, disturbance rejection, and parametric adaptation simultaneously. Specifically, control actions generated by CARIC consist of robust feedback, adaptive feedforward, and online trajectory compensation components. Robust feedback and adaptive feedforward terms initially stabilize single-axis closed-loop control systems and adapt to parameter variations. An online contouring error prediction model subsequently captures upcoming contouring errors in advance, enabling the iterative calculation of optimal online trajectory compensation signals at each sampling instant during real-time motion. This mechanism proactively suppresses potential contouring errors before their occurrence. Comparative simulations and experiments demonstrate that the proposed CARIC method reaches the accuracy limit previously attainable only by iterative learning control (ILC) while enhancing trajectory generalization, disturbance rejection, and parametric adaptation. Notably, practical experiments on a nano-accuracy air-bearing motion stage showcase consistent 7-nm-level accuracy across various 100-mm stroke contouring tasks even under varying contours, payloads, and disturbances. Owing to these advantages, CARIC offers promising potential to enhance ultraprecision manufacturing performance through advanced motion control techniques.

Relevance of body weight adaptation and modern obesity-defining parameters in the analysis of isokinetic trunk strength in people with obesity - A retrospective analysis.

Pathologic values of body mass index (BMI), body weight, and waist circumference correlate with higher absolute and lower relative trunk strength. Whether waist-to-height ratio (WHtR) is appropriate for showing trunk strength differences in people with obesity and whether a continuous linear relationship exists between the increase in obesity and trunk strength is unknown. This retrospective cross-sectional study included 1174 subjects (1114 men and 60 women). Measured values included body weight, height, waist circumference, WHtR, BMI, and both absolute and body weight-adapted trunk flexor/extensor strength. Statistical analyses included t-tests, Welch tests, Pearson correlations, mixed-linear, and nonlinear regression analyses. Positive correlations with absolute trunk strength were found in subjects without obesity for all anthropometric parameters except WHtR. Weaker positive and partly negative correlation and linear regression coefficients were found in subjects with obesity. Nonlinear relationships were found between age, BMI, WHtR, and absolute respective body weight-adapted trunk strength. The relationship between obesity-defining measures/ indices and trunk strength is non-linear. Increasing BMI, waist circumference, or WHtR above cut-off values known from cardiovascular research is linked to a decrease or weaker increase in trunk strength. Body weight adaptation is recommended to avoid misinterpretation of apparently good absolute trunk strength values in people with obesity.

Dynamic Adaptation of Hematological Parameters, Albumin, and Non-Esterified Fatty Acids in Saddlebred and Standardbred Horses During Exercise

The response to exercise following a rest period may vary among horse breeds based on the importance of the hematological and hematochemical profiles of athletic horses. Ten Standardbred and ten Italian Saddlebred mares were subjected to a 4-week training program after a seasonal rest, and hematological parameters (red blood cells—RBCs; hemoglobin—Hb; hematocrit—Hct; platelets—PLTs; platelet aggregation—AG; aggregation slope—Slope; fibrinogen—Fb), as well as Albumin (Alb) and non-esterified fatty acids (NEFAs), were analyzed. Blood samples were obtained each week during the training program following a simulated exercise performed at T0 and T4 (T0pre-T0post; T1, T2, T3, and T4pre-T4post). A two-way ANOVA revealed an increase in all assessed parameters post vs. pre at T0 and T4 (p < 0.01) and a decrease in PLTs (p < 0.01) at T0 and T4 in both breeds. A significant effect of breed was observed, with higher values for RBCs and Hb at each time point (p < 0.001); Hct at T0 post, T2, T3, and T4 post; and NEFAs (p < 0.001) at T0 post and T4 post in Standardbred compared to Saddlebred horses. Positive correlations were identified among RBCs, Hb, Hct, PLTs, Alb, and NEFAs in both breeds and between AG and Slope in Saddlebred horses. Negative correlations were identified among AG and RBCs, Hb, Hct, PLTs, Alb, and NEFAs in Standardbred horses and for AG and Slope with RBCs, Hb, Hct, and PLTs in Saddlebred horses. A comparable reaction to training was observed in both breeds following the seasonal rest.

Optimization of Underwater Images Based on Gray World Algorithm and Jaffe-McGlamery Models

In this paper, a comprehensive scheme for underwater image processing is proposed based on the grayscale world algorithm and the Jaffe-McGlamery model. Firstly, a color bias detection based on grayscale world theory, a low light detection based on HSV color space, and a fuzzy detection method based on frequency domain and Laplace operator are designed to classify different types of image degradation. Subsequently, the corresponding scene degradation models are constructed for different degradation types through the simplified Jaffe-McGlamery model, and the image features under different water conditions are analyzed. Next, an improved gray world algorithm is used to eliminate color bias, the limiting contrast adaptive histogram equalization (CLAHE) technique is utilized to improve the image quality under low-light conditions, and the dark-channel a priori algorithm is optimized by the depth estimation and parameter adaptation modules to remove blurring. The proposed method in this study significantly improves the image quality in different scenarios and provides a new idea for underwater image processing.

The Simulation of Pose Control for Stewart Platform Based on Gain-Enhanced PID with Generalized Boolean Algebra

Due to the nonlinear and time-varying uncertainties commonly present in practical industrial systems, traditional PID controllers often fail to achieve optimal control performance under significant input variations. This paper proposes a gain-enhanced PID control method optimized by generalized Boolean algebra, applying Boolean algebraic logic control strategies to traditional PID control to improve parameter adaptability and real-time control performance. Using MATLAB/Simulink simulation tools, a dual-loop control simulation model is established for the Stewart platform, incorporating both conventional PID control and the gain-enhanced PID control optimized by generalized Boolean algebra. Both models are configured with identical PID parameters for simulation, and comparative experimental results are presented. The experimental results demonstrate that, in the Simulink simulation model of the Stewart platform's pose control system, the proposed gain-enhanced PID control system optimized by generalized Boolean algebra improves the dynamic performance and response speed compared to the traditional PID control system, enhancing the real-time performance and stability of the Stewart platform control.

Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications

The use of a static modulation coding scheme (MCS), such as 7, and resource keep probability (Prk) value, such as 0.8, was proven to be insufficient to achieve the best packet reception ratio (PRR) performance. Various adaptation techniques have been used in the following years. This work introduces a novel optimization algorithm approach called the fuzzy inference reinforcement learning (FIRL) sequence for adaptive parameter configuration in cellular vehicle-to-everything (C-V2X) mode-4 communication networks. This innovative method combines a Sugeno-type fuzzy inference system (FIS) control system with a Q-learning reinforcement learning algorithm to optimize the PRR as the key metric for overall network performance. The FIRL sequence generates adaptive configuration parameters for Prk and MCS index values each time the Long-Term Evolution (LTE) packet is generated. Simulation results demonstrate the effectiveness of this optimization algorithm approach, achieving up to a 169.83% improvement in performance compared to static baseline parameters.