A Novel Genetic Algorithm and Its Application in Variable Structure Control of Robot

A novel genetic algorithm (NGA) is proposed, which possesses micro-regulation and renascence operation. The optimized variable searching interval is regulated gradually according to the sub-group of excellent individuals. The NGA is used to optimize the parameters of the variable structure control (VSC), which satisfies the new reaching law and sliding mode. It is used in robot control systems. Simulation results are given.

A Novel Sliding Mode Variable Structure Controller Based on a Genetic Algorithm

A novel control method has been proposed by using the genetic algorithm （GA） for nonlinear and complex plants. The proposed control strategy is based on a variable structure control, it overcomes the defects of other adaptive methods such as strong dependence to the system. A GA is used to learn to optimally select integral coefficient C. Simulation results verified the effectiveness of the controller. For position control of Direct Current （DC） motor in practice, this method has good performance and strong robustness, and both dynamic and steady performances were improved.

Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

An Approach to Polynomial NARX/NARMAX Systems Identification in a Closed-loop with Variable Structure Control

Many physical processes have nonlinear behavior which can be well represented by a polynomial NARX or NARMAX model. The identification of such models has been widely explored in literature. The majority of these approaches are for the open-loop identification. However, for reasons such as safety and production restrictions, open-loop identification cannot always be done. In such cases, closed-loop identification is necessary. This paper presents a two-step approach to closed-loop identification of the polynomial NARX/NARMAX systems with variable structure control （VSC）. First, a genetic algorithm （GA） is used to maximize the similarity of VSC signal to white noise by tuning the switching function parameters. Second, the system is simulated again and its parameters are estimated by an algorithm of the least square （LS） family. Finally, simulation examples are given to show the validity of the proposed approach.

Optimal variable structure control with sliding modes for unstable processes

In this work,a variable structure control(VSC)technique is proposed to achieve satisfactory robustness for unstable processes.Optimal values of unknown parameters of VSC are obtained using Whale optimization algorithm which was recently reported in literature.Stability analysis has been done to verify the suitability of the proposed structure for industrial processes.The proposed control strategy is applied to three different types of unstable processes including non-minimum phase and nonlinear systems.A comparative study ensures that the proposed scheme gives superior performance over the recently reported VSC system.Furthermore,the proposed method gives satisfactory results for a cart inverted pendulum system in the presence of external disturbance and noise.

VARIABLE STRUCTURE CONTROL APPLIED IN ELECTRO-HYDRAULIC SERVO SYSTEM WITH ANN

The variable structure control （VSC） theory is applied to the electro-hydraulic servo system here. The VSC control law is achieved using Lyapunov method and pole placement. To eliminate the chattering phenomena, a saturation function is adopted. The proposed VSC approach is fairly robust to load disturbance and system parameter variation. Since the distortion. including phase lag and amplitude attenuation occurs in the system sinusoid response, the amplitude and phase control （APC） algorithm, based on Adaline neural network and using LMS algorithm, is developed for distortion cancellation. The APC controller is simple and can on-line adjust, thus it gives accurate tracking.

STUDIFS ON OPTIMAL TOPOLOGY DES IGN OF STRUCTURES WITH DISCRETE VARIABLES

Some problems in the optimal topology design of structures with discrete variables are studied in this paper.The problem of a model of discrete optimization is discussed and a neglected fact that discrete optimum design may be controlled by the discreteness of sizing variables and global con- straints is pointed out.A heuristic algorithm for solving discrete topology optimization problems of trusses and frames is proposed.

Theoretical and experimental research on a new system of semi-active structural control with variable stiffness and damping

In this paper,a new system of semi active structural control with active variable stiffness and damping (AVSD) is suggested.This new system amplifies the structural displacement to dissipate more energy,and in turn,effectively reduces the structural response in the case of relatively small story drifts,which occur during earthquakes.A predictive instantaneous optimal control algorithm is established for a SDOF structure equipped with an AVSD system Comparative shaking table tests of a 1/4 scale single story structural model with a full scale control device have been conducted.From the experimental and analytical results,it is shown that when compared to structures without control or with the active variable stiffness control alone, the suggested system exhibits higher efficiency in controlling the structural response,requires less energy input,operates with higher reliability,and can be manufactured at a lower cost and used in a wider range of engineering applications.

A NEW METHOD FOR STRUCTURAL TOPOLOGICAL OPTIMIZATION BASED ON THE CONCEPT OF INDEPENDENT CONTINUOUS VARIABLES AND SMOOTH MODEL

A concept of the independent-continuous topological variable is proposed to establish its corresponding smooth model of structural topological optimization. The method can overcome difficulties that are encountered in conventional models and algorithms for the optimization of the structural topology. Its application to truss topological optimization with stress and displacement constraints is satisfactory, with convergence faster than that of sectional optimizations.

Comparative Study of Variable Selection Using Genetic Algorithm with Various Types of Chromosomes

In this study,different methods of variable selection using the multilinear step-wise regression（MLR） and support vector regression（SVR） have been compared when the performance of genetic algorithms（GAs） using various types of chromosomes is used.The first method is a GA with binary chromosome（GA-BC） and the other is a GA with a fixed-length character chromosome（GA-FCC）.The overall prediction accuracy for the training set by means of 7-fold cross-validation was tested.All the regression models were evaluated by the test set.The poor prediction for the test set illustrates that the forward stepwise regression（FSR） model is easier to overfit for the training set.The results using SVR methods showed that the over-fitting could be overcome.Further,the over-fitting would be easier for the GA-BC-SVR method because too many variables fleetly induced into the model.The final optimal model was obtained with good predictive ability（R2 = 0.885,S = 0.469,Rcv2 = 0.700,Scv = 0.757,Rex2 = 0.692,Sex = 0.675） using GA-FCC-SVR method.Our investigation indicates the variable selection method using GA-FCC is the most appropriate for MLR and SVR methods.

Variable structure multiple model fixed-interval smoothing

This paper focuses on fixed-interval smoothing for stochastic hybrid systems.When the truth-mode mismatch is encountered,existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable.We develop a fixedinterval smoothing method based on forward-and backward-filtering in the Variable Structure Multiple Model(VSMM)framework in this paper.We propose to use the Simplified Equivalent model Interacting Multiple Model(SEIMM)in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters,and design a re-filtering procedure in the model-switching stage to enhance the estimation performance.To improve the computational efficiency,we make the basic model-set adaptive by the Likely-Model Set(LMS)algorithm.It turns out that the smoothing performance is further improved by the LMS due to less competition among models.Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.

Semi-active Control with Variable Damper for Pier-Beam Transversal Vibration

In this paper, the method of semi-active control with variable damper is presented to deal with the relatively poor transversal seismic condition of bridge. Based on the LQR control algorithm the control effectiveness for transverse vibration of pier-beam structure of bridge are discussed. Taking the structure as a multiple-degree of freedom system, the calculating model of structure-variable damper system is set up and the differential equation is derived, combined with practical example the control system is simulated and studied by various semi-active control algorithms and passive strategy with MATLAB. The results show that the semi-active control with variable damper can decrease the transverse vibration effectively and the control effect is obvious.

基于混沌控制的采摘机器人防碰撞系统设计

针对采摘机器人在作业过程中遇到障碍物导致路径规划出现混沌现象的问题,基于混沌控制对采摘机器人防碰撞系统进行设计,并采用模糊自适应控制算法和滑模变结构控制算法结构的方式对系统进行控制。为了验证防碰撞系统的有效性,对采摘机器人进行了防碰撞和采摘仿真试验,结果表明:采摘机器人在作业过程中可以避开障碍物,按照规划路线到达预设位置,完成果蔬的采摘。

面向壁厚均匀的液压成形三通管的坯料设计方法与参数优化

目的研究连续变厚度管液压成形过程中三通管的壁厚均匀性问题。方法首先,利用有限元软件分析得到了等厚管液压成形三通管过程中的成形规律,根据其成形规律,反向设计了用于液压成形三通管的连续变厚度管坯;其次,采用正交试验方法,研究了连续变厚度管厚区壁厚、过渡区长度对液压成形三通管成形质量的影响;最后,采用多岛遗传算法,对连续变厚度管的结构参数进行了优化验证,提高了液压成形三通管的成形质量。结果在等厚管液压成形三通管成形过程中,支管顶部壁厚值持续减小、轴向过渡圆角及直管底部中间位置处壁厚值持续增大;环向过渡圆角处等效应力最大,其他位置连续变化;轴向压应力使得三通管壁厚增大,拉应力使得壁厚减小;正交试验结果表明,当连续变厚度管厚区厚度、环向过渡区长度达到某一值时将出现较好的壁厚均匀性;在4种过渡区曲线线型中,直线型的壁厚均匀性最好;通过多目标优化得到最佳连续变厚度管结构参数,并通过有限元仿真进行验证,相对误差均在2%以内,使用优化后的连续变厚度管液压成形三通管相对于等厚管,壁厚均匀值减小了0.423 mm。结论可以采用厚度补偿或减薄的方式来提高成形三通管的壁厚均匀性;连续变厚度管坯相比于等厚管,显著提高了液压成形三通管的壁厚均匀性。

基于改进麻雀搜索算法的变电构架优化方法

为了解决变电构架设计中的优化问题,采用改进麻雀搜索算法对其进行优化设计。在基本麻雀搜索算法中引入Circle混沌映射,以提高种群的多样性和算法的全局搜索能力;引入萤火虫算法对麻雀搜索算法进行扰动更新,使其易于跳出局部最优。建立尺寸优化的数学模型,采用罚函数法处理约束条件。先将改进后的算法用于典型桁架算例分析,证明其稳定性与有效性,再将其用于变电构架的优化设计,结果表明,采用改进后的麻雀搜索算法能够有效提升变电构架的优化效果。

动态内全潜结构投影的空间扩展故障检测方法

动态内偏最小二乘(DiPLS)方法是基于数据驱动的潜结构投影的动态扩展算法,用于动态特征提取和关键性能指标预测.在大型装备系统中,传感器采集的当前时刻样本受历史样本的影响且可能包含较大噪声.在动态特征提取中,因DiPLS算法未按降序提取主成分,导致残差空间仍存在较大变异,动态和静态信息难以有效分离,影响故障检测性能.为此,本文提出了一种基于动态内全潜结构投影的故障检测方法(DiTPLS).首先,使用动态内偏最小二乘方法和向量自回归模型建立动态模型并检测故障,用于捕捉质量相关动态信息;基于结构化动态主成分分析算法建立一种改进的动态潜在变量模型,用于残差分解,提取质量无关的动态信息和静态信息,并构造合适的统计量进行故障检测.数值仿真和田纳西–伊斯曼过程实验验证了DiTPLS算法的有效性.

基于灰狼优化算法的变截面点阵结构设计及性能优化

点阵结构因其比强度高、比刚度高,减振降噪性好和隔热吸能能力强等特点,已被广泛应用于航空航天和交通运输等领域主承力零部件设计.但现有的点阵结构设计大都基于等截面假设开展,严重制约了材料分布优化的寻优空间,无法满足质量敏感领域极致轻量化设计的迫切需求.而目前关于变截面点阵结构设计的研究大都基于实验的“试错式”经验调配模式,其相应的理论设计方法尚不完善.文章基于灰狼智能优化算法,提出了一种高效的变截面点阵结构设计方法.首先,基于水平集函数构建变截面点阵的显式几何描述模型,以实现变截面点阵几何形状的自由描述;其次,采用能量均匀化方法预测变截面点阵单胞的宏观等效力学属性,建立宏观点阵结构与变截面单胞构型间的内在联系;然后,以变截面点阵的几何描述参数为设计变量,材料用量为约束条件,最小化柔度为目标函数,构建变截面点阵优化数学模型,并采用灰狼优化算法实现上述模型的高效求解,得到性能优化的变截面点阵结构;最后,通过二维和三维数值算例和仿真分析共同验证了所提方法的正确性和有效性,并与相同条件下等截面点阵结构的承载能力进行了比较.结果表明:优化后变截面点阵结构的柔度相较于相同条件下的等截面点阵结构可降低30%以上,具有更优异的承载能力.研究结果丰富了变截面点阵结构设计理论,在高端装备极致轻量化设计领域具有重要应用前景.

基于改进海洋捕食者算法的输电塔结构优化

为确保输电塔架线施工时取消临时拉线工况下输电塔架线施工安全,需对其结构进行优化设计。文章建立输电塔结构的计算模型,采用罚函数法确定海洋捕食者算法(marine predators algorithm,MPA)的目标函数;考虑到MPA易陷入局部最优、收敛速度慢的缺点,提出一种融合折射反向学习策略和黄金正弦算法的改进海洋捕食者算法(improved marine predators algorithm,IMPA),并将其在6个基准测试函数数值实验中与其他算法进行比较,结果表明IMPA具有更好的收敛性和求解精度。利用IMPA对输电塔结构在特殊工况下进行优化,结果表明IMPA可以对三维杆系结构进行优化设计,优化后的输电塔具有较好的力学性能。

线性测量误差回归模型下Sparse Group Lasso方法研究

双重稀疏结构的线性回归模型是一种描述解释变量组间和组内同时具有稀疏性的统计模型,我们常用Sparse Group Lasso对此模型进行变量选择.然而在很多应用中,解释变量很难做到精确测量,从而我们在应用Sparse Group Lasso方法时需要考虑测量误差的影响.针对这一问题,本文提出了一种具有双重稀疏结构的线性测量误差回归模型的Sparse Group Lasso变量选择方法(MESGL).该方法先利用半正定投影算子对观测数据的误差进行修正,然后借助ADMM算法对修正后的数据进行恢复,最后利用Sparse Group Lasso方法进行变量选择和参数估计.在一些正则条件下,我们建立了参数估计量的非渐近Oracle不等式,并且通过随机模拟分析验证了MESGL方法在变量选择和参数估计上取得的良好效果.

基于模糊变结构补偿的机械臂运动路径避障规划算法

机械臂的活动范围可以自由调整,其准确的运动规律较难获取,导致机械臂运动路径避障规划存在规划效果差、规划路径长以及规划耗时长的问题,为此提出基于模糊变结构补偿的机械臂运动路径避障规划算法。该算法首先对机械臂进行相关动力学分析,将分析结果与模糊变结构相结合对机械臂力矩进行补偿控制;然后使用Q-learning算法计算补偿控制结果,获取路径最佳规划策略;最后通过该策略完成机械臂避障路径的规划。实验结果表明,使用该算法进行路径规划时的规划结果较为准确,规划路径短且规划耗时短。