Control Strategy For System Research Articles

Optimal Control Strategies for Constrained Linear Time-Delay Systems with Disturbances

Optimal Control Strategies for Constrained Linear Time-Delay Systems with Disturbances

99.6% efficiency DC-DC coupling for green hydrogen production using PEM electrolyzer, photovoltaic generation and battery storage operating in an off-grid area

This paper presents a novel connection and control strategy for a hydrogen generation system using a proton exchange membrane electrolyzer powered by solar energy in an off-grid area without network backup. Given that, the proposed architecture is based on the indirect control of the Photovoltaic plant (achieved by removing a power-converter), the presented solution is more efficient and more reliable than the traditional scheme. Furthermore, with this innovation one can reach the same hydrogen production with smaller electrolyzers.The methodology includes detailed models of the photovoltaic panel and the electrolyzer, along with a control strategy that considers the degradation mechanisms of the electrolyzer to ensure reliable and prolonged operation. The results show that the proposed strategy keeps the operating power of the electrolyzer constant, even under variations in irradiance, thanks to energy storage in batteries. It is demonstrated that the proposed system offers efficiency above 99.6% during the analyzed period, with a 100% utilization rate of the electrolyzer, avoiding periods of inactivity and high current peaks. The study also includes simulations and experimental tests that confirm the feasibility and effectiveness of the presented solution, highlighting its advantages in terms of efficiency and investment costs compared to direct connections and other existing methods.

Distributed control strategy of central heating system based on multi-agent consensus

Distributed control strategy of central heating system based on multi-agent consensus

Comparative Analysis of Fuzzy Logic, PID, and FOPID Controllers in DC Microgrid Voltage Regulation for Power Plants: Integrating Renewable Energy Sources

The worldwide direction moving towards sustainable energy solutions requires more advanced control mechanisms within power systems, especially in DC microgrids that combine renewable energy sources like solar panels and wind turbines with conventional power setups. This paper addresses the necessary challenge of maintaining steady and efficient DC voltage control—important for the dependability and performance of power plants. Specifically, it looks at the efficiency of four different control strategies: classic PID (Proportional-Integral-Derivative) controllers, Fuzzy Logic Controllers (FLC), Fractional Order PID (FOPID) controllers, and a new mixed system that merges Fuzzy Logic with PID controllers in a Fuzzy-PI layout. Our approach utilizes a strict comparative analysis using MATLAB Simulink simulations, assessing each controller’s capacity to manage dynamic load shifts, adjust to the unpredictability of renewable energy inputs, and preserve strong voltage stability under various operational cases. The simulations are crafted to single out the most effective control strategy to boost efficiency and toughness in microgrid operations. This study adds substantially to the progress of control strategies in hybrid energy systems, offering important insights into the creation of more complex and reliable power management solutions. By evaluating the performance of these varied controllers, we aim to underline potential upgrades in control systems that could support better energy management in intricate grid setups, thereby encouraging the wider inclusion of renewable energy technologies into power grids.

Enhanced Fault Detection in Satellite Attitude Control Systems Using LSTM-Based Deep Learning and Redundant Reaction Wheels

Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, reaction wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to faults—a factor with the potential to precipitate catastrophic failures such as total satellite loss. In light of this, we introduce a fault detection methodology grounded in deep learning techniques specifically designed for satellite attitude control systems. Our proposed method utilizes a Long Short-Term Memory (LSTM) model adept at learning temporal patterns inherent to both healthy and faulty system behaviors. Incorporated into our model is a torque allocation algorithm designed to circumvent specific velocities known to induce torque disturbances, a factor known to influence LSTM performance adversely. To bolster the robustness of our fault detection technique, we also incorporated denoising autoencoders within the LSTM framework, thereby enabling the model to identify temporal patterns in healthy and faulty system behavior, even amidst the noise. The method was evaluated using cross-validation on simulated satellite data comprising 1000 time series samples and across different fault scenarios, such as stiction and resonance at varying intensities (90%, 50%, and 30%). The results confirm achieving performance metrics such as Mean Squared Error for accurate fault identification. This research underscores a stride in the evolution of fault detection and control strategies for satellite attitude control systems, holding promise to boost the reliability and efficiency of future space missions.

Cooperative Control Strategy of Optical Storage System Based on an Alternating Sequence Filter

Due to photovoltaic (PV) power generation depending on the environment, its output power is volatile, and effectively dealing with its power fluctuation has become a key concern. Aiming at this problem, this article presents an optical storage cooperative control technology based on an Alternating Sequence Filter (ASF), which controls the power management of the Energy Storage System (ESS) consisting of a vanadium redox battery, battery, and supercapacitor. Firstly, an ASF is designed to stabilize the PV power generation by alternating sequence and improve system response speed. Secondly, according to the output signal of the filter, the charge and discharge of the three energy storage units are dynamically adjusted, and the power fluctuation is compensated in real-time to improve the system stability and conversion efficiency. Finally, the simulation results of actual illumination show that the control strategy calls the ESS to stabilize the power fluctuation, so that the power of the direct current bus is stabilized at about 15 kw, and the fluctuation is maintained between −4.48% and 4.05%. The strategy significantly reduces power fluctuation and improves the dynamic response ability and energy storage utilization of the system.

Power-controllable variable refrigerant flow system with flexibility value for demand response

With the increase of renewable energy sources in the power supply, the issues of balancing power supply and demand are becoming severe. Demand response, by altering the demand-side load state, has been proven as a good method to solve the supply and demand balance problem. Variable refrigerant flow system has great potential in demand-side loads and is poised to become the mainstay of demand response. However, the traditional temperature or compressor state control of variable refrigerant flow systems does not allow for better power regulation for the power system. To address this problem, this paper proposes a power-controllable variable refrigerant flow system with demand response flexibility value for power regulation. The demand response flexibility value is designed as a subjective indicator to satisfy the heterogeneous customer comfort requirement. The predictive mean vote as an objective indicator of customer comfort coordinates with the demand response flexibility value. A fuzzy control strategy for variable refrigerant flow systems is designed to balance power system requirements and customer comfort requirements. The experiment result demonstrates that the power-controllable variable refrigerant flow system can provide power regulation service to the power system with a maximum power regulation deviation of 3.67%. By guaranteeing consumer comfort, the higher the flexibility value, the shorter the consumer’s participation time in demand response. The demand response flexibility values can provide regulation configuration for demand response in practical engineering.

Event-triggered [formula omitted] predictive control for networked control systems with sensor and actuator saturation

This paper concerns an event-triggered H∞ predictive control scheme for a networked control system in which sensor and actuator saturation as well as external disturbances are involved. In the scheme, a state estimator is firstly designed to acquire state estimations from the saturated output with measurement noises. An event generator is given to pick out necessary state estimations for saving feedback channel transmission resources. Furthermore, a buffer-based predictive controller is constructed to compensate for network-induced delays and lighten the communication burden of forward channel networks. Then a co-design criterion is shown to find estimator and controller parameters for the resulting closed-loop system with desired H∞ performance. An inverted pendulum model is employed in numerical simulation to verify the proposed control scheme.

A new dynamic control strategy for a solar-driven absorption thermal energy storage system: Modeling and performance evaluation

Solar heating is a vital technology to promote the decarbonization of building energy supply systems. However, the mismatch between the intermittency of solar energy supply and the fluctuating heating demands poses significant challenges to its application. Current heat storage systems often exhibit low energy density, significant heat losses, and limited temperature regulation capabilities, which restrict their effectiveness in practical applications. This paper proposed a new real-time control strategy for a solar-driven absorption thermal energy storage system, integrated with an absorption heat pump, which can resolve the mutual constraints between solar energy utilization efficiency and the heating temperature, and then improve the system flexibility. The dynamic control strategy is implemented to manage concentration and temperature by regulating the inlet and outlet flow rates of three tanks, namely the absorbate tank, the weak solution tank, and the strong solution tank. A thermodynamic model is developed based on Aspen Plus and Matlab. Results show that within a 24-hour cycle, the system, driven by intermittent solar energy, can effectively meet the varying user-side heating demands through flow rate regulation. An optimized tank design, based on flow variation analysis, reduces the total volume by approximately 35 %. Additionally, the system can recover low-grade heat from the environment through the absorption heat pump, achieving an energy storage efficiency of 1.07 and an energy storage density of 56.13 kWh/m3. The proposed system and its dynamic control method are well-suited to intermittent solar energy, increasing the system flexibility, and thus expanding renewable energy utilization potential, especially those involving multi-energy complementary systems.

Optimal control strategy for a cutting-edge hybrid ventilation system in classrooms: Comparative analysis based on air pollution levels across cities

Natural ventilation has the potential to enhance indoor air quality in classrooms with elevated CO2 levels, although it may introduce outdoor pollutants. This study introduces a novel controller for automatic windows that simultaneously monitors outdoor air pollution and temperature, synchronizing window openings with mechanical ventilation system to create a comfortable, healthy, and energy-efficient indoor environment. The practicality of the proposed controller is assessed for a classroom in Delhi, Warsaw, and Stockholm, each with contrasting climates and outdoor pollution levels, specifically PM2.5 and NO2. The controller parameters are optimized for each city using a non-dominated sorting genetic algorithm (NSGA-II) to find the best trade-off between thermal comfort, CO2 levels, and energy consumption. The results show that the controller successfully met the indoor air quality standards in all cities; however, its operation was significantly influenced by the climate and pollution levels. While natural ventilation was utilized for 44% and 31% of the year in Warsaw and Stockholm, respectively, it was used for only 11% of the year in Delhi, the most polluted city. The optimization process significantly reduced energy use across all cities while also successfully reducing indoor CO2 concentrations. Although thermal comfort decreased slightly, it remained within acceptable thermal comfort conditions.

Review on recent control system strategies in Microgrid

Microgrids (MGs) are integral to the evolving global energy landscape, facilitating the integration of renewable energy sources such as solar and wind while enhancing grid stability and resilience. This review presents a comprehensive analysis of control strategies in MG systems, addressing both conventional and advanced methodologies. We explore traditional control methods, such as droop control and Proportional Integral Derivative (PID) controllers, for their simplicity and scalability, but acknowledge their limitations in handling non-linearities and real-time adaptation. Model Predictive Control (MPC), Adaptive Sliding Mode Control (ASMC), and Artificial Neural Networks (ANN) are some of the more advanced techniques that make systems more flexible, better at managing energy, and stable even when operations change quickly. The review further delves into the role of the Internet of Things (IoT), predictive analytics, and real-time monitoring technologies in MGs, emphasizing their importance in enhancing energy efficiency, ensuring real-time control, and improving system security. The review places emphasis on energy management systems (EMS), which optimize supply and demand balance, reduce uncertainty, and enable seamless integration of distributed energy resources (DERs). The paper also highlights emerging trends such as blockchain, AI-driven controls, and deep learning for MG optimization, security, and scalability. Concluding with future research directions, the paper underscores the need for more robust control frameworks, advanced storage technologies, and enhanced cybersecurity measures, ensuring that MGs continue to play a pivotal role in the transition to a decentralized, low-carbon energy future.

A Simulation-Assisted Field Investigation on Control System Upgrades for a Sustainable Heat Pump Heating

Heat pump-based renewable energy and waste heat recycling have become a mainstay of sustainable heating. Still, configuring an effective control system for these purposes remains a worthwhile research topic. In this study, a Smith-predictor-based fractional-order PID cascade control system was fitted into an actual clean heating renovation project and an advanced fireworks algorithm was used to tune the structural parameters of the controllers adaptively. Specifically, three improvements in the fireworks algorithm, including the Cauchy mutation strategy, the adaptive explosion radius, and the elite random selection strategy, contributed to the effectiveness of the tuning process. Simulation and field investigation results demonstrated that the fitted control system counters the adverse effects of time lag, reduces overshoot, and shortens the settling time. Further, benefiting from a delicate balance between heating demand and supply, the heating system with upgraded management increases the average exergetic efficiency by 11.4% and decreases the complaint rate by 76.5%. It is worth noting that the advanced fireworks algorithm mitigates the adverse effect of capacity lag and simultaneously accelerates the optimizing and converging processes, exhibiting its comprehensive competitiveness among this study’s three intelligent optimization algorithms. Meanwhile, the forecast and regulation of the return water temperature of the heating system are independent of each other. In the future, an investigation into the implications of such independence on the control strategy and overall efficiency of the heating system, as well as how an integral predictive control structure might address this limitation, will be worthwhile.

A study of external machine control pathways based on brain-computer interfaces

Abstract. This research analyses three fundamental paradigms in brain-computer interface (BCI) systems: motor imagery (MI), sensorimotor rhythms (SMRs), and P300-based paradigms, focusing on their methodology and applications. Motor imagery-based brain-computer interface (MI-BCI) entails mentally practicing motor activities while adjusting electroencephalography (EEG) patterns and utilising multimodal stimulation to enhance the accuracy of classification. SMR-BCI employs precise EEG band oscillations to operate prosthetics and virtual environments. Sensory threshold neuromuscular electrical stimulation is used to improve performance. The P300 paradigm utilizes event-related potentials to control devices, and includes advancements such as the omitted elicited paradigm to enhance efficiency. In addition, the study examines control strategies for BCI systems, such as PID, MPC, and shared control approaches. By utilizing MATLAB as a tool, one may ascertain the distinction between different control strategies, highlighting their respective contributions to improving system responsiveness and stability.

Dynamic power allocation for the new energy hybrid hydrogen production system based on WOA-VMD: Improving fluctuation balance and optimizing control strategy

The new energy hydrogen production system is subject to intermittent fluctuations, which compromise efficiency and equipment lifespan. This study proposes a new dynamic power allocation and control strategy for hybrid hydrogen production systems. By using the Whale Algorithm Optimized Variable Modal Decomposition (WOA-VMD), the fluctuating power of new energy is quantitatively decomposed and reconstructed. Based on a multi-objective optimization function, low-frequency variation components and high-frequency fluctuation components are accurately allocated between alkaline electrolyzers (ALK) and proton exchange membrane electrolyzers (PEM). The strategy effectively improves the source-load balancing capability, reducing the low-frequency fluctuation rate by 60% and reducing the high-frequency power amplitude by 70%, significantly reducing the operational demands on ALK electrolyzers, the capacity requirements of PEM electrolyzers, and the need for energy storage configuration, while also increasing hydrogen production efficiency by 7%. Compared to existing methods, the strategy has a transparent analysis process, strong interpretability, and good data reusability, making it valuable for engineering applications.

Exploration on the liquid-based energy storage battery system from system design, parametric optimization, and control strategy

Lithium-ion batteries are increasingly employed for energy storage systems, yet their applications still face thermal instability and safety issues. This study aims to develop an efficient liquid-based thermal management system that optimizes heat transfer and minimizes system consumption under different operating conditions. A thermal-fluidic model which incorporates fifty-two 280 Ah batteries and a baffled cold plate is established. The reliability of battery heat generation is confirmed experimentally, with a maximum deviation of 14.8 %. Then, comparative studies are performed to evaluate the impacts of cold plate structure and attachment on thermal management performance. Results found that while side-attachment shows better heat transfer, bottom-attachment coupled with a baffled cold plate reduces the temperature gradient and power consumption at both cooling and preheating scenarios. Furthermore, the effects of flow rate and inlet temperature are studied systematically, and a decision making method is utilized to assist in multi-attribute optimization. Finally, a surrogate model is proposed to unveil the collaborative effects between these factors. Based on this, an optimal thermal management strategy with controllable flow rate and inlet temperature is obtained according to ambient temperatures. The proposed framework provides a feasible approach to improve the design and control of liquid-based battery thermal management.

PEMFC Gas-Feeding Control: Critical Insights and Review

Proton exchange membrane fuel cells (PEMFCs) are currently a relatively mature type of hydrogen energy device due to their high efficiency and low noise compared to traditional power devices. However, there are still challenges that hinder the large-scale application of PEMFCs. One key challenge lies in the gas supply system, which is a complex, coupled nonlinear system. Therefore, an effective control strategy is essential for the efficient and stable operation of the gas control system. This paper aims to provide a comprehensive and systematic overview of the control strategies for PEMFC anode and cathode supply systems based on an analysis of 182 papers. The review covers modern control theories and optimization algorithms, including their design, objectives, performance, applications, and so on. Additionally, the advantages and disadvantages of these control methods are thoroughly evaluated and summarized.

Integration of a solid oxide electrolysis system with solar thermal and electrical energy: A testing campaign for operation and control strategy definition

AbstractThe EU project PROMETEO has the scope of testing a 25 kW solid oxide electrolysis system integrated with a concentrated solar power plant via thermal energy storage in a relevant environment. Given the plant layout and the hydrogen demand characteristics, this work aims to identify how to operate the system effectively when renewable electricity is unavailable and how to modulate the load during hydrogen generation, thus defining the system's operation modes and control strategy. A 5 kWe stack has been tested at the FBK facility. The hot standby tests show that feeding a reducing gas at the negative electrode and air at the positive electrode, without polarizing the stack, effectively keeps the stack hot at 750°C and prevents degradation. Conversely, the electric protection approach leads to significant stack degradation (15% voltage drop in 200 h for one cluster). Regarding modulation of hydrogen generation, with low steam flowrates, the stack current and the flow rate of produced hydrogen mainly depend on the steam flow rate, while it is not affected by the stack temperature; conversely, with high steam flowrates, the current depends only on the stack temperature (from 25 A at 670°C to 65 A at 760°C). Based on the results, two hot standby modes and two load control strategies to be implemented and tested in the PROMETEO prototype are proposed.

A Neural Network-Based State-Constrained Control Strategy for Underactuated Aerial Transportation Systems Within Narrow Workspace

The aerial transportation system belongs to a symmetrical system and has recently garnered increasing attention from researchers due to its broad application range and convenient operation. The control difficulty of the aerial transportation system lies in the fact that the load is not directly actuated, posing a significant challenge for state-constrained control. Taking the motion of an unmanned aerial vehicle (UAV) suspension transportation system within complex pipelines as an example, this paper employs the the swept volume signed distance field (SVSDF) method to search for state boundaries, which is an aspect not considered or elaborated in many state-constrained control approaches. Furthermore, adaptive state-constrained control based on the radial basis function (RBF) neural network is utilized for the case of experiencing unknown air resistance. The convergence of the proposed method for underactuated and actuated state variables is theoretically demonstrated based on the Lyapunov technique. Compared with existing methods, the error integral index demonstrates that the proposed method displays better convergence capability in the simulation section when considering state constraints under disturbance and air resistance.

Modeling and Analysis of Micro-Turbine Integrating PV Generation Systems

Abstract. As a matter of fact, in the emerging field of distributed generation, micro turbines (MT) are seen as having significant potential in power generation especially in recent years. In reality, it can provide flexibility as well as robustness for PV generation system with great performances. Therefore, with this in mind, this paper proposes a hybrid power system integrating MT into PV generation system. To be specific, this study investigates as well as discusses the operating principles of MT. On this basis, a coordinated PQ control strategy for the hybrid PVMT power generation system is proposed. At the same time, the inertia enhancement brought by the MT is analyzed validating the potential and feasibility of the proposed strategy. According to the analysis, the current limitations as well as future improvements are discussed in the meantime. Overall, these results shed light on guiding further exploration of MT integrating PV generation systems and pave a novel path for designs.

Distributed model predictive control for asynchronous multiagent systems with self‐triggered coordinator

AbstractThis paper investigates a distributed model predictive control strategy for asynchronous nonlinear multiagent systems (MASs) with external disturbances via self‐triggered generators and prediction horizon regulators. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enhance the robustness of the system. Then, the triggering interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade‐off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the MASs is achieved under asynchronous communication. Finally, the recursive feasibility and closed‐loop stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach.