Search Results (379)

Aiming at the trajectory tracking control problem of wheeled mobile robots under unknown slip ratio conditions, this paper designs a trajectory tracking controller based on an improved rapid variable power reaching law and a sliding mode observer. First, a kinematic model of the wheeled mobile robot is established, explicitly considering the influence of slip ratio. Then, a sliding mode observer is developed for online estimation of the slip ratio, addressing the difficulty of direct slip ratio measurement. On this basis, a trajectory tracking controller is designed based on the improved rapid variable power reaching law, enabling fast tracking of multiple complex trajectories under slip conditions. Simulation and experimental results show that the proposed trajectory tracking controller not only effectively eliminates the influence of unknown slip disturbances on trajectory tracking, improving smoothness and tracking accuracy but also greatly accelerates the convergence process. The shortest convergence time is only 20.56% of that achieved by a fuzzy PID trajectory tracking controller and 61.43% of that achieved by a rapid double power reaching law trajectory tracking controller with a sliding mode observer. Full article

This study explores the issue of fixed-time dynamic event-triggered consensus control for uncertain nonlinear multi-agent systems (MASs) within directed graph frameworks. In practical applications, the system encounters multiple constraints such as unknown time-varying parameters, unknown external disturbances, and input dead zones, which may increase the communication burden of the system. Therefore, achieving fixed-time consensus tracking control under the aforementioned conditions is challenging. To address these issues, an adaptive fixed-time consensus tracking control method based on boundary estimation and fuzzy logic systems (FLSs) is proposed to achieve online compensation for the input dead zone. Additionally, to optimize the utilization of communication resources, a periodic adaptive event-triggered control (PAETC) is designed. The mechanism dynamically adjusts the frequency at which the trigger is updated in real time, reducing communication resource usage by responding to changes in the control signal. Finally, the efficacy of the proposed approach is confirmed via theoretical evaluation and simulation. Full article

In this work, a fault detection method and a distributed consensus fault-tolerant control (FTC) scheme are proposed for multiple quadrotor unmanned aerial vehicles (multi-QUAVs) with actuator faults. In order to identify the actuator faults in time, an auxiliary state observer is constructed first. Subsequently, a fault detection scheme based on the observer error is presented, which can improve the early warning ability of the multi-QUAVs. Meanwhile, to handle unknown sudden faults, the Nussbaum function approach is combined with the consensus theory to design a distributed consensus FTC strategy for multi-QUAVs. Compared with the traditional direct fault estimation method using the projection function technique, the proposed Nussbaum-based FTC method can avoid the singularity problem of the controller in a simple way. Moreover, all error signals of the closed-loop system are proved to be uniformly ultimately bounded via Lyapunov stability theory and the consensus control algorithm. Finally, simulation comparison results indicate the early warning capability of the fault detection method and the formation maintenance performance of the developed fault-tolerant controller. Full article

Intelligent driving is a key technology in the field of automotive manufacturing due to its advantages in environmental protection, energy efficiency, and economy. However, since the intelligent driving model is an uncertain multi-input multi-output dynamic system, especially in an interactive environment, it faces uncertainties such as non-uniform trial lengths, unknown nonlinear parameters, and unknown control direction. In this paper, an adaptive iterative learning control method is proposed for vehicle systems with non-uniform trial lengths and asymmetric control gain matrices. Unlike the existing research on adaptive iterative learning for non-uniform test lengths, this paper assumes that the elements of the system’s control gain matrix are asymmetric. Therefore, the assumption made in traditional adaptive iterative learning methods that the control gain matrix of the system is known or real, symmetric, and positive definite (or negative definite) is relaxed. Finally, to prove the convergence of the system, a composite energy function is designed, and the effectiveness of the adaptive iterative learning method is verified using vehicle systems. This paper aims to address the challenges in intelligent driving control and decision-making caused by environmental and system uncertainties and provides a theoretical basis and technical support for intelligent driving, promoting the high-quality development of intelligent transportation. Full article

A combination of high potential productivity and ecological stability is essential for current cultivars, which is achievable by breeding. Interspecific/intergeneric hybridization remains a key approach to producing new high-yielding and resistant cultivars. Interspecific reproductive barriers (IRBs) appear in the interaction between the pollen and pistil of interspecific/intergeneric hybrids. The mechanisms underlying these hybridization barriers are to a considerable degree unknown. The pollen–pistil interaction is decisive because the pollen of distantly related plant species either is not recognized by stigma cells or is recognized as foreign, preventing pollen tube (PT) germination and/or penetration into the stigma/style/ovary. This review mainly focuses on (1) the pollen–pistil system; (2) IRB classification; (3) similarity and differences in the function of self-incompatibility (SI) barriers and IRBs; and (4) physiological and biochemical control of IRBs and their overcoming. The main goal is to illuminate the physiological, biochemical, and molecular mechanisms underlying the growth arrest of incompatible PTs and their death. In general, this review consolidates the current understanding of the interaction of the male gametophyte with the sporophyte tissues of the pistil and outlines future research directions in the area of plant reproductive biology. Full article

Background/Objectives: The experience of pregnancy and parturition has been associated with long-term health effects in mothers, imparting protective effects against some diseases while the risk of other diseases is increased. The mechanisms that drive these altered disease risks are unknown. This study examined DNA methylation (DNAm) changes from pre-pregnancy to several years after giving birth in parous women compared to nulliparous controls over the same time interval. Methods: Using 180 parous-associated CpGs, three analyses were carried out to test DNAm changes from pre-pregnancy at age 18 years to gestation; from gestation to post-pregnancy at age 26 years in parous women; and from 18 to 26 years in nulliparous women using linear mixed models with repeated measures. Results: The directions of DNAm changes were the same between the parous and nulliparous groups. Most CpG dinucleotides (67%, 121 of 180) had a decreasing trend while a small number (7%, 13 of 180) had an increasing trend. Of the CpGs showing increasing or decreasing DNAm, approximately half had DNAm change to a smaller extent in parous women and the other half changed more in parous women than nulliparous controls. 9% (17 of 180) changed significantly in nulliparous women only, leading to a significant difference in DNAm levels in parous women at the post-pregnancy 26 years time point. Conclusions: Pregnancy and parturition may accelerate methylation changes in some CpGs, but slow down or halt methylation changes over time in other CpGs. Full article

The effects of 1-N-naphthylphthalamic acid (NPA) applied as an aqueous solution on uncooled grape hyacinth (Muscari armeniacum) bulbs were investigated, focusing on histological measurements and the determination of various metabolites in developing roots. M. armeniacum bulbs were kept for a defined number of days in distilled water (control) or aqueous NPA solutions, and then 2 cm sections of root tips were taken for histological measurements. Longitudinal and cross sections were taken in these root pieces, followed by measurements of their basic parts and microscopic images. Determinations of polar compounds by GC/MS and phenolic metabolites by HPLC/MS/MS were carried out in freeze-dried root samples. NPA inhibited the growth of the roots and caused swelling of their elongation parts, as well as changes in the dimensions of other parts of the roots and disruption of the gravitropic direction of their growth. However, NPA did not affect leaf growth and the amino acid, organic acid, and major carbohydrate content in the roots, but increased the level of unknown saccharides, probably oligofructans. The decrease in the contents of many phenolic compounds observed in our study under the influence of NPA may indicate that this could be one of the symptoms/causes of root growth disorders. In turn, the reduction in polyphenol levels may have been related to an increase in the number and length of root hairs. Full article

In time-sensitive aerial missions such as urban surveillance, emergency response, and adversarial airspace operations, achieving rapid and reliable formation control of multi-UAV systems is crucial. This paper addresses the challenge of ensuring robust and efficient formation control under stringent time constraints. The proposed singularity-free prescribed-time formation (PTF) control scheme guarantees task completion within a user-defined time, independent of initial conditions and control parameters. Unlike existing scaling-based prescribed-time methods plagued by unbounded gains and fixed-time strategies with non-tunable convergence bounds, the proposed scheme uses fixed-time stability theory and systematic parameter tuning to avoid singularity issues while ensuring robustness and predictable convergence. The method also accommodates directed communication topologies and unknown external disturbances, allowing follower UAVs to track a dynamic leader and maintain the desired geometric formation. Finally, some simulation results demonstrate the effectiveness of the proposed control strategy, showcasing its superiority over existing methods and validating its potential for practical applications. Full article

This research proposes a fractional-order adaptive neural control scheme using an optimized backstepping (OB) approach to address strict-feedback nonlinear systems with uncertain control directions and predefined performance requirements. The OB framework integrates both fractional-order virtual and actual controllers to achieve global optimization, while a Nussbaum-type function is introduced to handle unknown control paths. To ensure convergence to desired accuracy within a prescribed time, a fractional-order dynamic-switching mechanism and a quartic-barrier Lyapunov function are employed. An input-to-state practically stable (ISpS) auxiliary signal is designed to mitigate unmodeled dynamics, leveraging classical lemmas adapted to fractional-order systems. The study further investigates a decentralized control scenario for large-scale stochastic nonlinear systems with uncertain dynamics, undefined control directions, and unmeasurable states. Fuzzy logic systems are employed to approximate unknown nonlinearities, while a fuzzy-phase observer is designed to estimate inaccessible states. The use of Nussbaum-type functions in decentralized architectures addresses uncertainties in control directions. A key novelty of this work lies in the combination of fractional-order adaptive control, fuzzy logic estimation, and Nussbaum-based decentralized backstepping to guarantee that all closed-loop signals remain bounded in probability. The proposed method ensures that system outputs converge to a small neighborhood around the origin, even under stochastic disturbances. The simulation results confirm the effectiveness and robustness of the proposed control strategy. Full article

Aiming at the requirements of rapidity, high precision, and robustness for the longitudinal trajectory control of the receiver aircraft in autonomous aerial refueling, a direct lift control (DLC) strategy that integrates incremental nonlinear dynamic inversion (INDI) and nonlinear extended state observer (NESO) is proposed. First, a control strategy for generating direct lift through the coordinated action of the flaperons and elevators is presented, and a longitudinal dynamics model is established. Secondly, based on the INDI and DLC methods, the rapid tracking and control of altitude are achieved. Finally, an NESO is designed. The observer gains are designed through the pole placement method and the robust optimization method to achieve the estimation of states such as airspeed, angle of attack, pitch rate, and pitch angle, as well as unknown force and moment disturbances. The estimated force and moment disturbances are used to implement the active disturbance rejection control. Simulation results show that the strategy has no altitude tracking error under normal operating conditions, and the altitude tracking error is less than 0.2 m under typical disturbance conditions, indicating high control accuracy. Under disturbance conditions, the estimation errors of true airspeed, angle of attack, pitch angle, and pitch angular velocity are less than 0.3 m/s, 0.12°, 0.1°, and 0.2°/s, respectively, demonstrating the high-precision estimation capability of the observer. The NESO exhibits high accuracy in state estimation, the rudder deflection is smooth, and the anti-disturbance capability is significantly better than traditional methods, providing an engineered solution for the longitudinal control of the receiver aircraft. Full article

An isocratic reverse-phase high-performance liquid chromatography (RP-HPLC) method, coupled with photodiode array detection (PDA), was developed for the identification and characterization of stress degradation products and an unknown process-related impurity of rivaroxaban in bulk drug form. Rivaroxaban, a selective and direct Factor Xa inhibitor, underwent forced degradation under hydrolytic (acidic, alkaline, and neutral), photolytic, thermal, and oxidative stress conditions, following the ICH’s guidelines. The drug displayed significant susceptibility to acid, base, and oxidative environments leading to the formation of eleven degradation products. All degradation products, along with process impurities and Rivaroxaban, were effectively separated using a (4.6 × 250 mm, 5 µm) C18 Thermo ODS Hypersil column at ambient temperature. The mobile phase composed of acetonitrile and monobasic potassium phosphate (pH 2.9) in a 30:70 (v/v) ratio, with a flow rate of 1.0 mL/min, and detection was carried out at 249 nm. The LC-PDA method was validated in accordance with the ICH’s guidelines and USP38-NF33, demonstrating specificity, linearity, accuracy, precision, and robustness. Recovery studies showed results within the range of 98.6–103.4%, with a % RSD LT 2%. The limits of detection (LOD) and quantitation (LOQ) for rivaroxaban were determined to be 0.30 ppm and 1.0 ppm, respectively. Stress studies confirmed that the degradation products did not interfere with rivaroxaban detection, establishing the method as stability-indicating. Specific impurities were identified, including impurity G at 2.79 min, impurity D at 3.50 min, impurity H at 5.32 min, impurity C at 6.14 min, impurity E at 8.36 min, impurity A at 9.03 min, and impurity F at 9.49 min. Additionally, several unknown impurities were observed at 3.20, 4.00, 4.59, and 4.77 min. Statistical evaluation confirmed the method’s reliability, making it suitable for routine analysis, quality control of raw materials, formulations of varying strengths, dissolution studies, and bioequivalence assessments of rivaroxaban formulations. Full article

Over the past decade, more than a thousand new psychoactive substances (NPSs) have emerged worldwide. This rapid proliferation of “designer drugs” poses significant challenges for drug control, forensic analysis, and public health. Artificial intelligence (AI) has increasingly been applied to address these challenges in NPS design and analysis. This review provides a comprehensive overview of AI methodologies—including deep learning, generative models, and quantitative structure–activity relationship (QSAR) modeling—and their applications in the synthesis, prediction, and identification of NPSs. We discuss how AI-driven generative models have been used to design novel psychoactive compounds and predict their pharmacological activity, how QSAR models can forecast potency and toxicological profiles, and how machine learning is enhancing analytical chemistry workflows for NPS identification. Special emphasis is placed on mass spectrometry (MS)-based techniques, where AI algorithms (e.g., for spectral prediction and pattern recognition) are revolutionizing the detection and characterization of unknown NPSs. A dedicated section examines the legal and regulatory implications of AI-generated psychoactive substances in the European Union (EU) and United States (USA), highlighting current policies, potential gaps, and the need for proactive regulatory responses. The review concludes with a discussion of the benefits and limitations of AI in this domain and outlines future directions for research at the intersection of AI, analytical chemistry, and drug policy. Full article

This paper focuses on the ship control system and studies the problem of unknown control directions. Considering that the traditional Nussbaum gain method has to consider the complex situation where the gain converges to both positive and negative infinity when proving the stability of a system, a unilateral Nussbaum function is defined in this paper. By constructing this function, the design and proof process of the adaptive Nussbaum gain method are simplified. Taking the ship course–keeping control system as the research object, a course angle tracking controller is designed by combining neural network, robust adaptive, and sliding mode control techniques. A dual-input RBF single-output neural network is used to approximate the uncertain part of the system, and the robust adaptive control is adopted to deal with the unknown disturbance. The simulation results at the end of the article show that when the direction suddenly switches, the overshoot of the system reaches 40%, and the adjustment time is approximately 3 s. However, the system can still adapt to the change of the control direction and maintain stability, indicating that the method proposed in this paper is reasonable and effective. And the proposed method can effectively cope with the problems of the unknown control direction and its jump, keeping the system stable, which has great theoretical and engineering application value. Full article

Distributed interconnected systems are complex dynamic systems where every single subsystem has an impact on other subsystems. Actuators are key components in interconnected dynamic systems, which are prone to faults due to age and unexpected conditions. Therefore, there is motivation to develop an effective diagnosis algorithm for distributed interconnected systems, which is a starting point for predictive maintenance. In this study, an actuator fault estimation approach is proposed for a class of nonlinear interconnected systems with direct feedthrough inputs. Specifically, the original interconnected system is transformed into an augmented system by setting an extended state vector composed of an original state vector and actuator fault vector. An additional control term is used to eliminate the impact from unknown disturbances on the estimator error dynamics. Regional pole constraints are considered in the design of the distributed robust observer so that the poles are placed into a desired stable region. The observer gains are obtained by solving simultaneous linear matrix inequalities. Finally, the effectiveness of the proposed method is demonstrated by simulation studies, and a comparison is also provided. Full article

Green belts, consisting mainly of natural forests, woodlands, and agricultural areas surrounding major cities, play an essential role in regulating urban development and controlling the expansion of metropolitan areas. Although this concept has been extensively studied in the world’s major metropolitan areas, it remains relatively unknown in many countries, particularly in Africa. There is a great need for research to better understand urban vegetation cover on the continent. This article proposes a systematic review of African publications on green cover for the period 2010 to 2024. A descriptive and thematic analysis of the selected scientific papers was carried out using a database established to examine the state of existing research and understanding of the management of these plant formations in Africa. The results of these analyses highlight several major challenges facing urban forestry, including increasing anthropogenic pressures, lack of urban planning that integrates urban forestry, and shortcomings in the management of existing forest landscapes. The thematic analysis has also helped to identify the topics addressed by African researchers, identify gaps in research, and suggest directions for future studies. Three priority areas emerge from this analysis: the conservation of natural or artificial green belts around cities, the impact of these forest landscapes on urban heat islands (climate impact), and the sustainability of ecosystem management in the context of sustainable urbanization. These guidelines will enable a better understanding and valorization of green belts in Africa, thus contributing to the construction of more sustainable cities and the efficient management of forest landscapes. Full article