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Chinese Automation Congress (CAC)

作者： Yanxu Su School of Artificial Intelligence Anhui University Hefei P. R. China Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Southeast University Nanjing P. R. China

Abstract: In this paper, we study the nonconvex optimization problems with consideration of globally coupled constraints. The constrained optimization problem is converted into an augmented Lagrangian function, and a distributed primal-dual algorithm is exploited in the context of finite-time consensus for solving such a problem. Particularly, we do not require the globally coupled constraints to be separable. Instead, we introduce some local variables for estimating the constraint violations. Under some mild assumptions in the research of nonconvex optimization, we prove the convergence of the presented algorithm to a KKT point of the problem. The numerical experiment verifies the correctness and the effectiveness of the theoretical results.

关键词： Automation Heuristic algorithms Aggregates Numerical simulation Linear programming Optimization Lagrangian functions

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ICP matching algorithm-based localization of indoor mobile robots

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Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) 2013年第SUPPL.I期41卷 262-266页

作者： Gu, Wenhua Zhou, Bo Dai, Xianzhong Key Laboratory of Measurement and Control of Complex System Engineering of Ministry of Education School of Automation Southeast University Nanjing 210096 China

The localization of indoor mobile robots was studied. Point cloud data of surrounding environments were obtained by a two-dimensional laser range finder, and then three scan-matching algorithms, including Trimmed ICP (iterative closest point) algorithm, corner feature matching based HAYAI algorithm, as well as point-line matching based PLICP algorithm, were used respectively to estimate the relative displacement between two consecutive measurements to calibrate the localization result from odometer approaches. Better accuracy of long-range localization of indoor mobile robot could be achieved thereby. In the simulation, both the real-time property and accuracy of the mentioned three methods used were compared, showing the effectiveness and application field of the methods.

关键词： Mobile robots

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Research on panoramie vision system of mobile robot

Research on panoramie vision system of mobile robot

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Chinese control and Decision Conference, CCDC

作者： Qiaobing Yao Hualong Yu Wankou Yang Changyin Sun Key Lab of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing China School of Autmation Southeast University Nanjing Jiangsu CN Key Laboratory of Child Development and Learning Science of Ministry of Education Southeast University Nanjing China

This paper presents an imaging method for 360-degree panoramic view based on four wide angle cameras. In order to complete the image mosaic, all the parameters such as focal length, principal point and distortion coefficients, etc are calibrated by our proposed calibration toolbox. Then, our approach does not adopt the scheme which stitching all the images to the surrounding view after distortion correction. The proposed method directly calculates the mapping relationship between the wide-angle lens images and cylindrical projection images to generate lookup tables which can greatly simplifies the computation and reduces the loss of information in each image. Finally, panoramic image is composed by image registration and image fusion. Experimental results show that this method is valid.

关键词： Machine vision Mobile robots

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MetaComp: Learning to Adapt for Online Depth Completion

arXiv

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arXiv 2022年

作者： Chen, Yang Zhao, Shanshan Ji, Wei Gong, Mingming Xie, Liping Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China JD Explore Academy Beijing China National University of Singapore Singapore School of Mathematics and Statistics University of Melbourne Australia

Relying on deep supervised or self-supervised learning, previous methods for depth completion from paired single image and sparse depth data have achieved impressive performance in recent years. However, facing a new environment where the test data occurs online and differs from the training data in the RGB image content and depth sparsity, the trained model might suffer severe performance drop. To encourage the trained model to work well in such conditions, we expect it to be capable of adapting to the new environment continuously and effectively. To achieve this, we propose MetaComp. It utilizes the metalearning technique to simulate adaptation policies during the training phase, and then adapts the model to new environments in a self-supervised manner in testing. Considering that the input is multi-modal data, it would be challenging to adapt a model to variations in two modalities simultaneously, due to significant differences in structure and form of the two modal data. Therefore, we further propose to disentangle the adaptation procedure in the basic meta-learning training into two steps, the first one focusing on the depth sparsity while the second attending to the image content. During testing, we take the same strategy to adapt the model online to new multi-modal data. Experimental results and comprehensive ablations show that our MetaComp is capable of adapting to the depth completion in a new environment effectively and robust to changes in different modalities. Copyright © 2022, The Authors. All rights reserved.

关键词： E-learning

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GPIO Based Iterative Learning control for Manipulators with Time-Varying Disturbances

GPIO Based Iterative Learning Control for Manipulators with ...

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第42届中国控制会议

作者： Huangyuwei Lu Zuo Wang Jiankun Sun Xi Xiao Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex systems of Engineering Ministry of Education Shenzhen Research Institute Southeast University School of Artificial Intelligence and Automation Huazhong University of Science and Technology Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration

To improve the disturbance rejection ability and repetitive tracking accuracy of manipulators,a composite iterative learning control(ILC) scheme via generalized proportional integral observer(GPIO) is proposed.A high-order polynomial is first employed to model the time-varying disturbances that include unmodeled dynamics,parameter uncertainties and load fluctuations.A GPIO is then designed to estimate the disturbances and high-order time *** introducing estimations into a PD-type ILC at each iteration,a composite control scheme is obtained,such that the undesirable influence of time-varying disturbances can be effectively attenuated in the repetitive tracking process of *** this paper,the rigorous stability analysis of the closed-loop system is presented to guarantee that the tracking error exponentially converges to zero as the iteration number tends to *** effectiveness of the proposed control scheme is finally verified by simulation results.

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Pinr: A Physics-Integrated Neural Representation for Dynamic Fluid Scenes

SSRN

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SSRN 2024年

作者： Zhao, Ziyu Zhou, Sifan Lu, Xiaobo Zeng, Weili Qian, Jian School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China College of Civil Aviation Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of ASIC and System Fudan University Shanghai China

Recent research based on neural representation has achieved impressive results in dynamic reconstruction. However, reconstructing high-fidelity dynamic fluid scenes from multi-view videos remains challenging due to the complex fluid phenomena such as translucency, scattering, occlusion, and visual ambiguity. In this paper, we present a novel neural representation that simultaneously exploits physics knowledge and differentiable volume rendering techniques to construct a physically plausible neural field. We first propose a simple yet effective state field to separate fluid and non-fluid contents apart, which essentially reduces the amount of computation and appearance ambiguity. To achieve robust optimization and fast convergence, we introduce intermediate velocity in neural representation based on Helmholtz Decomposition to decouple physical quantities (velocity, pressure and density) that are tightly coupled in physical equations. In contrast to previous physics-informed reconstruction methods, we explore a novel solution by introducing a fast and stable fluid solver to update the intermediate velocity to a physically conserved incompressible velocity. This solver is based on a parallelizable multi-scale architecture, which can effectively avoid degenerate solutions. By adopting the proposed physics-aware loss, we achieved better convergence and superior performance in dynamic fluid scenes. Extensive experiments show that the proposed method can achieve state-of-the-art results for reconstructing dynamic fluid scenes, and the performance and efficiency have been significantly improved in different scenarios. © 2024, The Authors. All rights reserved.

关键词： Computer vision

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A Hierarchical Optimization-Based Model for Microgrids with Energy Storage Considering Operation Cost and Net Load Mean Square Error

A Hierarchical Optimization-Based Model for Microgrids with ...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Zexiang Guan Dongsheng Cai Olusola Bamisile Jian Li Zhenyuan Zhang Qi Huang School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control University of Electronic Science and Technology of China Chengdu China

When a high proportion of renewable energy is connected to the grid, the net load fluctuation can be suppressed by configuring energy storage. However, the current cost of energy storage is high. This paper proposes a bi-level optimization model that takes into account the dynamic allocation of shared energy storage (SES) capacity in response to electric load demand. The upper-layer objective function adopts EO optimization algorithm, which is to maximize the income of the shared energy storage operator (SESO). The lower-layer multi-objective function is to minimize the sum of the operating cost and the net load mean square error of multiple microgrids, which can be classified as the mixed integer linear programming problem. Then, the solution flow of the algorithm is given. Finally, under the constraints of the hierarchical optimization-based model, solving the problem by the CPLEX solver on the MATlab platform. The results demonstrate that the proposed method can allocate optimal capacity and reduce operating costs for microgrids. Simultaneously, this method increases the revenue of SESO and promotes the consumption of renewable energy.

关键词： Renewable energy sources Costs Fluctuations Microgrids Mean square error methods Mathematical models Classification algorithms

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Optimization of Low-carbon Dispatching for Microgrid Based on Improved Particle Swarm Optimization

Optimization of Low-carbon Dispatching for Microgrid Based o...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Huan Yang Dongsheng Cai Olusola Bamisile Jian Li Zhenyuan Zhang Qi Huang School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control University of Electronic Science and Technology of China Chengdu China

In the context of carbon neutrality, the power industry is becoming the main battlefield of carbon emission reduction. However, the low-carbon transformation of energy structure is a complex long-term task, it needs to be analyzed in combination with the actual situation. This paper is based on the microgrid in the grid connection mode, considering microgrid power output and carbon emission constraints. An optimal model is proposed to reduce carbon emission cost, the optimal economic operation, and build a low-carbon scheduling model of a microgrid system. In this paper, the improved multi-objective particle swarm optimization algorithm is used to solve the problem. Finally, the simulation results verify the effectiveness of the model and the superiority of the improved algorithm.

关键词： Job shop scheduling Costs Wind energy Heuristic algorithms Optimal scheduling Microgrids system integration

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Contraction analysis on primal-dual gradient optimization

arXiv

引用

arXiv 2019年

作者： Su, Yanxu Shi, Yang Sun, Changyin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Southeast University Nanjing210096 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 3P6 Canada

This paper analyzes the contraction of the primaldual gradient optimization via contraction theory in the context of discrete-time updating dynamics. The contraction theory based on Riemannian manifolds is first established for convergence analysis of a convex optimization algorithm. The equality and inequality constrained optimization cases are studied, respectively. Under some reasonable assumptions, we construct the Riemannian metric to characterize a contraction region. It is shown that if the step-sizes of the updating dynamics are properly designed, the convergence rates for both cases can be obtained according to the contraction region in which the convergence can be ***, the augmented Lagrangian function which is projection free is adopted to tackle the inequality constraints. Some numerical experiments are simulated to demonstrate the effectiveness of the presented contraction analysis results on primal-dual gradient optimization algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

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Distributed model predictive control under inexact primal-dual gradient optimization based on contraction analysis

arXiv

引用

arXiv 2019年

This paper develops a distributed model predictive control (DMPC) strategy for a class of discrete-time linear systems with consideration of globally coupled constraints. The DMPC under study is based on the dual problem concerning all subsystems, which is solved by means of the primal-dual gradient optimization in a distributed manner using Laplacian consensus. To reduce the computational burden, the constraint tightening method is utilized to provide a capability of premature termination with guaranteeing the convergence of the DMPC optimization. The contraction theory is first adopted in the convergence analysis of the primal-dual gradient optimization under discrete-time updating dynamics towards a nonlinear objective function. Under some reasonable assumptions, the recursive feasibility and stability of the closed-loop system can be established under the inexact solution. A numerical simulation is given to verify the performance of the proposed strategy. Copyright © 2019, The Authors. All rights reserved.

关键词： Closed loop systems

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