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IEEE International Conference on robotics and automation (ICRA)

作者： G.C. Nandy Yangsheng Xu Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong China The Robotics Institute Camegie Mellon University Pittsburgh PA USA

We develop a dynamic model of a gyroscopic wheel, an important component of Gyrover, a single-wheel robot developed at Carnegie Mellon University. The Gyrover robot consists of a single wheel, and is actuated through a spinning flywheel attached through a two-link manipulator at the wheel bearing. The flywheel can be tilted to achieve steering, and can be driven forwards and backwards to accelerate the robot. This paper focuses on developing a 3D model of the wheel part of the Gyrover. We first describe the Gyrover robot. We then develop the dynamic model of the wheel through the Lagrangian constrained generalized formulation. Finally, we implement the resulting equations of motion and present simulation results for the unactuated Gyrover in the different gravitational environments of Earth, the Moon, and Mars.

关键词： Wheels Mobile robots Flywheels Spinning Manipulator dynamics Acceleration Lagrangian functions Differential equations Earth Moon

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Reduced-dimension representations of human performance data for human-to-robot skill transfer

Reduced-dimension representations of human performance data ...

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Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190)

作者： C. Lee Yangsheng Xu Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China

Despite the large amount of research currently directed toward programming robots by demonstration, a significant problem with this method of human-to-robot skill transfer has not yet been addressed: developing representations of human performances which isolate the intrinsic dimensions of the performances (and thus the skills which guide them) within high-dimensional, raw human performance data. In this paper we propose the use of three methods for representing high-dimensional human performance data within lower-dimensional spaces: principal component analysis (PCA), nonlinear principal component analysis (NLPCA), and sequential nonlinear principal component analysis (SNLPCA). We compare the appropriateness of these methods for modeling a simple human grasping operation.

关键词： Humans Performance evaluation robotics and automation Principal component analysis Performance analysis Grasping Robot sensing systems Instruments Fingers Manifolds

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Analysis of actuation and dynamic balancing for a single-wheel robot

Analysis of actuation and dynamic balancing for a single-whe...

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Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190)

作者： Yangsheng Xu Kwok Wai Au G.C. Nandy H.B. Brown The Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China Mechanical Engineering Department REC Durgapur India

We develop a dynamic model of the steering and actuation mechanism of Gyrover, a single-wheel robot which can be considered as a single wheel, actuated through a spinning flywheel attached through a two-link manipulator at the wheel bearing and a drive motor. The spinning flywheel acts as a gyroscope to stabilize the robot, and at the same time it can achieve steering. We develop a dynamic model, investigate its motion equation, and nonholonomic constraints, and present a simulation study. The work is significant in understanding this type of dynamically stable but statically unstable system, and in developing automatic control of the system.

关键词： Wheels Mobile robots robotics and automation Spinning Flywheels Torque Robot kinematics Prototypes Stability Automatic control

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A fuzzy system compensator for backlash

A fuzzy system compensator for backlash

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IEEE International Conference on robotics and automation (ICRA)

作者： K.T. Woo Li-Xin Wang F.L. Lewis Z.X. Li Department of Electrical and Electronic Engineering Hong Kong University of Science and Technology Hong Kong China Automation and Robotics Research Institute University of Texas Arlington USA

We design a fuzzy system to compensate the delays due to backlash nonlinearity. The fuzzy compensator is constructed from some common-sense rules. We consider the general case of the unknown backlash parameter and develop an adaptation algorithm to estimate the backlash parameter online. We prove that under certain conditions the fuzzy compensator with the adaptation algorithm guarantees that the backlash output converges to the desired trajectory. Simulation and hardware implementation results show that the fuzzy compensator is robust to the estimation errors in the backlash parameters. Application to an industrial CNC machine tool is described.

关键词： Fuzzy systems Fuzzy logic Fuzzy control robotics and automation Delay Parameter estimation Linearity Control systems Adaptive control Design engineering

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New developments in neurocontrol

New developments in neurocontrol

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IEEE Conference on Control Technology and Applications (CCTA)

作者： F.L. Lewis T. Parisini Automation and Robotics Research Institute University of Texas Arlington Fort Worth TX USA Department Electrical Electronic Computer Engineering DEEI-University of Trieste Trieste Italy

The complexity of modern day systems is increasing and performance requirements for industrial and military systems are becoming more stringent. With new results based on nonlinear stability theory, neural network (NN) based controllers are now able to provide guaranteed closed-loop stability, performance, and robustness for such complex dynamical systems. This paper presents a family of NN controllers developed for robotic systems, force control, backstepping control of industrial motors, friction compensation, deadzone compensation of actuators, etc. Some high-level NN control architectures are discussed, including reinforcement techniques and optimal design.

关键词： Neural networks Robust stability Control systems Force control Electrical equipment industry Defense industry Nonlinear control systems Robust control Robot control Service robots

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Two performance measures for evaluating human control strategy

Two performance measures for evaluating human control strate...

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IEEE International Conference on robotics and automation (ICRA)

作者： Jingyan Song Yangsheng Xu M.C. Nechyba Yeung Yam Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Hong Kong China The Robotics Institute Camegie Mellon University Pittsburgh PA USA Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong China

In the last few years, modeling dynamic human control strategy (HCS) is becoming an increasingly popular paradigm in a number of different research areas, such as the intelligent vehicle highway system, virtual reality and robotics. Usually, these models are derived empirically, rather than analytically, from real human input-output control data. As such, there is a great need to develop adequate performance criteria for these models, as few guarantees exist about their theoretical performance. It is our goal in this paper to develop several such criteria. In this paper, we first collect driving data from different individuals through a real-time graphic driving simulator. We then model each individual's control strategy through the flexible cascade neural network learning architecture. Next, we develop two performance measures for evaluating the resulting HCS models, one dealing with obstacle avoidance, the other with tight-turning behavior. Finally, we evaluate the relative skill of different HCS models through the proposed performance criteria.

关键词： Anthropometry Humans Vehicle dynamics Neural networks Intelligent robots Automatic control robotics and automation Intelligent vehicles Road transportation Nonlinear dynamical systems

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Robust control of cooperative underactuated manipulators

Robust control of cooperative underactuated manipulators

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： M. Bergerman Yangsheng Xu Yun-Hui Liu Informatics Technology Center Automation Institute Campinas SP Brazil The Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Hong Kong China

We propose in this work the first model-based robust control method for a team of underactuated manipulators jointly manipulating a load. The method is based on feedback linearization of the nonlinear dynamic coupling between the torques applied at the actuated joints and the Cartesian acceleration of the load, combined with a variable structure controller. Singularities in the control method are addressed, and a sufficient condition for a singularity-free controller implementation is obtained. Simulation and experimental results are presented to validate the theory presented.

关键词： Robust control Manipulator dynamics robotics and automation Mechanical variables control Sufficient conditions Kinematics Force control Trajectory Switches Informatics

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Optimization of human control strategy with simultaneously perturbed stochastic approximation

Optimization of human control strategy with simultaneously p...

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： Jingyan Song Yangsheng Xu Yeung Yam M.C. Nechyba Department of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong NT China The Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong NT China

Modeling the dynamic human control strategy (HCS) is becoming an increasingly popular paradigm in a number of different research areas, ranging from robotics to intelligent vehicle highway systems. Usually, HCS models are derived empirically, rather than analytically, from real human input-output data. While these empirical models offer an effective means of transferring intelligent behaviors from humans to robots and other machines, the models are not explicitly optimized with respect to potentially important performance criteria. We therefore propose an iterative algorithm for optimizing an initially stable HCS model with respect to an independent, user-specified performance criterion. We first collect driving data from different individuals through a real-time graphic driving simulator. Next, we describe how we model each individual's control strategy through flexible cascade neural networks. Once we have initially stable HCS models, we propose simultaneously perturbed stochastic approximation (SPSA) to optimize these models with respect to a chosen performance criterion. Finally, we describe and discuss some experimental results with the proposed algorithm.

关键词： Humans Intelligent robots Vehicle dynamics Intelligent vehicles Road transportation Machine intelligence Iterative algorithms Graphics Neural networks Stochastic processes

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Guest editorial: Neural network feedback control with guaranteed stability

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International Journal of Control 1998年第3期70卷 337-339页

作者： Lewis, Frank L. Parisini, Thomas Automation and Robotics Research Institute University of Texas Arlington TX United States Department of Electrical Electronic and Computer Engineering DEEI—University of Trieste Trieste Italy

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COCOLOG Logic Control for Hierarchical and Hybrid Systems 1

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IFAC Proceedings Volumes 1998年第29期31卷 35-38页

作者： Peter E. Caines Tom Mackling Carlos Martínez-Mascarúa Department of Electrical Engineering McGill University 3480 University Street Montreal Quebec Canada H3A 2A7 and Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin N. T.Hong Kong. Optimal-Robotics Corp. Montreal Quebec Canada Cegelec ESCA 11120 NE 8th Place Bellevue WA 98004

COCOLOG (Conditional Observer and Controller Logic) (Caines and Wang 1990, 1995) is a framework for the design and implementation in first order logic of controllers for finite deterministic input-state-output machines. A COCOLOG control system consists of a partially ordered family of first order logical theories which describe and control the evolution of the state of a given partially observered finite machine M. In order to increase the efficiency of COCOLOG we introduce the so-called systems of Markovian fragments which, in a precise sense, have the same control power as standard COCOLOG systems but for which the amount of information to be stored does not increase with time. Furthermore, we present an enhancement of the basic COCOLOG system involving, first, Macro (COCOLOG) Languages and, second, Macro (COCOLOG) Actions, which, together, consititute Macro COCOLOG. These extensions and enhancements yield a COCOLOG system which, for certain classes of logic control problems, is (i) syntatically and logically general and flexible, (ii) efficient in its problem expression and solution, and (iii) efficient in the execution of feedback control. Applications of COCOLOG include robotics and elevator control. An implemented COCOLOG controller utilized by a team at McGill University, Montreal, for part of the control system of a physical mobile robot called the MMRA (McGill Mobile robotics Architecture) is briefly described.

关键词： logic control artificial intelligence automated theorem proving intelligent control hierarchical systems hybrid systems

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