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An Efficient PC-Based Environment for Power System Studies - Application to the Restoration Problem

IFAC Proceedings Volumes 1998年第20期31卷 659-664页

作者： N.A. Fountas N.D. Hatziargyriou K.P. Valavanis Electric Energy Systems Laboratory Department of Electrical and Computer Engineering National Technical University of Athens Greece Robotics and Automation Laboratory The Center for ACS The University of Southwestern Louisiana Lafayette

advanced powerful tools are sought for in order to conduct power system studies, especially for large scale systems. In this paper, an efficient PC-based environment for power system studies is presented. It is based on both analytical and artificial intelligence techniques. To demonstrate its effectiveness, this environment has been used for restoration studies following a recent blackout in the Hellenic power system, with very promising results.

关键词： computer environment analytic approximations Timed Petri-nets Power system

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Robust Control of a Vibrating Link Robot Manipulator

Robust Control of a Vibrating Link Robot Manipulator

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Southeastern Symposium on System Theory

作者： D. Shukla D.M. Dawson F.W. Paul Department of Mechanical Engineering Robotics and Machine Automation Laboratory Clemson SC USA Department of Electrical and Computer Engineering Center for Advanced Manufacturing Clemson SC USA Department of Mechanical Engineering Clemson University Clemson SC USA

This paper presents an approach for designing robust tracking controller for lumped mass model of a vibrating link (VL) robot manipulator. The control methodology is intuitively simple since it is based on concepts which are familiar to most control engineers. The approach is illustrated by developing a robust tracking controller that achieves global uniform ultimate boundedness (GUUB) stability of the link tracking error in spite of model uncertainty and the additional link dynamics.

关键词： Robust control Robots Manipulators Vibration control robotics and automation Automatic control Uncertainty Force control Error correction Manufacturing automation

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Adaptive Artificial Potential Fields with Orientation Control Applied to Robotic Manipulators

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IFAC-PapersOnLine 2020年第2期53卷 9924-9929页

作者： Caio Cristiano Barros Viturino Ubiratan de Melo Pinto Junior André Gustavo Scolari Conceição Leizer Schnitman LaR - Robotics Laboratory Department of Electrical and Computer Engineering Federal University of Bahia Salvador Brazil CTAI - Training Center in Technology in Industrial Automation Department of Chemical Engineering Federal University of Bahia Salvador Brazil

This paper proposes the integration of an Adaptive Artificial Potential Fields algorithm with a new end effector orientation control technique for real-time robot path planning. The development of autonomous robotic systems has undergone several advances in path planning algorithms. These systems generate object collision-free paths in the robot’s workspace. In this context, the Artificial Potential Fields technique has been the focus of improvements in recent years due to its simplicity of application and efficiency in real-time systems, since it does not require a global mapping of the robot’s workspace. In spite of its efficiency, this technique is susceptible to local minimum problems of different natures, such as Goals Non-Reachable with Obstacles Nearby (GNRON). To solve this problem, we suggest the use of an improvement called Adaptive Artificial Potential Fields used in conjunction with the proposed end effector orientation control technique, which allows reaching a desired orientation of the end effector. The resulting force, generated from the Adaptive Artificial Potential Field, guides the robot end effector to the goal. The Robot Operating System (ROS) framework and a collaborative robot manipulator UR5 are used to validate the proposed method on an approaching task for an object on a 3D printer tray.

关键词： Artificial Potential Field Orientation Control Robotic manipulators Path planning

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Modeling and feedback control of nonholonomic mobile vehicles

Modeling and feedback control of nonholonomic mobile vehicle...

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IEEE Conference on Decision and Control

作者： G.J. Pappas K.J. Kyriakopoulos N.Y. State Center for Advanced Technology in Automation and Robotics Electrical Computer and Systems Engineering Departmennt Rensselaer Polytechnic Institute Troy NY USA

A complete dynamic model of mobile vehicles moving on nonplanar surfaces under nonholonomic and dynamic constraints is presented. The kinematic control of such systems is examined, and the invariant manifold technique is introduced for steering a nonholonomic mobile vehicle to an invariant manifold which is relatively locally-locally controllable. A closed-loop control strategy based on the invariant manifold technique is formulated and applied to the control of nonholonomic vehicles. Tracking control of such vehicles is demonstrated with both holonomic and nonholonomic reference trajectories.< >

关键词： Feedback control Vehicle dynamics Control systems Kinematics Trajectory

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IEEE Transactions on Haptics: Editorial

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IEEE Transactions on Haptics 2008年第1期1卷 2-8页

作者： Edward Colgate, J. Lederman, Susan Prattichizzo, Domenico Barbagli, Federico Basdogan, Cagatay Buss, Martin Ernst, Marc Frisoli, Antonio Gillespie, Brent Grant, Danny Harders, Matthias Hayward, Vincent Jones, Lynette Kajimoto, Hiroyuki Kappers, Astrid Kheddar, Abderrahmane Lin, Ming C. MacLean, Karon Miller, Brian Noma, Haruo Okamura, Allison M. O'Malley, Mareia Ryu, Jeha Tan, Hong Z. Yokokohji, Yasuyoshi Department of Psychology School of Computing Queen's University Canada IEEE Robotics and Automation Society Tübingen Germany Stanford University's Computer Science Department Tübingen Germany Institute of Automatic Control Engineering Technische Universität München Germany Independent Research Group Multisensory Perception and Action Max Planck Institute for Biological Cybernetics Tübingen Germany Department of Applied Mechanics SSSA Japan Research Division Virtual Reality and Telerobotic Systems PERCRO Laboratory Japan Department of Mechanical Engineering Japan Department of Robotics McGill University Canada Computer Vision Lab ETH Zurich Japan Virtual Reality in Medicine Group Japan IEEE CS/RAS Technical Committee on Haptics Japan Department of Electrical and Computer Engineering McGill University Canada Department of Mechanical Engineering MIT Japan University of Electro-Communications Japan Department of Physics and Astronomy Utrecht University Netherlands University of Evry France Chapel Hill United States Department of Computer Science University of British Columbia Canada ATR Knowledge Science Lab United Kingdom Department of Mechanical Engineering Denmark Johns Hopkins University United States Laboratory for Computational Sensing and Robotics United Kingdom Engineering Research Center for Computer-Integrated Surgical Systems and Technology United States Mechanical Engineering and Materials Science Department Rice University United States Korea Republic of Department of Electrical and Computer Engineering Mechanical Engineering and Psychological Sciences Purdue University United States Department of Mechanical Engineering and Science Graduate School of Engineering Kyoto University Japan

No abstract available

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Formation Control with Orientation Alignment and Constrained Input

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IFAC-PapersOnLine 2020年第2期53卷 3397-3402页

作者： Yu-Wen Chen Ming-Li Chiang Li-Chen Fu Department of Electrical Engineering National Taiwan University Taipei Taiwan Graduate Institute of Automation Technology National Taipei University of Technology Taipei Taiwan Department of Electrical Engineering and the Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan

We design a formation controller for Multi Agent Systems such that the agents can form into the desired shape and track a given reference trajectory. The main feature of the proposed design is that not only the orientation of individual agents, but also the orientation of the whole formation is considered and is designed to be aligned with the moving direction of the reference trajectory, which helps the tracking movement to be smoother compared with the common tracking results. Moreover, the control inputs are designed in predefined input ranges to reflect the practical system. System stability is proved based on nonlinear system theory and some simulations are given to validate the proposed results.

关键词： Multi-agent systems Nonlinear adaptive control Coordination of multiple vehicle systems

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Robust neural network control of rigid-link electrically-driven robots

Robust neural network control of rigid-link electrically-dri...

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IEEE International Symposium on Intelligent Control (ISIC)

作者： C.M. Kwan F.L. Lewis D.M. Dawson Automation and Robotics Research Institute University of Texas Arlington Fort Worth TX USA Department of Electrical and Computer Engineering Center for Advanced Manufacturing Clemson University Clemson SC USA

A robust neural network (NN) controller is proposed for the motion control of rigid-link electrically-driven (RLED) robots. The NNs are used to approximate two very complicated nonlinear functions. The main advantage of our approach is that the NN weights are tuned online, with no off-line learning phase required. Most importantly, we can guarantee the uniformly, ultimately bounded (UUB) stability of tracking errors and NN weights. When compared with standard adaptive robot controllers, we do not require persistent excitation conditions and no lengthy and tedious preliminary analysis to determine a regression matrix is needed.

关键词： Robust control Neural networks Robots robotics and automation Stability Actuators Nonlinear dynamical systems Motion control Error correction computer aided manufacturing

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Tetrobot: A Modular System for Hyper-Redundant Parallel robotics

Tetrobot: A Modular System for Hyper-Redundant Parallel Robo...

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IEEE International Conference on robotics and automation (ICRA)

作者： G.J. Hamlin A.C. Sanderson Electrical Computer and Systems Engineering Department Rensselaer Polytechnic InstituteNew York State Center for Advanced Technology in Automation and Robotics Rensselaer Polytechnic Institute RPI Troy NY USA

A modular system for the design, implementation, and control of a class of highly redundant parallel robotic mechanisms is described. The Tetrobot system features a novel concentric multilink spherical (CMS) joint which allows an arbitrary number of links to share a common center of rotation. The CMS joint facilitates the construction of a wide variety of variable geometry truss mechanisms using an integrated control and computational framework. A distributed control algorithm is based on an iterative virtual force based solution which propagates the goal positions of target nodes to determine systematic adjustments of actuators. Capabilities of the Tetrobot system have been demonstrated by the construction of several configurations of up to 18 nodes, 48 links, and 15 actuators. Implementations of a double Stewart platform and a six-legged walker, constructed from the same set of parts, are described in this paper.

关键词： Parallel robots Collision mitigation Actuators Concurrent computing Design engineering Systems engineering and theory Electric variables control Control systems Computational geometry Centralized control

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Robust adaptive control of robots using neural network: global tracking stability

Robust adaptive control of robots using neural network: glob...

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IEEE Conference on Decision and Control

作者： C.M. Kwan D.M. Dawson F.L. Lewis Automation and Robotics Research Institute University of Texas Arlington Fort Worth TX USA Department of Electrical and Computer Engineering Center for Advanced Manufacturing Clemson University Clemson SC USA

A desired compensation adaptive law-based neural network (DCAL-NN) controller is proposed for the robust position control of rigid-link robots. The NN is used to approximate a highly nonlinear function. The controller can guarantee the global asymptotic stability of tracking errors and boundedness of NN weights. In addition, the NN weights here are tuned on-line, with no off-line learning phase required. When compared with standard adaptive robot controllers, one does not require persistent excitation conditions, linearity in the parameters, or lengthy and tedious preliminary analysis to determine a regression matrix. The controller can be regarded as a universal reusable controller because the same controller can be applied to any type of rigid robots without any modifications.

关键词： Robust control Adaptive control Neural networks Programmable control Adaptive systems Position control Asymptotic stability Error correction Robot control Linearity

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Training hard-limiting neurons using back-propagation algorithm by updating steepness factors

Training hard-limiting neurons using back-propagation algori...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiangui Yu N.K. Loh W.C. Miller Department of Electrical Engineering University of Windsor Windsor ONT Canada Center for Robotics and Advanced Automation Dodge Hall of Engineering Oakland University Rochester MI USA

This paper presents one kind of modified backpropagation algorithm for training the multilayer feedforward neural networks with hard-limiting neurons. Adaptive steepness factors in the analog sigmoidal neuron activation functions are updated in the training process. With the decrease of the sum-square error, these steepness factors are varied from a small positive value to infinite. It makes the sigmoidal neuron transferred to hard-limiting one after the training process complete. Thus, a multilayer feedforward neural network can be trained with the resultant architecture is only composed of hard-limiting neurons. The learning algorithm is similar to the conventional backpropagation algorithm, only the derivatives of the hidden neural activation functions are modified according to the proposed idea. Extensive numerical simulations are presented to show the feasibility of the proposed algorithm. In addition, the numerical properties of the proposed algorithm are also discussed in detail. Comparisons of the proposed algorithm with algorithms are given, and some useful conclusions are drawn.< >

关键词： Neurons Backpropagation algorithms Multi-layer neural network Neural networks Feedforward neural networks Very large scale integration robotics and automation Multilayer perceptrons Numerical simulation Artificial neural networks

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