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Locust Behaved Particle Swarm optimization Technique

Journal of Donghua University(English Edition) 2014年第2期31卷 190-196页

作者：钟伟民谢雪勤梁毅罗娜张娟钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

The collective behavior of certain animals and insects has the characteristic of self-organization. The simple interactions among individuals can produce complex adaptive patterns at the level of the group. Recently,new scientific investigation pointed out that desert locusts show extreme phenotypic plasticity in transforming between the lonely phase and the swarming gregarious phase depending on the population density,which is controlled by a serotonin called 5-hydroxytryptamine( 5HT). In this paper,based on the mechanism of the locusts' collective behavior,a new particle swarm optimization technique called LBPSO is studied. The number of swarms is selfadaptively adjusted by the acquired outstanding particles coming from behind the previous global best solution. The swarm sizes are related to the corresponding serotonin 5HT,which is determined by the optimization parameters such as global best and iteration number. And each swarm adopts one of three rules below according to its density, generalized social evolution strategy, generalized cognition evolution strategy and the independent moving strategy. A comparative study of LBPSO,social particle swarm optimization( SPSO), improved SPSO and the standard particle swarm optimization( StdPSO) on their abilities of tracking optima is carried out. And the results under four static benchmark functions and a dynamic function generator moving peaks benchmark( MPB)show that LBPSO outperforms the other three functions in both static and dynamic landscapes due to the introduced locusts' collective behavior.

关键词： evolutionary algorithm particle swarm optimization(PSO) locust collective behavior

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Novel Ensemble Modeling Method for Enhancing Subset Diversity Using Clustering Indicator Vector Based on Stacked Autoencoder

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Computer Modeling in Engineering & Sciences 2019年第10期121卷 123-144页

作者： Yanzhen Wang Xuefeng Yan Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai200237China

A single model cannot satisfy the high-precision prediction requirements given the high nonlinearity between *** contrast,ensemble models can effectively solve this *** key factors for improving the accuracy of ensemble models are namely the high accuracy of a submodel,the diversity between subsample sets and the optimal ensemble *** study presents an improved ensemble modeling method to improve the prediction precision and generalization capability of the *** proposed method first uses a bagging algorithm to generate multiple subsample ***,an indicator vector is defined to describe these subsample ***,subsample sets are selected on the basis of the results of agglomerative nesting clustering on indicator vectors to maximize the diversity between ***,these subsample sets are placed in a stacked autoencoder for ***,XGBoost algorithm,rather than the traditional simple average ensemble method,is imported to ensemble the model during *** machine learning public datasets and atmospheric column dry point dataset from a practical industrial process show that our proposed method demonstrates high precision and improved prediction ability.

关键词： Ensemble model deep learning bagging stacked autoencoder XGBoost

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Multi-objective optimization of steam system based on GPU acceleration

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IFAC-PapersOnLine 2018年第21期51卷 183-188页

作者： Zhao, Liang Ye, Zhencheng Du, Wenli Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai200237 China

The steam system is an important part of the utility systems in process industry. The energy consumption and operation cost of the existing plant were increased due to the inefficient configuration of the steam system. Meanwhile, the fossil fuels such as coal and oil were used to produce steam for the process industry, and the poisonous gases released from the burning process caused a lot of damage to the environments. Therefore, it is of great practical significance to reduce the operation cost and pollutant emissions simultaneously. This paper proposes an evolutionary multi-objective optimization (EMOO) algorithm to deal with this problems. But because of the complexity, higher dimension and rigid constraint conditions of the process industry, the computation time of EMOO algorithm can not meet the requirements of real-time optimization. Thus, Graphics Processing Unit (GPU) computing, which was running on the CUDA platform was introduced to shorten the running time of the algorithm. A constraint handling mechanism was presented to improve the performance of the algorithm. The case study indicates that the proposed GPU-based EMOO algorithm can obtain the Pareto optimal solution of the steam system in a minute-level time. © 2018

关键词： Multiobjective optimization

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A Hybrid Differential Evolution Algorithm Integrated with Particle Swarm optimization

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Journal of Donghua University(English Edition) 2014年第2期31卷 197-200页

作者：范勤勤颜学峰 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

To implement self-adaptive control parameters, a hybrid differential evolution algorithm integrated with particle swarm optimization （PSODE） is proposed. In the PSODE, control parameters are encoded to be a symbiotic individual of original individual, and each original individual has its own symbiotic individual. Differential evolution （ DE） operators are used to evolve the original population. And, particle swarm optimization （PSO） is applied to co-evolving the symbiotic population. Thus, with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the realtime optimum control parameters are obtained. The proposed algorithm is compared with some DE variants on nine functious. The results show that the average performance of PSODE is the best.

关键词： differential evolution algorithm particle swann optimization self-adaptive co-evolution

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A New Sample-Selection and Modeling Method Based on Near-Infrared Spectroscopy and Its Industrial Application

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Journal of Donghua University(English Edition) 2014年第2期31卷 207-211页

作者：贺凯迅程辉钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

Near-infrared( NIR) spectroscopy has been widely employed as a process analytical tool( PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to capture process properties. In quantitative online applications,the robustness of the established NIR model is often deteriorated by process condition variations,nonlinear of the properties or the high-dimensional of the NIR data set. To cope with such situation,a novel method based on principal component analysis( PCA) and artificial neural network( ANN) is proposed and a new sample-selection method is mentioned. The advantage of the presented approach is that it can select proper calibration samples and establish robust model effectively. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional partial leastsquares( PLS),principal component regression( PCR) and several other modeling methods, the proposed approach was found to achieve good accuracy in the prediction of gasoline properties. An application of the proposed method is also reported.

关键词： gasoline blending near-infrared spectroscopy sample selection modeling method

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Unsupervised learning of depth and ego-motion with absolutely global scale recovery from visual and inertial data sequences

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Cyber-Physical Systems 2021年第3期7卷 133-158页

作者： Meng, Yanwen Sun, Qiyu Zhang, Chongzhen Tang, Yang Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai China

In this paper, we propose an unsupervised learning method for jointly estimating monocular depth and ego-motion, which is capable to recover the absolute scale of global camera trajectory. In order to solve the general problems of scale drift and scale ambiguity of monocular camera, we fuse geometric movement data from inertial measurement unit (IMU), and use Bi-directional Long Short-Term Memory (BiLSTM) to extract temporal features. Besides, we add a lightweight and efficient attention mechanism, Convolutional Block Attention Module (CBAM), to Convolutional Neural Networks (CNNs) to complete the extraction of image features. Considering the scenes with severe illumination changes, ambiguous structures, moving objects and occlusions, especially scenes with progressively-variant textures, the geometric features can provide adaptive estimation results in the case of the degeneration of visual features. Experiments on the KITTI driving dataset reveal that our scheme achieves promising results in the estimation of camera pose and depth. Moreover, the absolute scale recovery for the global camera trajectory is effective. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Recovery

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Design of fractional order control using predictive functional control structure 35

Design of fractional order control using predictive function...

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第35届中国控制会议

作者： ZOU Qin ZHANG Junfeng LU Renquan ZHANG Ridong Information and Control Institute Hangzhou Dianzi University Key Laboratory of Advanced Control and Optimization for Chemical Processes

ISBN: (纸本)9781509009107

In practical processes,lots of phenomena are unable to be accurately described by conventional integer order models,while fractional order models can describe the characteristics more *** this paper,a new fractional order predictive functional control(FPFC) method is presented for the fractional order *** Oustaloup approximation is employed to derive the approximate model of the original ***,the Griinwald-Letnikov definition and fractional operator are used to extend the integer order predictive functional control to the *** with traditional predictive functional control,simulation results reveal that the fractional order controller can achieve improved control performance.

关键词： Fractional order calculus Oustaloup approximation fractional order system predictive functional control

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Vision-based formation control of multiple UAVs with event-triggered integral sliding mode control

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Cyber-Physical Systems 2020年第4期6卷 231-251页

作者： Chen, Siqing Jin, Xin Zhang, Wenle Tang, Yang Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai China

Vision-based formation control offers an alternate solution for unmanned aerial vehicles (UAVs) to work together in the external position system denied environment. In this paper, we present a vision-based formation control strategy with the advantages of high tracking performance and low consumption of communication resources. A vision-based multi-robot localisation methods is applied to get precise position information, and the extended Kalman filter (EKF) is used to fuse measurement data from different sensors. The integral sliding mode controller (ISMC) is utilised to ensure the fast convergence and robustness to the desired formation, and the event-triggered communication scheme is employed to reduce redundant transmission of state information used in the formation controller. The simulation results in Gazebo with a team of three UAVs are given to illustrate the effectness of the proposed control strategy. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Extended Kalman filters

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Trajectory planning in 3D dynamic environment with non-cooperative agents via fast marching and Bézier curve

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Cyber-Physical Systems 2019年第2期5卷 119-143页

作者： Zong, Xuepeng Sun, Qiyu Yao, Dachen Du, Wei Tang, Yang Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai China

In this paper, we propose a framework for trajectory planning in a 3D dynamic environment where other non-cooperative agents may obstruct the active quadrotor. A trajectory predictor is designed for the non-cooperative agent via least squares. In addition, based on Euclidean signed distance field (ESDF), a velocity map configuration is developed for both dynamic and static obstacles, and then utilized in fast marching method to find a feasible discrete path. The smooth and dynamic feasible trajectory generation problem is formulated as a quadratic programming (QP) problem, where a safe flight corridor generator is developed for safe constraints in a dynamic environment. Piecewise Bézier curves are used to represent the generated trajectory. With the help of flight corridor, along with the convex hull property of Bézier curve, the quadratic programming problem turns into a convex optimization problem. Simulation results show that the proposed approach is validated for collision avoidance in a dynamic environment. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Collision avoidance

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Model predictive control for constrained uncertain piecewise linear systems

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控制理论与应用（英文版） 2013年第2期11卷 230-236页

作者： Dewei LI Yugeng XI Yuanyuan ZOU Department of Automation and Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

For constrained piecewise linear （PWL） systems, the possible existing model uncertainty will bring the difficulties to the design approaches of model predictive control （MPC） based on mixed integer programming （MIP）. This paper combines the robust method and hybrid method to design the MPC for PWL systems with structured uncertainty. For the proposed approach, as the system model is known at current time, a free control move is optimized to be the current control input. Meanwhile, the MPC controller uses a sequence of feedback control laws as the future control actions, where each feedback control law in the sequence corresponds to each partitions and the arbitrary switching technique is adopted to tackle all the possible switching. Furthermore, to reduce the online computational burden of MPC, the segmented design procedure is suggested by utilizing the characteristics of the proposed approach. Then, an offline design algorithm is proposed, and the reserved degree of freedom can be online used to optimize the control input with lower computational burden.

关键词： Model predictive control Constrained piecewise linear system Structured uncertainty Segmented designprocedure

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