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Online fault accommodation control for catastrophic system failures

Online fault accommodation control for catastrophic system f...

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American control Conference (ACC)

作者： Liang-Wei Ho G.G. Yen Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University USA

Prompted by the increasing demands in system reliability and survivability, fault detection and accommodation has quickly become one of the most active research areas in the intelligent control community. In this work, the necessary and sufficient conditions for system online stability under catastrophic failures have been derived based upon discrete-time Lyapunov stability theory. An online fault accommodation control framework is proposed to deal with the desired trajectory-tracking problem for systems suffering from various unknown and unanticipated catastrophic component failures. The approach is to continuously monitor the system performance and identify the system's current state by using an efficient online fault detection scheme based upon our best knowledge of the nominal system and nominal controller. Once a fault is detected, a neural network is suggested as the online estimator to learn and approximate the unknown system failure dynamics. Effective control signals to accommodate the dynamic failures are then computed through the realization of the neural network estimator for the unknown failures based only upon the partially available information of the faults.

关键词： control systems Fault detection Neural networks Reliability intelligent control Sufficient conditions Stability Lyapunov method Condition monitoring System performance

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A hybrid intelligent system for medical diagnosis

A hybrid intelligent system for medical diagnosis

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International Joint Conference on Neural Networks (IJCNN)

作者： P. Meesad G.G. Yen School of Electrical and Computer Engineering Intelligent Systems and Control Laboratory Oklahoma State University Stillwater OK USA

ISBN: (纸本)0780370449

We propose a novel hybrid intelligent system (HIS) that is a combination of numerical and linguistic knowledge representation. The proposed HIS is a hierarchical integration of an incremental learning fuzzy neural network (ILFN) and a fuzzy linguistic model optimized via the genetic algorithm. The ILFN is self-organizing network with the capability of fast, online, incremental learning. The linguistic model is constructed based on knowledge embedded in the trained ILFN. The knowledge captured from the low-level ILFN can be mapped to the higher-level linguistic model and vice versa. The GA is applied to optimize the linguistic model to maintain high accuracy and comprehensibility. The resulted HIS is capable of dealing with low-level numerical computation and higher-level linguistic computation. After the system completely constructed, it can incrementally learn new information in both numerical and linguistic structures. To evaluate the system's performance the well-known benchmark Wisconsin breast cancer data was studied as an application to medical diagnosis. The simulation results show that the proposed HIS perform better than the individual standalone systems.

关键词： Hybrid intelligent systems Medical diagnosis Fuzzy neural networks Knowledge representation Genetic algorithms Self-organizing networks System performance Breast cancer Computational modeling Medical simulation

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Coordination of exploration and exploitation in a dynamic environment

Coordination of exploration and exploitation in a dynamic en...

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International Joint Conference on Neural Networks (IJCNN)

作者： G. Yen Fengming Yang T. Hickey M. Goldstein School of Electrical and Computer Engineering Intelligent Systems and Control Laboratory Oklahoma State University Stillwater OK USA

ISBN: (纸本)0780370449

One much researched issue in reinforcement learning is the trade off between exploration and exploitation. Being able to effectively balance exploration and exploitation activities becomes even more crucial in a dynamic environment. An algorithm is proposed herein that provides one solution to the exploration vs. exploitation dilemma. The algorithm is presented in the context of a path-finding agent in a dynamic grid-world problem. The state-value function used is penalty based, allowing the agent to act over the space of paths with minimal penalties. A forgetting mechanism is implemented that allows the agent to explore paths that were previously determined to be suboptimal. Simulation results are used to analyze the behavior of the proposed algorithm in a dynamic environment.

关键词： Learning systems Genetic algorithms systems engineering and theory intelligent systems intelligent control control systems Analytical models Algorithm design and analysis Heuristic algorithms Acceleration

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Reconfigurable control system design for fault diagnosis and accommodation

Reconfigurable control system design for fault diagnosis and...

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IEEE Conference on Decision and control

作者： Liang-Wei Ho G.G. Yen Intelligent Systems and Control Laboratory School of Electrical and Computer Engineerin Oklahoma State University USA

ISBN: (纸本)0780370619

The online fault tolerant control problem for dynamic systems under unanticipated failures is investigated from a realistic point of view without any specific assumption on type of system dynamical structure or failure scenarios. The necessary and sufficient conditions for system online stability under catastrophic failures have been derived using the discrete-time Lyapunov stability theory. Based upon existing control theory and modern intelligent techniques, an online fault accommodation control strategy is proposed to deal with the desired trajectory-tracking problems for systems suffering from various unknown and unanticipated catastrophic component failures. Through the online estimator, effective control signals to accommodate the dynamic failures can be computed using only the partially available information of the faults. To investigate the feasibility of using the developed technique for unanticipated fault accommodation in real hardware under the real-time environment, an online fault tolerant control test bed has been constructed to validate the proposed technology. Both online simulations and the real-time experiment show encouraging results and promising futures of online real-time fault tolerant control based solely upon insufficient information of the system dynamics and the failure modes.

关键词： control systems Fault diagnosis Fault tolerant systems Real time systems Sufficient conditions Stability Lyapunov method control theory Hardware Fault tolerance

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A connectionist-symbolic approach to modeling agent behavior: Neural networks grouped by contexts 3rd

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3rd International and Interdisciplinary Conference on the Topic of Context, CONTEXT 2001

作者： Henninger, Amy E. Gonzalez, Avelino J. Georgiopoulos, Michael DeMara, Ronald F. Intelligent Systems Laboratory School of Electrical Engineering and Computer Science University of Central Florida United States Soar Technology Inc 317 North First Street Ann ArborMI48103-3301 United States

ISBN: (纸本)9783540423799

A recent report by the National Research Council (NRC) declares neural networks "hold the most promise for providing powerful learning models". While some researchers have experimented with using neural networks to model battlefield behavior for computer Generated Forces (CGF) systems used in distributed simulations, the NRC report indicates that further research is needed to develop a hybrid system that will integrate the newer neural network technology into the current rule-based paradigms. This paper supports this solicitation by examining the use of a context structure to modularly organize the application of neural networks to a low-level Semi-Automated Forces (SAF) reactive task. Specifically, it reports on the development of a neural network movement model and illustrates how its performance is improved through the use of the modular context paradigm. Further, this paper introduces the theory behind the neural networks’ architecture and training algorithms as well as the specifics of how the networks were developed for this investigation. Lastly, it illustrates how the networks were integrated with SAF software, defines the networks’ performance measures, presents the results of the scenarios considered in this investigation, and offers directions for future work. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Hybrid systems

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Reducing OS overhead for real-time industrial controllers with adjustable timer resolution

Reducing OS overhead for real-time industrial controllers wi...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Tae Rim Park Jae Hyun Park Wook Hyun Kwon Control Information Systems Laboratory School of Electrical Engineering and Computer Science Seoul National University Seoul South Korea Real-time Embedded Systems Laboratory School of Information and Communication Engineering Inha University Incheon South Korea

ISBN: (纸本)0780370902

In this paper, a new method, named adjustable timer resolution (ATR) is proposed to control tasks in a timely manner, with enhanced performance on real-time operating systems. It prevents useless execution of the timer interrupt service routine (ISR). Features of the proposed method are compared with the conventional method, and the schedulability is analyzed to estimate the performance. As an implementation model, uC/OS-II is modified and ported to a power PC processor system. The metrics and results of the model are presented and discussed.

关键词： Industrial control Real time systems control systems Operating systems Communication system control Performance analysis Embedded system Processor scheduling Power system modeling Time factors

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BIFURCATION control AND NONSMOOTH FEEDBACK

BIFURCATION CONTROL AND NONSMOOTH FEEDBACK

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第二十届中国控制会议

作者： Hua O.Wang Yiguang Hong Laboratory for Intelligent and Nonlinear Control(LINC)Department of Electrical and Computer Engineering Duke University DurhamNC 27708U.S.A and Center for Nonlinear and Complex Systems Dept.of Control Science and Engineering Huazhong University of Science and Technology Institute of Systems Sciences Chinese Academy of Sciences

In the paper,we exhibit the usefulness of nonlinearity in two aspects:analysis of nonlinear dynamics and nonsmooth feedback *** illustrative examples are given to show how the new bifurcation p

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Inverse kinematics learning by modular architecture neural networks with performance prediction networks

Inverse kinematics learning by modular architecture neural n...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： E. Oyama Nak Young Chong A. Agah T. Maeda Intelligent Systems Laboratory National Institute of Advanced Industrial Science and Technology Ibaraki Japan Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS USA School of Engineering University of Tokyo Tokyo Japan

ISBN: (纸本)0780365763

Inverse kinematics computation using an artificial neural network that learns the inverse kinematics of a robot arm has been employed by many researchers. However, the inverse kinematics system of typical robot arms with joint limits is a multivalued and discontinuous function. Since it is difficult for a well-known multilayer neural network to approximate such a function, a correct inverse kinematics model cannot be obtained by using a single neural network. In order to overcome the discontinuity of the inverse kinematics function, we proposed a novel modular neural network system that consists of a number of expert neural networks. Each expert approximates the continuous part of the inverse kinematics function. The proposed system uses the forward kinematics model for selection of experts. When the number of the experts increases, the computation time for calculating the inverse kinematics solution also increases without using the parallel computing system. In order to reduce the computation time, we propose a novel expert selection by using the performance prediction networks which directly calculate the performances of the experts.

关键词： Kinematics Neural networks Artificial neural networks Multi-layer neural network computer networks Robots computer architecture Manipulators Concurrent computing Parallel processing

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Fault tolerant control: An intelligent sliding mode control strategy

Fault tolerant control: An intelligent sliding mode control ...

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作者： Yen, Gary G. Ho, Liang-Wei Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078-5032 United States

We investigate the fault tolerant control problem and propose an intelligent online sliding mode control strategy using artificial neural networks to handle the desired trajectories tracking problem for systems suffering from catastrophic faults or incipient failures. The approach is to continuously monitor the system performance and identify the system's current state by using a fault detection method based upon our best knowledge of the nominal system and nominal controller. Once a fault is detected, the proposed intelligent controller will adjust its control signal by adding a corrective sliding mode control signal to confine the system performance within a boundary layer. Meanwhile, an artificial neural network is initialized and compensates for the unknown fault dynamics online. When the online learning process converges, the control input is tuned again by using the output of the identification model and a new least upper bound for the remaining uncertainty is estimated to further reduce the tracking error. © 2000 American Automatic control Council.

关键词： Sliding mode control

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Fault tolerant control: an intelligent sliding mode control strategy

Fault tolerant control: an intelligent sliding mode control ...

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American control Conference (ACC)

作者： G.G. Yen L.-W. Ho Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

关键词： Sliding mode control Fault tolerance intelligent control control systems Artificial intelligence Artificial neural networks System performance Fault detection Fault tolerant systems intelligent networks

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