Multiple Performance Indices Research Articles

Superior Control of Spacecraft Re-Entry Trajectory

This paper focuses on the re-entry phase of lunar return spacecraft and addresses the design optimization of their re-entry trajectories in real-world conditions. Considering various constraints of re-entry flights, this study introduces a refined superior control theory, drawing from Xuesen Qian’s descriptions in engineering control theory, and presents a specific superior control algorithm. The designed superior control algorithm and the traditional weighted optimal control algorithm were employed to simulate the lunar return and re-entry processes. Two representative trajectories were selected for a comparative analysis to obtain various parameters. Results indicate that the trajectory optimized using the weighted optimal control algorithm can only ensure that multiple performance indexes are optimized according to preset weights but cannot achieve superior performance in all metrics. In contrast, trajectories optimized using the superior control algorithm effectively leverage the permissible floating range of performance indexes without exceeding the maximum limit, thereby ensuring superior performance in all metrics. This paper is the first to refine the superior control theory proposed by Xuesen Qian, to design a specific algorithm theory for superior control, and to apply it to aerospace re-entry trajectory optimization—providing a theoretical foundation for future non-weighted control algorithm developments.

Comprehensive design method of controller parameters for three‐phase LCL‐type grid‐connected inverter based on D‐partition

AbstractThe LCL‐type inverter is a core component in grid‐connected renewable energy systems, with its performance heavily influenced by the controller. Conventional design methods of controller parameters generally rely on approximation or trial and error, making it difficult to optimize parameters for multiple performance indices. This paper proposes a comprehensive design method of controller parameters for a three‐phase LCL‐type grid‐connected inverter based on the D‐partition method, obtaining a multi‐objective parameter stability domain of controller parameters that simultaneously satisfies multiple performance indices, such as gain margin, phase margin, and current loop bandwidth. The stability domain can be visually presented, which can avoid repetitive adjustments. Additionally, the variation trend of the stability domain when there are deviations in the LCL filter parameters is analyzed. Finally, the accuracy and effectiveness of the proposed design method are validated through simulations and experiments, achieving precise parameter design for the controller of LCL grid‐connected inverters even in the presence of deviations in filter parameters.

Explainable highway performance degradation prediction model based on LSTM

With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including Pavement Surface Condition Index (PCI), Pavement Rutting Depth Index (RDI), Pavement Riding Quality Index (RQI) and Pavement Skidding Resistance Index (SRI) and the hyperparameters of the model were optimized by Bayesian optimization algorithm and the casual analysis of the model was conducted based on SHapley Additive exPlanations (SHAP), providing a reference for China's highway network-level preventive maintenance decision-making. The results prove that the established model can effectively predict the performance of the following year through the data of the previous two years. The established model has a better comprehensive prediction effect in comparison to similar multi-output models. The average coefficient of determination (R2) of the four prediction indexes can reach 0.823. Besides, the prediction effect among the indexes is stable and multiple pavement performance indexes can be effectively and reliably predicted at the same time. In addition, the contribution of input feature variables to output is quantified based on SHAP. According to quantified the contribution, the association relationship of the neural network model is set-valued mapped to the causality relationship in the post hoc. The input feature variables with the main contribution are extracted for causal analysis. The causal analysis shows that the degradation of the four pavement performances all have obvious time memory characteristics and have significant differences, but their pavement performance degradation is the result of nonlinear changes caused by the coupling effects of traffic load, road age, climate, and pavement structure.

Research on the PDC rock cuttings image instance segmentation method based on improved U-Net++ network and multi-feature fusion

Currently, PDC bits dominate the petroleum bit market. It is a small cut of polycrystalline diamond, which is embedded in the body of the drill bit. Due to the small number of cuttings generated through the PDC bit, the individual cuttings are small, and the cuttings edge is blurred, which results in poor effect and low precision of traditional image segmentation. Therefore, this paper proposes an image segmentation method regarding deep learning. Firstly, a multi-task learning method is introduced based on the U-Net segmentation model. A multi-task-learning-U-Net++ instance segmentation model is proposed. The results of semantic segmentation and edge segmentation are obtained by this segmentation model. Then, the cuttings are segmented by superpixel to obtain the sub-blocks of superpixel. Finally, a multi-feature fusion method is proposed, which integrates semantic information, edge information and superpixel sub-blocks to obtain the segmentation result of cuttings image. Experimental results show that the proposed method can effectively segment each cutting in the image and this method has certain robustness. Compared with the segmentation algorithms such as U-Net and U-Net++, this algorithm performs better in multiple image segmentation performance indexes.

Assessing the use of PEC and jf methods for strengthening the evaluation of new heat exchangers

The new type of heat exchanger uses the micro heat pipe as the main heat transfer material. Compared with the traditional heat exchanger of the same kind, the performance of the new type of heat exchanger has significantly improved. By studying the evaluation method of strengthening the performance of the heat exchanger, the performance of the heat exchanger can be further optimized. Most of the current literature study the heat transfer coefficient of the heat exchanger, but there are many influencing factors. Therefore, the evaluation lacks accuracy, and there is no clear research description of fin strengthening performance of the heat exchanger. In this paper, the PEC and jf methods were used in evaluating the enhanced performance of a new type of heat exchanger. Multiple performance indexes were used as evaluation factors and the characteristics of flow and heat transfer were analyzed by studying the working medium, heat transfer coefficient and pressure drop in the heat exchanger. The study, therefore, proposed an optimization direction and the results proved that PEC and jf methods can be used to evaluate the performance of the new heat exchanger efficiently, and the two conclusions were similar. The fins were more capable to improve the performance of heat exchanger when Re >31000.

Analysis and multi-objective optimization design of sinusoidal DSEM with two different excitation modes based on Gray-Fuzzy-Taguchi method

The nonlinearity of sinusoidal doubly salient electro-magnetic machine (SDSEM) leads to the fuzzy optimization range of its design parameters. And there is a conflict problem in multiple optimization objectives. Therefore, the multi-objective optimization of SDSEM with two different excitation modes is carried out based on the Gray-Fuzzy-Taguchi method. Firstly, the proposed sinusoidal method of square wave back electromotive force (EMF) phase modulation is introduced. And the key parameters to improve the symmetry and sinusoidal degree of the back EMF waveform are pointed out. Then, the sensitivity of key parameters is analyzed and the signal-to-noise (S/N) ratio is calculated by Taguchi orthogonal test. Secondly, the S/N ratio is linearly normalized by grey correlation analysis. Then, fuzzy reasoning is used to synthesize multiple performance indexes of the motor into a multiple performance characteristics index (MPCI) value, and the optimal scheme is obtained. Finally, the optimized SDSEM with concentrated magneto-motive forces (SDSEM-CF) and SDSEM with distributed magneto-motive forces (SDSEM-DF) are simulated and compared. The SDSEM-DF prototype is developed and verified by experiments. The results show that the no-load back EMF of the prototype has a good sinusoidal degree. Based on vector control, the sinusoidal drive of DSEM is realized without increasing the control complexity, which greatly reduces the torque ripple of the flux reluctance motor.

EFFECT OF PROCESS VARIABLES ON THE CORROSION INHIBITION PERFORMANCE OF AGERATUM CONYZOIDES ON STRUCTURAL STEEL IN HCl – FUZZY LOGIC APPROACH

The aim of this study is to optimize the process variables to achieve a more efficient Ageratum Conyzoides extract (ACE) inhibitive performance on structural steel in 1 M HCl acid, using the integrated Taguchi-fuzzy logic technique. Optimization of multiple performance characteristics of weight and corrosion rate; was achieved via a multiple response performance index (MRPI) from corrosion inhibition parameters of inhibitor concentration, temperature and exposure time. The predictive model determined that the setting of inhibitor concentration of 2 g/cm3, temperature of 30oC and exposure time of 2 days were the optimal setting for maximum inhibitive corrosion performance. The optimal setting was verified and validated by a confirmatory experiment, indicating a significant improvement of 0.634 in the actual MRPI value when the integrated Taguchi-Fuzzy logic optimization model was employed.

Interlaminar shear and impact properties of particle reinforced jute fiber/epoxy hybrid composites

In today’s world, there is a growing emphasis on using fiber-reinforced composites carefully across diverse sectors like automotive, construction, machinery and appliances. These materials are valued for their capacity to be efficiently repurposed, recycled or disposed of with minimal ecological consequences. Thus, integrating sustainable, eco-friendly and environmental mindful approaches into developing new materials and processes has become increasingly imperative. This study aims to analyze the impact strength, inter laminar shear strength and water absorption capacity of the jute fiber composite filled with the particles like alumina (Al2O3), boron carbide (B4C) and silicon carbide (SiC). The response surface methodology (RSM), employing three levels and three parameters, enables the identification of distinct combinations of input parameters essential for the fabrication of suitable polymer composites. The effects of process factors on interlaminar shear strength (ILSS), impact strength and % weight increase are examined. For each output measures, the connotation of the input parameters is determined by analysis of variance (ANOVA). To establish multiple performance indexes, a hybrid model called adaptive neuro-fuzzy inference system (ANFIS) based on Grey theory is developed. The model’s capability is validated and the results confirm the effective prediction of the preferred performance indicator.

The impact of mechanical properties on the cutting performance of Al2O3-based ceramic cutting tools for green turning of hardened steel

The utilization of green turning as an alternative to grinding has the potential to enhance the processing efficiency of hardened steel while also preserving the environment. However, the intermittent turning of hardened steel necessitates the tool to possess exceptional comprehensive mechanical properties. Consequently, this study aimed to investigate the impact of mechanical properties on the cutting performance of three Al2O3-based ceramic cutting tools, namely SG4, CC650, and CC670 by conducting interrupted turning experiments on hardened steel at both low and high speeds. The findings revealed that for different ceramic tool, it was not the smaller the speed, the smaller the cutting force when intermittently cutting hardened steels. In general, an optimal cutting speed existed where the workpiece material softened at a faster rate than the tool material due to cutting heat, resulting in minimized main cutting forces. Consequently, during the initial cutting stage, the cutting force of CC670 measured at 110 m/min was slightly higher than that at 230 m/min. The primary failure mechanism of CC670 at high speeds was mechanical fatigue. In high-speed cutting scenarios where tool failure was not primarily caused by thermal shock, the tool CC670, possessing superior mechanical properties, demonstrated a longer lifespan. When the multiple mechanical performance indices do not consistently reach their maximum values simultaneously under the current process conditions, an ideal cutting tool structure would feature a tool nose characterized by exceptional hardness and chemical stability, while the remaining section of the cutting tool should demonstrate high fracture toughness and flexural strength.

Resilient Event Triggered Interval Type‐2 Fuzzy Sliding Mode Control for Connected and Autonomous Vehicles Subjected to Multiple Cyber Attacks

Connected and autonomous vehicles (CAVs) are considered a hot area of research in the field of intelligent transportation systems. However, over the past few years, cybersecurity threats have posed significant challenges to such systems, given the ever‐evolving automotive industry. Thus, there is a growing need to design resilient control strategies to address the issue of cyberattacks. This article proposes the design of a distributed multiagent expert control scheme for cyberattack‐resilient control of CAVs. The study implemented an event‐triggered consensus‐based attack detection scheme capable of distinguishing between replay (RA), denial‐of‐service (DoS), and false data injection (FDI) attacks. The attacks in this study occur randomly, are bounded and time‐varying, and can overlap with each other. It was demonstrated that by considering an estimator error boundεfor the attacked signal reconstruction, the SMC controller in feedback with a vehicle remains stable in the sense of Lyapunov. Conditions were then provided that guarantee global asymptotic stability for a minimum dwell‐time constraint , and the platoon was shown to be string stable for the minimum distance between vehicles, denoted as . Finally, the performance of the control strategy was evaluated using multiple performance indices, considering platoons of varying sizes.

A hybrid RSA-IPA optimizer for designing an artificial neural network to study the Jeffery-Hamel blood flow with copper nanoparticles: Application to stenotic tapering artery

In this study, the effects of copper nanoparticles are taken into account to create a new hybrid metaheuristic for solving the nonlinear MHD Jeffery-Hamel blood flow problem. Since copper nanoparticles are effective in lowering the hemodynamics of stenosis, the subject has scientific and biological implications. The coupled partial differential equations are converted to ordinary differential equations using suitable transformations. Considering an artificial neural network as a solution, the error function has been formed for the differential equations. A hybrid of the reptile search algorithm (RSA) and the interior point algorithm (IPA) is used to minimise the error function (≤10−05). The initial weights are first updated using the RSA algorithm (maximum 20 iterations), which is used as a tool for global search; later, IPA is used for quick local convergence. To validate the proposed technique’s accuracy, comparison studies are conducted using the fourth-order Runge-Kutta method and the hybrid PSO-IPA algorithm. Statistical analysis is provided using multiple performance indices to demonstrate the suggested approach’s precision, efficiency, and reliability. The research findings indicate that the outcomes achieved by the developed RSA-IPA technique exhibit superior performance compared to previously established methodologies, with an accuracy level of 10−04. The findings indicate that a rise in the volume fraction of cu-nanoparticles results in a decrease in the rate of blood flow. The research advances studies of blood flow in the medical sciences.

Pendulum tuned mass damper (PTMD) with geometric nonlinear dampers for seismic response control

In engineering applications, the geometric nonlinear (GN) arrangement of pendulum-tuned mass dampers (PTMDs) may induce the damping force to deviate from its intended state. Therefore, this study extends the scope of the structural control strategy by introducing a pendulum-tuned mass damper considering geometric nonlinear dampers (GND). Specifically, the damper developed in this study, termed PTMD with GND, is an inconspicuous pendulum-type tuned mass damper with different GN settings in the initial state. A nonlinear mathematical model of the PTMD with GND is developed, identifying its significant nonlinear characteristics, particularly the "unavailable" locations. The stochastic linearization method (SLM) is adopted to simplify the nonlinear model using the Kanai-Tajimi modified spectrum. The SLM agrees well with the numerical results obtained using the Monte Carlo simulation method. Furthermore, the control strategy of PTMD with GND is proposed with the consideration of filtered random excitation. Several numerical cases have been conducted to investigate the availability of PTMD with GND and the proposed optimal design strategy under 178 real seismic excitations. The results show that the PTMD with GND is nearly as effective as the ideal transitional TMD in mitigating the seismic response of the primary structure and more effective than the inverted rough linear adjustment. It can be concluded that the proposed optimal design strategy for the PTMDs with GND efficiently suppressed the structural vibration through multiple performance indices and used the nonlinearity of the damping mechanism of the GND to provide a sufficient damping energy dissipation effect adaptively.

Distributed Fuzzy Optimal Consensus Control of State-Constrained Nonlinear Strict-Feedback Systems.

This article investigates the distributed fuzzy optimal consensus control problem for state-constrained nonlinear strict-feedback systems under an identifier-actor-critic architecture. First, a fuzzy identifier is designed to approximate each agent's unknown nonlinear dynamics. Then, by defining multiple barrier-type local optimal performance indexes for each agent, the optimal virtual and actual control laws are obtained, where two fuzzy-logic systems working as the actor network and critic network are used to execute control behavior and evaluate control performance, respectively. It is proved that the proposed control protocol can drive all agents to reach consensus without violating state constraints, and make the local performance indexes reach the Nash equilibrium simultaneously. Simulation studies are given to verify the effectiveness of the developed fuzzy optimal consensus control approach.

Satisfaction-Based Multiobjective Optimization of the Design of a 65 GHz Millimeter-Wave Connector

It is difficult to simultaneously ensure the radio frequency (RF) performance and mechanical strength of 65 GHz millimeter-wave connectors. To address this, satisfaction-based multiobjective method for connector design optimization is proposed. An optimization model is developed for the structural dimensions of 65 GHz millimeter-wave connectors to minimize the voltage standing wave ratio, insertion loss, and deflection and maximum stress of the center conductor. The proposed optimization model concurrently satisfies the design requirements in terms of characteristic impedance, transmission frequency, and dielectric withstanding voltage. The model is optimized by innovatively using the multiobjective chaotic optimization algorithm in microwave finite-element simulations. The resulting optimal structure satisfies all performance requirements, thereby proving the rationality and feasibility of the proposed method. Moreover, this study establishes a novel design for other microwave devices to solve the problem of multiple performance index restrictions.

Optimisation of Building Green Performances Using Vertical Greening Systems: A Case Study in Changzhou, China

The benefits of greening systems on buildings have been frequently examined using experimental methods. However, few studies have adopted dynamic monitoring of real operational buildings to quantify the effects of greening systems on multiple building green performance indexes, such as thermal comfort, indoor air quality, and energy consumption. In this study, a type of multi-in-one indoor environmental quality monitoring device was adopted for vertical greening systems in a green-certified building in Changzhou, China, with real-time data collection through an Internet of Things platform. Measurements of the indoor thermal environment and air quality were recorded from four testing points during a 90 day period from spring to summer in 2021. For comparison, the testing points were divided into group A (office zone) and group B (exhibition zone). Our results demonstrated that, in the presence of a vertical greening system, the seasonal average indoor temperatures decreased by up to 0.7 °C. The green facade outperformed the ordinary exterior wall, optimising both indoor thermal comfort and thermal inertia. Furthermore, judicious indoor greening designs significantly reduced the indoor air-pollutant concentrations, such as particulate matter, carbon dioxide, and organic pollutants. The median values for particulate matter 10 and formaldehyde concentration decreased by 20.7% and 33.3%, respectively, thus improving the indoor air quality. Lastly, the annual electricity consumption of the building with vertical greening systems was about 25% lower than that of similar buildings, underlining the potential contribution of vertical greening systems to building energy conservation. Such findings collectively demonstrate that greening systems offer quantifiable benefits for building parameters such as thermal properties, indoor air quality, and energy conservation.

Autonomous Underwater Vehicle Path Tracking Based on the Optimal Fuzzy Controller with Multiple Performance Indexes

Autonomous underwater vehicles (AUVs) are increasingly being used in missions involving submarine cable detection, underwater archaeology, pipeline inspection, military reconnaissance, and so on. It is very important to realize AUV path tracking to accomplish these missions. In this paper, a fuzzy controller based on the established kinematic and dynamic models of AUV systems is presented to solve the AUV path-tracking problem. In order to design the fuzzy controller to exhibit good performance, we select the path length, smoothness, and cross-track position error as the multiple optimization performance indexes for the fuzzy controller. We propose the particle swarm optimization (PSO) algorithm to determine the parameters of the membership functions. Different scenarios are presented to test the performance of the proposed algorithm, including the straight line, sine curve, half-moon shape, Archimedean spiral, and practical paths. The results are given to illustrate the effectiveness and feasibility of the fuzzy controller with the optimization of multiple performance indexes.

Comprehensive Performance Evaluation of Landing Gear Retraction Mechanism in a Certain Model of Aircraft Based on RPCA Method

The performance of landing gear retraction mechanism in aircraft directly affects its safe operation. Therefore, it is important to analyze and evaluate its comprehensive performance during the design process. Multiple single kinematic and dynamic performance indexes of landing gear retraction mechanism could be solved by CAD/CAE software. The weighting factors of every single performance index are used to distinguish the different effects of comprehensive evaluation, and also achieved by the expert investigation method. Combining the a priori information of the mechanism, the comprehensive performance of landing gear retraction mechanism could be analyzed by Relative Principal Component Analysis (RPCA) method, and the scale of the landing gear retraction mechanism with the best comprehensive performance could be effectively selected. Further, RPCA could also provide a scientific reference basis for the optimization design of landing gear retraction mechanism.

Mechanical characterization of particle reinforced jute fiber composite and development of hybrid Grey-ANFIS predictive model

ABSTRACT Natural fiber composites are a potential material in a range of engineering applications because of their excellent properties, which include reduced weight, better strength, and economic affordability. Aside from these features, these materials are biodegradable and renewable. The intent of this study is to look through into mechanical properties of aluminum oxide (Al2O3), boron carbide (B4C), and silicon carbide (SiC) particle-filled jute fiber polymer composite. The response surface methodology (RSM) with three levels/three factors is used to achieve the different combinations of process variables required to fabricate the desired polymer composites. In this regard, the effect of process variables on tensile characteristics, increase in weight %, and flexural characteristics is examined in detail. Further, the best combination of process parameters is chosen to produce composites with the desired mechanical qualities. The significance of such variables on each output variable is assessed using analysis of variance. A hybrid grey-based adaptive neuro-fuzzy inference system model is constructed for establishing multiple performance indexes. From the validation outcomes obtained, it is proved that the evolved model is proficient for precise prediction.

A comparative study of manipulator teleoperation methods for debris retrieval phase in nuclear power plant decommissioning

ABSTRACT This paper presents a comparative study of teleoperation methods of robotic manipulators aiming at application to the decommissioning operation of the primary containment vessel (PCV) of Fukushima Daiichi Nuclear Power Plant Unit 3. A detailed set of mockup is constructed from data collected by internal investigation of the damaged PCV, and a series of teleoperation experiments using a dual-arm robotic manipulator simulating the actual obstacle removal operation is conducted. Three teleoperation methods, manual, teaching-based, and planning-based, are compared in terms of multiple performance indices. The manual teleoperation resulted in low efficiency and safety, and high cognitive load. The method based on pre-recorded teaching data showed the highest safety score, but required large amount of time for preparation. The planning-based method achieved efficient teleoperation with small amount of preparation time, while it was found that it needs further improvement in terms of safety.

Fractional‐order filter‐based enhanced full state feedback control design with multi‐objective formulation using grey wolf optimization algorithm

AbstractA perturbed fractional‐order filter (FOF)‐based LQR control design with multiple performance indices is proposed in this paper. Three variants of the FOF‐based linear quadratic regulator (LQR) system have been proposed along with choices on the commensurate order of state feedback law. The multi‐objective problem formulation has been done with three performance objectives to address the different issues of design. The first performance index constitutes a weighted sum of ITAE (Integral Time of Absolute Error) and the difference between the eigenvalues of the plant and the LQR controlled plant, chosen to minimize the large oscillations as well as relative stability with respect to eigenvalues. A new lower bound of the singular values have been addressed to ensure the robust stability of the system through the formulation of a second performance objective. It is the minimization of singular values of the return ratio matrix at the plant's input. The third and the last performance index aims minimization of maximum singular values of the perturbation transfer function at the plant's output, so as to guarantee no closed‐loop right half plane zeros. The multi‐objective optimization problem is solved and the optimal solutions are obtained via Grey Wolf Optimization Algorithm. The simulation results validate the performance and effectiveness of the proposed control design.