Advanced Control Strategies Research Articles

Dynamic Envelope Optimization of Articulated Vehicles Based on Multi-Axle Steering Control Strategies

Steer-by-wire technology, critical for autonomous driving, enables full-wheel steering in articulated vehicles, significantly enhancing maneuverability in complex driving environments. This study investigates dynamic envelope optimization for articulated multi-body vehicles by integrating coordinated multi-axle steering control strategies with higher-order Bezier curve designs. Unlike traditional approaches that primarily focus on single-axle steering, this research emphasizes the advantages of multi-axle steering control, which significantly reduces the dynamic envelope and enhances maneuverability. To address the challenges posed by constrained road environments, a comparative analysis of Septimic Bezier curves under various control point configurations was conducted, demonstrating their effectiveness in achieving smoother curvature transitions and steering comfort. The results highlight the pivotal role of reducing curvature peaks and increasing curvature continuity in optimizing vehicle performance. Furthermore, advanced steering control strategies, such as Articulation Angle Reference (AAR) and Dual Ackermann Steering (DAS), were shown to outperform conventional methods by ensuring precise trajectory control and improved stability. This study provides actionable insights for enhancing vehicle handling and safety in complex driving scenarios, offering a framework for future road design and multi-axle steering system development.

Integration of neuromuscular control for multidirectional horizontal planar reaching movements in a portable upper limb exoskeleton for enhanced stroke rehabilitation.

Globally, the prevalence of stroke is significant and increasing annually. This growth has led to a demand for rehabilitation services that far exceeds the supply, leaving many stroke survivors without adequate rehabilitative care. In response to this challenge, this study introduces a portable exoskeleton system that integrates neural control mechanisms governing human arm movements. This design leverages neuroplasticity principles to simulate natural movements, aiming to reactivate and strengthen neuromuscular connections and thus enhance rehabilitation outcomes. A tailored musculoskeletal model of the human arm and an associated cost function were developed to accurately replicate the planar motion trajectories of a healthy human arm across 32 directions. The application of a Proportional-Derivative (PD) controller enables precise tracking of these trajectories by the exoskeleton. Individual testing has demonstrated high consistency between the exoskeleton-driven motion paths and the simulated trajectories, especially in trajectory accuracy along the X and Y axes. These findings support the efficacy of integrating advanced neural control strategies with practical exoskeleton designs in stroke rehabilitation.

Review on Consensus Control of Multi-Agent Systems

This review provides a comprehensive analysis of consensus control strategies for multi-agent systems (MASs) mainly by researching and analysing the control formula designs of different representative MAS. Key approaches are highlighted ,such as finite-time consensus, pinning control, consensus control under DoS attacks, and other advanced control strategies. Each control strategy is discussed in detail regarding its applicability, advantages, and limitations. Furthermore, the strategies ability to address rapid convergence, robustness, and security in complex network environments is emphasized. Besides, several current research trends and key challenges, such as robustness under dynamic uncertainties and resource-constrained communication, are also explored. Future opportunities for enhancing the stability and security of MASs are explained in the final section of this review

Research on the Application of PID Control Algorithm and Fuzzy Control Theory in the Field of Temperature Control

In the field of industrial automation control systems, PID control algorithms are widely used in various control fields such as temperature, speed and position. Among them, fuzzy PID control can improve control accuracy and stability, can significantly improve product quality and production efficiency, in food processing, chemical engineering, pharmaceutical and textile industries have a wide range of applications. Therefore, this paper comprehensively discusses the theory of fuzzy PID control algorithm and its application in the field of temperature control. The advantages of fuzzy PID control in complex industrial environments are emphasized through a comparative analysis with other temperature control methods as well as traditional linear PID control. The research indicates that by optimizing the control algorithm and hardware design, the fuzzy PID control algorithm can reduce the system overshoot and steady state error, thus improving the control accuracy and response speed. At the same time, the introduction of multivariable PID controllers or the use of advanced multivariable control strategies can achieve coordinated control of multiple variables, thereby improving the performance of the entire system. In addition, the integration of advanced energy management system and data analysis technology can comprehensively monitor and optimize the management of energy consumption, further improving the energy efficiency of the system

Adaptive Energy Management Strategy for Sustainable xEV Charging Stations in Hybrid Microgrid Architecture

Integrating electric vehicles (EVs) into power grids presents critical energy management challenges, especially in microgrid systems powered by renewable energy sources. This study introduces a novel energy management strategy for EV charging stations utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. The system dynamically optimizes the coordination of renewable energy sources solar photovoltaic (PV) panels and wind turbines energy storage, and EV chargers. By leveraging real-time data and predictive algorithms, the ANFIS controller adapts to fluctuations in energy supply and demand, ensuring optimal performance. The innovation of this work lies in combining fuzzy logic with neural network-based learning to enhance decision-making under uncertain and variable renewable energy conditions. The proposed approach employs a robust design methodology, integrating neural network training with fuzzy logic system development, to create an adaptive and intelligent control system. Simulation results using MATLAB/Simulink demonstrate a 92% increase in energy efficiency and an 89% enhancement in load-handling capacity compared to conventional methods. The system effectively manages renewable energy variability, battery state-of-charge, and load demand, maintaining stable electrical characteristics even under dynamic wind and solar conditions. This work underscores the importance of advanced AI-driven control strategies in enabling sustainable EV charging infrastructure within microgrid environments.

Research on boundary control of vehicle-mounted flexible manipulator based on partial differential equations.

Vehicle-mounted flexible robotic arms (VFRAs) are crucial in enhancing operational capabilities in sectors where human intervention is limited due to accessibility or safety concerns, such as hazardous environments or precision surgery. This paper introduces the latest generation of VFRAs that utilize advanced soft materials and are designed with elongated structures to provide greater flexibility and control. We present a novel mathematical model, derived using Hamilton's principle, which simplifies the analysis of the arm's dynamic behaviors by employing partial differential equations (PDEs). This model allows us to understand how these arms behave over time and space, classifying them as distributed parameter systems. Furthermore, we enhance the practical utility of these robotic arms by implementing a proportional-derivative (PD) based boundary control law to achieve precise control of movement and suppression of vibrations, which are critical for operations requiring high accuracy. Our approach's effectiveness and practical utility are evidenced by numerical simulations, which verify that our advanced control strategy greatly enhances the performance and dependability of VFRAs in actual applications. These advancements not only pave the way for more sophisticated robotic implementations but also have broad implications for the future of automated systems in various industries.

Advanced control strategies for grid-following inverter fault response: Implementation and analysis in MATLAB for protection studies in medium voltage distribution networks

Advanced control strategies for grid-following inverter fault response: Implementation and analysis in MATLAB for protection studies in medium voltage distribution networks

Integrating Advanced Control Strategies for Enhanced Motor Efficiency in Robotics

Motor efficiency requires diversified paradigm integration to facilitate the advancement of robotics applications through precision, energy optimization, and reliability. Advanced control strategies like AI-driven predictive mechanisms, harmonic drive systems, and real-time feedback tools in motor performance underline the efficiency required in robotic technologies. As a collective role for robotic motors, the approaches enable precise torque and speed modulation, dynamic adaptability to environmental changes, and energy-efficient operation in controlled strategies. Understanding theoretical foundations in motor efficiency guides decisions on the selection and implementation of robotic technologies in the automation of industrial and manufacturing processes. Comparative robotic role analyses pinpoint optimization for the technology to harness dynamic responsiveness and energy utilization efficiency. The ways to implement advanced motor controls underscore the potential of combining intelligent algorithms with innovative motor designs to elevate robotics reliance in complex situations. The advanced control strategies showcase the need for efficiency, adaptability, and relevance of robotic solutions in trending technological applications.

Advanced Harmonic Mitigation in Photovoltaic Grid-Connected Systems: Jellyfish Search Optimization Approach

Increasing prevalence of nonlinear loads (NLL) in modern power systems poses significant challenges, particularly in terms of harmonic distortion. This distortion degrades the performance and efficiency of photovoltaic (PV) grid-connected systems (PV-GCS), demanding advanced control strategies. This study presents a unique optimization-based method that employs the Jellyfish Search Optimization (JSO) algorithm to enhance the Shunt Active Power Filter (SAPF) for effective harmonic reduction in PV-GCS with a high penetration of NLL in the distribution network. The study aims to reduce total harmonic distortion (THD) and regulate the DC-bus voltage (Vdc) in SAPF. A comprehensive comparison examination is performed between JSO and three frequently utilized optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and golden eagle algorithm (GE), to investigate the efficiency of JSO algorithm. The reduced THD by the JSO is only 1.32 percent, smoothly regulated the Vdc with a short settling time, without overshooting. These findings indicate that JSO outperforms others, emphasizing that it has the potential to improve power quality (PQ) in PV-GCS with NLL. This study presents a strong foundation for minimizing harmonics in PV integrating to the grids, which contributes to improved system reliability and efficiency.

Advanced control strategy for AC microgrids: a hybrid ANN-based adaptive PI controller with droop control and virtual impedance technique

In this paper, an improved voltage control strategy for microgrids (MG) is proposed, using an artificial neural network (ANN)-based adaptive proportional-integral (PI) controller combined with droop control and virtual impedance techniques (VIT). The control strategy is developed to improve voltage control, power sharing and total harmonic distortion (THD) reduction in the MG systems with renewable and distributed generation (DG) sources. The VIT is used to decouple active and reactive power, reduce negative power interactions between DG’s and improve the robustness of the system under varying load and generation conditions. Simulation findings under different tests have shown significant improvements in performance and computational simulation. The rise time is reduced by 60%, the overshoot is reduced by 80%, the THD of the voltage is reduced by 75% (from 0.99 to 0.20%), and the THD of the current is reduced by 69% (from 10.73 to 3.36%) compared to the conventional PI controller technique. Furthermore, voltage and current THD values were maintained below the IEEE-519 standard limits of 5% and 8%, respectively, for the power quality enhancement. Fluctuations in voltage and frequency were also maintained at 2% tolerance and 1% tolerance, respectively, across all voltage limits, which is consistent with international norms. Power-sharing errors were reduced by 50% after conducting the robustness tests against the DC supply and load disturbances. In addition, the proposed strategy outperforms the previous control techniques presented at the state of the art in terms of adaptability, stability and, especially, the ability to reduce the THD, which validates its effectiveness for MG systems control and optimization under uncertain conditions.

Developments in smart wall-climbing robots for corrosion inspection through IoT integration, deep learning and advanced control strategies

Developments in smart wall-climbing robots for corrosion inspection through IoT integration, deep learning and advanced control strategies

Consensus-Based Formation Control and Gyroscopic Obstacle Avoidance for Multiple Autonomous Underwater Vehicles on SE(3)

To address the control challenges posed by increasingly complex mission scenarios, this paper aims to develop an advanced formation control and obstacle avoidance strategy for autonomous underwater vehicles (AUVs) in SE(3). This study establishes a dynamic model for fully actuated AUVs and designs a consensus-based formation control strategy to achieve coordinated movement. Motivated by limitations of existing obstacle avoidance strategies such as local minima issues and mutual interference between formation members in high-density environments, this paper introduces a novel gyroscopic force-based obstacle avoidance method. The proposed approach leverages the principles of rotation and angular momentum conservation to enable effective obstacle avoidance while maintaining formation integrity. Simulation results demonstrate the effectiveness of the proposed methodology in achieving robust formation control and collision avoidance under challenging conditions.

An advanced backstepping control scheme via active and reactive powers for DFIM-based on variable-speed turbine energy

An advanced backstepping control scheme via active and reactive powers for DFIM-based on variable-speed turbine energy

Improving real-time wind turbine deloading using advanced control strategy

Improving real-time wind turbine deloading using advanced control strategy

Performance Enhancement of Microgrid Systems Using Backstepping Control for Grid Side Converter and MPPT Optimization

Microgrid networks represent a crucial model in the field of power transmission and distribution, integrating renewable energy sources with modern control technologies. This research focuses on enhancing the performance of a microgrid system connected to the main grid through a three-phase converter controlled using Voltage Oriented Control (VOC). The microgrid comprises a storage element and a photovoltaic (PV) generation system. To improve system efficiency and power quality, backstepping (BS) controllers are implemented and compared with traditional control methods. Three control systems are evaluated: a conventional system using Perturb and Observe (P&O) algorithm for Maximum Power Point Tracking (MPPT) and Proportional-Integral (PI) controllers for grid-side converter (GSC) control, a hybrid system with BS-modified P&O (BS-P&O) for MPPT and PI controllers for GSC, and an advanced system employing BS-P&O for MPPT and BS controllers for GSC. The research demonstrates that BS controllers exhibit superior dynamic performance, contributing to improved regulation of DC-link voltage, active and reactive powers. Additionally, they achieve lower Total Harmonic Distortion (THD) values and increase the overall efficiency of the PV system. The proposed advanced control strategy shows particular effectiveness in tracking speed, disturbance rejection, and power quality enhancement. This study underscores the potential of nonlinear control techniques in optimizing microgrid operations and facilitating the integration of renewable energy sources into the main grid.

Advanced Control Strategies for Cleaner Energy Conversion in Biomass Gasification

The escalating climate crisis necessitates urgent and decisive action to mitigate greenhouse gas emissions. Gasification stands out as a highly adaptable process for energy conversion, capable of handling a wide range of feedstocks, from coal to biomass. The process plays a significant role in improving sustainability by converting these feedstocks into synthesic gas (syngas), which can be used as a cleaner energy source or as a building block for producing various chemicals. The utilization of syngas obtained through gasification not only reduces the reliance on fossil fuels but also helps in reducing greenhouse gases (GHGs), thereby contributing to a more sustainable energy landscape. To maintain optimal operational conditions and ensure the quality and safety of the product, an effective control system is crucial in the gasification process. This paper presents a comparative analysis of three control strategies applied to a numerical model of rice husk gasification: classical control, fuzzy logic control, and dynamic matrix control. The analysis is based on a comprehensive model that includes the equations necessary to capture the dynamic behavior of the gasification process across its various stages. The goal is to identify the most effective control strategy, and the performance of each control strategy is evaluated based on the integral of the absolute value of the error (IAE). The results indicatethat fuzzy logic control consistently outperforms classical control techniques, demonstrating superior disturbance rejection, enhanced stability, and overall improved control accuracy. These findings highlight the importance of selecting an appropriate advanced control strategy to optimize sustainable gasification processes.

Model-based Evaluation of Advanced Energy Management Control Strategies for Heavy-Duty Fuel Cell Powertrains

<div>In recent years, fuel cell electric vehicles (FCEV) have become a promising alternative to battery electric vehicles in medium- and heavy-duty on-road applications, which specifically require long vehicle range, high payload capacity, and fast refueling times. While FCEVs are more likely to meet these requirements, they come with their own challenges of high upfront system cost, reduced system efficiency at high load, on-board hydrogen storage system packaging, and fuel cell system (FCS) durability. To address these challenges, it is critical to ensure optimal propulsion system component sizing during the concept phase as well as ensure optimal propulsion system energy management during vehicle operation. In a previous publication, authors presented a model-based approach for system sizing and optimization of FCEV propulsion system components for a Class 8 long-haul application. In this study, the authors have evaluated and optimized multiple advanced propulsion system energy management control strategies to maximize the FCEV propulsion system efficiency during vehicle operation.</div> <div>Specifically, several energy management strategies were evaluated with the primary objective of reducing hydrogen consumption through efficient power split between FCS and high-voltage battery, while maintaining vehicle performance and sustaining battery state of charge (SOC). A 1D multi-physics-based plant model of the vehicle propulsion and thermal system was developed in GT-SUITE and validated against vehicle test data. The validated plant model was then used for model-in-loop (MiL) simulations to evaluate multiple control strategies such as rule-based, equivalent consumption minimization strategy (ECMS), and dynamic programming (DP), on real-world drive cycles.</div>

The Modeling and Simulation of Non-Isolated DC–DC Converters for Optimizing Photovoltaic Systems Applied in Positive Energy Districts

DC–DC converters are critical for energy management in positive energy districts (PEDs) because they allow for efficient conversion between different voltage levels, enabling the integration of various renewable energy sources, energy storage systems, and loads. The demand for high-voltage gain DC–DC converters in photovoltaic power systems has surged in recent times. Despite the numerous converter topologies reported, there is a focused effort to streamline components, particularly switching devices, passive elements, and overall converter losses. This paper introduces the single switching impedance network (SSIN)-based converter as a unique DC–DC converter topology, designed in both one-stage and double-stage configurations for photovoltaic applications. One of the main characteristics of the SSIN converter is that it needs just one switch and three capacitors for the n-stage. A comparative analysis with conventional boost converter topology demonstrates the SSIN-based converter’s capability to achieve a desirable output voltage that closely approximates an ideal sine waveform. Furthermore, the application of advanced control strategies to the proposed converter highlights its superior performance and robustness in maintaining output voltage stability under varying conditions. These characteristics make the SSIN-based converter particularly well-suited for PED applications, where efficiency, reliability, and the seamless integration of renewable energy sources are crucial.

Advanced control and optimization strategies for a 2-phase interleaved boost converter

Renowned for their adeptness in smoothing current flow and maintaining balanced operation, 2-phase interleaved boost converters (IBC) demonstrate remarkable efficiency, especially when confronted with demanding loads. This makes them a preferred choice for high-power applications such as renewable energy systems, high-power supplies, and electric vehicle power trains. In contrast, standard boost converters are typically favored in low-power, low-demand scenarios. The control of a 2-phase IBC involves running two boost converters in parallel but with a phase shift to reduce ripple currents, improve efficiency, and increase power handling capabilities. To ensure stability and optimal performance, the control strategies for these converters focus on achieving balanced operation between the phases. Hence, the control of 2-phase IBC presents a significant challenge due to their non-minimum phase behavior. The core focus of this article is the implementation of a composite model predictive control (MPC) technique to regulate a 2-phase interleaved boost converter. It introduces a novel approach, model predictive sliding mode control (MPSMC), which leverages the strengths of both MPC and sliding mode control (SMC). The benefits of this hybrid method, termed MPSMC, are thoroughly developed and simulated using MATLAB/Simulink. The results, as discussed in the respective section, provide an in-depth understanding of its effectiveness in practical applications.

Enhancing water usage efficiency in agriculture through smart systems: a review

Abstract As the scarcity of clean water becomes more pronounced, significant challenges emerge in efforts to uphold food security and achieve sustainable progress, particularly on a global scale. Agriculture stands out as one of the primary sectors significantly contributing to water consumption. Thus, enhancing water usage efficiency within irrigated agricultural systems holds paramount importance to ensure the continued development of agricultural sustainability. One promising avenue lies in the advancement of intelligent irrigation systems, particularly leveraging wireless communications technology, monitoring systems, and advanced control strategies to optimize irrigation scheduling. This study adopts a qualitative methodology, grounded in a thorough examination of literature adhering to specific criteria, including publications in indexed journals, articles, or research papers published in the past ten years. It introduces diverse irrigation control and observation methodologies employed in recent times. An intriguing proposition emerges regarding the integration of soil, temperature, and humidity monitoring systems with predictive control models, potentially augmenting water usage efficiency. Through this review, researchers will be able to monitor and create control prospects related to irrigation agriculture as well as identify directions and gaps for future research activities in this field.