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Robust decentralized control of power systems utilizing only swing angle measurements

INTERNATIONAL JOURNAL OF control 1997年第4期66卷 581-601页

作者： Jain, S Khorrami, F Control/Robotics Research Laboratory Department of Electrical Engineering Polytechnic University Six Metrotech Center Brooklyn NY 11201 United States

This paper considers an application of the decentralized adaptive output feedback scheme developed by Jain and Khorrami (1997) to maintain global robustness to parametric and dynamic uncertainties among interconnections in large-scale power systems and also rejection of any bounded unmeasurable disturbances entering the system. The proposed design utilizes only feedback from local rotor angle measurements. The scheme is applied to a two-axis (fourth-order) model of the generator with a second-order turbine-governor model and an IEEE type I excitation control (fifth order) for each subsystem. The design presented is also applicable to higher-order models of the generator and turbine-governor system.

关键词： Decentralized control

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Evolutionary Optimisation for Robot Motion

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IFAC Proceedings Volumes 1997年第3期30卷 179-184页

作者： A.S. Rana A.M.S. Zalzala Robotics Research Group Department of Automatic Control and Systems Engineering University of Sheffield Sheffield SI 3JD UK

This paper presents a detailed analysis of a motion planner based on genetic algorithms for collision free motion planning of robotic manipulators through simulation. The problem is formulated for a 2-DOF planar manipulator moving in the presence of a static circular obstacle in its operational space. The algorithm is then extended to 3-DOF planar manipulator moving among multiple static obstacles.

关键词： Genetic algorithms robotic manipulators optimised collision free motion planning

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Fuzzy inference-based reinforcement learning of dynamic recurrent neural networks

Fuzzy inference-based reinforcement learning of dynamic recu...

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SICE Annual Conference

作者： Hyo-Byung Jun Dong-Wook Lee Dae-Joon Kim Kwee-Bo Sim Robotics and Intelligent Control System Lab Department of Control and Instrumentation Engineering Chung-Ang University Seoul South Korea

ISBN: (纸本)0780342089

This paper presents the fuzzy inference-based reinforcement learning algorithm of dynamic recurrent neural network, similar to the psychological learning scheme of the higher animals. The proposed method follows the way linguistic and conceptional expressions have an effect on human's behavior by reasoning reinforcement based on fuzzy rules. The intervals of fuzzy membership functions are found optimally by genetic algorithms. By using the recurrent neural network composed of dynamic neurons as action-generation network, not only the current state but also the past state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying it to the inverted pendulum control problem.

关键词： Learning Recurrent neural networks Fuzzy neural networks Heuristic algorithms Inference algorithms Psychology Animals Fuzzy reasoning Genetic algorithms Neurons

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Advanced control system design for high speed ultra accurate manufacturing systems

Advanced control system design for high speed ultra accurate...

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American control Conference (ACC)

作者： F. Khorrami H. Melkote J. Ish-Shalom Control Robotics Research Laboratory Department of Electrical Engineering Polytechnic University Brooklyn NY USA

In this paper we describe the application of a robust adaptive control design to an ultra accurate (sub-micron) linear and planar step motor known as the Sawyer motor (1969). Simulation results presented show promisin... 详细信息

关键词： control systems Manufacturing systems Planar motors Torque Equivalent circuits Teeth Robot control Laboratories Robust control Adaptive control

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A unified approach to adaptive nonlinear control of stepper motors

A unified approach to adaptive nonlinear control of stepper ...

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IEEE Conference on Decision and control

作者： H. Melkote F. Khorrami Control/Robotics Research Laboratory Department of Electrical Engineering Polytechnic University Brooklyn NY USA

In this paper, a robust adaptive nonlinear controller for various types of stepper motors is presented. The control design is applicable to both variable reluctance and permanent magnet stepper motors. It is also shown that the methodology is applicable to other peculiar configurations of stepper motors (e.g. Sawyer motors). Furthermore, the motors may be either rotary or linear. To this end, a general electromechanical model of stepper motors is utilized. The model is comprised of the mechanical dynamics and electrical dynamics of the motor including the effects of cogging, viscous friction, winding resistance or other uncertainties. The robust adaptive design is based on our earlier work (1997) and does utilizes backstepping in the case that voltage level control is used rather than current controls. Simulation studies are presented to show the efficacy of the control design approach.

关键词： Programmable control Adaptive control Reluctance motors Permanent magnet motors Magnetic variables control Robust control control design Forging Friction Electric resistance

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Friction compensation for drives with and without transmission compliance

Friction compensation for drives with and without transmissi...

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American control Conference (ACC)

作者： S. Jain F. Khorrami N. Ahmad S. Sankaranarayanan Control/Robotics Research Laboratory (CRRL) Department of Electrical Engineering Polytechnic University Brooklyn NY USA

In this paper, nonlinear robust adaptive controller is proposed for friction compensation. The design has been performed for a six parameter dynamic model of friction. The advocated control design does not utilize any knowledge of these six parameters and requires just an upper bound on the static friction level, which can be obtained experimentally. The controller is robust to dynamic uncertainties and can compensate the effect of friction which is separated from the control input through drive compliance. Simulation and experimental results are also presented.

关键词： Friction Programmable control Adaptive control Robust control Torque Robot control Laboratories control design Upper bound Temperature

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Artificial immune network-based cooperative control in collective autonomous mobile robots

Artificial immune network-based cooperative control in colle...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Dong-Wook Lee Kwee-Bo Sim Robotics and Intelligent Information System Laboratory Department of Control and Instrumentation Engineering Chung Ang University Seoul South Korea

In this paper, we propose a method of cooperative control based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying the immune system to DARS, a robot is regarded as a B lymphocyte (B cell), each environmental condition as an antigen and a behavior strategy as an antibody respectively. The executing process of proposed method is as follows: when the environmental condition changes, a robot selects an appropriate behavior strategy, and its behavior strategy is stimulated and suppressed by other robot using communication. Finally, such stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis, and it is used for decision making of an optimal swarm strategy.

关键词： Intelligent networks Mobile robots Immune system control systems Intelligent robots Mobile communication Decision making Humans Robot sensing systems Intelligent systems

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Behavior learning and evolution of collective autonomous mobile robots based on reinforcement learning and distributed genetic algorithms

Behavior learning and evolution of collective autonomous mob...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Hyo-Byung Jun Kwee-Bo Sim Robotics and Intelligent Information System Laboratory Department of Control and Instrumentation Engineering Chung Ang University Seoul South Korea

In this paper, we present the reinforcement learning and distributed genetic algorithm based behavior learning of the distributed autonomous mobile robots. The internal reinforcement signal for the reinforcement learning is generated by fuzzy inference, and dynamic recurrent neural networks are used as action generation module. We adopt the distributed genetic algorithms for the cooperative behavior emergence. We show the validity of the proposed learning and evolution algorithm by computer simulation.

关键词： Mobile robots Learning Genetic algorithms Inference algorithms Signal generators Intelligent robots Animals Mobile communication Education Intelligent systems

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Robust adaptive control of direct drive brushless DC motors and applications to robotic manipulators

Robust adaptive control of direct drive brushless DC motors ...

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IEEE Conference on control Technology and Applications (CCTA)

作者： H. Melkote F. Khorrami Control Robotics Research Laboratory CRRL Department of Electrical Engineering Six MetroTech Center Polytechnic University Brooklyn NY USA

Application of robust adaptive nonlinear control to brushless DC motors is considered in this paper. The advocated robust adaptive controller enjoys robustness to parametric and dynamic uncertainties in the motor dynamics. In addition, the controller can reject any bounded disturbances acting on the motor. The controlled variables are phase voltages which are designed based on the backstepping technique. The closed-loop stability of the system is shown using Lyapunov techniques. The tracking errors are shown to be globally uniformly bounded. The design procedure is shown to be also applicable to multi-link manipulators actuated by brushless DC motors. The control design may be improved to achieve asymptotic tracking.

关键词： Robust control Adaptive control Brushless DC motors Programmable control Nonlinear dynamical systems DC motors Uncertainty Voltage control Backstepping Stability

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Model-based objects recognition in industrial environments for autonomous vehicles control

Model-based objects recognition in industrial environments f...

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IEEE International Conference on robotics and Automation (ICRA)

作者： J. Marti J. Batlle A. Casals Computer Vision and Robotics Group Universíty of Girona Girona Spain Department of Automatic Control and Computer Engineering Polytechnic University of Catalonia Barcelona Spain

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system.

关键词： Object recognition Navigation Remotely operated vehicles Mobile robots Layout Shape Image recognition Data mining Image analysis Expert systems

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