Simulation Comparison Experiments Research Articles

Modified crayfish optimization algorithm with adaptive spiral elite greedy opposition-based learning and search-hide strategy for global optimization

Abstract Crayfish optimization algorithm (COA) is a novel bionic metaheuristic algorithm with high convergence speed and solution accuracy. However, in some complex optimization problems and real application scenarios, the performance of COA is not satisfactory. In order to overcome the challenges encountered by COA, such as being stuck in the local optimal and insufficient search range, this paper proposes four improvement strategies: search-hide, adaptive spiral elite greedy opposition-based learning, competition-elimination, and chaos mutation. To evaluate the convergence accuracy, speed, and robustness of the modified crayfish optimization algorithm (MCOA), some simulation comparison experiments of 10 algorithms are conducted. Experimental results show that the MCOA achieved the minor Friedman test value in 23 test functions, CEC2014 and CEC2020, and achieved average superiority rates of 80.97%, 72.59%, and 71.11% in the WT, respectively. In addition, MCOA shows high applicability and progressiveness in five engineering problems in actual industrial field. Moreover, MCOA achieved 80% and 100% superiority rate against COA on CEC2020 and the fixed-dimension function of 23 benchmark test functions. Finally, MCOA owns better convergence and population diversity.

Intelligent Diagnosis Method of Transformer Based on Oil Chromatographic Data

Abstract As one of the most important equipment in the operation of power system, ensuring the safe and stable operation of power transformer is a prerequisite for ensuring the normal supply of power grid. The failure of the power transformer will lead to the interruption of the power supply of the power grid and cause huge losses to the national economy. With the rapid development of China’s power grid scale and the continuous improvement of the intelligent construction of modern power system, the number of power equipment such as transformers is increasing. Therefore, it is necessary to make timely and accurate judgment on the status of key power transformation and distribution equipment in the power system, and grasp the operation of electrical equipment in real time, so as to ensure the reliability of power supply in the power system. This paper takes 220kV oil-immersed transformer as the research object. This paper considers the problems and characteristics of the current transformer fault diagnosis methods. By making full use of dissolved gas analysis (DGA) information in oil, a method for diagnosing faults in electrical transformers based on particle swarm optimization and generalized regression neural networks has been proposed. The simulation comparison experiment is carried out to select the most suitable transformer intelligent diagnosis method based on oil chromatographic data.

Weighted three-way conflict analysis in multi-attribute decision-making perspective

In real life, multiple issues may have different weights in leading to conflicts. Some of the existing conflict analysis literature related to weight does not involve an explicit weight method, and some use common methods such as entropy weight method. This paper aims to design a reasonable weighting method for the inherent characteristics of conflict analysis. The main motivation is that the issues getting vast majority of support (or opposition) are assumed to be more likely to cause intense conflicts. Firstly, a novel weighting method using N-bounded symmetric concave reciprocal function is brought in. Some properties of the method such as duality, objectivity and symmetry are discussed. Then, by embedding the weights into the existing distance function a weighted three-way conflict analysis model is proposed. Next the thresholds of conflict, alliance and neutrality relations are given by decision-theoretic rough set theory. In addition, as a main characteristic of the model, the conflict distance between any two agents is determined by the overall conflict table rather than their own. Finally, the model of this paper is compared with the existing conflict analysis weight model. At the same time, a comparative simulation experiment was carried out on the weight change and the number of maximal coalitions. The new weighted conflict model proposed in this paper can effectively compensate for the limitations of existing weighted models, especially in three-valued information systems and multi-valued information systems. It can also theoretically expand the field of three-way conflict analysis.

Effects of different fire slash artificial promotion regeneration and natural material regeneration on ecological function

IntroductionIn the aftermath of a fire, prompt reforestation of the affected areas is crucial to mitigate economic losses and ecological impacts.MethodsThis paper introduces an ecological function assessment model leveraging the Back Propagation Neural Network (BPNN). The model's efficacy is validated through simulation comparison experiments. Subsequently, an analysis of the ecosystem's material circulation and energy flow capabilities is undertaken.ResultsSimulation outcomes reveal that our proposed model attains convergence by the 10th training iteration, with a loss function value of just 0.28, highlighting minimal training loss. This underscores the model's rapid convergence and impressive training performance. Our method proves superior to the comparison method in both initial and later operational phases. Notably, it offers a significantly faster response speed and boasts an accuracy rate exceeding 95%.DiscussionConsequently, employing this model to analyze ecological function changes is deemed feasible. The analysis of ecosystem material circulation and energy flow capabilities reveals that while initial assessments show minimal change, scores exhibit a clear acceleration as the cycle progresses.

Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle

In response to the challenges posed by the low energy utilization of single-power pure electric vehicles and the limited lifespan of power batteries, this study focuses on the development of a compound power system. This study constructs a composite power system, analyzes the coupling characteristics of multiple systems, and investigates the energy management and optimal control mechanisms. Firstly, a power transmission scheme is designed for a hybrid electric vehicle. Then, a multi-state model is established to assess the electric vehicle’s performance under complex working conditions and explore how these conditions impact system coupling. Next, load power is redistributed using the Haar wavelet theory. The super capacitor is employed to stabilize chaotic and transient components in the required power, with low-frequency components serving as input variables for the controller. Further, power distribution is determined through the application of fuzzy logic theory. Input parameters include the system’s power requirements, power battery status, and super capacitor state of charge. The result is the output of a composite power supply distribution factor. To fully exploit the composite power supply’s potential and optimize the overall system performance, a global optimization control strategy using the dynamic programming algorithm is explored. The optimization objective is to minimize power loss within the composite power system, and the optimal control is calculated through interpolation using the interp function. Finally, a comparative simulation experiment is conducted under UDDS cycle conditions. The results show that the composite power system improved the battery discharge efficiency and reduced the number of discharge cycles and discharge current of the power battery. Under the cyclic working condition of 1369 s, the state of charge of the power battery in the hybrid power system decreases from 0.9 to 0.69, representing a 12.5% increase compared to the single power system. The peak current of the power battery in the hybrid power system decreases by approximately 20 A compared with that in the single power system. Based on dynamic programming optimization, the state of charge of the power battery decreases from 0.9 to 0.724. Compared with that of the single power system, the power consumption of the proposed system increases by 25%, that of the hybrid power fuzzy control system increases by 14.2%, and that of the vehicle decreases by 14.7% after dynamic programming optimization. The multimode energy shunt relationship is solved through efficient and reasonable energy management and optimization strategies. The performance and advantages of the composite energy storage system are fully utilized. This approach provides a new idea for the energy storage scheme of new energy vehicles.

Adaptive fuzzy sliding mode control of the manipulator based on an improved super-twisting algorithm

The high-precision trajectory tracking and chatter-free performance of the manipulators are of great significance to the improvement of the automation level. Therefore, in view of the problems of chattering and tracking performance degradation of multi-joint manipulators caused by uncertain factors such as external disturbances and modeling errors, an adaptive fuzzy sliding mode control of the manipulator based on an improved super-twisting algorithm is proposed. First, fuzzy control is chosen to approximate the uncertainty of the manipulator model, which reduces the influence of unknown disturbances on the system, and then the adaptive law is utilized to automatically modify and improve the fuzzy rules to enhance the approximation capability; Second, an improved super-twisting algorithm is used on this basis to put the discontinuous term into the integral term to avoid chattering and eliminate the approximation error; Finally, a comparative simulation experiment is carried out with the 3-DOF manipulator as the research object. The experimental results show that the method in this paper improves the convergence speed by up to 69.74% and the tracking error accuracy by up to 85.11% over the fuzzy control based on adaptive reaching law with no chattering and little difference in control inputs, thus verifying the feasibility and superiority of the method.

Velocity tracking control of nodes for the nonlinear complex dynamical networks associated with outgoing links subsystem

In general, a complex dynamical network (CDN) can be viewed as a combination of a nodes subsystem(NS) and an outgoing links subsystem (OLS), both of which are coupled with each other. In this paper, a velocity tracking control protocol is used to achieve the velocity tracking control of nodes for the nonlinear CDN with the help of OLS. Firstly, the models of NS and OLS for the CDN are established, and three assumptions for CDN and the definition of the outgoing links vector are given. Further, the control objectives of this article are given. The appropriate control protocol and the scheduled auxiliary targets (SAT) are synthesized to realize the velocity tracking control of nodes for the nonlinear CDN. Finally, a numerical simulation comparison experiment is bench tested to validate the effectiveness of the proposed control protocol and the SAT.

State estimation and sliding-mode fault-tolerant control of a class of nonlinear systems with actuator faults under the event-triggered mechanism

The current work is an analytical study of the problem of state estimation and fault-tolerant control (FTC) based on dynamic event triggered mechanism (ETM) in the event of actuator fault for a class of nonlinear systems in a network environment. First, a state estimator module is designed in conjunction with the designed dynamic ETM, and an integral sliding surface (SS) is designed in the estimation space to obtain a variable time delay system after augmenting the system by combining the state estimator module and the SS. Second, the sufficient conditions that can make the augmented system stable under the ETM with H∞ performance index are obtained by the Lyapunov–Krasovskii function. The sufficient conditions are decoupled after the design of the sliding mode control (SMC) law that can stabilize the system, and the calculation of the state estimation gain matrix and the SMC gain matrix are obtained. Finally, the effectiveness of the method proposed in this study is verified with two simulation comparison experiments.

Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search

For underwater target exploration, multiple Autonomous Underwater Vehicles (AUVs) have shown significant advantages over single AUVs. Aiming at Multi-AUV task allocation, which is an important issue for collaborative work in underwater environments, this paper proposes a Multi-AUV task allocation method based on the Differential Evolutionary Gray Wolf Optimization (DE-GWO) algorithm. Firstly, the working process of the Multi-AUV system was analyzed, and the allocation model and objective function were established. Then, we combined the advantages of the strong global search capability of the Differential Evolutionary (DE) algorithm and the excellent convergence performance of Gray Wolf Optimization (GWO) to solve the task assignment of the Multi-AUV system. Finally, a reassignment mechanism was used to solve the problem of AUV failures during the task’s execution. In the simulation comparison experiments, the DE-GWO, GWO, DE, and Particle Swarm Optimization (PSO) algorithms were carried out for different AUV execution capabilities, respectively.

Energy consumption calculation and energy-saving measures of substation based on Multi-objective artificial bee colony algorithm

Abstract Electric energy is the most commonly used energy in people’s daily life. In the process of use, how to improve the utilization rate of resources, so as to achieve the purpose of energy saving and environmental protection of power resources is a very significant work. The power grid is crucial in the transmission of electricity, and the energy consumption of substations is a major factor affecting the operation cost. Therefore, it is a key condition to ensure the stable operation of the power grid to scientifically calculate its energy consumption and formulate appropriate energy-saving measures. After consulting a large number of relevant documents and data, it has been found that many researchers have provided new ideas for the calculation of energy consumption and the study of energy-saving measures in substations. This article was adding its own understanding and taking it as the direction and basis. In the introduction, the research significance of substation energy consumption calculation was introduced, and then the academic research and analysis were carried out on the two key sentences of substation energy consumption calculation and analysis and the research of multi-objective artificial bee colony (ABC) algorithm in substation. In the method part, a constructive algorithm theory was proposed, and the application of the algorithm in substation energy consumption was proposed to provide a theoretical basis for the energy consumption calculation and energy conservation measures research of substations based on multi-objective ABC algorithm. At the end of the article, a simulation comparison experiment was carried out and the experiment was summarized and discussed. Firstly, the performance of the two algorithms was analyzed. Then, through the first selection of power plant T and Y, it was concluded that the resistivity of the cable in the distribution room in power plant Y was much higher than the average value based on the experimental results. Therefore, it was taken as the sample subject of the following experiments and analyzed again. For the second time, the analysis of district power plant Y showed that through simple calculation, the average energy efficiency difference of the distribution room before and after use was 3.1. Finally, it was analyzed that the ABC algorithm could not only improve the energy efficiency of the distribution room in the power plant, but also improve the effective efficiency of its power station. It shows that this kind of algorithm is effective and worthy of further study.

Sliding mode control based on particle swarm optimization neural network and adaptive reaching law

This paper presents a sliding mode control based on particle swarm optimization neural network and adaptive reaching law, and the proposed control method solves the problem of chattering and tracking performance degradation of a multi-joint manipulator caused by uncertainties such as external disturbances and modeling error. First, to address the problem that the precise dynamic system of the manipulator is difficult to establish, the radial basis function neural network (RBFNN) is used to approximate the uncertainty of the manipulator model, and the parameters of the neural network are optimized through the adaptive natural selection particle swarm optimization algorithm (ASelPSO) to improve the approximation ability and reduce the approximation error. Second, to eliminate chattering, adaptive reaching law is selected to improve the dynamic quality of approaching motion. Finally, a comparative simulation experiment is carried out with a 3-DOF manipulator as the research object. The results show that the control method has obvious improvements in eliminating chattering, improving tracking accuracy, and increasing convergence speed, which verifies the feasibility and superiority of the control scheme.

High-Order Modeling, Zeroing Dynamics Control, and Perturbations Rejection for Non-Linear Double-Holding Water Tank

The double-holding water tank system is a common non-linear control system that plays a crucial role in process control in the chemical industry. It consists of two cylindrical glass containers: the preset tank and the main tank. The main challenge in controlling this system is adjusting the main control valve to ensure that the actual liquid level of the main tank tracks the desired liquid level. This paper explores the zeroing dynamics (ZD) method and its application in tracking control. A non-linear model is developed for the double-holding water tank system, and the ZD method is used to design an effective controller (called the ZD controller) for tracking control. Additionally, the robustness of the double-holding water tank system in the presence of time-varying perturbations is investigated. In order to substantiate the effectiveness and robustness of the ZD controller, simulation experiments on four different tracking trajectories corresponding to four different practical situations, as well as an extra simulation experiment considering time-varying perturbations, are conducted. Furthermore, a comparative simulation experiment based on the backstepping method is conducted. The presented results successfully illustrate the feasibility and effectiveness of the ZD method for the tracking control of double-holding water tank systems.

Integral back-stepping active disturbance rejection control for piezoelectric stick-slip drive nanopositioning stage

Piezoelectric stick-slip driven nanopositioning stage (PSSNS) with nanometer resolution has been widely used in the field of micro-operation. However, it is difficult to achieve nanopositioning over large travel, and its positioning accuracy is affected by the hysteresis characteristics of the piezoelectric elements, external uncertain disturbances, and other nonlinear factors. To overcome the above-mentioned problems, a composite control strategy combining stepping mode and scanning mode is proposed in this paper, and an integral back-stepping linear active disturbance rejection control (IB-LADRC) strategy is proposed in the scanning mode control phase. First, the transfer function model of the system in the micromotion part was established, and then the unmodeled part of the system and the external disturbance were treated as the total disturbance and extended to a new system state variable. Second, a linear extended state observer was used as the core of the active disturbance rejection technique to estimate displacement, velocity, and total disturbance in real time. In addition, by introducing virtual control variables, a new control law was designed to replace the original linear control law and improve the positioning accuracy and robustness of the system. Furthermore, the effectiveness of the IB-LADRC algorithm was verified by simulation comparison experiments and experimentally validated on a PSSNS. Finally, experimental results show that the IB-LADRC is a practical solution for a controller capable of handling disturbances during the positioning of a PSSNS with a positioning accuracy of less than 20 nm, which essentially remains constant under load.

Attitude control of quadrotor UAV based on fuzzy PID control under small disturbance

This paper proposes a attitude control method based on fuzzy control. Firstly, the basic model of quadrotor UAV is constructed, then the fuzzy PID controller based on fuzzy control strategy is introduced, and finally the simulation comparison experiments under PID control and fuzzy PID control are conducted. The experimental results show that the parameters of each link in the PID control method are easy to set, but the overshoot amount is large and the adjustment time is long; The simulation model of fuzzy PID control is relatively complex, and it is difficult to reach the predetermined expectation and difficult to accurately control. However, the output of overshoot, short regulation time and the waveform peak are small. In terms of anti-interference, fuzzy PID is more adaptive than PID control.

High precision structured H∞ control of a piezoelectric nanopositioning platform.

The inherent weakly damped resonant modes of the piezoelectric nanopositioning platform and the presence of model uncertainty seriously affect the performance of the system. A structured H∞ design is used in this paper to solve the accuracy and robustness problems respectively using a two-loop control structure. The multiple performance requirements of the system are constituted into an H∞ optimization matrix containing multi-dimensional performance diagonal decoupling outputs, and an inner damping controller d is set according to the damping of the resonant modes; the second-order robust feedback controller is preset in the inner loop to improve the robustness of the system; the tracking controller is connected in series in the outer loop to achieve high accuracy scanning; finally, the structured H∞ controller is designed to meet the multiple performance requirements. To verify the effectiveness of the proposed structured H∞ control, simulation comparison experiments are done with the integral resonant control (IRC) and H∞ controller. The results demonstrate that the designed structured H∞ controller achieves higher tracking accuracy compared to the IRC and H∞ controllers under grating input signals of 5, 10, and 20 Hz. Moreover, it has good robustness under 600g and 1000g loads and high frequency disturbances close to the resonant frequency of the system, meeting multiple performance requirements. Compared with the traditional H∞ control, yet with lower complexity and transparency, which is more suitable for engineering practice applications.

URLM: Utilising reinforcement learning to schedule subflows in MPTCP

SummaryParallel transmission in multiple access networks using MultiPath TCP (MPTCP) greatly enhances the throughput. However, critical packet disorder is commonly observed due to traffic fluctuation and path diversity. Although several predictive scheduling algorithms have been proposed to solve this problem, they cannot accommodate prediction accuracy and real‐time adaptation simultaneously in a dynamic network environment. The time overhead in modifying scheduling parameters to adapt to network changes leads to performance degradation in throughput and packet disorder. In this study, we propose a scheduling algorithm called Utilising Reinforcement Learning to Schedule Subflows in MPTCP (URLM). We apply reinforcement learning to select an optimal scheduling parameter in real time, which brings significant time benefits for modifying the parameters. The simulation comparison experiments show that URLM reduces the average number of out‐of‐order packets and the time overhead in adapting to network changes while improving global throughput.

Dynamic grasping of manipulator based on realtime smooth trajectory generation

For safe and effective grasping in a dynamic environment, planning algorithms need real-time to deal with changing the target’s movement and obstacles. This paper proposes a new sequential Sense-Plan-Act (SeqSPA) dynamic grasping framework to generate a robot’s real-time and smooth grasping trajectory. Specifically, we cluster all stable grasps of the target, transform the clustering centers into pregrasps, and predict the future motion of the moving objects by using the observed values. The trajectory optimization algorithm constructing the approximative joint space gradient field can generate a smooth trajectory for a 6-DOF industrial robot arm within 2 ms. Our method generates trajectories for multiple pregrasps and selects the time-optimal trajectory for execution. Simulation comparison and actual experiments verify that our framework can immediately respond to environmental changes and efficiently find a grasping trajectory of the near-optimal time. The trajectory optimization algorithm in the framework can also be used alone to generate a real-time grasping trajectory when the prediction module cannot accurately predict the target motion.

A local opposition-learning golden-sine grey wolf optimization algorithm for feature selection in data classification

The classification problem is an important research topic in machine learning and data mining. Feature selection can remove irrelevant and redundant features and improve classification accuracy. The traditional Grey Wolf algorithm (GWO) has the defects of low convergence efficiency and easy to falls into local extremes in solving the feature selection process, leading to ineffective removal of irrelevant and redundant features. This paper proposes a binary version of the local opposing learning golden sine grey wolf optimization algorithm (OGGWO). First, the OGGWO algorithm uses local opposing learning mapping to initialize the positions of individual grey wolves to enrich population diversity and improve convergence speed. Secondly, mix the golden sine algorithm and the grey wolf optimization algorithm to control the direction and distance of α wolves by using the golden mean coefficient to improve the autonomous search ability of individual grey wolves and avoid the algorithm from falling into the local optimum. Finally, the updated grey wolf position is binary converted by pre-setting the threshold value to reduce the feature subset’s size and improve the classification effect. To verify the effectiveness of the OGGWO algorithm, 18 international standard datasets were selected, and compare the OGGWO algorithm with the improved Grey Wolf algorithm and the popular metaheuristic algorithm for the fitness value comparison test and the simulation comparison experiment for classification accuracy. The results show that: (1) the OGGWO algorithm has good convergence and high search accuracy on all 18 test data; (2) the improved strategy of the OGGWO algorithm can effectively improve the classification accuracy and reduce the number of selected features compared with the traditional GWO algorithm in the classification accuracy simulation. The experimental results show that the superiority and robustness of the OGGWO algorithm in feature selection are verified.

Research on Two-Round Self-Balancing Robot SLAM Based on the Gmapping Algorithm

Aiming at the inconvenience of inspection and monitoring of coal mine pump room equipment in a narrow and complex environment, this paper proposes and designs a two-wheel self-balancing inspection robot based on laser SLAM. Using SolidWorks, the three-dimensional mechanical structure of the robot is designed, and the overall structure of the robot is analyzed by finite element statics. The kinematics model of the two-wheel self-balancing robot was established, and the multi-closed-loop PID controller was used to design the two-wheel self-balancing control algorithm of the robot. The 2D LiDAR-based Gmapping algorithm was used to locate the robot and construct the map. Through the self-balancing test and anti-jamming test, it is verified that the self-balancing algorithm designed in this paper has a certain anti-jamming ability and good robustness. By using Gazebo to build a simulation comparison experiment, it is verified that the selection of the particle number is of great significance for improving the map accuracy. The actual test results show that the constructed map has high accuracy.

A single-step measurement method for transmission loss of the muffler based on improved transfer matrix

Transmission loss is a very important acoustic characteristic of the muffler. In order to improve the efficiency of the double-load method for measuring the transmission loss of the muffler, this paper proposes a single-step measurement method for transmission loss of muffler. In addition, the simulation model of the single-step measurement method is established based on the finite element acoustic analysis. For the model, the calculation method of the transmission loss of the muffler is deduced from the theoretical knowledge of the transfer matrix. In the simulation, the sound pressure data of six positions in the model are obtained, and the transfer matrix is used to calculate the transmission loss of the muffler. Then, two simulation comparison experiments were done. In order to verify the validity of the single-step measurement method, the first comparison experiment was to compare the transmission loss of the muffler simulated by the single-step measurement method with their theoretical transmission loss in the finite element acoustic analysis. The results of the simulation validation test cases showed that the transmission loss of the muffler obtained by the single-step measurement method was consistent with the theoretical value. The second comparison experiment was to compare the transmission loss obtained by the single-step measurement method with the transmission loss obtained by the double-load method. The results of the simulation validation test cases showed that at certain frequencies, the transmission loss obtained by the single-step measurement method was more accurate than the transmission loss obtained by the double-load method. In conclusion, the single-step measurement method proposed in this paper can accurately obtain the transmission loss of the muffler with only one measurement, which improves the measurement efficiency.