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A unified approach to adaptive nonlinear control of stepper motors

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

引用

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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Development of power assist system with individual compensation ratios for gravity and dynamic load

Development of power assist system with individual compensat...

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： Y. Hayashibara K. Tanie H. Arai H. Tokashiki Department of Control and System Engineering Toin University of Yokohama Yokohama Kanagawa Japan Robotics Department Mechanical Engineering Laboratory M.I.T.I. Tsukuba Ibaraki Japan Institute of Engineering Mechanics University of Tsukuba Tsukuba Japan

This paper present the design concept of a power assist system. In such system, when the controller is designed without considering the maximum torque of the actuators, the actuators can sometimes become saturated, resulting in a loss of stability and manoeuvrability. We propose a method for dealing with this problem. The load force is divided into gravitational and dynamic component, and each component is attenuated by an individual ratio. These ratios are determined considering the maximum power of the operator and the actuators.

关键词： Gravity Actuators control systems Force control Force sensors Stability Power engineering and energy Laboratories Torque control Aerodynamics

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Region-based Q-learning using convex clustering approach

Region-based Q-learning using convex clustering approach

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： J.H. Kim I.H. Suh S.R. Oh Y.J. Cho Y.K. Chung Intelligent Control and Robotics Laboratory Department of Electronics Engineering Hanyang University Ansan Kyunggi South Korea Advanced Robotics Research Center KIST Seoul South Korea School of Mechanical Engineering Information Research Laboratories PIRL Pohang University of Science and Technology South Korea

For continuous state space applications, a novel method of Q-learning is proposed, where the method incorporates a region-based reward assignment being used to solve a structural credit assignment problem and a convex clustering approach to find a region with the same reward attribution property. Our learning method can estimate a current Q-value of an arbitrarily given state by using effect functions, and has the ability to learn its actions similar to that of Q-learning. Thus, our method enables robots to move smoothly in a real environment. To show the validity of our method, the proposed Q-learning method is compared with conventional Q-learning method through a simple two dimensional free space navigation problem, and visual tracking simulation results involving a 2-DOF SCARA robot are also presented.

关键词： Orbital robotics Learning systems Intelligent robots State-space methods Mechanical engineering Mechanical factors State estimation Motion planning Discrete event simulation Humans

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Fuzzy-Q learning for autonomous robot systems

Fuzzy-Q learning for autonomous robot systems

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International Conference on Neural Networks

作者： Il Hong Suh Jae-Hyun Kim F.C.-H. Rhee Intelligent Control and Robotics Laboratory Department of Electronic Engineering Hanyang University Ansan South Korea Computational Vision and Fuzzy Systems Laboratory Hanyang University Ansan South Korea

It is desirable for autonomous robot systems to posses the ability to behave in a smooth and continuous fashion when interacting with an unknown environment. Since Q-learning is normally used for optimizing a series of discrete actions, it may be undesirable when applied to a real environment which involves continuous states and actions. In this paper, we propose a new method of Q-learning that incorporates a fuzzy interpolation technique which is used to approximate a continuous state. Our learning method can estimate current state by its neighboring states and has the ability to learn its actions similar to that of Q-learning. Thus, our method can enable robots to react smoothly in a real environment. Simulation results involving an autonomous robot are given to show the validity of our method.

关键词： Robot sensing systems control systems Laboratories Interpolation State estimation Intelligent robots Learning Robot control Robot vision systems Machine vision

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Fuzzy modeling and control of a nonlinear magnetic bearing system

Fuzzy modeling and control of a nonlinear magnetic bearing s...

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IEEE Conference on control Technology and Applications (CCTA)

作者： Sung-Kyung Hong R. Langari Joongseon Joh Department of Mechanical Engineering and Center for Fuzzy Logic Roboticsand Intelligent Systems Research Texas A and M University College Station TX USA Department of Control and Instrumentation engineering Changwon NatE28099l University South Korea

This paper presents an approach, called fuzzy model based nonlinear fuzzy control, that overcomes position dependent nonlinearity of magnetic bearing systems. We represent the nonlinear magnetic bearing by the Takagi-Sugeno-Kang fuzzy model. Based on such model, nonlinear fuzzy controllers can be derived by means of a systematic synthesis approach. Moreover, the stability analysis of the fuzzy control system can be done efficiently by using linear matrix inequality method. Simulation results demonstrate that the proposed fuzzy controller yields not only maximized stability boundary but also better performance than a single operating point linear controller.

关键词： Fuzzy control Fuzzy systems Nonlinear control systems Magnetic levitation Fuzzy logic control systems Nonlinear dynamical systems Nonlinear magnetics Rotors Magnetic forces

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