基于Super-Twisting滑模S面的无人机路径跟踪控制

针对小型固定翼无人机在执行任务时跟踪精度低以及容易受外界风影响的问题,设计基于Super-Twisting滑模S面(STSM S-Plane)的路径跟踪控制器,同时采用内外双环控制模式。外环即速度环采用Super-Twisting滑模控制,内环即姿态环采用S面控制。考虑到S面控制求导易导致积分爆炸的问题引入了二阶微分器,并对外界风组成进行建模研究。最后通过空间特殊曲线来验证所设计算法的控制性能。仿真结果表明,所设计的算法可以实现固定翼无人机对期望路径的精确跟踪,并具有良好的鲁棒性和抗干扰性能。

基于自适应Super-twisting算法的永磁同步电机驱动控制

在永磁同步电机调速系统中,良好的速度控制器要同时兼顾跟踪性能及抗干扰性能,非线性控制具有快速响应及强鲁棒性的优点,适用于速度控制器的设计,但传统滑模控制存在着严重的抖振问题。自适应Super-twisting算法属于二阶滑模,能将相轨迹的状态量以级数的形式收敛到原点,又能在线调整控制器的增益,因此能有效削弱抖振现象。采用自适应Super-twisting算法来设计速度控制器,并通过李雅普诺夫函数给出其稳定性证明。试验结果表明,所设计的控制器具有良好的跟踪性能及抗干扰性能,同时能减小电流的纹波,达到削弱抖振的目的。

基于改进Super-Twisting算法和电流预测的PMSM调速控制

针对永磁同步电机传统PI控制器存在响应速度慢、控制精度低、鲁棒性差等问题,该文提出一种改进调速控制方案。首先,将全局快速积分型终端滑模理论与广义Super-Twisting算法相结合,基于Anti-Windup原理设计改进广义Super-Twisting积分终端滑模速度控制器,并采用新型分段指数函数优化控制律;其次,设计改进扩展扰动观测器对系统未知扰动进行前馈补偿;最后,在电流环引入无差拍电流预测控制器,进一步改善系统的动态响应。仿真结果表明,所提出的改进控制方案能够有效提高调速系统的动态控制性能、控制精度和鲁棒性。

大长径比远程制导火箭弹自适应Super-twisting控制方法

针对具有轻质薄壳结构的大长径比远程制导火箭弹刚体-弹性体耦合动力学控制难题,提出了一种自适应Super-twisting控制方法。首先,将大长径比远程制导火箭弹的弹性模态与外部干扰项视为归一化扰动,建立了考虑参数、模型不确定性的刚体-弹性体耦合动力学模型。针对外部扰动难以实时精准获取的问题,设计了有限时间收敛干扰观测器,能够在大范围气动参数摄动情形下对远程制导火箭弹外部扰动进行实时精准估计。为提高系统鲁棒性,设计了基于Super-twisting算法的自适应有限时间控制方法,使系统能根据状态误差自动调节控制参数,在削弱高频抖振的同时有效提高抗干扰能力。仿真结果表明:所提控制方法在弹体±15%气动参数摄动的条件下,由平飞状态转变为跟踪幅值为10的正弦弹道倾角指令时,实现3.2 s内弹道倾角跟踪误差收敛至0.002°,可实现远程制导火箭弹姿态平稳跟踪控制弹道倾角指令。

Cooperative guidance law based on super-twisting observer for target maneuvering

To solve the problem that multiple missiles should simultaneously attack unmeasurable maneuvering targets,a guidance law with temporal consistency constraint based on the super-twisting observer is proposed.Firstly,the relative motion equations between multiple missiles and targets are established,and the topological model among multiple agents is considered.Secondly,based on the temporal consistency constraint,a cooperative guidance law for simultaneous arrival with finite-time convergence is derived.Finally,the unknown target maneuver-ing is regarded as bounded interference.Based on the second-order sliding mode theory,a super-twisting sliding mode observer is devised to observe and track the bounded interfer-ence,and the stability of the observer is proved.Compared with the existing research,this approach only needs to obtain the sliding mode variable which simplifies the design process.The simulation results show that the designed cooperative guidance law for maneuvering targets achieves the expected effect.It ensures successful cooperative attacks,even when confronted with strong maneuvering targets.

高速列车分布式super-twisting滑模控制研究

针对不确定因素及外界干扰下高速列车分布式协同控制问题,提出基于super-twisting滑模一致性算法的高速列车速度跟踪控制策略。首先,考虑列车受到的外部干扰、基本阻力及车厢间耦合作用力,构建高速列车多智能体模型;其次,利用相邻车厢的位移和速度信息设计一致性滑模函数,引入super-twisting算法削弱控制输入抖振;最后,设计分布式二阶滑模控制律,并采用Lyapunov理论验证算法稳定性。以高速列车实际参数进行仿真研究,并加入外界干扰,利用本文方法、普通一致性、PID一致性及滑模一致性方法进行仿真。结果表明,相较于其他3种方法,所提算法能使车厢单元快速、精准跟踪目标速度曲线,速度误差在(-0.8~1.1)×10^(-3)m/s内,同时使相邻车厢距离保持在安全范围内,且控制输入较平滑,对外部干扰有较好的鲁棒性。

Revised barrier function-based adaptive finite-and fixed-time convergence super-twisting control

This paper presents an adaptive gain,finite-and fixedtime convergence super-twisting-like algorithm based on a revised barrier function,which is robust to perturbations with unknown bounds.It is shown that this algorithm can ensure a finite-and fixed-time convergence of the sliding variable to the equilibrium,no matter what the initial conditions of the system states are,and maintain it there in a predefined vicinity of the origin without violation.Also,the proposed method avoids the problem of overestimation of the control gain that exists in the current fixed-time adaptive control.Moreover,it shows that the revised barrier function can effectively reduce the computation load by obviating the need of increasing the magnitude of sampling step compared with the conventional barrier function.This feature will be beneficial when the algorithm is implemented in practice.After that,the estimation of the fixed convergence time of the proposed method is derived and the impractical requirement of the preceding fixed-time adaptive control that the adaptive gains must be large enough to engender the sliding mode at time t=0 is discarded.Finally,the outperformance of the proposed method over the existing counterpart method is demonstrated with a numerical simulation.

基于Super-Twisting滑模观测器无刷直流电机调速控制

无位置传感器无刷直流电机作为动力送风呼吸防护装备的动力来源,以它为核心的动力送风系统动态性能指标决定了使用者的安全性和舒适性。针对常规滑模观测器在无位置传感器无刷直流电机控制过程中存在高频抖振以及转子位置相位滞后的问题,设计了一种基于Super-Twisting的二阶滑模观测器,其抑制了因一阶滑模面存在的不连续相而产生的抖振,以及因采用低通滤波器而导致的相位滞后和幅值衰减,同时对滑模观测器的开关函数进行改进。在此基础上采用磁场定向控制的双闭环控制系统进行系统调速仿真,同常规滑模观测器进行对比。实验结果表明:基于Super-Twisting滑模观测器的控制系统有效地抑制了系统抖振,降低了转子位置估算中存在的相位滞后,提升了电机转子位置的估算精度。其动态性能指标均优于常规滑模观测器的控制系统,提升了系统控制性能,达到了送风呼吸防护装置预期的控制效果。

基于Super-Twisting无位置滑膜观测器的永磁同步电机控制

针对永磁同步电机在一些工作场合精度要求较高的情况,在永磁同步电机数学模型的基础上,文中提出了一种二阶Super-Twisting滑膜理论算法。该方法对电机的反电动势观测值进行运算处理,通过得到电机速度和位置信息来实现永磁同步电机无位置传感器控制。根据Lyapunov稳定性理论可知,该控制系统稳定。在MATLAB/Simulink工具中搭建系统仿真模型来验证该理论算法的有效性。仿真结果表明,相较于传统滑膜观测器,该算法有效削弱了滑膜抖振,提高了系统的估算精度和响应速度,使其能更好地跟踪转子的位置和速度信息。

基于分数阶Super-Twisting滑模的四旋翼无人机轨迹跟踪控制

针对存在外界干扰的四旋翼无人机轨迹跟踪问题,提出了一种分数阶Super-Twisting滑模控制器。基于双闭环控制策略,将四旋翼无人机系统解耦为位置子系统和姿态子系统;利用Super-Twisting算法设计出滑模控制器,可在消除系统抖振的同时实现全局有限时间收敛;为了进一步提高系统的控制精度和抗干扰性,引入了分数阶微分积分算子;最后,与传统Super-Twisting滑模和积分终端滑模进行的仿真对比,验证了所提算法的优越性。

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

Quantitatively characterizing sandy soil structure altered by MICP using multi-level thresholding segmentation algorithm

The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.

Genetic algorithm assisted meta-atom design for high-performance metasurface optics

Metasurfaces,composed of planar arrays of intricately designed meta-atom structures,possess remarkable capabilities in controlling electromagnetic waves in various ways.A critical aspect of metasurface design involves selecting suitable meta-atoms to achieve target functionalities such as phase retardation,amplitude modulation,and polarization conversion.Conventional design processes often involve extensive parameter sweeping,a laborious and computationally intensive task heavily reliant on designer expertise and judgement.Here,we present an efficient genetic algorithm assisted meta-atom optimization method for high-performance metasurface optics,which is compatible to both single-and multiobjective device design tasks.We first employ the method for a single-objective design task and implement a high-efficiency Pancharatnam-Berry phase based metalens with an average focusing efficiency exceeding 80%in the visible spectrum.We then employ the method for a dual-objective metasurface design task and construct an efficient spin-multiplexed structural beam generator.The device is capable of generating zeroth-order and first-order Bessel beams respectively under right-handed and left-handed circular polarized illumination,with associated generation efficiencies surpassing 88%.Finally,we implement a wavelength and spin co-multiplexed four-channel metahologram capable of projecting two spin-multiplexed holographic images under each operational wavelength,with efficiencies over 50%.Our work offers a streamlined and easy-to-implement approach to meta-atom design and optimization,empowering designers to create diverse high-performance and multifunctional metasurface optics.

Product quality prediction based on RBF optimized by firefly algorithm

With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

Path Planning for AUVs Based on Improved APF-AC Algorithm

With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.