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IEEE International Workshop on intelligent Robots and systems (IROS)

作者： A. Heale L. Kleeman Intelligent Robotics Research Centre Department of Electrical and Computer Systems Engineering Monash University VIC Australia

This paper describes a new highly accurate fast self-contained sonar sensor. Transmission and echo analyses are performed at repetition rates exceeding 27 Hz for ranges up to 5.4 metres. The sensor contains low noise variable gain preamplifiers, two 1 MHz 12 bit ADC receivers and a DSP echo processor. Optimal arrival time estimation is performed by the DSP using matched filtering of echoes with short duration and wide bandwidth. With the sensor mounted on a mobile robot, map results are derived from scanning with the sensor as the robot moves continuously.

关键词： Digital signal processing Sonar Mobile robots Sensor phenomena and characterization Performance analysis Gain Preamplifiers Matched filters Filtering Bandwidth

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A Genetic Algorithm for Warehouse Multi-Objective Optimisation

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IFAC Proceedings Volumes 2000年第17期33卷 963-969页

作者： S.G. Tzafestas P. Poulos G.G. Rigatos A. Koukos Intelligent Robotics and Automation Laboratory Department of Electrical and Computer Engineering National Technical University of Athens Zografou Campus 15773 Athens Greece

The rapid increase of warehouse use demands automated management services in order to make decisions for all tasks concerned. These decisions must ensure optimised usage of resources, which leads to cost reduction and better customer service. A major consideration is the way the warehouse area, which consists of different storage types and similar product groups, is exploited. The optimisation of the warehouse's occupied area is the target of replenishment, which iS essentially a constrained placement problem. In this paper, a genetic algorithm with revised operators iS developed. This algorithm is applied to real warehouse data and results show that it produces successful replenishments in a complex environment where many criteria have to be considered and met to some user-defined extent.

关键词： genetic algorithms multi-objective optimisation constrained placement warehouse management

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Color recognition

Color recognition

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International Conference on Information Technology Interfaces

作者： M.S. Stachowicz D. Lemke Laboratory for Intelligent Systems Department of Electrical and Computer Engineering University of Minnesota Duluth MN USA

ISBN: (纸本)9539676916

Color provides a wealth of information about an image. Color reduction and color feature extraction can be used to compress this information into a manageable image characteristic. These simple image-processing techniques can be used to quickly describe images while providing enough information to allow image classification and identification. This paper investigates three possible applications of color recognition: flag identification, stamp identification, and landscape classification. The results from these three examples show that color reduction can provide a useful tool for image understanding. In addition, the low processor cost of this system allows it to be easily incorporated into larger systems.

关键词： Pixel Color Image coding Information management computer displays Matrix converters Laboratories intelligent systems Drives Feature extraction

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Fuzzy boundaries

Fuzzy boundaries

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International Conference on Information Technology Interfaces

作者： M.S. Stachowicz Laboratory for Intelligent Systems Electrical and Computer Engineering Department University of Minnesota Duluth MN USA

ISBN: (纸本)9539676916

The primary purpose of this lecture is to provide the participant with a comprehensive coverage of theoretical foundation of digital fuzzy set theory (DFS) and multivalued logic (MVL), as well as a broad overview of the increasingly important applications of these new areas of mathematics. Due to computational efficiency DFS and MVL could be used extensively, especially in real time implementations. Throughout the paper, many examples are used to illustrate concepts, methods, and genetic applications. The applications, which are multi-disciplinary in nature, include computing with words, automatic control, image processing, database management, pattern recognition and so on.

关键词： Fuzzy logic Fuzzy set theory Set theory Fuzzy systems Laboratories intelligent systems Multivalued logic Mathematics Genetics Automatic 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

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.

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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Hierarchical genetic algorithm based neural network design

Hierarchical genetic algorithm based neural network design

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IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (CoEN)

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

In this paper, we propose a novel genetic algorithm based design procedure for multi-layer feedforward neural network. Hierarchical genetic algorithm is used to evolve both neural network topology and parameters. Compared with traditional genetic algorithm based designs for neural network, the proposed hierarchical approach addressed several deficiencies highlighted in literature. A multi-objective function is used herein to optimize the performance and topology of the evolved neural network. Two benchmark problems are successfully verified and the proposed algorithm proves to be competitive or even superior to the traditional back-propagation network in Mackey-Glass chaotic time series prediction.

关键词： Neural networks Biological neural networks Algorithm design and analysis Genetic algorithms Neurons Feedforward neural networks Network topology Multi-layer neural network Training data Force measurement

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Winner take all experts network for sensor validation

Winner take all experts network for sensor validation

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IEEE Conference on Control Technology and Applications (CCTA)

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

The validation of sensor measurements has become an integral part of the operation and control of modern industrial equipment. The sensor under a harsh environment must be shown to consistently provide the correct measurements. Analysis of the validation hardware or software should trigger an alarm when the sensor signals deviate appreciably from the correct values. Neural network based models can be used to estimate critical sensor values when neighboring sensor measurements are used as inputs. The discrepancy between the measured and predicted sensor values may then be used as an indicator for sensor health. The proposed winner take all experts (WTAE) network is based on a 'divide and conquer' strategy. It employs a growing fuzzy clustering algorithm to divide a complicated problem into a series of simpler sub-problems and assigns an expert to each of them locally. After the sensor approximation, the outputs from the estimator and the real sensor value are compared both in the time domain and the frequency domain. Three fault indicators are used to provide analytical redundancy to detect the sensor failure. In the decision stage, the intersection of three fuzzy sets accomplishes a decision level fusion, which indicates the confidence level of the sensor health. Two data sets, the Spectra Quest Machinery Fault Simulator data set and the Westland vibration data set, were used in simulations to demonstrate the performance of the proposed WTAE network. The simulation results show the proposed WTAE is competitive with or even superior to the existing approaches.

关键词： Industrial control Signal analysis Hardware Neural networks Clustering algorithms Frequency estimation Frequency domain analysis Failure analysis Redundancy Fault detection

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intelligent fault tolerant control using artificial neural networks

Intelligent fault tolerant control using artificial neural n...

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International Joint Conference on Neural Networks (IJCNN)

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

As dynamic systems become more complex, experience more rapidly changing environments, and encounter a greater variety of unexpected component failures, solving the control problems of such systems is a grand challenge for control engineers. We investigate the fault tolerant control problem and propose an intelligent 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 what the system's current state is 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 robust term to confine the system performance within a boundary layer. At the same time, an artificial neural network is initialized and compensates for the unknown fault dynamics online. Once 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. The simulation results show a significant improvement in trajectory following performance based upon the proposed intelligent control strategy.

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

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Dense range image smoothing using adaptive regularization

Dense range image smoothing using adaptive regularization

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IEEE International Conference on Image Processing

作者： Yiyong Sun Joon-Ki Paik J.R. Price M.A. Abidi Department of Electrical and Computer Engineering Imaging Robotics and Intelligent Systems Laboratory University of Tennessee Knoxville USA Department of Image Engineering Graduate School of Advanced Imaging Science Chung-Ang University Seoul South Korea

We propose an adaptive regularization algorithm for smoothing dense range images using a novel, first order stabilizing function. The stabilizer we suggest is based upon minimizing the reconstructed surface area and is derived in the native, spherical coordinate system of the range scanner. This allows adjustments to be made along only the direction of measurement, thereby preventing the data overlapping problem that can arise in dense images. Adaptation is achieved by adjusting the regularization parameter according to the results of 2D edge analysis. Results indicate effective noise suppression along with well preserved edges and details in the reconstructed, 3D surfaces.

关键词： Smoothing methods Surface reconstruction Image reconstruction Image edge detection intelligent robots Robot kinematics intelligent systems Laboratories Laser noise Noise measurement

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Discrete-time tracking control of robotic manipulators based on dynamic inversion using dynamic neural networks

Discrete-time tracking control of robotic manipulators based...

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IEEE International Symposium on intelligent Control (ISIC)

作者： F.C. Sun Z.Q. Sun R.J. Zhang Y.B. Chen Department of Computer Science and Technology State Key Laboratory of Intelligent Technology and Systems Beijing China Departmentof Electrical Engineering Purdue University Indianapolis IN USA

A stable discrete time tracking control approach based on dynamic inversion using dynamic neural networks (DNNs) is developed in the paper for robotic manipulators with unknown dynamics nonlinearities. Two novel design technologies-dynamic inversion constructed by DNNs and the NN variable structure control-are used for adaptive tracking controller design. The robot control law is composed of the dynamic inversion of the DNN system, adaptive compensation and the NN variable structure control (VSC) components. The developed control scheme guarantees the global stability and tracking error convergence of the NN control system. Finally, the control performance of the proposed control approach is illustrated through the comparison studies with robot tracking control approach using static NNs.

关键词： Robot control Manipulator dynamics Neural networks Nonlinear dynamical systems Control systems Programmable control Adaptive control Control nonlinearities Adaptive systems Stability

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