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O(N) recursive Algorithm for the Operational Space Inertia Matrix of a Robot Manipulator

IFAC Proceedings Volumes 1990年第8期23卷 275-279页

作者： K.W Lilly D.E Orin Department of Electrical Engineering The Ohio State University Columbus OH 43210 USA

The operational space inertia matrix reflects the dynamic properties of a robot manipulator to its tip. It mav be used to decouple force and/or motion control about the manipulator workspace axes, and it also plays an important role in the development of efficient algorithms for the dynamic simulation of closed-chain robotic mechanisms. The traditional approach for computing this matrix has a computational complexity of O(N 3 ) for an N degree-of-freedom manipulator. This paper presents the development of a new recursive algorithm for the operational space inertia matrix which reduces the computational complexity to O(N) for any manipulator. The Hew algorithm is based on a single recursion which begins at the base of the manipulator and progresses out to the last link, and it is the most efficient method known for N ≥ 6. The numerical accuracy of the new algorithm is tested for a PUMA 560 robot with a fixed base.

关键词： Robots inertia matrix recursive algorithms dynamic simulation force control computational methods

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Soft-Constrained Stochastic Nash Games for Weakly Coupled Large-Scale Discrete-time Systems

Soft-Constrained Stochastic Nash Games for Weakly Coupled La...

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IEEE Conference on Decision and Control

作者： Hiroaki Mukaidani Hua Xu Vasile Dragan Graduate School of Education Hiroshima University 1-1-1 Kagamiyama Higashi-Hiroshima 739-8524 Japan Graduate School of Business Sciences The University of Tsukuba 3-29-1 Otsuka Bunkyo-ku Tokyo 112-0012 Japan Institute of Mathematics of the Romanian Academy P.O.Box 1-764 RO-014700 Romania

ISBN: (纸本)9781612848006

In this paper, we discuss infinite-horizon soft-constrained stochastic Nash games involving state-dependent noise and deterministic uncertainties in weakly coupled large-scale discrete-time systems. First, we formulate linear quadratic soft-constrained Nash games in which robustness is attained against external disturbance. Then, the conditions for the existence of robust equilibrium are derived based on the solutions of sets of the discrete version of cross-coupled stochastic algebraic Riccati equations (CSAREs). Moreover, various reliable features such as mean square stability are analyzed. After establishing an asymptotic structure along with positive definiteness for CSAREs solutions, we derive the recursive algorithm for solving CSAREs. Finally, we provide a numerical example to verify the efficiency of the proposed method.

关键词： Stochastic recursive algorithms mean square numerical example Riccati equations infinite horizon

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Discrete recursive Algorithm for Estimation of Non-Stationary Noise in Deep-Submicron Integrated Circuits

Discrete Recursive Algorithm for Estimation of Non-Stationar...

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2010 10th IEEE International Conference on Solid-State and Integrated Circuit Technology(第十届固态和集成电路技术国际会议 ICSICT-2010)

作者： Amir Zjajo Qin Tang Nick van der Meijs Michel Berkelaar Circuits and Systems Group Delft University of Technology Mekclweg 4 2628 CD DelftThe Netherland Circuits and Systems Group Delft University of Technology Mekclweg 4 2628 CD Delft The Netherland Circuits and Systems Group Delft University of Technology Mekclweg 4 2628 CD Delft The Netherlan

ISBN: (纸本)9781424457977

This paper reports a new recursive algorithm for efficient estimation of the noise content in timedomain noise analysis of non-linear dynamic integrated circuits with arbitrary excitations. Statistical simulation of specific circuit fabricated in 65 nm CMOS process shows that the proposed algorithm offers accurate and efficient solution.

关键词： recursive algorithms Integrated circuits Electronic circuits Non-stationary noise CMOS process noise analysis

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Q-matrix Learning and DINA Model Parameter Estimation

Q-matrix Learning and DINA Model Parameter Estimation

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International Conference on Behavior, Economic and Socio-cultural Computing

作者： Yuan Sun Shiwei Ye Guiping Su Yi Sun National Institute of Informatics University of China Academy of Science

ISBN: (纸本)9781509061655

The DINA model is one of the most widely used models in cognitive and skills diagnosis, and several algorithms have been developed for estimating the model parameters. However, since the parameter space is very large and has a mix of binary variables, even medium-sized testing is extremely challenging. To make the model practical, a fast optimization algorithm for parameter estimation is needed. In this study, we converted the deterministic Q-matrix learning problem into a Boolean matrix factorization (BMF) problem and developed a recursive algorithm to find an approximate solution while solving the uncertainty parameters analytically using maximum likelihood estimation (MLE). We proved that the MLE is equivalent to the minimum information entropy of the DINA model. Simulation results demonstrated that our proposed algorithm converges rapidly to the optimal solution under suitable initial values of skill - item association and is insensitive to the initial values of the uncertainty parameters.

关键词： maximum likelihood estimation model parameters parameter space methods recursive algorithms Optimization algorithms optimal solution Parameter estimation BMF gene

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A STOCHASTIC 3MG ALGORITHM WITH APPLICATION TO 2D FILTER IDENTIFICATION

A STOCHASTIC 3MG ALGORITHM WITH APPLICATION TO 2D FILTER IDE...

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European Signal Processing Conference

作者： Emilie Chouzenoux Jean-Christophe Pesquet Anisia Florescu Universite Paris-Est Dunarea de Jos University Electronics and Telecommunications Dept.

ISBN: (纸本)9781479946037

Stochastic optimization plays an important role in solving many problems encountered in machine learning or adaptive processing. In this context, the second-order statistics of the data are often unknown a priori or their direct computation is too intensive, and they have to be estimated on-line from the related signals. In the context of batch optimization of an objective function being the sum of a data fidelity term and a penalization (e.g. a sparsity promoting function), Majorize-Minimize (MM) subspace methods have recently attracted much interest since they are fast, highly flexible and effective in ensuring convergence. The goal of this paper is to show how these methods can be successfully extended to the case when the cost function is replaced by a sequence of stochastic approximations of it. Simulation results illustrate the good practical performance of the proposed MM Memory Gradient (3MG) algorithm when applied to 2D filter identification.

关键词： Stochastic approximation Optimization Sub-space algorithms Memory gradient methods Descent methods recursive algorithms Majorization-minimization Filter identification Newton method Sparsity Machine learning Adaptive filtering

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RANGE recursive SPACE TIME ADAPTIVE PROCESSING (STAP) FOR MIMO AIRBORNE RADAR

RANGE RECURSIVE SPACE TIME ADAPTIVE PROCESSING (STAP) FOR MI...

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European signal processing conference

作者： Sylvie Marcos Laboratoire des Signaux et Systemes CNRS UMR8506 SUPELEC Plateau de Moulon 3 rue Joliot-Curie 91192 Gif-sur-Yvette cedex FRANCE

ISBN: (纸本)9781617388767

This paper presents a range recursive algorithm for the space time adaptive processing (STAP) of multi input multi output (MIMO) airborne radar signals involved in clutter rejection for the detection of slow moving ground targets. The MIMO aspect comes from the fact that the transmitter consists of an array of spaced elements sending non coherent waveforms and that the receiver is a conventional array used for spatial clutter rejection. The transposition of the STAP algorithms from the single input multi output (SIMO) systems to the MIMO ones has been claimed to theoretically improve performance in clutter resolution and rejection. However, even if this transposition is conceptually easy, in practice, the convergence and the computational complexity of the MMO-STAP algorithms are higher than for SIMO models. After reconsidering the advantages and drawbacks of the extended MIMO-STAP, namely the sample matrix inversion (SMI) and eigencanceller (EC) algorithms, we propose the fast approximated power iteration (FAPI) range recursive algorithm as an alternative to resolve the convergence and complexity problems.

关键词： clutter suppression Space time adaptive processing Converge multi-input Spacetime RADAR SIGNALS radar recursive algorithms complexity classes

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recursive Identification for Hammerstein Systems with Bounded Noise

Recursive Identification for Hammerstein Systems with Bounde...

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IEEE Annual Conference on Decision and Control

作者： Mathieu Pouliquen Eric Pigeon Olivier Gehan Control Group GREYC CNRS UMR 6072 ENSICAEN 06 Bd du Marechal Juin 14050 Caen Cedex France

ISBN: (纸本)9781467357159

In this paper, we consider the identification of Hammerstein systems in presence of bounded disturbances. The proposed identification algorithm is a recursive algorithm. It ensures that the estimation error is bounded and allows a real-time separation of the nonlinear part and linear part. Furthermore, some stability and convergence results are demonstrated and a simulation example is given to illustrate the performance of the presented method.

关键词： Bounded recursive algorithms recursive Authentication IDENTIFICATION SYSTEMS Estimation error

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ON DATA SPARSIFICATION AND A recursive ALGORITHM FOR ESTIMATING A KERNEL-BASED MEASURE OF INDEPENDENCE

ON DATA SPARSIFICATION AND A RECURSIVE ALGORITHM FOR ESTIMAT...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Pierre-Olivier Amblard Jonathan H. Manton Dept. of Mathematics&Statistics The University of Melbourne Australia Control and Signal Processing Lab The University of Melbourne Australia

ISBN: (纸本)9781479903573

Technological improvements have led to situations where data sets are sufficiently rich that in the interests of processing speed it is desirable to throw away samples that provide little additional information. This is referred to here as data sparsification. The first contribution is a study of a recently proposed data sparsification scheme;ideas from vector quantisation are used to assess its performance. Informed by this study, a modification of the data sparsification algorithm is proposed and applied to the problem of estimating a kernel-based measure of independence of two datasets. (Given i.i.d. observations from two random variables, x and y, the underlying problem are to determine whether or not x and y are independent of each other.) The second contribution of this paper is to make recursive an existing algorithm for measuring independence and able to operate on both raw data and on sparsified data generated by the aforementioned data sparsification algorithm. Compared with the original algorithm, the recursive algorithm is significantly faster due to its lower memory and computational requirements.

关键词： sparse dictionary kernel quantisation independence Quantization recursive algorithms Dictionaries as Topic Seeds Kernel Vector quantization algorithms Dataset extra message

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Position Based Visual Servoing for Catching a 3-D Flying Object Using RLS Trajectory Estimation from a Monocular Image Sequence

Position Based Visual Servoing for Catching a 3-D Flying Obj...

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2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)

作者： R. Herrejon S. Kagami K. Hashimoto Intelligent Control Systems Laboratory at the Department of System Information Sciences of Tohoku University SendaiJapan

ISBN: (纸本)9781424447749

Online coordination of visual information with slow speed manipulator control is studied in the specific task of three dimensional robotic catching using position based visual servoing. The problem involves the design and application of a recursive algorithm to extract and predict the position of an object in a 3D environment from one feature correspondence from a monocular image sequence. Target translational model involves an object moving in a parabolic path using projectile physics. A state-space model is constructed incorporating kinematic states, and recursive techniques are used to estimate the state vector as a function of time. The measured data are the noisy image plane coordinates of object match taken from image in the sequence. Image plane noise levels are allowed and investigated. The target trajectory estimation is formulated as a tracking problem, which can use an arbitrary large number of images in a sequence and is done using recursive Least Squares (RLS). Results are demonstrated by both simulations and experiments using a a real-time vision system and a sixdegree- of-freedom robotic arm with speed capabilities of up to 1.0 m/s.

关键词： Visual servoing image sequences Restless Legs Syndrome tracking problem images State space models recursive algorithms trajectory state vectors visual information

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Zonotope-based Set-membership Parameter Identification of Linear Systems with Additive and Multiplicative Uncertainties: A New Algorithm

Zonotope-based Set-membership Parameter Identification of Li...

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American Control Conference

作者： Hao Wang Ilya Kolmanovsky Jing Sun the Department of Naval Architecture & Marine Engineering University of Michigan Ann Arbor MI 48109 USA the Department of Aerospace Engineering University of Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781509045839

This paper develops a set-membership estimation algorithm for identification of time-varying parameters in linear models, where both additive and multiplicative uncertainties are considered explicitly. We show that a recursive algorithm we develop is capable of providing tight overbounds on the feasible solution set. Examples are considered to demonstrate the utility and effectiveness of the algorithm, including an application to health monitoring in marine dual fuel engines.

关键词： Linear Systems Additive Additives Linear system Linear Multiplication time varying parameters recursive algorithms Parameter identification Uncertainty linear models DUAL-FUEL ENGINES algorithms

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