Comprehensive Performance Index Research Articles

An advanced RIME Optimizer with Random Reselection and Powell Mechanism for Engineering Design

Abstract RIME is a recently introduced optimization algorithm that draws inspiration from natural phenomena. However, RIME has certain limitations. For example, it is prone to falling into Local Optima (LO), thus failing to find the Global Optima (GO), and has the problem of slow convergence. To solve these problems, this paper introduces an improved RIME algorithm (PCRIME), which combines the random reselection strategy and the Powell mechanism. The random reselection strategy enhances population diversity and helps to escape LO, while the Powell mechanism helps to improve the convergence accuracy and thus find the optimal solution. To verify the superior performance of PCRIME, we conducted a series of experiments at CEC 2017 and CEC 2022, including qualitative analysis, ablation studies, parameter sensitivity analysis, and comparison with various advanced algorithms. We used the Wilcoxon signed-rank test and the Friedman test to confirm the performance advantage of PCRIME over its peers. The experimental data show that PCRIME has superior optimization ability and robustness. Finally, this paper applies PCRIME to five real engineering problems and proposes feasible solutions and comprehensive performance index definitions for these five problems to prove the stability of the proposed algorithm. The results show that the PCRIME algorithm can not only effectively solve practical problems, but also has excellent stability, making it an excellent algorithm.

Energy management strategy and operation strategy of hybrid energy storage system to improve AGC performance of thermal power units

With the continuous increase of the renewable energy power generation penetration, the intermittency and fluctuation of renewable energy power generation make the traditional thermal power units (TPU) which play the main role of regulation need to undertake more frequent and more severe regulation tasks. In order to improve the automatic generation control (AGC) command response capability of TPU, an operation strategy of hybrid energy storage system (HESS) is proposed in this paper. While assisting TPU to complete the regulation tasks, it gives full play to the advantages of power-type and energy-type energy storage. Moreover, an energy management strategy of energy storage array (ESA) is proposed to improve the overall operation efficiency of ESA while making the state of charge (SOC) of all energy storage subunits consistent. Finally, the effectiveness of the proposed strategy is verified by simulation experiments. The results show that after using HESS to assist TPU operation, the comprehensive regulation performance index of TPU increased from 4.0198 to 7.8112, which significantly improved the operational flexibility of TPU. Meanwhile, the strategy proposed in this paper makes different types of energy storage systems in HESS operate in a relatively healthy SOC range, and the SOC of the flywheel energy storage system (FESS) reaches the limitation value decrease from 6 times to once, ensuring the overall charge and discharge capacity of HESS.

Working performance of vertical spiral stirred mill based on discrete element method

The main structures and characteristic parameters that affect the working performance of vertical spiral stirred mill were analyzed and studied, and a method for selecting key working parameters was proposed. Based on the discrete element method, a simulation model of vertical spiral stirred mill was established, and the effects of spindle speed, agitator lead and grinding media size distribution on grinding performance were analyzed. A comprehensive grinding performance index was proposed, and orthogonal tests were conducted on each parameter to obtain the quantitative value of the grinding effect, and then the optimal working parameter combination under a specific weight coefficient was obtained, which provided a reference method for the optimal design of the mill.

Selection Process Based on Advancing the Development of a Science and Technology Law System with Chinese Features

The significance of advancing socialist S&T legal system building with Chinese features is examined in this paper, along with the extensive effects it has on S&T innovation and economic growth. Against the backdrop of an increasingly competitive global scientific and technology landscape, China’s ability to innovate and achieve sustainable growth hinges on the establishment of an ideal science and technology legislative framework. This essay first examines the primary obstacles to China’s development of a science, technology, and innovation (S&T) legal system, including inadequate protection for intellectual property rights, a flawed process for transforming scientific and technological advancements, and an insufficient system for encouraging enterprise innovation. Then, this research presents a quantitative analysis model to optimize the path of science and technology legal building by applying the improved particle swarm optimization method (PSO). The model takes into account a wide range of variables, including the degree of intellectual property protection, the strength of legal backing, the pace at which scientific and technological advancements are transformed, etc. Through the analysis of simulation data, the model also confirms the promotion effect of the legal system construction on the quantity of patent applications, the success rate of innovation projects, the enterprise R&D expenditure, and the expansion of the local economy. The study’s findings demonstrate that bolstering the science and technology legal system can effectively encourage businesses to boost R&D investment and foster regional economic development in addition to greatly raising the quantity of patent applications and the success rate of innovation projects. The rigorous intellectual property protection laws and ideal legal framework for the conversion of accomplishments greatly boost the regional innovation vitality and economic efficiency, particularly in the case study of Zhongguancun in Beijing and East China. Moreover, adaptive weighting is used to enhance the PSO algorithm and optimize the development of science and technology legal system’s comprehensive performance index, thereby confirming the model’s viability and efficacy. The study’s findings offer theoretical justification and helpful advice for China’s development of a science and technology legal framework, which is crucial for fostering innovation in these fields and boosting the country’s competitiveness.

Numerical study on heat transfer and pressure performance of different suspension nozzles

The drying process of the lithium battery pole pieces makes extensive use of the suspension nozzle. It is of great significance to study the heat transfer and pressure steady-state characteristics of the suspension nozzle and to select the appropriate nozzle structure for the production of pole pieces. Based on the SSTk-ω\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{SST }}k - \\omega$$\\end{document} turbulence model, this article numerically simulates the impact jet process of suspension nozzles with slits, injection holes, and effusion holes. There is a qualitative and quantitative analysis of the distribution of their velocity field, temperature field, local Nusselt number, average Nusselt number, local pressure coefficient, and average pressure coefficient, and the comprehensive performance index of the nozzle is proposed. The results show that when the weight factor of heat transfer performance α is less than 21.61% and the weight factor of pressure performance β is more than 78.39%, the comprehensive performance of the traditional suspension nozzle with double slits is the best. As the α is increasing, the β is decreasing. The comprehensive performance of the suspension nozzle with effusion holes is the best. The turbulent intermittence, interaction between neighbouring jets, and edge effects affect the heat transfer and pressure uniformity of the suspension nozzle.

Time course of cognitive functions and physiological response under heat exposure: Thermal tolerance exposure time based on ECG and fNIRS

Thermal discomfort has a great dynamic impact on cognitive performance and occupants’ well-being during prolonged working. However, few studies have focused on the dynamic changes in cognitive functions and physiological responses under extreme heat stress. This study investigated the cognitive impairment over exposure time at various ambient temperatures, ranging from 21.15℃ WBGT to 38.93℃ WBGT, and its underlying physiological mechanism. 30 well-trained volunteers and 8 occupational operators participated in the experiments, with typical cognitive tests, ECG, and fNIRS measurements. Negative correlations were mainly observed between cognitive test accuracy, ECG RRI, ECG RMSSD, ECG HF/LF, fNIRS ALFF, fNIRS functional connectivity (CORRz, COH, PLV), and exposure time. This indicated that increased physical load, stress, and decreased regulation ability to sudden changes, cognitive resource input, and inter-regional brain cooperation were key factors contributing to cognitive impairments over time. A comprehensive performance index (CPI) was established by the weighted sum of accuracy in cognitive tests and physiological features of ECG and fNIRS. The weights suggested fNIRS ALFF, ECG RRI, and fNIRS functional connectivity could be potential biomarkers for cognitive assessment. A set of linear quantitative CPI curves over exposure time was supplied to calculate the thermal tolerance exposure time at various risk tolerances and ambient temperatures. A power-law relationship (R2>0.997) was observed between the thermal tolerance exposure time and the standardized temperature. This study provides a physiological insight into cognitive impairment under prolonged heat exposure, and quantitative thermal tolerance exposure curves to evaluate safe thermal working duration for both low-risk groups (officers and drivers) and high-risk groups (operators in industry).

A Data-Driven Comprehensive Evaluation Method for Electromagnetic Suspension Maglev Control System

As new advanced vehicles, the safety and stability of electromagnetic suspension maglev trains have always been a subject of concern. This study introduces the improved R index and τ-distance index into the performance evaluation of the suspension control system, respectively assessing the stability of the suspension gap and the smoothness of train operation, combining them with grey relational analysis to achieve data-driven comprehensive evaluation. Furthermore, feasibility tests on the Fenghuang Maglev Express validate the effectiveness and superiority of the comprehensive evaluation method based on measured data. Experimental results demonstrate that the data-driven comprehensive evaluation method, through designing specialized evaluation metrics and increasing assessment dimensions, effectively evaluates the performance of the suspension system control loop. Compared to a traditional error integral comprehensive performance index, it offers greater comprehensiveness and accuracy, along with real-time state-monitoring capabilities.

Computational Analysis of the Comprehensive Lifetime Performance Index for Exponentiated Fréchet Lifetime Distribution Products with Multi-Components

Computational Analysis of the Comprehensive Lifetime Performance Index for Exponentiated Fréchet Lifetime Distribution Products with Multi-Components

Enhancing thermal-hydraulic performance in heat exchangers with metal foam inserts: a comprehensive experimental investigation

ABSTRACT This research investigates the use of metal foam inserts to enhance the thermal-hydraulic performance of double-tube heat exchangers, relevant for energy-efficient systems such as solar flat plate collectors. Motivated by the need for improved heat transfer efficiency, this study hypothesizes that metal foam integration can significantly boost performance. Through systematic experimental study, the impact of metal foam inserts on heat transfer and pressure drop characteristics is comprehensively evaluated across various configurations, spanning horizontal and vertical orientations. Experimental evaluations were conducted on four configurations, including both conventional and metal foam-integrated heat exchangers in horizontal and vertical orientations, using water as the working fluid and Nickel metal foam with 0.9 porosity and 10 PPI in the annular space. The findings highlight significant enhancements in heat transfer performance with metal foam integration across both horizontal and vertical heat exchanger configurations compared to conventional counterparts, particularly showcasing superior effectiveness and efficiency in the horizontal configuration. Results show that metal foam integration substantially enhances heat transfer, with horizontal configurations exhibiting a 14.56% higher heat transfer coefficient than vertical ones. Additionally, the comprehensive performance index improved by 1.17 times, indicating a better balance between heat transfer enhancement and pressure loss. These findings were validated against established correlations from the literature, confirming the superior effectiveness of horizontal metal foam heat exchangers.

Mitigating heat-induced yield loss in peanut: Insights into 24-epibrassinolide-mediated improvement in antioxidant capacity, photosynthesis, and kernel weight

Context or problemAs global temperatures steadily increase, the frequent occurrence of extreme high-temperature events has significantly hampered peanut (Arachis hypogaea L.) production in low-latitude regions. Objective or research questionPreviously, 24-epibrassinolide (EBR) was identified as a substance capable of mitigating abiotic stress damage in plants. However, it remains unclear whether and by what mechanisms EBR can diminish the yield loss caused by heat stress in peanuts. MethodsDuring the flowering phase, two distinct peanut cultivars, Qinghua7 (heat-resistant type) and Shanhua101 (heat-sensitive type) were exposed to a 10-day heat stress treatment (+4.2 ℃). EBR or water was sprayed on the 1st, 3rd, and 5th days of heating, and water-sprayed natural peanuts was used as control, to assess the effect of EBR on antioxidant capacity, photosynthetic performance, and yield in heat-stressed peanuts. ResultsEBR application increased activities of superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase in heat-stressed peanut leaves. Simultaneously, EBR decreased hydrogen peroxide and superoxide anion production, along with a reduction in malondialdehyde content. Additionally, EBR notably alleviated the oxidation damage to chloroplast membranes and grana lamella under heat stress. Thus, an increase in maximum photochemical efficiency, comprehensive performance index, rubisco activity, net photosynthetic rate, and biomass accumulation was observed in heat-stress peanuts. Synergistic enhancement provided by EBR on antioxidant capacity and photosynthetic performance resulted in improved plant growth, kernel weight, and effective pods per plant, led to a reduction in yield loss for heat-stressed cultivars Qinghua7 and Shanhua101 by 26.92 % and 55.18 %, respectively. ConclusionsThe application of EBR enhanced the antioxidant capacity of peanut leaves. This, in turn, mitigated oxidative damage to chloroplast membranes, resulting in improved photosynthetic performance. Ultimately, this intervention led to a reduction in yield loss for heat-stressed peanuts, achieved through an increase in kernel weight. Implications or significanceThe foliar spraying of EBR holds significant promise in crop production, offering a broad application prospect. This practice is beneficial for enhancing the heat resistance of peanuts and potentially other field crops, equipping them to better withstand the increasingly severe climate challenges anticipated in the future.

Experimental studies of thermal behavior, engine performance and emission characteristics of biodiesel / diesel / 1 pentanol blend in diesel engine

The present study investigates the significance of the utilization of biodiesel derived from waste cooking oil in diesel engines and its impact on engine output and environmental pollution after blending with diesel and 1-pentanol. The higher values of the ignition index and comprehensive performance index of 4.34 ×10−4mass/min°C2 and 13.71×10−6 mass2/min2 °C3, respectively, for D70B20P10 (70 % diesel, 20 % biodiesel & 10 % 1-pentanol by volume) indicate superior combustion performance. At full load, carbon monoxide and unburned hydrocarbon emissions from 1-pentanol blended fuels decrease significantly, ranging from 40 % to 52 % and 30.76–46.15 % compared to diesel. The D70B20P10 also showed an improved thermal efficiency of 28.68 % among all tested fuels at full load but slightly lower than diesel (29.56 %). Diesel demonstrated superior in-cylinder pressure (ICP) of 77 bar and heat release rate (HRR) of 41.1 J/ºCA owing to its excellent combustion characteristics. Introducing 5–10 % 1-pentanol in blend improved combustion, elevating ICP and HRR, attributed to enhanced fuel atomization and oxygen content. The sustainable process index value obtained for a global index per person is 0.002×10−4cap Litre-1, which lies between 0 and 1, indicates sustainability and compatibility of biodiesel production demonstration by universal biofuels (Andhra Pradesh).

Reliable identification based intelligent PID tuning for long-period process control under different working conditions

BackgroundChemical process models change frequently with operating conditions. However, the industry mostly adopts an off-line calibration and online fixed value commissioning scheme, which can be difficult to maintain control quality. Therefore, a long-cycle online intelligent PID controller calibration scheme is necessary. MethodsFirstly, a high-order linear dynamic model is used as the identification model, and online recursive identification technology is utilized. Secondly, a comprehensive performance index is selected to ensure both stability and rapidity of dynamic transitions. Finally, the slow rate updated PID parameters are obtained during the process control, and the Levy Memory Particle Swarm Optimization (LMPSO) search is applied to harvest the optimal solution. Significant findingsThis paper has presented an intelligent PID tuning method based on reliable identification technology. The main contributions include: (i) By using Lyapunov theory, the boundedness of identification error using the proposed algorithm is guaranteed; (ii) The LMPSO algorithm is proposed to enhance global search capability and search efficiency; (iii) A novel optimization scheme is proposed for a full-process online closed-loop intelligent PID controller. The scheme aims to improve the industrial controller's performance without altering its structure.

A novel composite bionic leaf vein and honeycomb microchannel heat sink applied for thermal management of electronic components

Based on the bionic concept and fractal theory, a composite bionic microchannel radiator based on veins and honeycombs is designed in this work to address the problem of heat dissipation in electronic components. The convective heat transfer within the novel microchannel structure is numerically investigated, and the effects of the coolant (Fe3O4-water nanofluids) and the surface bump structure on the overall performance of the microchannel are explored. Compared to the traditional ordinary leaf vein-shaped fractal microchannels, the proposed microchannel heat sink in this work demonstrated a significant heat dissipation effect, with an optimal increase in the Nusselt number of 64.5%. The best microchannel heat dissipation was achieved using Fe3O4-water nanofluids with a mass fraction of 0.5%. Additionally, changing the height and arrangement of the bump structure allows for improving heat transfer by a maximum of 8% compared to microchannels without the bump structure. Furthermore, the novel bionic microchannel heat sinks are evaluated, and the resulting optimized microchannel structure has a comprehensive performance index of 1.11. Overall, the study results provide useful information and serve as a reference for designing new bionic heat sinks for the thermal management of electronic components.

Multi-objective integrated optimization of geothermal heating system with energy storage using digital twin technology

Heat energy storage technology plays a significant role in energy systems, and the various technological solutions brought about by digitalization are especially valuable in the field of energy storage. This article proposes an innovative model based on digital twin technology to solve the supply–demand mismatch problem in geothermal heating systems. This model achieves multi-objective optimization of comprehensive cost, geothermal energy utilization rate, and carbon emission by constructing a heat storage geothermal heating system. Digital twin technology integrates data and information models of public buildings and facilitates their sharing and transmission throughout the entire lifecycle of the geothermal heating system. Initially, the proposed method employs a machine learning-based approach to accurately predict heating demand. Subsequently, the operation of the heat storage water tank and heat pump units is optimized to resolve difficulties in matching energy supply and demand. Finally, the method takes full advantage of time-of-use electricity pricing policies to reduce costs. The data utilized were collected from an office building in China over a period of six months. Experimental results demonstrate that: (1) In terms of predicting heating demand, the improved neural network proposed in this study achieved a prediction accuracy of 98%, which is a 10% improvement over comparative algorithms. Additionally, the experimental comparison of four types of errors showed that the machine learning method proposed had smaller errors across the board. (2) The method realized collaborative multi-objective optimization, and in five scenarios, the comprehensive performance index increased by up to 38.03% compared to the benchmark system. This indicates that intelligent technology is an effective means of enhancing the energy sustainability of geothermal heating systems, and the use of geothermal energy as a clean energy source effectively addresses issues related to the storage, utilization, management, and energy conservation of buildings.

A novel reduced thermal conductivity ductility cement-based composite material with containing aerogels: Towards multi-function in buildings

Fully exploring and expanding the performance of building materials has become an important direction for the sustainable development of building materials. In this study, low-density silica aerogel (LSA) with a density of 74.3 kg/m³ and conventional silica aerogel (CSA) with a density of 141.6 kg/m³ were used as partial aggregates to prepare a reduced thermal conductivity ductility cement-based composite material (RTCDCC). Based on this, the effects of LSA and CSA at different contents on the compressive strength, four-point bending performance, water absorption, thermal conductivity, and density of RTCDCC were compared and studied. The results showed that when the aerogel content was the same, the influence of LSA on the mechanical and physical properties of the mixture was more significant compared to CSA. Furthermore, the mixture exhibits good ductility when the content of aerogel was less than 30 %. Additionally, the impact of different density aerogels on the internal pore distribution of the mixture was investigated through low-field nuclear magnetic resonance (NMR) tests, providing further insights into the reasons for the variations in performance between LSA and CSA mixtures. Notably, the RTCDCC prepared in this study not only has good compressive strength but also maintains a lower thermal conductivity, and an average comprehensive performance index (Icp) of 0.63, which promotes the application of aerogel in the realm of multi-function building materials.

Reinforcement effects on the tensile properties of seawater sea-sand engineered cementitious composites reinforced with multi-scale hybrid fibers

The preparation of seawater sea-sand engineered cementitious composites (SS-ECC) holds great potential for development in coastal and marine infrastructure. To improve its compressive strength and tensile properties, this study investigated the crack characteristics, micro-mechanics, fibers reinforcement index, and micro-structure of SS-ECC using a multi-scale hybrid fibers system consisting of CaCO3 whiskers, PE fibers, and stainless steel fibers (SSF). The aim was to gain an in-depth understanding of the reinforcement effects of multi-scale hybrid fibers. The results showed that CaCO3 whiskers increased the number of cracks and the stability of crack evolution in SS-ECC during the ultimate tensile state by bridging the matrix micro-cracks, improving the matrix micro-structure, and filling the interfacial transition zones between the macroscopic fibers (PE fibers and SSF) and the matrix. Additionally, it reduced the crack width and spacing. These combined effects increased the compressive strength, tensile strength, and tensile strain capacity of SS-ECC by 12.8%, 15.7%, and 11.9%, respectively. Finally, the reinforcement effects of multi-scale hybrid fibers were analyzed based on the pseudo-strain hardening performance indices, fibers reinforcement index, and hybrid coefficient. This analysis provides reference and insight for the design of multi-scale hybrid fibers SS-ECC with a high comprehensive performance (σcσtε/w) index.

Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm

The detection of two-line element (TLE) outliers and space events play a crucial role in enhancing spatial situational awareness. Therefore, this paper addresses the issue of TLE outlier detection methods that often overlook the mutual influence of multiple factors. Hence, a Multivariate Gaussian Mixture Model (MGMM) is introduced to consider the interdependencies among various indicators. Additionally, a Multi-strategy Genetic Algorithm (MGA) is employed to adjust the complexity of the MGMM, allowing it to accurately learn the actual distribution of TLE data. Initially, the proposed method applies probabilistic fits to the predicted error rate changes for both the TLE semi-major axis and the orbital inclination. Chaos initialization, a posterior probability penalty, and local optimization iterations are subsequently integrated into the genetic algorithm. These enhancements aim to estimate the MGMM parameters, addressing issues related to poor robustness and the susceptibility of the MGMM to converge to local optima. The algorithm’s effectiveness is validated using TLE data from typical space targets. The results demonstrate that the optimized algorithm can efficiently detect outliers and maneuver events within complex TLE data. Notably, the comprehensive detection performance index, measured, using the F1 score, improved by 15.9% compared to the Gaussian mixture model. This significant improvement underscores the importance of the proposed method in bolstering the security of complex space environments.

A novel segmented random search based batch scheduling algorithm in fog computing

In fog computing, batch scheduling is an important and challenging task aiming at reducing the response latency. Response time includes the scheduling time, execution time and other factors such as network latency. However, the majority of the existing batch scheduling algorithms primarily focus on minimizing the execution time of tasks for IoT devices, ignoring the algorithms' scheduling time. This contributes to suboptimal overall response time, which is a critical quality-of-service (QoS) indicator and significantly impacts performance. Optimizing both execution and scheduling time is therefore crucial for achieving minimum response time, improving QoS, and alleviating load-balancing issues. This work introduces a novel approach for the load-balancing difference variable and uses dimensionality reduction techniques for dynamic task organization. The main contribution comprises two distinctive algorithms: the Pre-allocation Minimum Completion Time (PMT) algorithm and the Segmented Random Search Fog Computing Batch Scheduling Algorithm (SRS). These algorithms are designed to address the inherent characteristics of the problem. To evaluate the performance and applicability of batch scheduling algorithms, we establish a model incorporating compliance and application performance indicators. We introduce the Logical Recursive Indirect Comparison Analysis method to assess and evaluate batch scheduling algorithms. Simulation results demonstrate the exceptional effectiveness of the SRS algorithm. It exhibits a remarkable improvement in comprehensive performance indices, ranging from 139.10% to 261.18%, and optimization quality enhancement rates of 64.25%–154.13% compared to classical standard optimization algorithms. Compared with advanced algorithms, the SRS algorithm also outperforms, with optimization quality improvement rates of 22.74% and 71.11% compared to AEOSSA and CHMPAD.

Effect of a stranded hole type on the performance of corn stover composite pipe.

To promote the comprehensive utilization of corn stover and the development of field water-saving irrigation technology, a method of returning corn stover to the field was prosed; in this method, the crop stalks were crushed, mixed with soil in different proportions of adulteration, and then extruded to form hollow round tubes. To compare the influence of the winch blade with or without a diameter change on the composite pipe molding performance, two composite pipe molding devices were theoretically designed, simulated, and analyzed using discrete element simulation software, and a composite pipe molding bench test was performed. The simulation test revealed that the composite pipe molding rate of the winch blade without the reducer molding device was 3.45 kg/s, the output power of the winch shaft was 20.7 kW, the composite pipe molding rate of the winch blade with the reducer molding device was 1.20 kg/s, and the output power of the winch shaft was 18.75 kW. By calculating the weighted average of two indices, the composite pipe forming rate and the winch shaft output power, the comprehensive performance index of the composite pipe forming device without a reducer was greater than that of the device with a reducer. The composite pipe forming bench test revealed two kinds of molding devices with an extrusion molding with an outer diameter of 100 mm and an inner diameter of 30 mm. The composite pipe density test average was greater than 1.30 g/cm3 and met the requirements of composite pipe molding; the winch blade without a reducer molding device had an average composite pipe molding rate of 3.23 kg/s, and the winch blade with an average reducer molding rate of 2.07 kg/s. The forming rate of the composite pipe without a reducer was faster. Therefore, a winch blade without a reducer composite pipe molding device is more conducive to improving the composite pipe molding performance.

Augmented Kalman Estimator and Equivalent Replacement Based Taylor Series-LQG Control for a Magnetorheological Semi-Active Suspension

This research presents an augmented Kalman estimator and an equivalent replacement-based Taylor series (ERBTS)-linear quadratic Gaussian (LQG) control strategy to cope with the control accuracy and response delay of magnetorheological (MR) dampers for vehicle semi-active suspensions. The parameters in the MR model are identified from experimental measurements. Then, two main sources of control error, namely, modelling error and real-time variety of the MR damper output force, are defined as an integrated compound real-time variety. Subsequently, they are written into a differential equation with characteristics of the minimum system to augment the state equation of the semi-active suspension system. The augmented Kalman estimator is constructed to estimate the abovementioned compound real-time variety. To calculate an acceptable time-delay compensation predictive control force, an equivalent operation is implemented beforehand in the suspension comprehensive performance index by replacing a part of the squared time-delay control force with the corresponding predictive control force. Simulation results verify the effectiveness of the proposed augmented Kalman estimator, and the newly developed ERBTS-LQG controller almost achieves control effectiveness of the ideal time delay free semi-active suspension.