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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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Sequential Graphical Lasso for Fault Detection and Isolation

Sequential Graphical Lasso for Fault Detection and Isolation

引用

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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Design of missile longitudinal controller based on deep deterministic policy gradient learning algorithm

Design of missile longitudinal controller based on deep dete...

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ieee Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Wanchao Zhang Guangshan Chen Hao Ni TingShuai Tong YiWen Zhou Shanghai Aerospace Control Technology Institute Shanghai China

For the longitudinal control problem of missiles under highly nonlinear conditions, traditional controllers designed by fixed points have good static characteristics, but in the face of highly nonlinear systems, the control parameters designed at a certain characteristic point will not be able to satisfy the entire control system. In this paper, a combination of reinforcement learning and deep neural network architecture is used to propose an intelligent adaptive controller design scheme based on a deep deterministic policy gradient algorithm. Firstly, the control problem is reconstructed in the framework of deep reinforcement learning. the environment is composed of a two-degree-of-freedom nonlinear model of missile longitudinal dynamics. Secondly, the design of the reward function not only considers the stability of the longitudinal dynamics, but also uses the ITAE index as the reward function. the penalty item is to comprehensively consider the balance of error and execution output. Finally, simulation results are shown to check the ability of the proposed data-driven method to achieve the specified closed-loop response in a fully model-free manner under a complete flight condition, and the method is verified by model parameter-pull Monte Carlo simulations. Feasibility and effectiveness.

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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...

引用

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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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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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

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.

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

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Two-Player Multiagent Graphical Games with Reinforcement learning*

Two-Player Multiagent Graphical Games with Reinforcement Lea...

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Industrial Cyber-Physical systems (ICPS)

作者： Bosen Lian Jiacheng Wu Electrical and Computer Engineering Department Auburn University Auburn AL USA State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

this paper studies the synchronization problem of two-player multiagent systems through reinforcement learning methods. A Nash-minmax strategy is formulated, where the interactions of two players in the same agent are non-zero-sum, while interactions of players between agents are zero-sum games. We propose an offline model-based reinforcement learning algorithm to identify Nash solutions for players within each agent, as well as the worst control solutions for players in neighboring antagonistic agents. On this basis, a data-driven off-policy algorithm is provided to alleviate the requirement for accurate system dynamics in the offline algorithm. Besides, the convergence of the proposed algorithms is analyzed. Finally, simulation results verify the effectiveness of the designed algorithms.

关键词： System dynamics Heuristic algorithms Simulation Games Reinforcement learning Cyber-physical systems Synchronization

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Robust data-driven Model Predictive control via On-policy Reinforcement learning for Robot Manipulators

Robust Data-driven Model Predictive Control via On-policy Re...

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Industrial Cyber-Physical systems (ICPS)

作者： Tianxiang Lu Kunwu Zhang Yang Shi Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

In this paper, a robust data-driven model predictive control (MPC) via on-policy reinforcement learning (RL) is presented for the regulation of constrained robot manipulators subject to both model mismatch and bounded additive disturbances. Based on the Euler-Lagrangian model of the robot manipulator, the model mismatch is characterized by the difference between the system matrix describing the Coriolis and centrifugal torques and its initial approximation. the additive disturbances considered in this work affect the torques applied to the joints. To reduce the complexity of the prediction model adopted by MPC and thus the computational load of solving the online control problem in MPC, the inverse dynamics controller is incorporated into the control framework. the rigid tube-based MPC with adaptive design of the terminal weighting matrix and terminal set is presented to ensure robust constraint satisfaction. the use of the inverse dynamics control policy leads to the selection of the on-policy RL algorithm: Sarsa for designing the policy to update key parameters of the MPC optimization problem. the efficacy of the proposed control framework is validated by a case study using a robot manipulator with two revolute joints in simulation.

关键词： Additives Uncertainty Heuristic algorithms Computational modeling Reinforcement learning Predictive models Prediction algorithms

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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...

引用

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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Research on Fault Self-Healing control Method of Intelligent Power Distribution System Based on Deep learning

Research on Fault Self-Healing Control Method of Intelligent...

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ieee conference on Energy Internet and Energy System Integration (EI2)

作者： Jingru Li Shigong Jiang Hongjun Li Wei Chai Jiacheng Fo Fengzhang Luo Nan Ge Shengyuan Wang State Grid Economic and Technological Research Institute Co. Ltd. Beijing China Distribution Network Planning Center State Grid Economic and Technological Research Institute Co.Ltd. Beijing China Electric Power Research Institute State Grid Shanghai Municipal Electric Power Company Shanghai China Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin China

Aiming at the deficiencies of the current distribution network self-healing control, the high time cost of the heuristic algorithm in the recovery control algorithm after fault isolation and the insolvability arising from the intelligent optimization algorithm in finding fault recovery generation strategies, the traditional distribution network self-healing control scheme cannot meet the application requirements of intelligent distribution systems. this paper proposes a data-driven fault recovery strategy for distribution networks. Firstly, a distribution network self-healing control model based on the mechanism model is proposed, and the distribution network reconstruction method is used to realize the distribution network self-healing function. Finally, the effectiveness of the self-healing control model is verified through the simulation analysis of the ieee 33-node system case, which demonstrates the advantages of the proposed strategy in generating self-healing control solutions for distribution networks quickly and improving network reconfiguration efficiency.

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Compact Deep Neural Networks for Computationally Efficient Gesture Classification From Electromyography Signals

Compact Deep Neural Networks for Computationally Efficient G...

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ieee International conference on Biomedical Robotics and Biomechatronics

作者： Adam Hartwell Visakan Kadirkamanathan Sean R. Anderson Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK Sl 3JD Department of Automatic Control and Systems Eng

ISBN: (纸本)9781538681848

Machine learning classifiers using surface electromyography are important for human-machine interfacing and device control. Conventional classifiers such as support vector machines (SVMs) use manually extracted features based on e.g. wavelets. these features tend to be fixed and non-person specific, which is a key limitation due to high person-to-person variability of myography signals. Deep neural networks, by contrast, can automatically extract person specific features - an important advantage. However, deep neural networks typically have the drawback of large numbers of parameters, requiring large training data sets and powerful hardware not suited to embedded systems. this paper solves these problems by introducing a compact deep neural network architecture that is much smaller than existing counterparts. the performance of the compact deep net is benchmarked against an SVM and compared to other contemporary architectures across 10 human subjects, comparing Myo and Delsys Trigno electrode sets. the accuracy of the compact deep net was found to be 84.2 ±0.06% versus 70.5 ±0.07% for the SVM on the Myo, and 80.3 ±0.07% versus 67.8 ± 0.09% for the Delsys system, demonstrating the superior effectiveness of the proposed compact network, which had just 5,889 parameters - orders of magnitude less than some contemporary alternatives in this domain while maintaining better performance.

关键词： Electrodes Neural networks Feature extraction Support vector machines Graphics processing units Convolution Embedded systems Support Vector Network Neural network Embedded systems Convolution Feature extraction Electrodes Graphics Processing Unit Compact Compact Pattern Electromyogram human subject Myography

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