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第33届中国控制与决策会议

作者： Huijun Guo Jintao Liang School of Automation and Information Engineering Xi'an University of Technology Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing Guilin University of Electronic Technology

This paper studies the problem of distributed consensus tracking control for multi-robot systems with different friction coefficients and external disturbances based on the multi-agent *** the case where the communication topology is a weighted directed graph and leader node is the neighbor of a small portion of the follower *** follower nodes are modeled as two second-order nonlinear dynamical system with coupling parts.A distributed robust adaptive control law based on the neural networks(NN) is designed for each follower node,where the controller requires only relative state information from its adjacent *** such control scheme,the states of all follower nodes ultimately synchronize to the leader node with bounded residual *** results are provided to validate the effectiveness of the algorithm.

关键词： Consensus control Multi-robot systems Agent systems Neural networks Sliding mode control

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Scene Classification Using Deep Networks Combined with Visual Attention

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Journal of Sensors 2022年第0期2022卷

作者： Shi, Jing Zhu, Hong Li, Yuxing Li, Yanghui Du, Sen School of Automation and Information Engineering Xi'An University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'An University of Technology Xi'an710048 China

In view of the scene's complexity and diversity in scene classification, this paper makes full use of the contextual semantic relationships between the objects to describe the visual attention regions of the scenes and combines with the deep convolution neural networks, so that a scene classification model using visual attention and deep networks is constructed. Firstly, the visual attention regions in the scene image are marked by using the context-based saliency detection algorithm. Then, the original image and the visual attention region detection image are superimposed to obtain a visual attention region enhancement image. Furthermore, the deep convolution features of the original image, the visual attention region detection image, and the visual attention region enhancement image are extracted by using the deep convolution neural networks pretrained on the large-scale scene image dataset Places. Finally, the deep visual attention features are constructed by using the multilayer deep convolution features of the deep convolution networks, and a classification model is constructed. In order to verify the effectiveness of the proposed model, the experiments are carried out on four standard scene datasets labelMe, UIUC-Sports, Scene-15, and MIT67. The results show that the proposed model improves the performance of the classification well and has good adaptability. © 2022 Jing Shi et al.

关键词： Convolution

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A Multi-Kernel Principal Component Analysis Method for Quality-Related Fault Detection

A Multi-Kernel Principal Component Analysis Method for Quali...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Lingxia Mu Biyu Lei Ding Liu School of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (纸本)9781665465373

In this paper, a multi-kernel principal component analysis (MKPCA) method for quality-related fault detection is proposed. The initial space is firstly mapped to a new space. The correlated information between the new space and output quality is then obtained by the kernel function. Meanwhile, with consideration of the advantage of global function and local function, a weight factor which combines them together is introduced to construct a multi-kernel function. In this way, the algorithm achieves better learning ability. The new space is projected to two mutually orthogonal subspaces, i.e., quality-related part and quality-unrelated part. In each subspace, fault information is expressed by different statistical indicators. The numerical example is presented to evaluate the performance of the MKPCA. The results show better reliability and high fault detection rate through proper spatial decomposition and kernel function construction.

关键词： Correlation Automation Fault detection Feature extraction Eigenvalues and eigenfunctions Reliability Matrix decomposition

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Adaptive Back-stepping Based control for Active Suspension systems with Prescribed Performance

Adaptive Back-stepping Based Control for Active Suspension S...

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第40届中国控制会议

This paper proposes an adaptive back-stepping tracking control with prescribed performance function for vehicle suspension systems with uncertain parameters. The prescribed performance function can lead the tracking error from a large set to an arbitrarily small residual set. Firstly, the constrained tracking error system incorporating the prescribed performance functions is transformed into unconstrained form. Then an adaptive updating scheme based on the projection algorithm is designed to estimate the uncertain parameter. Following that an adaptive nonlinear controller is designed using the back-stepping technique, and the stability of whole controlled systems is proved by the Lyapunov theory. Meanwhile, the state convergence of the internal dynamical system is also guaranteed. Finally, simulation results are provided to verify the proposed approach.

关键词： Active Suspension system Adaptive control Back-stepping technique prescribed performance

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Scheduling optimization of silicon single crystal production process based on adaptive improved particle swarm optimization

Scheduling optimization of silicon single crystal production...

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Chinese Automation Congress (CAC)

作者： Lu Kang Ding Liu Yali Wu Guozheng Ping School of Automation and Information Engineering Xi’an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China School of Automation and Information Engineering Xi’an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China School of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China

ISBN: (纸本)9781665426480

Aiming at the potential safety hazards of silicon single crystal production enterprises in the process of multi-furnace silicon single crystal production, and considering the maximum power load requirements in actual enterprises, a silicon single crystal production process scheduling model with the goal of minimizing the maximum completion time was established. Based on this model, an adaptive improved particle swarm optimization algorithm (Adaptive GPSO) is proposed to solve it. The algorithm retains the individual optimal value and global optimal value of the particle swarm algorithm, and introduces the crossover operation and mutation operation in the genetic algorithm to improve the new individual generation mechanism. At the same time, it performs dynamic iterative calculations on the mutation probability and crossover probability to avoid the algorithm fall into the local optimum, and improve the diversity and convergence of the algorithm. By solving different scales scheduling problems of silicon single crystal production process, the experiments on real-world data shows that the feasibility and effectiveness of the proposed algorithm.

关键词： Adaptation models Heuristic algorithms Crystals Production Probability Silicon Scheduling

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Inverse Problem Estimation of Heat Conduction using Parameters Adaptive PID Algorithm

Inverse Problem Estimation of Heat Conduction using Paramete...

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第34届中国控制与决策会议

作者： Huang Weichao Li Jiahao Liu Ding Faculty of automation and Information Engineering Xian University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing

A boundary inverse problem estimation method for heat conduction system based on a parameters adaptive PID algorithm is proposed in the *** method can solve the boundary heat flux estimation problem of onedimensional heat conduction model based on the inverse heat conduction *** reduce the error between the temperatures of the measurement points and the calculation values constantly by using feedback control of PID *** parameters of PID algorithm is optimized by Whale optimization *** is enhances the rapidity and stability of the system and solves the problem that PID parameters are difficult to *** experimental results show that,the method that proposed in the paper can realize the inverse estimation of thermal boundary conditions accurately and quickly while ensuring the stability and convergence of the system.

关键词： Inverse heat conduction problem PID algorithm Finite difference method Whale optimization algorithm

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Optimization of Extreme Learning Machine Based on Improved Beetle Antennae Search for Slot Die Coating Prediction

Optimization of Extreme Learning Machine Based on Improved B...

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第35届中国控制与决策会议

作者： Haonan Yang Ding Liu Jun-Chao Ren Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

In the actual production of slot die coating,the minimum coating thickness and the maximum substrate moving speed could only be judged by production experience,and there was no accurate prediction model due to the nonlinear characteristics of fluid ***,building a reasonable and efficient prediction model for slot die coating is now an urgent and challenging *** this paper,an optimized extreme learning machine(ELM) based on improved beetle antennae search(IBAS) algorithm is proposed for slot die coating *** optimized ELM model can well learn the nonlinear characteristics of the system and make accurate predictions,thus solving the traditional inaccurate empirical *** the prediction accuracy of ELM depends on the selection of weights and biases,the IBAS optimization algorithm is used to quickly search for the optimal value of weights and biases in the ELM *** algorithm improves the generation mechanism of antennae on the basis of the original algorithm,so that the algorithm can converge *** the same time,the search strategy of the algorithm is improved to avoid falling into the local optimal *** predicting the production data of slit coating,the feasibility and effectiveness of IBAS-ELM model are proved.

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Support Vector Regression Modeling based Data-Driven Iterative Learning control for Czochralski Crystal Growth Process

Support Vector Regression Modeling based Data-Driven Iterati...

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第三十八届中国控制会议

作者： Junchao Ren Ding Liu Yin Wan Ni Zhang National & Local Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

In view of the previous Czochralski(Cz) monocrystalline silicon growth control method based on mechanism model, it is difficult to accurately reflect the nonlinear and real-time variation characteristics of crystal growth process. This paper proposes a data-driven iterative learning control method based on support vector regression, which combines crystal diameter prediction model by support vector regression(SVR) off-line training with crystal growth mechanism model, and then control the crystal diameter and output key variable information in the framework of iterative learning algorithm. Simulation results for diameter control of crystal growth process and the analysis of key variable information show that the proposed method is effective and has a fast crystal diameter learning speed, and good performances of setpoint tracking and disturbance rejection, high crystal growth speed.

关键词： Data-Driven Czochralski Monocrystalline Silicon Support Vector Regression Iterative Learning control

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Optimization Deployment of Temperature Measurement Points for CZ Silicon Single Crystal Growth Equipment Based on Improved Fuzzy C-Means Clustering

Optimization Deployment of Temperature Measurement Points fo...

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第43届中国控制会议

作者： Weichao Huang Siyuan Cao Ding Lui School of Automation and Information Engineering Xi'an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The inverse heat transfer method is an effective inversion algorithm for solving the heat transfer at non-measurable places of the study object. However, the current inverse heat transfer inversion method has the problems of low accuracy and difficult selection of temperature measurement point locations. To solve this problem, this paper proposes an optimization algorithm for measurement point locations based on improved fuzzy c-means clustering(IFCM). The method first generates the initial clustering center through a specific density function and introduces the Mahalanobis distance to solve the problem that the traditional fuzzy c-means clustering results depend on the initial clustering center and the results are unstable. Then, an optimized deployment algorithm based on IFCM temperature measurement points is proposed and compared with other mean value clustering algorithms. Finally, the optimal temperature measurement position on the crystal surface is selected by the IFCM temperature measurement point optimization deployment algorithm on the CZ(Czochraski)silicon single crystal growth equipment, and the inversion results of the randomly selected temperature measurement point scheme and the optimized temperature measurement point scheme are compared by the real-time inversion algorithm based on the dynamic matrix control multi-boundary heat flux, and the results of the optimized temperature measurement point scheme are obtained to be better than that of the randomly selected temperature measurement point scheme.

关键词： Fuzzy c-means clustering IFCM Temperature measurement point optimization Inverse heat transfer

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Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment

Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surf...

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2020 IEEE International Conference on information Technology, Big Data and Artificial Intelligence, ICIBA 2020

作者： Qi, Xianzhi Fu, Wenxing An, Pei Wu, Bingli Ma, Jie National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100071 China

ISBN: (纸本)9781728152240

Point cloud preprocessing is still a challenging task in the marine environment, for it is difficult to filter out non-obstacle points while avoiding damage to the obstacle completeness. In this paper, we propose a novel data preprocessing method on 3D LiDAR data for the unmanned surface vehicle in the marine environment. It consists of two tasks: outlier removal and wake filtering. As the spatial resolution of LiDAR changes with distance, we exploit distance normalization on statistical outlier filter for robust outlier removal. Considering the gradient difference between wave wake and obstacle surface near the water, we define and calculate the vertical state of point cloud on the range image to obtain the pre-filtering point set, and then use the RANSAC method to filter out the wake points. Experiments on real data has demonstrated the effectiveness of the proposed algorithm both in terms of feasibility and accuracy. © 2020 IEEE.

关键词： Wakes

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