Development Of Control Strategies Research Articles

Strategi Inovatif Pemberantasan Hama Jagung Dengan Sistem Fuzzy Dan Iot Untuk Meningkatkan Produktivitas (Studi Kasus Pemberantasan Hama Jagung Di Ponorogo)

Corn pest control is a significant challenge for corn farmers in Indonesia. Pests can cause substantial losses for farmers, especially if left unattended. Therefore, innovative strategies are needed to enhance productivity and reduce farmer losses. This study explores the potential of Internet of Things (IoT) technology in innovative corn pest control strategies. The study aims to investigate how IoT can assist corn farmers in monitoring and controlling pests, thereby enhancing productivity and reducing losses.The study employs a case study approach, focusing on the application of IoT technology in corn pest control. Data collection involves field observations, interviews with farmers, and analysis of existing literature. The findings indicate that IoT technology can significantly improve the accuracy and speed of pest monitoring, enabling farmers to make more informed decisions about pest control.The study concludes that the integration of IoT technology into corn pest control strategies can lead to increased productivity and reduced losses for farmers. The findings of this study contribute to the development of innovative and effective pest control strategies, ultimately benefiting the corn farming industry in Indonesia.

Intra-event variations of organic micropollutants in highway runoff and a presedimentation-biofilter treatment facility

The study assessed the quality of highway runoff and a stormwater treatment system, focusing on intra-event variations (IEVs: variations within a runoff/effluent event) of the concentration of organic micropollutants (OMPs) including bisphenol-A, alkylphenols, polycyclic aromatic hydrocarbons (PAHs), and petroleum hydrocarbons (PHCs). IEVs of OMPs varied considerably with no particular recurring pattern in highway runoff and presedimentation effluent, displaying sporadic strong first flushes. IEVs are significantly associated with rainfall intensity variations, especially for particle-bound substances such as PAHs and PHCs. However, phenolic substances showed distinct IEV patterns compared to total suspended solids, PAHs, and PHCs, likely due to their higher solubility and mobility. Downstream sand filter (SF) and vegetated biofilter (BFC) mitigated IEVs, leading to more uniform discharge during outflow events. Although BFC’s IEVs were indiscernible due to low effluent concentrations, SF’s IEVs often peaked at the beginning of events (within the first 100 of ⁓600 m3), exceeding the lowest predicted non-effect concentrations for five PAHs, bisphenol-A, and octylphenol. This study highlights the advantage of IEV analysis over conventional event mean concentration analysis for identifying critical effluent stages, crucial for developing control strategies to protect sensitive water recipients or for reuse applications.

Human-Environment Interactions Shape Mosquito Seasonal Population Dynamics.

Mosquito species, including the Asian tiger mosquito, can transmit disease-causing pathogens such as dengue, Zika, and chikungunya, with their population dynamics influenced by a variety of factors including climate shifts, human activity, and local environmental conditions. Understanding these dynamics is vital for effective control measures. Our study, conducted in Jardí Botanic Marimurtra from May to November 2021, monitored Ae. albopictus activity using BG-Traps and investigated larval control effects. We employed Generalized Linear Mixed Models to analyze variables like weather, human presence, and larvicidal control on adult mosquito abundance. Adults of Ae. albopictus exhibited a seasonal pattern influenced by temperature but with bimodal peaks linked to cumulative rainfall. Proximity to stagnant water and visitor influx directly affected mosquito captures. Additionally, the effectiveness of larvicide treatments depended on interactions between preceding rainfall levels and treatment timing. Our research emphasizes the significance of studying vector ecology at local scales to enhance the efficacy of control programs and address the escalating burden of vector-borne diseases. Considering the impacts of extreme weather events and climate shifts is essential for the development of robust vector control strategies. Furthermore, our distinct findings serve as a prime illustration of utilizing statistical modeling to gain mechanistic insights into ecological patterns and processes.

Improved Sliding Mode Control for Tracking Global Maximum Power of Triple Series Parallel Ladder Photovoltaic Array under Uneven Shadowing

Abstract This paper presents an innovative way for enhancing the performance of photovoltaic arrays under uneven shadowing conditions. The study focuses on a triple series parallel ladder configuration to exploit the benefits of increased power generation while addressing the challenges associated with uneven shadowing. The proposed methodology focuses on the implementation of improved sliding mode Control technique for the global maximum power point tracking efficiently. Sliding mode control is known for its robustness in the presence of uncertainties and disturbances, making it suitable for dynamic and complex systems such as photovoltaic arrays. This work employs a comprehensive simulation framework to comment the performance of the suggested improved sliding mode control strategy at uneven shadowing scenarios. Comparative analysis has been done to show the effectiveness of the suggested method than the traditional control strategies. The results demonstrate a remarkable enhancement in the tracking accuracy of the global maximum power point, leading to enhanced energy harvesting capabilities under challenging environmental conditions. Furthermore, the proposed approach exhibits robustness and adaptability in mitigating the effect of shading on the photovoltaic array, thereby increasing overall system efficiency. This research contributes valuable insights into the development of advanced control strategies for photovoltaic arrays, particularly in the context of triple series parallel ladder configurations operating under uneven shadowing conditions. Under short narrow shading condition, the improved sliding mode control method tracking the maximum power more compared to perturb & observe is 20.68%, incremental conductance is 68.78%, fuzzy incremental conductance is 19.8%, and constant velocity sliding mode control is 1.25%. The improved sliding mode control method has 60% less chattering than constant velocity sliding mode control under shading conditions.

Interpretable GHG emission prediction for papermaking wastewater treatment process with deep learning

The content of pollutants in papermaking wastewater is highly varied due to heterogeneous raw materials, product types, and biochemical process conditions, resulting in significant uncertainty of GHG emissions mitigation in the treatment process. This study established a modified Benchmark Simulation Model no.1-based mechanism model to reveal the characteristics of GHG emissions and support GHG reduction in the papermaking wastewater treatment process. Due to its heavy dependence on the Benchmark Simulation Model No. 1 (BSM1) and plentiful parameters, it was instead further proposed Deep Neural Networks (DNN) models and Long Short-Term Memory (LSTM) models to estimate GHG emissions with high accuracy (R2 > 0.94) greatly reduced computational complexity (8920.6 times). The global sensitivity analysis upon DNN revealed key factors for GHG emission with strong explainability, supporting the development of GHG reduction control strategies for the papermaking wastewater treatment process.

Setosphaeria turcica, the Maize Leaf Blast Pathogen: Current Status and Infection Mechanisms

The fungal pathogen Setosphaeria turcica causes northern corn leaf blight (NCLB) in maize, sorghum, and related grasses. NCLB is a serious fungal foliar disease of cultivated maize that causes devastating yield losses. S. turcica infects maize leaves by means of a specialized cell, the appressorium, but the regulatory mechanisms that underlie appressorium-mediated infection remain largely unknown. Many regulatory pathways and a large number of genes have been described in S. turcica, and many of these genes have been cloned. Characterization of such disease-related genes is important for understanding the biological mechanisms of interaction between pathogen and host and can guide the development of strategies for disease control. There is a significant level of concern regarding the possible dissemination of the S. turcica pathogen to regions where NCLB is not presently prevalent. This scenario is of considerable concern and necessitates immediate research intervention. The present review brings together information on the epidemiology and infection mechanisms of S. turcica.

A Critical Review in Infectious Disease and Mathematical Modelling

Infectious diseases control and prevention relies on a thorough understanding of the factors determining transmission. The transmission dynamics of infectious diseases is susceptible to changes governed by several factors, whose recognition is critical for the rational development of strategies for prevention and control, as well as for developing health policies. This article summarizes the fundamental principles of infectious disease transmission and compartmental models. Basic principles of infectious disease diagnosis, reasons for formulating the infectious diseases and historical facts on epidemics are also reviewed.

Effects of Hypoxia on the Antibacterial Activity of Epidermal Mucus from Chilean Meagre (Cilus gilberti).

Comprehending the immune defense mechanisms of new aquaculture species, such as the Chilean meagre (Cilus gilberti), is essential for sustaining large-scale production. Two bioassays were conducted to assess the impact of acute and intermittent hypoxia on the antibacterial activity of juvenile Chilean meagre epidermal mucus against the potential pathogens Vibrio anguillarum and Vibrio ordalii. Lysozyme and peroxidase activities were also measured. In general, fish exposed to hypoxia showed a 9-30% reduction in mucus antibacterial activity at the end of hypoxic periods and after stimulation with lipopolysaccharide. However, following water reoxygenation, the activity of non-stimulated fish was comparable to that of fish in normoxic conditions, inhibiting bacterial growth by 35-52%. In the case of fish exposed to chronic hypoxia, the response against V. anguillarum increased by an additional 19.8% after 6 days of control inoculation. Lysozyme exhibited a similar pattern, while no modulation of peroxidase activity was detected post-hypoxia. These results highlight the resilience of C. gilberti to dissolved oxygen fluctuations and contribute to understanding the potential of mucus in maintaining the health of cultured fish and the development of future control strategies.

Performance-prescribed optimal neural control for hypersonic vehicles considering disturbances: An adaptive dynamic programming approach

The precise trajectory tracking control of hypersonic vehicles plays a crucial role in secure flight. To address this challenge, this article develops an optimal neural control framework with prescribed performance constraints for height-velocity control tasks with disturbances. To this end, the control-oriented error model is firstly established and transformed into an affine nonlinear form to provide a foundation for the design of controller. Then, in order to balance performance constraints and convergence speed, a performance-constraint function is introduced to convert the constrained tracking error into an unconstrained one. After that, an improved sliding mode compensation observer is proposed by combining low-fidelity approximation data with high-fidelity compensation model for eliminating disturbances. The developed control scheme is organized by an adaptive dynamic programming (ADP) algorithm based on the actor-critic policy structure to achieve the near-optimal control strategy. Meanwhile, the designed neural network (NN) weight updating laws are provided to obtain the solution of Hamiltonian-Jacobi-Bellman (HJB) equation rapidly without high calculation consumption. Finally, the stability conditions of the closed-loop system are performed and ensure the entire framework is semi-global uniformly ultimately bounded (SGUUB), two simulation cases verify the effectiveness and superiority of the proposed approach.

GPCR–MAPK signaling pathways underpin fitness trade-offs in whitefly

Trade-offs between evolutionary gain and loss are prevalent in nature, yet their genetic basis is not well resolved. The evolution of insect resistance to insecticide is often associated with strong fitness costs; however, how the fitness trade-offs operates remains poorly understood. Here, we show that the mitogen-activated protein kinase (MAPK) pathway and its upstream and downstream actors underlie the fitness trade-offs associated with insecticide resistance in the whitefly Bemisia tabaci. Specifically, we find a key cytochrome P450 gene CYP6CM1, that confers neonicotinoids resistance to in B. tabaci, is regulated by the MAPKs p38 and ERK through their activation of the transcription factor cAMP-response element binding protein. However, phosphorylation of p38 and ERK also leads to the activation of the transcription repressor Cap "n" collar isoform C (CncC) that negatively regulates exuperantia (Ex), vasa (Va), and benign gonial cell neoplasm (Bg), key genes involved in oogenesis, leading to abnormal ovary growth and a reduction in female fecundity. We further demonstrate that the transmembrane G protein-coupled receptor (GPCR) neuropeptide FF receptor 2 (NPFF2) triggers the p38 and ERK pathways via phosphorylation. Additionally, a positive feedback loop between p38 and NPFF2 leads to the continuous activation of the MAPK pathways, thereby constitutively promoting neonicotinoids resistance but with a significant reproductive cost. Collectively, these findings provide fundamental insights into the role of cis-trans regulatory networks incurred by GPCR-MAPK signaling pathways in evolutionary trade-offs and applied knowledge that can inform the development of strategies for the sustainable pest control.

Adaptive command filter control for switched time‐delay systems with dead‐zone based on an exact disturbance observer

AbstractThe focus of this article lies in the adaptive command filter tracking control problems for a class of switched time‐delay systems with an asymmetric dead‐zone and external disturbance under arbitrary switching. First, the external disturbance is estimated via an exact disturbance observer. Then, an echo state network (ESN) was adopted to approximate unknown function in system without tuning the weights between a reservoir and an input layer. Second, the time‐varying input of the dead‐zone model is considered as uncertain physical quantity of the system based on the dead‐zone slope boundary information. Third, an adaptive command filtered controller is employed to conquer the matter of “explosion of complexity” encountered in the traditional backstepping method. The filtering errors present in a dynamic surface control (DSC) approach are effectively eliminated by means of error compensation signals. A prescribed performance control (PPC) is resorted to confine the system output to meet prescribed steady‐state and transient tracking performances. The time delay terms are compensated by a Lyapunov–Krasovskii functional. The semi‐globally ultimately uniformly boundedness of all states in the closed‐loop system is ensured by the Liapunov's stability criterion. Finally, the validity of the developed control scheme is further verified through a simulation example.

Quantitative evaluation of the role of aging and non-aging factors for predicting threats from major chronic diseases and developing control strategies

Various indicators exist to assess the threat of chronic diseases. This paper presents new ones to evaluate the role of aging and non-aging factors for predicting threats from major chronic diseases. Age at zero mortality (AM0) and age at average mortality (AMa) can be calculated by regressing age and mortality (the intercept indicates AM0, the slope indicates the observed slope and r indicates random non-aging factors). A regression equation can be created using AMa at the age of 72 and mortality at the age of 82; thus, the expected slope can be obtained for the aging factor without considering non-aging factors. It is possible to distinguish between aging and non-aging factors using the observed and expected slopes, which should be multiplied by r to produce an index of aging (IA). The lower the AM0, AMa or IA of a disease is, the greater the threat it poses to a population. The AM0 and IA were calculated using data from China (2004 and 2019) for various diseases [cancer, heart disease (HD), cerebrovascular disease (CVD), and chronic obstructive pulmonary disease (COPD)]. We found the severity of threat was highest for cancer, CVD, other chronic diseases, HD and COPD in descending order in 2019. The results suggest that changes in threats may be related to socioeconomic development. Cancer was found to be the greatest threat to younger age groups, with IA<0.5, suggesting that non-aging risk factors may play an important role in cancers. Conversely, aging may play an important role in other chronic diseases, including HD, CVD, and COPD. Compared to 2004, the AM0 of cancer showed the greatest change. In conclusions, the different indicators explain different aspects of the problem and it would be beneficial to conduct in-depth research on the theoretical basis for the association of threats of disease with socioeconomic development in order to develop prevention and control strategies.

Current updates on the epidemiology, pathogenesis and development of small molecule therapeutics for the treatment of Ebola virus infections

Ebola virus disease (EVD) is a rare, highly contagious and a deadly disease with a variable fatality rate ranging from 30% to 90%. Over the past two decades, Ebola pandemic has severely affected the sub-Sahara region including Democratic Republic of the Congo (DRC), and Uganda. The causative agents of the most EVD cases are three distinct species out of six Ebolaviruses namely Zaire Ebolavirus (ZEBOV), Sudan Ebolavirus (SUDV) and Bundibugyo Ebolavirus (BDBV). In recent years, significant strides have been made in therapeutic interventions. Notably, the US Food and Drug Administration has approved two monoclonal antibodies: InmazebTM (REGN-EB3) and Ansuvimab or Ebanga™. Additionally, many small molecules are currently in the developmental stage, promising further progress in medical treatment. Addressing the critical need for preventive measures, this review provides an in-depth analysis of the licensed Ebola vaccines-Ervebo and the combination of Zabdeno (Ad26.ZEBOV) and Mvabea (MVA-BN-Filo) as well as the vaccines which are currently being tested for their efficacy and safety in clinical studies. These vaccines might play an important role in curbing the spread and mitigating the impact of this lethal disease. The current treatment landscape for EVD encompasses both nutritional (supportive) and drug therapies. The review comprehensively details the origin, pathogenesis, and epidemiology of EVD, shedding light on the ongoing efforts to combat this devastating disease. It explores small molecules in various stages of the development, discusses patents filed or granted, and delves into the clinical and supportive therapies that form the cornerstone of EVD management. This review aims to provide the recent developments made in the design and synthesis of small molecules for scientific community to facilitate a deeper understanding of the disease and fostering the development of effective strategies for prevention, treatment, and control of EVD.

A Precise Temperature Control Method for Lithium-ion Battery Pack based on the Nonlinear Model Predictive Control Algorithm

Abstract To utilize the maximum performance of the battery while ensuring its thermal safety, a battery thermal management system is used to control the battery maximum temperature within a safe range. This paper centres on the establishment of a temperature prediction model and the development of the nonlinear-based model predictive control (MPC) strategy. First, to address the need of predicting battery temperature, this paper develops a distributed parameter thermal resistance model to predict battery temperature quickly and accurately. Secondly, the open-loop formulation of the nonlinear-based MPC is derived based on the established state space equations. Then the soft and hard constraints of the model are established based on the actual current conditions, pump conditions, temperature difference and temperature rise indexes, so as to establish the objective function of the MPC algorithm. Finally, the established temperature nonlinear MPC algorithm is embedded on the board and the hardware platform of battery liquid cooling system is established. The experiment test result shows that the maximum error of temperature control is less than 0.1°C, and the effectiveness of the temperature control strategy of lithium-ion battery is verified through experiments.

A review of research on tire burst and vehicle stability control.

Tire burst is an accidental occurrence that poses a serious threat to the driving stability and road safety of vehicles. Therefore, it is of great practical significance to investigate early warning systems for tire burst and develop stability and safety control measures after burst incidents. The development of an accurate model that can effectively represent the impact of tire burst on vehicle dynamics is crucial for the design of control systems and the development of stability control strategies. Most of the existing research on tire burst models is based on static tire tests, the effectiveness of these models still needs to be further verified. The main approach to studying the impact of burst tires on vehicle performance is to embed a burst tire model into a vehicle dynamics model. Understanding the impact of tire burst on vehicle performance is essential for identifying burst incidents and developing stability control strategies. The research on burst identification primarily focuses on early warning systems and estimating vehicle state parameters after burst incidents, while the current research on stability control strategies focuses on enabling vehicles to continue running safely after burst incidents through braking, active steering, and collaborative control. Currently, there is no comprehensive review of research on vehicle tire burst stability control. Therefore, this paper primarily reviews five aspects: (a) the causes and prevention of tire burst, (b) the impact of tire burst on vehicle performance, (c) burst identification, (d) stability control strategies for burst incidents, and (e) future prospects for tire burst research.

Quantifying the effect of driving restrictions on fine particulate matter concentrations with WRF-Chem model: A case study in Kaifeng, China

One of the significant sources of urban air particulate matter (PM2.5 and PM10) has been found to be vehicle exhaust emission. Vehicle restriction measures have been taken to alleviate particulate matter pollution in the world. Here, we quantified the impact of vehicle restriction measures on the PM2.5 concentrations using the Weather Research and Forecasting model with Chemistry (WRF-Chem) in Kaifeng, China. This study set up three different scenarios to simulate the spatiotemporal changes of PM2.5 concentrations in four seasons in 2020. They were no emission reduction (S0), the restriction of two tailing license plate numbers (S1), and the restriction of odd–even tailing numbers of license plates (S2), respectively. In general, the odd–even license plate restriction policy has proven to be more effective than restricting two specific license plate numbers. In scenario S1, the range of PM2.5 concentrations reduction in Kaifeng City was between 0.2% and 1.28%. In scenario S2, the range of PM2.5 concentrations reduction was between 0.98% and 14.42%. The results showed that the effectiveness of vehicle restriction measures varies from season to season, and the effect in summer and winter is better than that in spring and autumn. We suggest that it may be difficult to reduce the PM2.5 concentrations to a great extent if simply reducing the traffic exhaust emissions through motor vehicle restrictions, on the contrary, would lead to the increase of O3 concentration, bringing some other effects. This study can serve as a foundation for the rational development of motor vehicle control strategies and help advance the ongoing improvement of air quality.

Seroprevalence of hepatitis c virus in Blood Donors in Kabul in 2023

Introduction: Understanding the prevalence and distribution of the hepatitis C virus (HCV) is crucial for the development of effective prevention and control strategies, given its significant impact on global public health. The objective of this study was to examine the seroprevalence of HCV among blood donors in Kabul and assess the possible gender-based variations in infection rates. Materials and Methods: This study analyzed blood donor records from the central blood bank in Kabul, including both male and female donors, from January to December 2023. HCV screening was performed using ELISA testing, and the data were analyzed using SPSS to determine HCV prevalence and investigate gender-based differences in infection rates. Results: In this study, 59 out of 15,080 individuals (0.39%) tested positive for HCV infection. Among the HCV-positive cases, 81.5% were male and 18.6% were female. The majority of infected males (64.4%) were in the 30-39 age range. A significant proportion of HCV-positive patients had no formal education (69.5%) and were unemployed (61.0%). Additionally, the majority of HCV-positive patients reported a low monthly income (66.1%). Discussion: The study found a low prevalence of HCV infection (0.39%) among the participants, with the highest rates observed in the 30-39 age group. Socioeconomic factors, such as lack of education, unemployment, and low income, were associated with HCV infection. Targeted interventions are needed to address gender disparities, age-specific risks, and socioeconomic determinants to improve prevention and control strategies for HCV in Kabul's blood donor population and enhance overall population health.

Epizootiological monitoring of wolf helminths in Northern and Central Kazakhstan.

Wolves (Canis lupus) play a role in nature, including the regulation of the number of ungulates and the use of dead animals. In addition, wolves are a natural link and carrier for the spread of many parasitic invasions. Hence, the main task in preventing the spread of parasitic invasions is to regulate the wolf population. This study aimed to monitor the endoparasitological fauna of wild wolves inhabiting Northern and Central Kazakhstan. Overall, 81 wolves were investigated for parasitic worms using the K. I. Scriabin method. Wolf intestinal materials were collected from the following six regions: North Kazakhstan, Pavlodar, Kostanay, Akmola, Ulytau, and Karaganda. The genetic diversity of the parasites was identified using a polymerase chain reaction with specific primers. After data collection, a comprehensive statistical analysis was performed. Several helminth types were identified in wolves, including Echinococcus granulosus, Taenia hydatigena, Mesocestoides spp., Toxascaris leonina, Trichinella nativa, Alaria alata, and Dirofilaria repens. Based on the results of this study, young male wolves aged 1-4 years were the most vulnerable to helminthiasis. Wolves living in steppe and semi-desert regions are often exposed to helminth infections. The prevalence of T. nativa in the wolves was 20.4%. This study also revealed the presence of echinococcosis among wolf populations in Karaganda and Kostanay, with prevalence rates of 4.1% and 4.7%, respectively. The overall prevalence of tapeworms in wolves was 54.3%. This study highlights the significance of understanding the potential risks associated with helminth infections in wild carnivores because helminths can act as disease reservoirs and pose a threat to humans, livestock, and other wild carnivores. These results can contribute to the development of effective control and management strategies for helminth infections in wolves, which can infect humans and livestock.

Comparative study of real-time A-ECMS and rule-based energy management strategies in long haul heavy-duty PHEVs

Road transport is a significant contributor to Green House Gas (GHG) emissions in the European Union (EU) and is responsible for approximately 25% of total GHG emissions in the EU13. The European Commission has proposed stricter CO2 emissions targets for heavy-duty vehicles that require manufacturers to achieve a reduction of 90% in average fleet emissions from new vehicles by 2040 compared to a 2019 baseline. To meet these ambitious targets, there is an urgent need to explore alternative pathways away from conventional fossil fuel-based powertrain systems like electrified powertrains and carbon–neutral fuels. One promising option is the plug-in hybrid electric powertrain concept, which combines the advantages of both power sources, enabling improved fuel efficiency and reduced CO2 emissions. However, the potential of such a plug-in hybrid powertrain needs to be evaluated for heavy-duty trucks.This study focuses on the development of control strategies for the Energy Management System (EMS) of the above powertrain concept. First, a control strategy for the non-predictive EMS’s start/stop functionality and optimization of the torque split between the combustion engine and electric machine is developed and calibrated for efficient engine operation depending on the battery state of charge. In addition, a predictive EMS’s control strategy is developed that uses horizon information of the driving route for optimal utilization of electrical energy within the prediction horizon, thereby further enhancing fuel consumption reduction. The predictive EMS uses Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with Pontryagin’s Minimization Principle (PMP) for online equivalence factor adaptation, providing local optimal solutions in real-time. The study concludes with the energy savings achieved through the implementation of these strategies using real-world driving cycles in the heavy-duty transport sector.

WITHDRAWN: Adaptive fuzzy fixed-time bipartite consensus control for stochastic nonlinear multi-agent systems with performance constraints

This paper investigates the fixed-time bipartite consensus control problem of stochastic nonlinear multi-agent systems (MASs) with performance constraints. A constraint scaling function is proposed to model the performance constraints with user-predefined steady-state accuracy and settling time without relying on the initial condition. Technically, the local synchronization error of each follower is mapped to a new error variable using the constraint scaling function and an error transformation function before being used to design the controller. To achieve fixed-time convergence of the local tracking error, a barrier function transforms the scaled synchronization error to a new variable to guarantee the prescribed performance. Then, an adaptive fuzzy fixed-time bipartite consensus controller is developed. The fuzzy logic system handles the uncertainties in the designing procedures, and one adaptive parameter needs to be estimated online. It is shown that the closed-loop system has practical fixed-time stability in probability, and the antagonistic network’s consensus error evolves within user-predefined performance constraints. The simulation results evaluate the effectiveness of the developed control scheme.