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第37届中国控制会议

作者： Wenguo Hou Peter X.Liu Minhua Zheng Shichao Liu School of Mechanical Electronic and Control EngineeringBeijing Jiaotong University Department of System and Computer Engineering Carleton University

An accurate and computationally efficient model for the deformation of brain tissue is very important in virtual neurosurgical simulation. In this paper, we introduced a new Finite Element Method(FEM) model, which is based on optimization implicit Euler method, for brain tissue deformation. Specifically, both the anisotropic and viscoelastic properties of brain tissue are incorporated into the model, providing more realistic and accurate description of the mechanical features of brain tissue. In the meantime, the model is particularly suitable for GPU-based computing, making it possible to achieve real-time performance for neurosurgical simulation. Simulation results show that the deformation model exhibits the behaviors of anisotropy and viscoelasticity. The proposed model was implemented on a neurosurgical simulator and it showed that the deformation of brain tissue can be rendered with a relatively high degree of visual realism at a refreshment rate of 23 frames per second in a normal PC.

关键词： Brain Tissue Deformation Model Neurosurgical Simulation Optimization Implicit Euler

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The NRSF-SVM based method for nonlinear rotor bearing fault diagnosis

The NRSF-SVM based method for nonlinear rotor bearing fault ...

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第30届中国控制与决策会议

作者： Ying Ma Zhong Luo Haopeng Liu Yunpeng Zhu School of Mechanical Engineering & Automation Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern University Department of Automatic Control and System Engineering University of Sheffield

The fault diagnosis scheme of the rotor bearing system often conducted by using either signal analysis approach or modeling method. In practice, the structure of the rotor bearing system is complex and contains many nonlinear factors. Therefore, it is hard to use the model-based method for fault detection. Thus, signal analysis approach is more efficient. In the signal analysis approach, frequency response function is widely applied. However, the existing analyzing methods of frequency response function have some limitations, such as multidimensional property. Thus, in this study, the concept of Nonlinear Response Spectrum Function(NRSF) is proposed to solve the problem. Finally, a simulation is conducted to identify the multi-fault rotor bearing systems by the proposed NRSFs feature and Support Vector Machine(SVM) classifier, showing that the NRSF-SVM approach has an excellent performance in fault identification of rotor bearing system.

关键词： rotor bearing system fault diagnosis NRSF SVM

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Semi-global leader-following output consensus of discrete-time linear multi-agent systems with input saturation

Semi-global leader-following output consensus of discrete-ti...

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2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018

作者： Shi, Liangren Li, Yuanlong Lin, Zongli Brown, Charles L. Innovation Center Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Department of Automation Shanghai Jiao Tong University China Department of Electrical and Computer Engineering University of Virginia P.O. Box 400743 CharlottesvilleVA22904-4743 United States

ISBN: (纸本)9781538668689

We consider the problem of semi-global leader-following output consensus for a group of agents, consisting of a leader agent and some follower agents, which are described by discrete-time linear systems and connected on a directed network. For each follower agent, a distributed observer is built that estimates the state of the leader. Based on the estimates of the leader state and the solution of a parameterized algebraic Riccati equation, we design low gain consensus protocols that are shown to solve the semi-global output consensus problem as long as the leader agent is reachable globally on the network. Numerical simulation shows the performance of the proposed consensus protocols. © 2018 IEEE.

关键词： Multi agent systems

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Tracking Filter for Nonballistic Targets in the Near Space Based on Modified Variable Structure Interacting Multiple Model 29

Tracking Filter for Nonballistic Targets in the Near Space B...

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第29届中国控制与决策会议

作者： Lei Qin Di Zhou Jun-long Li Beijing Institute of Electronic System Engineering Department of Control Science and Engineering Harbin Institute of Technology

ISBN: (纸本)9781509046584

As the representative of hypersonic vehicles in the near space,X-51 A and HTV-2 with high speed and irregular acceleration often adopt nonballistic maneuver flight motion,which is hard to be accurately *** paper focuses on three typical nonballistic maneuver models of near space targets,and proposes a new Modified Variable Structure Interacting Multiple Model(MVSIMM) *** the three nonballistic maneuver models,simulation results show that the MVSIMM algorithm is better than Fixed-Structure Interacting Multiple Model(FSIMM) filter algorithms.

关键词： Nonballistic Near Space Mathematical Models of Maneuver Motion Modified Variable Structure Interacting Multiple Model(MVSIMM)

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Functional Model and Stability Analysis of 18-Pulse Auto-Transformer Rectifier Unit（ATRU） 3

Functional Model and Stability Analysis of 18-Pulse Auto-Tra...

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2017 3rd IEEE International Conference on control Science and systems engineering (ICCSSE)

作者： Muhammad Saad Yong-Feng Ju Shahbaz Khan Husan Ali Bakht Muhammad Khan Department of Electronic and Control Engineering Chang'an University School of Automation Northwestern Polytechnical University

ISBN: (纸本)9781538604854

This work mainly focuses on designing and modeling of 18-pulse autotransformer rectifier unit. The dq rotating reference frame（Park’s Transformation） is used to design the average model. Polynomial fits are required to build up from essential parameters like α, kv and ki that must be extracted from switching model or actual setup. The designed model is computationally efficient, time invariant and has the key advantage of reduced simulation time, less convergence errors and having no numerical instabilities. The designed average model provides a much more accurate representation of the system dynamics both in frequency as well as in time domain. For this purpose, 18-pulse delta type autotransformer rectifier unit（ATRU） is modeled and analyzed for small signal behavior in order to realize the overall system stability. Finally average model is validated with hardware setup of 2 k VA prototype and its results are found to be closely matching with those of switching and average model proving the accuracy and reliability of the proposed model. Hence using these average models can greatly reduce the time consumption by avoiding mathematical instabilities while maintaining efficiency up to the mark on the other side.

关键词： more electric aircraft（MEA） ATRU prototype symmetric THD dq0 park’s transformation

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SOC Estimation of Lithium Battery Based on IPSO-BP Neural Network

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Journal of Physics: Conference Series 2020年第1期1684卷

作者： Xinjian Mao Shaojing Song Feng Ding Pan Tang Department of Communication and Information Engineering Shanghai Polytechnic University Shanghai 201209 P. R. of China Department of system& electronic control unit UAES Shanghai 201206 P.R of China

Based on the analysis of the accurate estimation method of the state of charge (SOC) of lithium battery for electric vehicles, aiming at the shortcomings of back propagation (BP) neural network model, an algorithm based on Improved Particle Swarm Optimization (IPSO) is proposed to optimize the parameters of BP neural network. In this algorithm, the particle swarm optimization algorithm is optimized by introducing shrinkage factor to limit the particle speed, so as to determine the initial parameters of BP neural network. Finally, the battery estimation model is established by using the data set of lithium battery published by NASA PCoE, and the simulation test is carried out by using MATLAB platform. The results show that the method can effectively reduce the SOC error and control the error within 2%. It has practical significance for SOC estimation in battery management system.

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Modal regression based structured low-rank matrix recovery for multi-view learning

arXiv

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arXiv 2020年

作者： Xu, Jiamiao Wang, Fangzhao Peng, Qinmu You, Xinge Wang, Shuo Jing, Xiao-Yuan Philip Chen, C.L. School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute China State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau 99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously, which thus leads to the performance degradation when there is a large discrepancy among multi-view data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of structured low-rank matrix. Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution. However, these models are not practical since complicated noise in practice may violate those assumptions and the distribution is generally unknown in advance. To alleviate such limitation, modal regression is elegantly incorporated into the framework of SLMR (term it MR-SLMR). Different from previous LMvSL based methods, our MR-SLMR can handle any zero-mode noise variable that contains a wide range of noise, such as Gaussian noise, random noise and outliers. The alternating direction method of multipliers (ADMM) framework and half-quadratic theory are used to efficiently optimize MR-SLMR. Experimental results on four public databases demonstrate the superiority of MR-SLMR and its robustness to complicated noise. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Robust MPC for constrained uncertain systems with data-driven improvement

Robust MPC for constrained uncertain systems with data-drive...

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第37届中国控制会议

作者： Lingyi Yang Dewei Li Jianbo Lu Yugeng Xi Bin Li Department of Automation Shanghai Jiaotong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education Department of Automation Sichuan UniversitySchool of Electrical Engineering and Information

The adaptive model predictive control is regarded as an effective control method for unknown constrained ***, the adaptive model predictive control needs model identification. The process of identification demands a certain amount of data and places some requirements on data which makes it difficult to be implemented. To handle it, this paper proposes a novel data-driven approach without model identification. The data of previous time is utilized to describe state space and input space of the uncertain system instead of identifying the true model. Based on the data-driven method, the Quasi-MinMax control strategy is used to design the robust data-driven MPC controller which directly calculates the input from the past data. Combined with the data-driven method, a free control variable is introduced to compensate for the insufficiency of past data. It is shown that adopting the data-driven controller can reduce conservatism by lessening model uncertainty and improve control performance. Meanwhile, the proposed design is proven to be recursively feasible and stabilizing. A numerical example demonstrates the effectiveness and advantages of the proposed control method.

关键词： Adaptive learning control Data-driven approach Model predictive control

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Short term electric load forecast with artificial neural networks

arXiv

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arXiv 2018年

作者： Cristian, Vasar Iosif, Szeidert Ioan, Filip Gabriela, Prostean Department of Control System Engineering Politehnica' University from Timisoara Faculty of Automation and Computer Science Timisoara300223 Romania

This paper presents issues regarding short term electric load forecasting using feedforward and Elman recurrent neural networks. The study cases were developed using measured data representing electrical energy consume from Banat area. There were considered 35 different types of structure for both feedforward and recurrent network cases. For each type of neural network structure were performed many trainings and best solution was selected. The issue of forecasting the load on short term is essential in the effective energetic consume management in an open market environment. Copyright © 2007 IFAC Copyright © 2018, The Authors. All rights reserved.

关键词： Electric load forecasting

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Axially and Radially Expandable Modular Helical Soft Actuator for Robotic Implantables

Axially and Radially Expandable Modular Helical Soft Actuato...

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IEEE International Conference on Robotics and automation

作者： Eduardo R. Perez-Guagnelli Sarunas Nejus Jian Yu Shuhei Miyashita YanQiang Liu Dana D. Damian Automatic Control and System Engineering Department University of Sheffield UK School of Mechanical Engineering and Automation Beihang University China Department of Electronic Engineering University of York UK Centre of Assistive Technology and Connected Healthcare University of Sheffield UK

Soft robotics has advanced the field of biomedical engineering by creating safer technologies for interfacing with the human body. One of the challenges in this field is the realization of modular soft basic constituents and accessible assembly methods to increase the versatility of soft robots. We present a soft pneumatic actuator composed of two elastomeric strands that provide interdependent axial and radial expansion due to the modularity of the components and their helical arrangement. The actuator reaches 35% of elongation with respect to its initial height and both chambers achieve forces of 1N at about 19kPa. We describe the design, fabrication, modeling and benchtop testing of the soft actuator towards realizing 3D functional structures with potential medical applications. An example of application for soft medical robots is tissue regenerative for the long-gap esophageal atresia condition.

关键词： Actuators Implants Soft robotics Esophagus Surgery Mathematical model

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