Innovative Control Strategies Research Articles

Improving Power Quality and Fault Ride-Through (FRT) in Grid-Connected Hybrid Energy Systems: Design and Optimization of Dynamic Voltage Restorers

The integration of renewable energy sources (RESs) into grid-connected systems has become pivotal in addressing climate change and building a sustainable, carbon-neutral society. However, challenges like power quality issues, grid stability, and fault ride-through (FRT) requirements remain significant. This study explores strategies to enhance FRT capabilities and improve power quality in hybrid energy systems. It emphasizes the role of advanced devices such as Dynamic Voltage Restorers (DVRs), EV charging stations, and energy storage solutions. The effectiveness of these technologies is analyzed under various fault scenarios, highlighting improvements in transient response, voltage stability, and harmonic reduction. Additionally, hybrid energy systems, combining multiple RESs and backup sources, are proposed as viable solutions to address intermittency and system reliability. MATLAB/SIMULINK simulations validate the proposed approaches, showcasing their effectiveness in mitigating grid disturbances and maintaining operational efficiency. The findings underline the importance of innovative control strategies and hybrid configurations in ensuring a resilient and sustainable energy infrastructure.

Methylation of foreign DNA overcomes the restriction barrier of Flavobacterium psychrophilum and allows efficient genetic manipulation.

Bacterial cold-water disease (BCWD) caused by Flavobacterium psychrophilum is a problem for salmonid aquaculture worldwide, and current control measures are inadequate. An obstacle in understanding and controlling BCWD is that most F. psychrophilum strains resist DNA transfer, thus limiting genetic studies of their virulence mechanisms. F. psychrophilum restriction enzymes that destroy foreign DNA were suspected to contribute to this problem. Here, we used F. psychrophilum DNA methyltransferases to modify and protect foreign DNA from digestion. This allowed efficient conjugative DNA transfer into nine diverse F. psychrophilum strains that had previously resisted DNA transfer. Using this approach, we constructed a gene deletion mutant that failed to cause disease in rainbow trout. Further genetic studies could help determine the molecular factors involved in pathogenesis and may aid development of innovative BCWD control strategies.

A conserved fungal Knr4/Smi1 protein is crucial for maintaining cell wall stress tolerance and host plant pathogenesis.

Filamentous plant pathogenic fungi pose significant threats to global food security, particularly through diseases like Fusarium Head Blight (FHB) and Septoria Tritici Blotch (STB) which affects cereals. With mounting challenges in fungal control and increasing restrictions on fungicide use due to environmental concerns, there is an urgent need for innovative control strategies. Here, we present a comprehensive analysis of the stage-specific infection process of Fusarium graminearum in wheat spikes by generating a dual weighted gene co-expression network (WGCN). Notably, the network contained a mycotoxin-enriched fungal module (F12) that exhibited a significant correlation with a detoxification gene-enriched wheat module (W12). This correlation in gene expression was validated through quantitative PCR. By examining a fungal module with genes highly expressed during early symptomless infection that was correlated to a wheat module enriched in oxidative stress genes, we identified a gene encoding FgKnr4, a protein containing a Knr4/Smi1 disordered domain. Through comprehensive analysis, we confirmed the pivotal role of FgKnr4 in various biological processes, including oxidative stress tolerance, cell cycle stress tolerance, morphogenesis, growth, and pathogenicity. Further studies confirmed the observed phenotypes are partially due to the involvement of FgKnr4 in regulating the fungal cell wall integrity pathway by modulating the phosphorylation of the MAP-kinase MGV1. Orthologues of the FgKnr4 gene are widespread across the fungal kingdom but are absent in other Eukaryotes, suggesting the protein has potential as a promising intervention target. Encouragingly, the restricted growth and highly reduced virulence phenotypes observed for ΔFgknr4 were replicated upon deletion of the orthologous gene in the wheat fungal pathogen Zymoseptoria tritici. Overall, this study demonstrates the utility of an integrated network-level analytical approach to pinpoint genes of high interest to pathogenesis and disease control.

Advances and trends in weed management: A comprehensive review

Weed management is a critical aspect of agricultural practices that can significantly impact crop yield and quality. As traditional methods face challenges such as herbicide resistance and environmental concerns, the agricultural sector is witnessing a shift towards innovative strategies for weed control. This review explores the emerging trends in weed management, focusing on sustainable and efficient approaches. Among them, one prominent trend is the adoption of agro ecological weed management strategies, which combine various control methods such as cultivation techniques, mechanical techniques, biological control, and reasonable herbicide use. This approach minimizes reliance on herbicides while maximizing weed suppression and preserving natural ecosystems. Another significant trend is developing and utilizing precision agriculture technologies for targeted weed control techniques such as satellite imaging, unmanned aerial vehicles (UAVs), and sensor-based systems. These innovations enable farmers to accurately identify and manage weeds, reducing herbicide usage and minimizing environmental impact. Furthermore, the exploration of alternative weed control methods, including thermal, electrical, and microwave-based technologies, is gaining momentum. These non-chemical approaches offer potential solutions to herbicide-resistant weeds and contribute to sustainable agricultural practices. Moreover, the integration of advanced breeding techniques and biotechnology for developing herbicide-resistant crops and enhancing allelopathic traits presents promising avenues for long-term weed management. In conclusion, this review highlights emerging technology for dealing with major problems, including increased understanding of weed biology linked with genomics; novel herbicide-resistant crops and redesigned weed-competing crops; multi-target herbicides; and enhanced biocontrol agents. When combined, these strategies could make up the elements of the next integrated packages designed to impede the emergence of new weed issues.

Navigating the Gridlock: Innovative Strategies for Traffic Management and Control

Urban traffic congestion is a critical challenge that impairs economic efficiency, environmental sustainability, and quality of life in cities worldwide. This article reviews the various strategies and technologies deployed to manage and mitigate urban traffic congestion. It begins with a discussion of traditional traffic control measures, such as traffic signals and law enforcement, and their limitations in densely populated urban areas. The narrative then transitions to innovative technologies, including Intelligent Transportation Systems (ITS), Internet of Things (IoT), and artificial intelligence (AI), which offer dynamic and efficient traffic management solutions. Additionally, the role of comprehensive public transportation systems and strategic urban planning is examined as essential components for reducing vehicular load and promoting sustainable urban mobility. The article concludes by exploring future directions in traffic management, emphasizing autonomous vehicles, smart infrastructure, and integrated mobility solutions. This review highlights the necessity for a multifaceted approach, combining technology, policy, and public engagement to address the complexities of urban traffic congestion effectively.

Adaptive Neural Network-Based PID Controller Design for Velocity Control of an Internal Combustion Engine Using Back Propagation Technique

Precise velocity control in internal combustion engines (ICEs) is essential for optimizing performance, improving fuel efficiency, and minimizing emissions. However, the nonlinear dynamics and inherent uncertainties within ICE systems present substantial challenges for traditional control methods. In this paper, we propose an adaptive control strategy that integrates a Proportional-Integral-Derivative (PID) controller with Back Propagation Neural Networks (BPNNs) to effectively address these complexities. The proposed controller architecture consists of two key components: a BPNN to estimate modelling uncertainties, such as unknown friction and external disturbances affecting the ICE, and a primary PID controller responsible for velocity regulation. The BPNN functions as a dynamic estimator, continuously learning and adapting to changes in system dynamics, thereby enhancing the robustness and adaptability of the control system. By accurately capturing the nonlinearities and uncertainties inherent in ICEs, the BPNN contributes to improved control performance and system stability. To validate the effectiveness of the proposed approach, extensive numerical simulations are performed using MATLAB Simulink. These simulations encompass a range of operating conditions and scenarios to thoroughly evaluate the controller's performance. Additionally, the proposed method is compared to conventional PID control techniques found in the literature, with a focus on robustness, tracking accuracy, and disturbance rejection. The results indicate that the adaptive PID controller, incorporating BPNNs, outperforms traditional PID methods, delivering superior velocity regulation and disturbance rejection. Furthermore, the proposed approach demonstrates significant potential for real-world applications in ICE systems, providing enhanced control performance and efficiency. This study advances the field of control engineering by introducing an innovative adaptive control strategy tailored specifically for velocity control in internal combustion engines. Leveraging the capabilities of BPNNs to address uncertainties, this approach contributes to improved system performance and offers a promising direction for future advancements in engine control technologies.

Adaptive Disturbance Rejection and Power Smoothing Control for Offshore Hydraulic Wind Turbines Based on Pitch and Motor Tilt Angles

This paper investigates an adaptive disturbance rejection control (ADRC) strategy for dual-variable power smoothing for hydraulic wind turbine systems deployed in marine environments. Initially, fluctuations in wind speed induce variations in the output torque and rotational speed of the wind turbine; this study examines the interaction between these two variables and subsequently decouples them. An innovative dual-variable anti-disturbance control strategy is proposed, which independently regulates the pitch angle of the rotor and the swing angle of the variable motor to mitigate fluctuations in both speed and torque, thereby achieving a smoother system output power. The simulation results obtained through MATLAB/Simulink (Version R2022a) indicate that employing the proposed control strategy leads to an 8.31% reduction in power generation compared to optimal power tracking strategies while enhancing output power stability by 56.67%. Furthermore, the effective smoothing of power fluctuations is accomplished without necessitating energy storage devices. Finally, the effectiveness of the power smooth output control strategy proposed in this paper was verified based on a semi-physical simulation experimental platform for a 30 kW hydraulic wind turbine. The control method proposed in this paper provides a theoretical basis for the promotion and application of hydraulic wind turbines with stable power output.

Optimizing microbe-infected mosquito release: a stochastic model for malaria prevention

Malaria remains a critical public health challenge in Africa, demanding innovative control strategies. This study introduces a novel approach using Microsporidia MB-infected mosquitoes and stochastic optimal control within a Lévy process framework to regulate mosquito release strategies. The primary goal is to optimize Microsporidia MB prevalence within mosquito populations to disrupt Plasmodium transmission to humans. By incorporating Lévy noise into the modeling process, we capture the inherent randomness of mosquito dynamics, improving intervention accuracy. The model, guided by the Hamilton–Jacobi–Bellman (HJB) equation, optimizes release protocols while accounting for key environmental factors like seasonality and temperature fluctuations. Results show that intervention success depends on local climatic conditions, underscoring the need for flexible, region-specific strategies in malaria-endemic areas. Focus regions include Kenya, Ghana, Niger, and Benin, where Microsporidia MB has been confirmed. Findings suggest that targeted mosquito releases could significantly reduce malaria transmission, offering valuable insights for public health efforts.

Foisc1 regulates growth, conidiation, sensitivity to salicylic acid, and pathogenicity of Fusarium oxysporum f. sp. cubense tropical race 4

The secreted isochorismatases derived from certain filamentous pathogens play vital roles in the infection of host plants by lowering salicylic acid (SA) levels and suppressing SA-mediated defense pathway. However, it remains unclear whether the fungus Fusarium oxysporum f. sp. cubense tropical race 4 (FocTR4), which causes vascular wilt in bananas, utilizes isochorismatases to modulate SA levels in the host and subvert the banana defense system for successful infection. In the current study, we selected and functionally characterized the foisc1 gene, one of 10 putative isochorismatase-encoding genes in FocTR4 that showed significant upregulation during early stages of infection. Deletion of foisc1 resulted in enhanced vegetative growth and conidiation, increased sensitivity to SA, reduced colonization within host plants, as well as impaired pathogenicity. Conversely, complementation restored phenotypes similar to those observed in the wild-type strain. Furthermore, deletion of foisc1 led to a notable rise in activities of defense-related enzymes such as catalase, peroxidase, and phenylalnine ammonialyase; along with an upregulated expression of several defense-related genes including PR genes and NPR1 genes within hosts' tissues. The non-secretory nature of Foisc1 protein was confirmed and its absence did not affect SA levels within host plants. Transcriptome analysis revealed that deletion of foisc1 resulted in decreased expression levels for numerous genes associated with pathogenicity including those involved in fusaric acid biosynthesis and effector genes as well as a catechol 1,2-dioxygenase gene essential for SA degradation; while increasing expression levels for numerous genes associated with hyphal growth and conidiation were observed instead. Therefore, our findings suggest that Foisc1 may influence hyphal growth, conidiation, sensitivity to SA, and pathogenicity of FocTR4 through modulation of various genes implicated in these processes. These findings provide valuable insights into the pathogenesis of FocTR4, and create a groundwork for the future development of innovative control strategies targeting vascular wilt disease of banana.

An Innovative Online Adaptive High-Efficiency Controller for Micro Gas Turbine: Design and Simulation Validation

In this article, an innovative online adaptive high-efficiency control strategy is proposed to improve the power generation efficiency of a marine micro gas turbine under partial load. Firstly, a mathematical model of the micro-gas turbine is established, and a control strategy consisting of an on-board prediction model and an online update model is proposed. To evaluate the performance changes of the gas turbine, we applied deep learning techniques to enhance the extreme learning machine (ELM) algorithm, resulting in the development of a high-precision, high-real-time deep extreme learning machine (DL_ELM) prediction model. This model effectively monitors changes in the gas turbine’s performance. Furthermore, an online time-series deep extreme learning machine with a dynamic forgetting factor (DFF_DL_OSELM) model is designed to achieve the real-time tracking of performance variations. When the DL_ELM model detects a gas turbine’s performance change, a particle swarm optimization (PSO) algorithm is employed to iteratively calculate the DFF_DL_OSELM model, determining the optimal speed control scheme to ensure the gas turbine operates at maximum efficiency. To validate the superiority of the proposed control strategy, a comparison is made with traditional high-efficiency control strategies based on polynomial fitting and BP neural networks. The results demonstrate that although all three strategies can achieve efficient operation under constant conditions, traditional strategies fail to identify and adjust to performance changes in real time, leading to decreased control performance and potential engine damage as engine characteristics degrade. In contrast, the proposed online adaptive control strategy dynamically adjusts the speed control plan based on performance degradation, ensuring that the gas turbine operates efficiently while keeping the turbine inlet and exhaust temperatures within safe limits.

DC microgrid for EV charging station with EV control by using STSM controllers

Abstract This study explores innovative control strategies for electric vehicle (EV) charging stations in a DC Microgrid powered by solar and wind energy. A new methodology regulates the dc-link voltage through various converters while a modified vector control method enhances the performance of switched reluctance motors (SRMs). The implementation of super twisting sliding mode (STSM) controllers show superior performance compared to traditional PI and Fuzzy controllers. The design of an asymmetrical converter with four battery banks also minimizes charging durations. The real-time test system (RTS) effectively managed power generation within a DC microgrid, demonstrating a stable voltage at the DC bus despite variations in total generation from photovoltaic (PVS) and wind systems. In Case 1, the controller successfully maintained power balance while charging electric vehicles and managing DC loads. During load torque adjustments, the system maintained a steady motor speed of 320 RPM even with a load torque increase from 5 Nm to 50 Nm at 3 s, showcasing its robust vector control strategy. Notably, the system facilitated a reverse operation at 10 km h−1 (80 RPM) by seamlessly adjusting the engine’s reference speed from 80 RPM to −80 RPM at t = 4.0 s. The vector control causes the engine speed heading to be opposite in a natural manner., indicating its innovative capability to handle diverse operational scenarios. The MATLAB/Simulink package serves as the foundation for the proposed model, which is then integrated into OPAL-RT modules to create a Hardware-in-the-Loop (HIL) system for showcasing diverse outcomes. Different outcomes are deliberated with validated justifications of the suggested approach. The research is linked to Sustainable Development Goals 7 (Affordable and clean energy) and 13 (Climate action).

Advanced heave control of a hydrofoil-based autonomous surface vehicle: CFD modeling with integrated variable propeller revolution control

An innovative control strategy employing the Proportional-Integral-Derivative (PID) algorithm is proposed to address the heave stability challenges in the in-house developed hydrofoil-based Autonomous Surface Vehicle (HASV). This method regulates the HASV's heave motion by adjusting the propeller's revolution speed. After confirming that the aerodynamic load on the superstructure contributes less than 2% compared to the hydrodynamic load, a detailed numerical simulation study and recirculating water tunnel test were conducted to validate the reliability of the established Unsteady Reynolds-Averaged Navier-Stokes (URANS) CFD model for the HASV substructure. The integration of the PD controller for propeller revolutions into the CFD free-running model, which incorporates fully nonlinear and viscous effects, was achieved using the ‘Region-Moving’ method for the background domain and the Body Force Method (BFM) for the propeller domain. By analyzing the impact of various PD coefficients on heave control, optimal settings for the HASV were determined. Subsequent studies examined the vehicle's capability in draft correction and draft maintenance under wave conditions. The results of direct CFD-PD simulations demonstrate that the proposed heave control method effectively stabilizes the HASV under complex nonlinear environmental conditions, offering valuable insights for addressing similar heave control challenges in hydrofoil-based vehicles.

Optimizing the tilt angle of kinetic photovoltaic shading devices considering energy consumption and power Generation— Hong Kong case

Photovoltaic Shading Devices (PVSDs) serve as integral components of Building-Integrated Photovoltaics (BIPV), fulfilling both architectural shading functions and supplying on-site renewable energy to buildings. Previous studies have primarily focused on the energy efficiency of PVSDs in single-story or low-rise buildings, often overlooking the thermal hotspot effects caused by the shading of upper devices on lower ones in multi-story settings, which significantly impairs the efficiency and lifespan of the PV systems. This study focuses on a residential high-rise in Hong Kong, where a kinetic PVSD was designed alongside three innovative control strategies aimed at minimizing energy consumption and optimizing photovoltaic efficiency. Results indicate that in Hong Kong’s context, a ratio of PV panel width to the vertical spacing of adjacent PVSDs below 1:10 prevents vertical shading between devices. Moreover, setting the PVSDs at a constant optimal angle of 65° throughout the year, adjusting to optimal monthly angles, or employing real-time angle optimization can reduce energy consumption by 25%, 31.9%, and 36.5%, respectively, compared to units without PVSDs. Furthermore, the hourly control strategy generated 6.4% and 11.4% more electricity than the monthly and yearly control strategies. The kinetic photovoltaic solar devices (PVSD) and the diverse control strategies discussed in this research provide valuable practical insights for the integration of building-integrated photovoltaics (BIPV) on urban facades in densely populated cities. These findings also support the advancement of zero-carbon building practices in high-density environments.

RNA communication between organisms inspires innovative eco-friendly strategies for disease control.

RNA communication between organisms inspires innovative eco-friendly strategies for disease control.

Backstepping-based tunable containment control for a class of second-order multi-agent systems

Aiming at the problem of non-tunable reference signals in the existing containment schemes for multi-agent systems (MASs), an innovative containment control scheme is proposed. The backstepping method is utilized to design controllers to guide agents, so as to realize the containment control objective by generating a set of tunable reference signals. If the reference signals are not available, then a set of estimators is designed to generate estimated reference signals to ensure that the agents are still able to accomplish the containment control task. Meanwhile, the proposed scheme incorporates a command filtered technique to reduce the computational complexity arising from the large number of partial derivative operations in the backstepping design. The tracking errors of MASs are proved to be bounded by Lyapunov stability analysis. The simulation results show that the proposed scheme is reasonable and effective.

Intelligent Vehicle Trajectory Tracking Based on Horizontal and Vertical Integrated Control

To address the common issues of accuracy and stability in trajectory tracking tasks for autonomous vehicles, this study proposes an innovative composite control strategy that skillfully integrates lateral and longitudinal dynamic control. For lateral control, model predictive control (MPC) theory is introduced to compute the front wheel steering angle that ensures optimal trajectory following. On the longitudinal control level, the vehicle’s acceleration and deceleration logic are finely tuned to ensure precise adherence to the preset speed trajectory. More importantly, by deeply integrating these two control methods, the comprehensive coordination of the vehicle’s lateral and longitudinal movements is achieved. To validate the effectiveness of the proposed control strategy, simulations were conducted using the CarSim and MATLAB/Simulink platforms. The analysis of the simulation results confirms that the proposed method effectively improves speed tracking stability and significantly enhances path tracking accuracy and overall driving stability.

Innovative strategies and challenges mosquito-borne disease control amidst climate change.

The revival of the transmission dynamics of mosquito-borne diseases grants striking challenges to public health intensified by climate change worldwide. This inclusive review article examines multidimensional strategies and challenges linked to climate change and the epidemiology of mosquito-borne diseases such as malaria, dengue, Zika, chikungunya, and yellow fever. It delves into how the biology, pathogenic dynamics, and vector distribution of mosquitoes are influenced by continuously rising temperatures, modified rainfall patterns, and extreme climatic conditions. We also highlighted the high likelihood of malaria in Africa, dengue in Southeast Asia, and blowout of Aedes in North America and Europe. Modern predictive tools and developments in surveillance, including molecular gears, Geographic Information Systems (GIS), and remote sensing have boosted our capacity to predict epidemics. Integrated data management techniques and models based on climatic conditions provide a valuable understanding of public health planning. Based on recent data and expert ideas, the objective of this review is to provide a thoughtful understanding of existing landscape and upcoming directions in the control of mosquito-borne diseases regarding changing climate. This review determines emerging challenges and innovative vector control strategies in the changing climatic conditions to ensure public health.

Deciphering the microbial communities in ticks of Inner Mongolia: ecological determinants and pathogen profiles

BackgroundTicks are vectors of numerous pathogens, with their bacterial composition, abundance, diversity, and interaction influencing both their growth and disease transmission efficiency. Despite the abundance of ticks in Inner Mongolia, China, comprehensive data on their microbial communities are lacking. This study aims to analyze the microbial communities within ticks from Inner Mongolia to inform innovative control strategies for interrupting pathogen transmission.MethodsTick samples were collected from animals and vegetation in multiple locations across Inner Mongolia and stored at − 80 °C. Ticks were identified using morphological keys and molecular biology methods. Full-length 16S rRNA gene sequencing was performed on collected samples. Bacterial community composition and diversity were mainly analyzed using bioinformatic tools such as QIIME, phyloseq, and DESeq2. Alpha diversity was assessed using Chao1, ACE, and Shannon indices, while beta diversity was evaluated using Bray-Curtis dissimilarity matrices. LEfSe analysis was applied to identify taxa associated with ecological and biological variables.ResultsA total of 5,048,137 high-quality read counts were obtained, forming an average of 789.3 OTUs per sample. Proteobacteria, Firmicutes, and Bacteroidetes were the most dominant phyla. Bacterial community composition varied significantly with geography, with Dermacentor nuttalli showing a higher abundance of Rickettsia in Xilingol League, while other regions had different dominant genera. The microbial community also differed based on the feeding status of ticks. Additionally, the microbiota of engorged ticks showed organ specificity. Pathogen detection efforts revealed the presence of nine pathogens across all three tick species. D. nuttalli was found to carry a significantly higher burden of pathogenic bacteria, making it the most potentially threatening tick species in Inner Mongolia.ConclusionsThe study highlights significant variations in tick microbiomes influenced by geographic location, feeding status, and tick species. It underscores the importance of enhancing tick and tick-borne disease surveillance in Inner Mongolia for early detection and control of emerging pathogens.Graphical

A composite fixed-time sliding mode control scheme for Unmanned Ground Vehicles affected by external disturbances

Accurate path-following control is crucial for Unmanned Ground Vehicles (UGV). However, influenced by external disturbances, UGV systems encounter challenges in meeting specific performance requirements, such as no overshoot and minimized chattering. In current sliding mode control (SMC) methods with disturbance observer (DOB), there still exists a serious chattering problem. To achieve fast chattering reduction and accurate path-following performance, this paper proposes a novel composite fixed-time sliding mode (CFTSM) control method according to fixed-time disturbance observer (FTDO). First, a innovative CFTSM control scheme is developed to achieve a significant gain near the origin in the UGV system. It facilitates quicker convergence and improved tracking performance than other SMC methods. Then, to mitigate the impact of the external disturbances, a FTDO is designed to estimate and effectively eliminate these disturbances in fixed-time. Finally, a composite control strategy, which integrated the FTDO into the CFTSM control scheme, is proposed to stabilize the UGV system and suppress the chattering phenomenon. A comprehensive simulation and experimental results demonstrate that the proposed CFTSM-FTDO control strategy performs better with strong robustness and stability. In real experiments, the CFTSM achieves RMSE reductions of 26.8% and 8.9% in lateral and heading errors compared to CFTSM-FFE, and 19.1% and 3.9% compared to FTSMC-AESO. These enhancements are expected to achieve more accurate and stable tracking performance for the UGV, thus giving rise to accurate autonomous navigation.

Barrier function-based prescribed performance trajectory tracking control of wheelchair upper-limb exoskeleton robot under actuator fault and external disturbance: experimental verification

This paper presents an innovative control strategy for the trajectory tracking of wheelchair upper-limb exoskeleton robots, integrating sliding mode control with a barrier function-based prescribed performance approach to handle actuator faults and external disturbances. The dynamic model of the exoskeleton robot is first extended to account for these uncertainties. The control design is then divided into two phases. In the first phase, the sliding mode control technique is applied to ensure robust trajectory tracking by defining the tracking error between the robot’s states and desired trajectories. A sliding surface is constructed based on this error, and to further enhance tracking performance, a prescribed performance control scheme is incorporated, which ensures fast error convergence and improves transient behavior. In the second phase, an advanced barrier function technique is introduced to mitigate the impact of actuator faults and disturbances, enhancing the overall robustness of the system. Stability and tracking accuracy are rigorously verified through Lyapunov theory, ensuring the system's resilience to uncertainties. The combined approach not only guarantees rapid error convergence but also prevents performance degradation due to excessive control action, maintaining system stability. Finally, the effectiveness of the proposed method is demonstrated through extensive simulations and hardware-in-loop experiments, highlighting its practical applicability for real-world exoskeleton systems.