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检索条件"任意字段=7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018"

共 474 条记录，以下是461-470 订阅

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Computationally Inexpensive Robust data driven Optimal Point-To-Point Tracking ILC for City Subway Trains subject to Iteration-Dependent Disturbances

Computationally Inexpensive Robust Data Driven Optimal Point...

引用

ieee data driven control and learning systems conference

作者： Genfeng Liu Zhongsheng Hou Advanced Control Systems Laboratory School of Electronic & Information Engineering Beijing Jiaotong University Beijing 100044

ISBN: (纸本)9781538626191

this paper presents a robust data driven optimal point-to-point ILC for subway trains with multiple-point tracking and subject to iteration-dependent disturbances by only utilizing input output data of the train system. Firstly, the tracking task requires that the control input is updated according to the prespecified measured multiple-point tracking error values rather than the complete output trajectory, which can reduce computational cost. Secondly, without model information of the train system, a robust data driven control law is designed. then, rigorous analysis is developed which demonstrates that the train tracking error is monotonic uniformly ultimately bounded convergence and the ultimate bound which only depends on the disturbances boundedness. Finally, a simulation is conducted for train system to verify the effectiveness of theoretical studies.

关键词： data driven control Robust optimal PTPILC City subway trains Low computational cost

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Bioinspired Adaptive Spiking Neural Network to control NAO Robot in a Pavlovian Conditioning Task 7

Bioinspired Adaptive Spiking Neural Network to Control NAO R...

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7th ieee RAS/EMBS International conference on Biomedical Robotics and Biomechatronics, BIOROB 2018

作者： Antonietti, Alberto Casellato, Claudia Angelo, Egidio Dr Pedrocchi, Alessandra Department of Electronics Information and Bioengineering Neuroengineering and Medical Robotics Laboratory Politecnico di Milano Piazza L. Da Vinci 32 I-Milano Italy University of Pavia Department of Brain and Behavioral Sciences Via Forlanini 6 Pavia Italy Brain Connectivity Center IRCCS Istituto Neurologico Nazionale C. Mondino Via Mondino 2 Pavia Italy

ISBN: (纸本)9781538681831

the cerebellum has a central role in fine motor control and in various neural processes, as in associative paradigms. In this work, a bioinspired adaptive model, developed by means of a spiking neural network made of thousands of artificial neurons, has been leveraged to control a humanoid NAO robot in real-time. the learning properties of the system have been challenged in a classic cerebellum-driven paradigm, the Pavlovian timing association between two provided stimuli, here implemented as a laser-avoidance task. the neurophysiological principles used to develop the model, succeeded in driving an adaptive motor control protocol with acquisition and extinction phases. the spiking neural network model showed learning behaviors similar to the ones experimentally measured with human subjects in the same conditioning task. the model processed in real-time external inputs, encoded as spikes, and the generated spiking activity of its output neurons was decoded, in order to trigger the proper response with a correct timing. three long-term plasticity rules have been embedded for different connections and with different time-scales. the plasticities shaped the firing activity of the output layer neurons of the network. In the Pavlovian protocol, the neurorobot successfully learned the correct timing association, generating appropriate responses. therefore, the spiking cerebellar model was able to reproduce in the robotic platform how biological systems acquire and extinguish associative responses, dealing with noise and uncertainties of a real-world environment. © 2018 ieee.

关键词： Neural network models

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Evolutionary Game Gynamics driven by Heterogeneous Self-learning Rules

Evolutionary Game Gynamics Driven by Heterogeneous Self-lear...

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ieee data driven control and learning systems conference

作者： Lei Zhou Bin Wu Vitor V. Vasconcelos Long Wang Center for Systems and Control Peking University Beijing 100871 P. R. China School of Sciences Beijing University of Posts and Telecommunications Beijing 100876 P. R. China Department of Ecology and Evolutionary Biology Princeton University Princeton New Jersey 08544 USA

ISBN: (纸本)9781538626191

How to achieve full cooperation among large numbers of individuals is essential for both artificial and biological systems. learning rules (or updating rules), which specify how individuals change their behavior over time, are vital to probe this problem. Here, we incorporate individual heterogeneity into the self-evaluation process and propose the heterogeneous self-learning dynamics. When the selection intensity is weak, we analytically derive that the final outcomes of the heterogeneous dynamics can be obtained by combining the outcomes of all the corresponding homogeneous dynamics in well-mixed populations. Meanwhile, a simple condition is found which tells whether one behavior will be more abundant than the other in the long run. All of our analytical results are verified by simulations. Our work thus reveals some interesting characteristics of heterogeneous self-learning dynamics.

关键词： Cooperation Self-learning rules Evolutionary dynamics Game theory

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Moving Object Real-time Detection and Tracking Method Based on Improved Gaussian Mixture Model

Moving Object Real-time Detection and Tracking Method Based ...

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ieee data driven control and learning systems conference

作者： Shanliang Zhu Xin Gao Haoyu Wang Guangwei Xu Qiuling Xie Shuguo Yang Research Center for Mathematical Modeling School of Mathematics and Physics Qingdao University of Science and Technology Qingdao 266061 China Finance Office Qingdao University of Science and Technology Qingdao 266061 China Institute of Intelligence Science & Data Technology School of Mathematics and Physics Qingdao University of Science and Technology Qingdao 266061 China

ISBN: (纸本)9781538626191

In order to improve the reliability of moving objects detection and tracking, this paper presents a method for moving object real-time detection and tracking based on Vibe and Gaussian mixture model (GMM). this method uses the "Virtual" background model that is trained by video sequence instead of the first frame image for background modeling. And then the foreground object is extracted based on the pixel classification. Finally, according to the morphological method, the clearer moving targets are conducted to realize the real-time detection and tracking. the experimental results show that, in comparison with the current mainstream background subtraction techniques, our approach effectively works on a wide range of complex scenarios, with faster detection speed and more reliable detection results.

关键词： Moving object detection Vibe Gaussian mixture model Pixel classification

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Design of Regional Logistics System Based on Unmanned Aerial Vehicle

Design of Regional Logistics System Based on Unmanned Aerial...

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ieee data driven control and learning systems conference

作者： Haoyuan Ni Xiaohui Deng Bo Gong Pangwei Wang Beijing Key Lab of Urban Intelligent Traffic Control Technology North China University of Technology China Highway Engineering Consultants Corporation

ISBN: (纸本)9781538626191

With the development of electronic commerce, a large number of labors are demanded to complete the express delivery, because of the complexity of labor management, the increasing amount of labors will result in increasing complication of the express distribution process. In addition, the express also has great limitations in mountainous areas, disaster areas and other special areas of harsh conditions. To solve the problem, this paper presents an intelligent regional logistics transportation system based on unmanned aerial vehicle (UAV). Firstly, this paper designs the application (APP) for mobile phone to send coordinate information and delivery route to the UAV. then the GPS and visual identification location module of UVA help implement the delivery for the residential area. this system can help achieve a safer, cheaper, less delayed, more orderly and unmanned regional logistics system in residential neighborhoods or other similar areas. Finally, the problem of "last kilometer logistics" will be solved to some extent by this method.

关键词： Intelligent transportation system(ITS) Unmanned aerial vehicle (UAV) Regional logistics system

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A Bidirectional Green Wave Band Method under Asymmetric Phase Sequence Mode based on Mobile Navigation data

A Bidirectional Green Wave Band Method under Asymmetric Phas...

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ieee data driven control and learning systems conference

作者： Jiyuan Tan Ming Zhang Honghai Li Weiwei Guo Li Wang Beijing Key Lab of Urban Intelligent Traffic Control Technology North China University of Technology Research Institute of Highway Ministry of Transport Beijing

ISBN: (纸本)9781538626191

With the continuous development of the city, traffic congestion is a serious problem. In order to reduce average delay and average numbers of stop, this paper proposed a bidirectional green wave band method under asymmetric phase sequence mode based on free-flow speed. the main idea to the method is to design a bidirectional wave band for arterial roads. Firstly, the different speed between each two intersections is considered and three speed calculation methods are proposed in this paper. then, intersections with asymmetric phase sequence are also mentioned and this method adjust the relative offset by set a random number which range of zero to the public cycle length. Finally, choose the maximum of all green wave bandwidths by using graphical way. In order to test the effectiveness of the arterial signal coordination method proposed in this paper, a simulation model is considered, where the performance indexes are the average delay and average numbers of stop. the simulation result shows that the method proposed in this paper can improve the traffic capacity by increasing bandwidth and reducing delay.

关键词： Traffic congestion Graphical method Trajectories Bidirectional green wave Speed

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Sequential Graphical Lasso for Fault Detection and Isolation

Sequential Graphical Lasso for Fault Detection and Isolation

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ieee data driven control and learning systems conference

作者： Yi Liu Jiusun Zeng Lei Xie Shihua Luo Hongye Su Institute of Cyber Systems and Control Zhejiang University College of Metrology and Measurement Engineering China Jiliang University School of Statistics Jiangxi University of Finance and Economics

ISBN: (纸本)9781538626191

this article proposes a sequential graphical Lasso based approach for monitoring of complex industrial systems. the graphical Lasso is a widely used algorithm to estimate the precision matrix (inverse covariance matrix), which encodes the conditional relationship between pairs of variables given other entities. Based on the estimated precision matrix, a graphical model can be constructed to represent the structured correlation information between process variables. the proposed approach utilizes the graphical model to localize anomalous variables. Different from the conventional graphical Lasso approach, the proposed method considers an additional fusseed lasso term and a similarity term in the objective function and the optimization problem can be solved by the alternative direction method of multiplier (ADMM). Using a moving window approach, the proposed method generates a sequence of sparse Gaussian graphs and a new monitoring statistic based on penalized likelihood ratio and matrix norm is constructed. Once a fault is detected, the problem of fault isolation becomes a graph matching problem and a fault score index is calculated for each variable. the validity of proposed method in fault detection and isolation is illustrated by a typical fault observed in the Tennessee Eastman (TE) process.

关键词： Graphical Lasso Penalized Likelihood Ratio Fault Score Moving Window

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Bioinspired Adaptive Spiking Neural Network to control NAO Robot in a Pavlovian Conditioning Task

Bioinspired Adaptive Spiking Neural Network to Control NAO R...

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ieee/RAS-EMBS International conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Alberto Antonietti Claudia Casellato Egidio D'Angelo Alessandra Pedrocchi Neuroengineering and Medical Robotics Laboratory Milano Italy University of Pavia Pavia Italy

关键词： Robots Neurons Task analysis Protocols Laser beams Timing Synapses

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Identifying Gait Phases from Joint Kinematics during Walking with Switched Linear Dynamical systems

Identifying Gait Phases from Joint Kinematics during Walking...

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ieee International conference on Biomedical Robotics and Biomechatronics

作者： Luke Drnach Irfan Essa Lena H. Ting Georgia Institute of Technology Atlanta GA 30332 USA Emory University Atlanta GA 30322 USA

ISBN: (纸本)9781538681848

Human-robot interaction (HRI) for gait rehabilitation would benefit from data-driven gait models that account for gait phases and gait dynamics. Here we address the current limitation in gait models driven by kinematic data, which do not model interlimb gait dynamics and have not been shown to precisely identify gait events. We used Switched Linear Dynamical systems (SLDS) to model joint angle kinematic data from healthy individuals walking on a treadmill with normal gaits and with gaits perturbed by electrical stimulation. We compared the model-inferred gait phases to gait phases measured externally via a force plate. We found that SLDS models accounted for over 88% of the variation in each joint angle and labeled the joint kinematics with the correct gait phase with 84% precision on average. the transitions between hidden states matched measured gait events, with a median absolute difference of 25ms. To our knowledge, this is the first time that SLDS inferred gait phases have been validated by an external measure of gait, instead of against pre-defined gait phase durations. SLDS provide individual-specific representations of gait that incorporate both gait phases and gait dynamics. SLDS may be useful for developing control policies for HRI aimed at improving gait by allowing for changes in control to be precisely timed to different gait phases.

关键词： Legged locomotion Hidden Markov models Superluminescent diodes Kinematics data models Switches Predictive models Gait Locomotion superluminescent diodes kinematics Biped locomotion data models Hidden Markov models Prediction models Switches DYNAMICAL systems Phase walking joint angle force plate

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Context-Aware learning from Demonstration: Using Camera data to Support the Synergistic control of a Multi-Joint Prosthetic Arm

Context-Aware Learning from Demonstration: Using Camera Data...

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ieee/RAS-EMBS International conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Gautham Vasan Patrick M. Pilarski University of Alberta

Ahstract- Muscle synergies in humans are context-dependent-they are based on the integration of vision, sensorimotor information and proprioception. In particular, visual information plays a significant role in the execution of goal-directed grasping movements. Based on a desired motor task, a limb is directed to the correct spatial location and the posture of the hand reflects the size, shape and orientation of the grasped object. Such contextual synergies are largely absent from modern prosthetic robots. In this work, we therefore introduce a new algorithmic contribution to support the context-aware, synergistic control of multiple degrees-of-freedom of an upper-limb prosthesis. In our previous work, we showcased an actor-critic reinforcement learning method that allowed someone with an amputation to use their non-amputated arm to teach their prosthetic arm how to move through a range of coordinated motions and grasp patterns. We here extend this approach to include visual information that could potentially help achieve context-dependent movement. To study the integration of visual context into coordinated grasping, we recorded computer vision information, myoelectic signals, inertial measurements, and positional information during a subject's training a robotic arm. Our approach was evaluated via prediction learning, wherein our algorithm was tasked with accurately distinguishing between three different muscle synergies involving similar myoelectric signals based on visual context from a robot-mounted camera. these preliminary results suggest that even simple visual data can help a learning system disentangle synergies that would be indistinguishable based solely on motor and myoelectric signals recorded from the human user and their robotic arm. We therefore suggest that integrating learned, vision-contingent predictions about movement synergies into a prosthetic control system could potentially allow systems to better adapt to diverse situations of daily-life p

关键词： Prosthetics Muscles Electromyography Grasping Robots Training Wrist

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