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2012 9th IEEE International Conference on Mechatronics and Automation, ICMA 2012

作者： Ma, Jianjun Li, Yanan Ge, Shuzhi Sam Social Robotics Laboratory Interactive Digital Media Institute National University of Singapore Singapore 119077 Singapore Department of Automatic Control College of Mechatronics and Automation National University of Defense Technology Changsha 410073 China NUS Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore 119613 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Singapore Robotics Institute School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China

ISBN: (纸本)9781467312776

The cable-driven mechanism is usually used in robotic systems for the efficient transmission of forces or torques from actuators to the end-effector. However, the control design is challenging for these systems due to the cable compliance and transmission elasticity. In this paper, adaptive control in a cascaded structure is designed for the trajectory tracking of a cable driven robot arm. The desired control input is obtained by developing an adaptation scheme, which does not require the computation of the regressor matrix. Proportional-derivative (PD) control is designed to specify the actual control input such that the trajectory tracking is achieved. Simulation results are provided to show the effectiveness of the proposed method. © 2012 IEEE.

关键词： Cables

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Intermediate point norm optimal iterative learning control

Intermediate point norm optimal iterative learning control

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51st IEEE Conference on Decision and control, CDC 2012

作者： Owens, David H. Freeman, Chris T. Dinh, Thanh Van Department of Automatic Control and Systems Engineering University of Sheffield Mappin Street Sheffield S1 3JD United Kingdom School of Electronics and Computer Science University of Southampton Highfield SO17 1BJ United Kingdom Advanced Robotics Division Italian Institute of Technology Genoa Italy

A Norm-Optimal Iterative Learning control (NOILC) solution is developed for the problem when tracking is only required at a subset of isolated time points along the trial duration. Well-defined convergence properties are presented, along with design guidelines and supporting experimental results using an electromechanical test facility. © 2012 IEEE.

关键词： Learning algorithms

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Design and control of a novel non-raster scan pattern for fast scanning probe microscopy

Design and control of a novel non-raster scan pattern for fa...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Y. K. Yong A. Bazaei S. O. R. Moheimani F. Allgöwer School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW Australia Director of Institute for Systems Theory and Automatic Control Mechanical Engineering Department University of Stuttgart Stuttgart Germany

A significant difficulty associated with achieving high scan speeds in scanning probe microscopes is that the probe is required to scan the sample in a zig-zag (raster) pattern. The fast axis of the scanner is required to track a non-smooth signal that contains frequency components beyond its mechanical bandwidth. Therefore, fast raster scans lead to distortions in the obtained image. This paper proposes analysis and design methods for a nonlinear but smooth scan pattern, known as Lissajous pattern, which enables us to achieve high-quality images at very high scan speeds where raster scanning typically leads to significant image distortions. Criteria are also proposed and formulated for constructing Lissajous trajectory and calculating the parameters and resolution. Together with the implementation of an internal model controller for high precision tracking, the proposed method is successfully employed to scan images in high speeds using a low resonance frequency SPM platform of only 825 Hz.

关键词： Resonant frequency Force Atomic force microscopy Nanopositioning Trajectory Tracking

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Fair scheduling in common-pool games by aspiration learning

Fair scheduling in common-pool games by aspiration learning

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Georgios C. Chasparis Ari Arapostathis Jeff S. Shamma Department of Automatic Control Lund University Lund Sweden Department of Electrical and Computer Engineering University of Texas at Austin Austin TX USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

We propose a distributed learning algorithm for fair scheduling in common-pool games. Common-pool games are strategic-form games where multiple agents compete over utilizing a limited common resource. A characteristic example is the medium access control problem in wireless communications, where multiple users need to decide how to share a single communication channel so that there are no collisions (situations where two or more users use the medium at the same time slot). We introduce a (payoff-based) learning algorithm, namely aspiration learning, according to which agents learn how to play the game based only on their own prior experience, i.e., their previous actions and received rewards. Decisions are also subject to a small probability of mistakes (or mutations). We show that when all agents apply aspiration learning, then as time increases and the probability of mutations goes to zero, the expected percentage of time that agents utilize the common resource is equally divided among agents, i.e., fairness is established. When the step size of the aspiration learning recursion is also approaching zero, then the expected frequency of collisions approaches zero as time increases.

关键词： Games Joints Markov processes Time frequency analysis Protocols Resource management Wireless networks

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Smart Energy-Aware Sensors for Event-Based control

Smart Energy-Aware Sensors for Event-Based Control

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IEEE Annual Conference on Decision and control

作者： Nicolas Cardoso de Castro Daniel E. Quevedo Federica Garin Carlos Canudas de Wit INRIA Rhone-Alpes NeCS Team School of Electrical Engineering & Computer Science The University of Newcastle Department of Automatic Control GIPSA-Lab CNRS NeCS Team Grenoble

ISBN: (纸本)9781467320658

This paper proposes an event-based scheme to control a networked control system and to manage the radio-modes of its smart sensor node. The smart node is battery driven and is in charge of sensing the system and computing the control law which is sent to the receiver using a wireless channel. To save energy and to limit the amount of communication with the receiver, the smart node combines two techniques: event-based control and radio-mode management. The control law and radio-mode switching policy are derived jointly in a predictive finite receding horizon optimization problem. We derive a Model Predictive controller using Dynamic Programming and we prove the stability of the obtained control law using the Input-to-State Stability framework. The main contribution of this paper is to take into account several low consuming radio-modes, e.g. Idle and Sleep and the energy-transition costs between modes. Most of the existing literature only considers one mode when the radio is not transmitting, i.e., the scheduling problem. As illustrated via simulations, our proposal has the potential of significant energy savings.

关键词： Networked control Systems Event-based control Radio-mode management Networked control systems Event-based control law energy saving Predictive controllers dynamic programming Receivers

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Synchronization of FitzHugh-Nagumo systems: An adaptive approach

Synchronization of FitzHugh-Nagumo systems: An adaptive appr...

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International Conference on Natural Computation (ICNC)

作者： Wei Wei Min Zuo Zaiwen Liu Jing Wang Department of Automatic Control Computer and Information Engineering School Beijing Technology and Business University Beijing China National Engineering Research Center of Advanced Rolling University of Science and Technology Beijing China

In this article, synchronization of FitzHugh-Nagumo (FHN) neurons is considered. Adaptive controller based on active compensation is adopted to drive the slave neuron to track the master neuron. Sufficient condition for asymptotic stability of the close-loop system is derived. Numerical simulation results are given to confirm the adaptive controller is valid.

关键词： Synchronization Neurons Adaptation models Closed loop systems Analytical models Adaptive systems Numerical simulation

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Editorial

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Automatika 2012年第2期53卷 103-106页

作者： Martínez-Salamero, Luis Milanovič, Miro Rovira i Virgili University School of Electrical and Computer Engineering Department of Electrical Electronic and Automatic Control Engineering Spain University of Maribor Faculty of Electrical Engineering and Computer Science Institute for Robotics Slovenia

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Parallel Implementation of Particle MCMC Methods on a GPU *

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IFAC Proceedings Volumes 2012年第16期45卷 1143-1148页

作者： Soren Henriksen Adrian Wills Thomas B. Schön Brett Ninness School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW 2308 Australia (Tel: +61 2 49216028 Division of Automatic Control Linköping University SE-581 83 Linköping Sweden (Tel: +46 13 281373

This paper examines the problem of estimating the parameters describing system models of quite general nonlinear and multi-variable form. The approach is a computational one in which quantities that are intractable to evaluate exactly are approximated by sample averages from randomized algorithms. The main contribution is to illustrate the viability and utility of this approach by examining how high computational loads can be simply managed using commodity hardware. The proposed algorithms and solution architectures are profiled on concrete examples.

关键词： Nonlinear dynamical systems nonlinear estimation Particle filter Markov Chain Monte Carlo parallel computation Graphics Processing Unit

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Estimation of Linear Systems using a Gibbs Sampler *

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IFAC Proceedings Volumes 2012年第16期45卷 203-208页

作者： Adrian Wills Thomas B. Schön Fredrik Lindsten Brett Ninness School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW 2308 Australia (Tel: +61 2 49216028 Division of Automatic Control Linköping University SE-581 83 Linköping Sweden (Tel: +46 13 281373

This paper considers a Bayesian approach to linear system identification. One motivation is the advantage of the minimum mean square error of the associated conditional mean estimate. A further motivation is the error quantifications afforded by the posterior density which are not reliant on asymptotic in data length derivations. To compute these posterior quantities, this paper derives and illustrates a Gibbs sampling approach, which is a randomized algorithm in the family of Markov chain Monte Carlo methods. We provide details on a numerically robust implementation of the Gibbs sampler. In a numerical example, the proposed method is illustrated to give good convergence properties without requiring any user tuning.

关键词： Parameter Estimation System Identification Bayesian statistics Markov Chain Monte Carlo techniques

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A nonlinear voltage controller using T-S fuzzy model for multimachine power systems

A nonlinear voltage controller using T-S fuzzy model for mul...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： A. Abbadi D. Boukhetala L. Nezli A. Kouzou Electrical and Computer Engineering Department Research Laboratory in Electrical Engineering and Automatic LREA University of Medea Algeria Electrical Engineering Department Process Control Laboratory El Harrach Polytechnic School Algiers Algeria Electrical Department University Centre of Djelfa Djelfa Algeria

ISBN: (纸本)9781467315906

In this paper we propose a decentralized nonlinear controller that has the ability to enhance the transient stability and achieve voltage regulation simultaneously for multimachine power systems. This paper extends the existing work on the construction of TS fuzzy model using linearly independent functions from nonlinear systems. This approach is based on Takagi-Sugeno (T-S) fuzzy controller via parallel distributed compensation (PDC) method. The stability analysis and control design problems can be reduced to linear matrix inequality (LMI) problems. Based on only local measurements, the designed fuzzy state feedback controller guarantees stability and satisfies desired transient responses. The proposed controller is applied to two-generator infinite bus power system. Simulation results illustrate the performance of the developed approach regardless of the system operating conditions.

关键词： Power system stability Stability analysis Transient analysis Generators Mathematical model Voltage control

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