Energy-efficient Control Research Articles

Cascade Model Predictive Control for Enhancing UAV Quadcopter Stability and Energy Efficiency in Wind Turbulent Mangrove Forest Environment

Unmanned aerial vehicle (UAV) modelling and control, particularly in quadrotors with high position-orientation coupling, present significant challenges for practical applications, such as environmental monitoring missions in windy mangrove forests. Conventional control strategies like the PID controller, often employed in simulations due to their simplicity, often underperform in real-world scenarios due to their linear assumptions. This research proposes a novel hierarchical cascaded model predictive control system for altitude, attitude, and battery efficiency for quadrotor in mangrove area. This control system addresses computational complexity by decomposing the overall MPC strategy into two distinct schemes, one for translational displacements and another for rotational movements, enhancing the UAV's resilience to wind turbulence, a significant disturbance factor in mangrove environments. Rigorous simulation and experiment test flight involving complex trajectory tracking and windy conditions demonstrate the proposed controller's superior performance compared to conventional PID controller, particularly in terms of stability, disturbance rejection, underscoring its potential for UAV applications in challenging environments.

Design of the predictive management and control system for combined propulsion complex

The object of this researching is the process of maneuvering a sea-based vehicle under compressed conditions, which requires one hundred percent reserve of thrusters (THRs) of various modifications and locations. The main problem is the provision of energy-efficient control over the ship's motion at low speed in the horizontal plane using a high-level predictive controller. The hierarchy of the motion control system (MCS) is usually divided into several levels with the help of a high-level motion controller and the THR motor control distribution algorithm. This allows for a modular software structure where a high-level controller (HLC) can be designed without using comprehensive information about the THR motors. However, for a certain reference of THR configurations, such a decoupling can lead to reduced control performance due to the limitations of HLC regarding the physical constraints of the vessel and the behavior of MCS. The main results of the researching are methods to improve control performance using a nonlinear model predictive control (MPC) as a basis for the designed motion controllers due to its optimized solution and ability to consider constraints. A decoupled system was implemented for two simple motor tasks showing dissociation problems. The shortcomings were eliminated through the development of a nonlinear MPC controller, which combines the motion controller and the distribution of control over THR motors. To preserve the discrete modularity of the control system and achieve adequate performance, a nonlinear MPC controller with time-varying constraints was designed. This has made it possible to take into account the current limitations of the THR control system, increase the accuracy of control, and reduce the response time of the system by 10 %.

Multidisciplinary Review of Induction Stove Technology: Technological Advances, Societal Impacts, and Challenges for Its Widespread Use

Induction stoves are increasingly recognized as the future of cooking technology due to their numerous benefits, including enhanced energy efficiency, improved safety, and precise cooking control. This paper provides a comprehensive review of the key technological advancements in induction stoves, while also examining the societal and health impacts that need to be addressed to support their widespread adoption. Induction stoves operate based on the principle of eddy currents induced in metal cookware, which generate heat directly within the pot, reducing cooking times and increasing energy efficiency compared with conventional gas and electric stoves. Moreover, induction stoves are considered an environmentally sustainable option, as they contribute to improvements in indoor air quality by reducing emissions associated with fuel combustion during cooking. However, ongoing research is essential to ensure the safe and effective use of this technology on a broader scale.

Comparison of Several Energy-Efficient Control Laws Using Energetic Macroscopic Representation for Electric Vehicles

Energy transition and decarbonization present significant challenges to transportation. Electric machines, such as motors and generators, are increasingly replacing internal combustion engines to reduce greenhouse gas emissions. This study focuses on enhancing the energy efficiency of electric machines used in vehicles, which are predominantly powered by batteries with limited energy capacity. By investigating various control strategies, the aim is to minimize energy losses and improve overall vehicle performance. This research examines two types of electric motors: Permanent Magnet Synchronous Motor (PMSM) and Induction Motor (IM). Real-time loss measurements were conducted during simulated driving cycles, including acceleration, constant speed, and braking phases, to mimic typical driving behavior. The simulation utilized characteristics from commercial vehicles, specifically the Renault Zoé and Bombardier eCommander, to assess the controls under different configurations. This study employed the Energetic Macroscopic Representation (EMR) formalism to standardize the analysis across different motors and controls. The results demonstrate significant loss reductions. The controls investigated in this study effectively reduce energy losses in electric motors, supporting their applicability in the automotive industry.

A benchmark study on the energy efficiency and environmental impacts of alternative fuels in gulet-type sailing yachts

Gulet-type yachts are one of the symbols of maritime culture with their unique hull forms and schooner or ketch-type riggings. In parallel with the targets aiming for decarbonization in the maritime industry, energy efficiency and emission control are on the agenda for these types of yachts. Driven by this motivation, a novel benchmark study for the gulet-type yachts is conducted to evaluate the energy efficiency and environmental impacts associated with the adoption of LNG or methanol as primary fuel alternatives to MDO. The benchmark study is constructed in two steps: Presenting the design and propulsion characteristics of existing gulets, followed by a detailed analysis of the energy efficiency and environmental impacts of using alternative fuels. Therefore, 57 gulets with round and transom sterns whose hull forms are analyzed, modeled and general characteristics are presented. After modeling, the resistance and effective power of hull forms are predicted using the Holtrop-Mennen method and CFD. The flow characteristics around the hulls are obtained and the results are validated using the previous experimental studies in the first step. Then, installed and computed engine capacities are compared, and the Annual Emission Ratio and Energy Efficiency Index (EEI) are calculated for environmental impact assessments. Finally, the applicability of using LNG or MeOH-fueled propulsion systems for gulet-type yachts is discussed considering energy efficiency and design. According to the results, transitioning to alternative fuels can increase the Energy Efficiency Index of gulet-type yachts by over 20%.

An energy efficient access control for secured intelligent transportation system for 6G networking in VANET

An energy efficient access control for secured intelligent transportation system for 6G networking in VANET

5CB-doped polymer-stabilised sphere-phase liquid crystal smart window with fast response and high transmittance

ABSTRACT Smart windows have become increasingly popular due to their ability to provide privacy protection, energy efficiency, and user control. Therefore, this technology has a wide range of applications in various fields such as construction, automotive, aviation, and renewable energy. However, commercialisation faces two significant challenges due to the slow response speed and low transmittance. This work demonstrates a 5CB-doped polymer-stabilised sphere-phase liquid crystal (PS-SPLC) smart window with sub-millisecond response time, which can be switched between transparent and scattering states by an electric field. The transmittance exceeds 90% when the voltage is on in the visible light range. This 5CB-doped PS-SPLC smart window has the potential to usher in a new era of smart window research.

Analytical model and comprehensive index construction of indoor air purification effect based on dynamic characteristics and energy efficiency

Meeting environmental regulation requirements while improving energy usage efficiency are crucial endeavors to achieve decarbonization and indoor environmental regulation. In this study, experimental testing, data analysis, and modeling are performed for the dynamic purification process of indoor PM2.5 (fine particulate matter) in the context of regulating the indoor environment of buildings. The results show that indoor PM2.5 levels are dynamically matched to the outdoor environment state and the requirements of environmental control. An analytical model of the effect of PM2.5 purification is constructed, which can accurately reflect the dynamic changes in the PM2.5 mass concentration during the indoor air purification process considering the outdoor PM2.5 concentration and the purification efficiency of the purification filters and purification systems, thus supporting the prediction of purification outcomes and accurately constraining the actual indoor air quality purification process. Meanwhile, an indoor air quality control index (IAQCI) is established based on the purification effect and purification energy efficiency under various combinations of air filters and purification systems. This approach is used to quantitatively assess the dual control of energy efficiency and environmental regulation, as well as the purification effect and efficiency of indoor purification schemes based on outdoor dynamic PM2.5 conditions.This study provides a set of innovative and feasible research methods for realizing comprehensive indoor environmental performance evaluations of buildings.

Review of Ti3C2Tx MXene Nanofluids: Synthesis, Characterization, and Applications

MXene-based nanofluids are important because of their thermal and rheological properties, influencing scientific and industrial applications. MXenes, made of titanium carbides and nitrides, are investigated for nanofluid enhancement. This review covers MXene nanofluid creation, characterization, and application. To produce nanoscale MXene particles, two-dimensional materials are dissolved and dispersed in a base fluid. The stability and efficacy of MXene nanofluids depend on production methods, such as chemical exfoliation, electrochemical etching, and mechanical delamination. Improved heat transfer coefficients and thermal conductivity from MXene nanofluids help resolve heat transfer, energy efficiency, and thermal control problems. This extensive review also addresses long-term safety and the necessity for standardized characterization methodologies, helping researchers optimize MXene-based nanofluids in many technological fields

An energy efficient control method of a photovoltaic system using a new three-phase inverter with a reduced common mode voltage

This paper presents a new energy-efficient space vector pulse width modulation (SVPWM) for controlling the switches of a New three-phase inverter (NTPI) for photovoltaic (PV) applications to reduce switching losses, the peak value, and the dv/dt of the common mode voltage (CMV) with fewer number of switches. The proposed system offers a reliable operation in PV energy system with less leakage current and increased efficiency because of the reduction of the CMV, the source of leakage current in PV inverter-based application. Moreover, this also optimizes the operation of electric vehicle application with lower bearing failure. The performance of the proposed system with the new SVPWM is evaluated to the existing PWM in the literature, as well as the active zero state pulse width modulation (AZSPWM) of the two-level inverter introduced under identical conditions. Experiments and MATLAB simulations have both been used in this study.

Multiscale experimental insights into vacuum drying of sludge for enhanced energy efficiency and emission control

This study provides a comprehensive analysis of the vacuum drying process for sludge drying, with a focus on optimizing energy efficiency and emission control. The study used both lab-scale static and pilot-scale vacuum drying systems to test various parameters like vacuum levels, heat source temperatures, and sludge thicknesses. The results indicated that optimal drying conditions were achieved at a vacuum level of −0.06 MPa, a heat temperature of 140 °C, and a sludge thickness of 3.4 mm, where the drying rate reaches 0.13278 g·g−1·min−1. The study underscores the significant influence of vacuum level, temperature, and sludge thickness on drying rates. The Page model was used to analyze drying kinetics, elucidating how changes in these parameters affect drying characteristics. Furthermore, the study also examined the pollutant emissions and energy efficiency at the pilot scale. It found that high vacuum environments could efficiently dry sludge using low-temperature heat source, leading to average energy consumption per unit evaporation of 3020.29 kJ/kg, which is lower compared to traditional methods. By harnessing low-grade industrial waste heat, this can be further reduced to 875.76 kJ/kg. This study offers valuable insights for sustainable sludge management systems, highlighting the environmental and economic benefits of vacuum drying technology. The detailed experimental approach and thorough analysis make a significant contribution to the field of the sludge drying.

A novel method of fuel consumption prediction for wing-diesel hybrid ships based on high-dimensional feature selection and improved blending ensemble learning method

Establishing an accurate fuel consumption prediction model is essential to optimize energy efficiency in wing-diesel hybrid ships. This study proposes an improved Blending ensemble learning method for predicting fuel consumption. Initially, the original data is cleaned using artificial experience and Interquartile Range (IQR) method. A high-dimensional feature selection method, variance analysis- Pearson correlation-mutual information-permutation importance (VA-PC-MI-PI), is then proposed to identify 16 key features from the initial 111 features. Furthermore, 9 algorithms are employed for prediction analysis under different temporal resolution data conditions. Based on the results, optimal time step and algorithm models are determined to construct the Blending ensemble model (BEM). Subsequently, the feature data of “sailing speed” and “bow draft”, which account for the highest permutation importance, are imported into the metadata set to alleviate insufficient BEM data utilization. Additionally, Extra Trees (ET) model is chosen as the meta-learner for adaptation and Bayesian method is employed for global optimization. The resulting improved Blending ensemble model (IBEM) exhibits optimal prediction accuracy and generalization ability with an RMSE of 66.04 kg/h, representing a reduction of 16.99% and 13.86% compared to the Support Vector Machine (SVM) model and BEM, respectively. Additionally, the IBEM exhibits significantly smaller residuals and absolute residuals, demonstrating superior predictive stability and consistency. This study provides an important reference and basis for energy efficiency prediction, control, and optimization of wing-diesel hybrid ships.

Unraveling the complexity: A taxonomy for characterizing and structuring smart energy services in the building sector

Achieving climate goals requires restructuring the current energy systems, which, among other things, involves decentralizing energy systems. This entails new challenges, e.g., a high degree of coordination and information exchange. Especially the building sector, being the largest energy consumer, offers great improvement potential to reduce emissions. Smart energy services promise various benefits in overcoming these challenges, e.g., through optimized energy generation, demand control, and more efficient energy control systems. Although smart energy services are crucial to transitioning to a more sustainable, reliable, and intelligent energy (eco)system, research lacks a holistic perspective of their essential characteristics and configuration options due to their novelty and heterogeneity. Nevertheless, this perspective is crucial as service providers, service innovators (e.g., business development and start-ups), and researchers face problems developing and deploying value-adding and sustaining smart energy services. Consequently, this work identifies the essential characteristics of building-related smart energy services by reviewing scientific and practitioner literature. It structures them by developing a taxonomy and organizing them in 15 dimensions and 54 characteristics, along smart energy service structure, value creation, delivery, and energy-related data input. The taxonomy and the derived theoretical and managerial implications support researchers and practitioners in designing and conceptualizing future smart energy service innovations.

CHEABC-QCRP: A novel QoS-aware cluster routing protocol for industrial IoT

Clustering routing protocols currently have problems such as Single point of failure of cluster head nodes, poor network dynamics, uneven data transmission, etc., which are critical to the optimization of energy efficiency, network lifespan and network topology control. However, this optimization problem is an NP hard problem that conventional algorithms are difficult to solve. This paper proposes a new multi-objective cluster routing protocol (CHEABC-QCRP) aimed at optimizing network energy consumption, system lifespan, and quality of services (QoS). The protocol is based on a new chaotic hybrid elite artificial bee colony algorithm (CHEABC) proposed in this paper, which has strong search ability and greatly reduces convergence time. At the same time, a new chaotic strategy was designed to effectively prevent falling into local optima and premature convergence. In simulation experiments, compared with multiple routing protocols, a large number of test results show that this protocol significantly reduces network energy consumption, greatly improves system lifespan, and effectively improves QoS in IWSN.

Renewal of the regression model for normalization of specific energy consumption

Purpose. To develop a method of updating the regression model for the normalization of specific energy consumption in the presence of frequent and significant changes in the energy efficiency of the production process. Methodology. Analysis of existing methods of updating regression models, comparison of their possibilities, and synthesis of the method of updating the model in conditions of frequent and significant changes in the energy efficiency of the production process. Findings. It was established that in the presence of a significant number of possible variants of structural and mode changes in the energy consumption of the control object, the introduction of associated variables into the regression model is problematic, as it requires an increase in the number of experimental data in conditions of their expected heterogeneity. The flaw of the well-known regression model for normalizing the power consumption of the object of control is revealed, which consists of the fact that the model does not take into account the last values in the sequence of their appearance of experimental data obtained in the process of energy efficiency control. This reduces the accuracy of predicted energy consumption values. It is proposed to update the regression model every time after performing the energy efficiency control and sample adjustment. Adjustments are implemented by checking the homogeneity of the obtained experimental data, followed by their addition to the elements of the existing sample and removal (if necessary) from the sample of outdated data. The defined sequence of adjustment of the initial data allows timely updating of the model and implementation of the forecast of specific energy consumption, entering data reflecting the latest changes that occurred in the facility's energy supply. The proposed method of updating the model implements the approximation in time of the moment of energy efficiency control to the moment of obtaining experimental data for building a regression dependence for normalizing energy consumption values. This helps to increase the accuracy of the forecast of normalized values. A significant change in the conditions of production of products with a violation of the homogeneity of data is accompanied by a transition to the transitional mode of adjustment, where it is proposed to reduce the number of elements of the existing sample, ensuring the sequential removal of the elements furthest from the next moment of control. Extraction continues until data homogeneity is achieved. During the daily control of the efficiency of electricity consumption, the change in the values of the regression model coefficients in the process of its renewal reflects the changes in the object's electricity consumption that occurred over the last day. This allows you to separate their impact from the impact of changes that occurred earlier and to assess the level of this impact. Originality. For the first time, the shortcomings of the existing methods of updating regression models in the conditions of frequent and significant changes in the energy efficiency of the production process were identified. A method of updating the model under these conditions has been developed, which involves adjusting the sample of experimental data by changing the number of its elements and checking the homogeneity of the data. Practical value is that the sequence of actions during the implementation of the developed method of updating the regression model is defined, which allows for an increase in the accuracy of calculating the normalized values of specific energy consumption.

Distributed control of autonomous watercraft dynamics using physicomimetics and robust synthesis for disturbance rejection

This study investigates the guidance and control of an autonomous swarm of surface watercraft with focusing on distributed control laws in scenarios with and without boat dynamics. Through comprehensive simulations, we explore the behavior of boats under varying conditions in terms of analyzing their trajectories, velocities, yaw rates, and angles. The inclusion of boat dynamics significantly impacts these parameters for affecting the efficiency and convergence of boat trajectories towards the homing point. We observe that boats operating under dynamic conditions show much smoother convergence and demonstrate improved collision avoidance behaviors although their movements are slower due to the increased complexities introduced. The introduction of noise in simulations enhances realism and robustness regarding mimicking real-world scenarios. Our findings emphasize the importance of considering boat dynamics in autonomous systems where offering insights into energy-efficient control strategies for surface watercraft. This research contributes to the understanding of swarm behavior and creates a path for practical applications in marine robotics.

PhotoROMP: The Future Is Bright.

Since the earliest investigations of olefin metathesis catalysis, light has been the choice for controlling the catalyst activity on demand. From the perspective of energy efficiency, temporal and spatial control, and selectivity, photochemistry is not only an attractive alternative to traditional thermal manufacturing techniques but also arguably a superior manifold for advanced applications like additive manufacturing (AM). In the last three decades, pioneering work in the field of ring-opening metathesis polymerization (ROMP) has broadened the scope of material properties achievable through AM, particularly using light as both an activating and deactivating stimulus. In this Perspective, we explore trends in photocontrolled ROMP systems with an emphasis on approaches to photoinduced activation and deactivation of metathesis catalysts. Recent work has yielded a myriad of commercial and synthetically accessible photosensitive catalyst systems, although comparatively little attention has been paid to achieving precise control over polymer morphology using light. Metal-free, photophysical, and living ROMP systems have also been relatively underexplored. To take fuller advantage of both the thermomechanical properties of ROMP polymers and the operational simplicity of photocontrol, clear directions for the field are to improve the reversibility of activation and deactivation strategies as well as to further develop photocontrolled approaches to tuning cross-link density and polymer tacticity.

Camera-based plant growth monitoring for automated plant cultivation with controlled environment agriculture

Building integrated photovoltaic thermal (BIPVT) greenhouse technology exhibits great potential to enhance crop yielding, energy harvesting, and water management for highly efficient farming and enables controlled environment agriculture (CEA) for automated plant cultivation. The proposed smart greenhouse system uses BIPVT, geothermal, image-based sensing, and control technologies for high energy efficiency and high-fidelity plant growth control. A camera-based, computer-controlled system was demonstrated to successfully monitor single and multiple food plants' growth. The growth rate over the life-cycle of the plant was automatically recorded by cameras without human interference. A novel algorithm was developed to extract the plants from the background of the image and then to measure the length from the ground to the top of the longest plant with different shapes or profiles. The growth rate was automatically generated as fundamental data for farming management and control. Because the plant growth is slow and the errors caused by image noise can be comparable with the actual growth when the interval between images is not large enough, the noise of a camera is evaluated by comparing multiple images under the same exposure condition, which is used to provide the error range of the measurements. The plant growth monitoring system will be integrated into an ongoing greenhouse project for smart greenhouse operation.

Coordinated energy-efficient walking assistance for paraplegic patients by using the exoskeleton-walker system

Overground walking can be achieved for patients with gait impairments by using the lower limb exoskeleton robots. Since it is a challenge to keep balance for patients with insufficient upper body strength, a robotic walker is necessary to assist with the walking balance. However, since the walking pattern varies over time, controlling the robotic walker to follow the walking of the human-exoskeleton system in coordination is a critical issue. Inappropriate control strategy leads to the unnecessary energy cost of the human-exoskeleton-walker (HEW) system and also results in the bad coordination between the human-exoskeleton system and the robotic walker. In this paper, we proposed a Coordinated Energy-Efficient Control (CEEC) approach for the HEW system, which is based on the extremum seeking control algorithm and the coordinated motion planning strategy. First, the extremum seeking control algorithm is used to find the optimal supporting force of the support joint in real time to maximize the energy efficiency of the human-exoskeleton system. Second, the appropriate reference joint angles for wheels of the robotic walker can be generated by the coordinated motion planning strategy, causing the good coordination between the human-exoskeleton system and the robotic walker. The proposed approach has been tested on the HEW simulation model, and the experimental results indicate that the coordinated energy-efficient walking can be achieved with the proposed approach, which is increased by 60.16% compared to the conventional passive robotic walker.

Dynamic Knowledge Management in an Agent-Based Extended Green Cloud Simulator

Cloud infrastructures operate in highly dynamic environments, and today, energy-focused optimization become crucial. Moreover, the concept of extended cloud infrastructure, which, among others, uses green energy, started to gain traction. This introduces a new level of dynamicity to the ecosystem, as “processing components” may “disappear” and “come back”, specifically in scenarios where the lack/return of green energy leads to shutting down/booting back servers at a given location. Considered use cases may involve introducing new types of resources (e.g., adding containers with server racks with “next-generation processors”). All such situations require the dynamic adaptation of “system knowledge”, i.e., runtime system adaptation. In this context, an agent-based digital twin of the extended green cloud infrastructure is proposed. Here, knowledge management is facilitated with an explainable Rule-Based Expert System, combined with Expression Languages. The tests were run using Extended Green Cloud Simulator, which allows the modelling of cloud infrastructures powered (partially) by renewable energy sources. Specifically, the work describes scenarios in which: (1) a new hardware resource is introduced in the system; (2) the system component changes its resource; and (3) system user changes energy-related preferences. The case study demonstrates how rules can facilitate control of energy efficiency with an example of an adaptable compromise between pricing and energy consumption.