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IEEE International Conference on Evolutionary Computation

作者： C. Dimopoulos A.M.S. Zalzala Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

The paper examines a cellular manufacturing optimisation problem in a new facility of a pharmaceutical company. The new facility, together with the old one, should be adequate to handle current and future production requirements. The aim of the paper is to investigate the potential use of evolutionary computation in order to find the optimum configuration of the cells in the facility. The objective is to maximise the total number of batches processed per year in the facility. In addition, two-objective optimisation search was implemented, using several evolutionary computation methods. One additional objective is to minimise the overall cost, which is proportional to the number of cells in the facility. The multi-objective optimisation programs were based on three approaches: the weighted-sum approach, the Pareto-optimality approach, and the multi-objective genetic algorithm (MOGA) approach.

关键词： Evolutionary computation Cellular manufacturing Optimization methods Inductors Genetic algorithms Cost function systems engineering and theory Pharmaceuticals Production group technology

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Segmentation of 3D acoustic images for object recognition purposes

Segmentation of 3D acoustic images for object recognition pu...

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OCEANS

作者： R.C. Patel A.R. Greig Automatic Control Group Department of Mechanical Engineering University College London London UK

This paper addresses the problem of segmenting 3D acoustic images for object recognition purposes. For remotely operated vehicle (ROV) navigation the 3D surroundings have to be understood. Sensors, such as 3D acoustic imaging sensors, can be used for this purpose. automatic segmentation and reconstruction of an underwater scene could make many underwater operations more effective and reliable. The characteristics of 3D acoustic images are different from conventional optical or range images and simple standard segmentation methods developed for optical imaging or range images are often rendered more or less useless. 3D acoustic images have usually got low resolution and exhibit characteristics inherent to acoustical imaging, such as speckle noise and target shadows. The main advantages are that the acoustic imaging sensor is functional even in turbid water conditions and is capable of providing 3D (range) information. A literature review of work in the field of acoustic image segmentation is given, summarising the state-of-the-art in acoustic image segmentation approaches for object recognition purposes. Four different approaches, thresholding, fuzzy clustering, Markov random fields and connected components approach, are implemented and tested on synthetic and real 3D acoustic images.

关键词： Image segmentation Object recognition Underwater acoustics Image sensors Sensor phenomena and characterization Acoustic sensors Acoustic imaging Remotely operated vehicles Optical noise Optical imaging

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Behavioral model identification

Behavioral model identification

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IEEE Conference on Decision and control

作者： R. Johansson M. Verhaegen C.T. Chou A. Robertsson Department of Automatic Control Lund Institute of Technology Lund University Lund Sweden Department of Electrical Engineering System and Control Engineering Group TU Delft Delft Netherlands

This paper presents theory and algorithms for system identification suitable for the framework of behavioral system theory. An algorithm based on algebraic system theory and realization theory (Ho-Kalman realization) in a subspace model identification framework is presented. The novel approach considered consists of the formulation of input-output data as an impulse response model and the subsequent application of state-space realization. As a special case, behavioral model identification from finite data sequences is demonstrated.

关键词： Power system modeling System identification Context modeling Matrix decomposition control engineering Fitting Mathematical model Data analysis Linear systems Regression analysis

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Multivariable controller tuning

Multivariable controller tuning

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1998 American control Conference, ACC 1998

作者： Johansson, Karl H. James, J. Ben Bryant, Greyham F. Åström, Karl J. Department of Automatic Control Lund Institute of Technology Box 118 S-221 00 Lund Sweden Exposure Management Bank of America Elmfield Road Bromley London 113277 United Kingdom Industrial Systems Group Centre for Process Systems Engineering Imperial College of Science Technology and Medicine Exhibition Road London SW7 BT United Kingdom

ISBN: (纸本)0780345304

The problem of tuning individual loops in a multivari-able controller is investigated. It is shown how the performance of a specific loop relates to a row in the controller matrix. Several interpretations of this relation are given. An algorithm is also presented that estimates the model required for the tuning via a relay feedback experiment. The algorithm does not need any prior information about the system or the controller. The results are illustrated by an example. © 1998 AACC.

关键词： controllers

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Markov chain Monte Carlo methods for tracking a maneuvering target in clutter

Markov chain Monte Carlo methods for tracking a maneuvering ...

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IEEE Conference on Decision and control

作者： A. Logothetis A. Doucet S3-Automatic Control Royal Institute of Technology Stockholm Sweden Signal Processing Group Department of Engineering University of Cambridge Cambridge UK

We address the problem of tracking a maneuvering target in a cluttered environment. We propose three algorithms based on stochastic sampling methods to solve the following combinatorial optimization problems: (a) data association, and (b) maneuver detection. The first proposed algorithm is a data augmentation (DA) scheme, that yields conditional mean state estimates of the maneuvering target in clutter. The second proposed scheme is a simulated annealing (SA) version of DA that computes the joint MAP state sequence estimates of the target state, the measurement to target associations and the input maneuvering control sequences. Finally, a SA Metropolis-Hastings DA scheme is designed to yield the MAP state sequence estimate of the measurement to target associations and the input maneuvering control sequences. The cost per iteration is linear in the data length. Furthermore, theoretical convergence results of the three proposed Markov chain Monte Carlo algorithms towards the desired estimates, have been obtained.

关键词： Target tracking State estimation Yield estimation Stochastic processes Sampling methods Optimization methods Computational modeling Simulated annealing Costs Convergence

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Multivariable controller tuning

Multivariable controller tuning

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American control Conference (ACC)

作者： K.H. Johansson B. James G.F. Bryant K.J. Astrom Department of Automatic Control Lund Institute of Technology Lund Sweden Exposure Management Bank of America London UK Industrial Systems Group Centre for Process Systems Engineering Imperial College of Science Technology and Medicine London UK

The problem of tuning individual loops in a multivariable controller is investigated. It is shown how the performance of a specific loop relates to a row in the controller matrix. Several interpretations of this relation are given. An algorithm is also presented that estimates the model required for the tuning via a relay feedback experiment. The algorithm does not need any prior information about the system or the controller. The results are illustrated by an example.

关键词： Relays automatic control control systems Tuning Electrical equipment industry Industrial control Feedback Costs Industrial economics Performance gain

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A genetic approach to motion planning of redundant mobile manipulator systems considering safety and configuration

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Journal of Field Robotics 1998年第7期14卷

作者： Mingwu Chen Ali M. S. Zalzala Robotics Research Group Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S1 3JD UK

This article presents a genetic algorithm approach to multi-criteria motion planning of mobile manipulator systems. For mobile robot path planning, traveling distance and path safety are considered. The workspace of a mobile robot is represented as a grid by cell decomposition, and a wave front expansion algorithm is used to build the numerical potential fields for both the goal and the obstacles. For multi-criteria position and configuration optimization of a mobile manipulator, least torque norm, manipulability, torque distribution and obstacle avoidance are considered. The emphasis of the study is placed on using genetic algorithms to search for global optimal solutions and solve the minimax problem for manipulator torque distribution. Various simulation results from two examples show that the proposed genetic algorithm approach performs better than the conventional methods.

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Dynamic modelling and genetic-based trajectory generation for non-holonomic mobile manipulators

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control engineering PRACTICE 1997年第1期5卷 39-48页

作者： Chen, MW Zalzala, AMS Robotics Research Group Department of Automatic Control and Systems Engineering The University of Sheffield UK

An approach for the modelling and motion planning of a mobile manipulator system with a non-holonomic constraint is presented in this paper. The Newton-Euler equations are used to obtain the complete dynamics of the system. Given the trajectory of the end-effector of the manipulator, near-optimal trajectories for the mobile platform and manipulator joints are obtained by using an efficient genetic algorithm with torque and manipulability optimisation and obstacle avoidance. Various simulations of a platform with a 3-link onboard manipulator are presented to show the effectiveness of the presented method.

关键词： robotics trajectory planning genetic algorithms dynamics obstacle avoidance

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Understanding the Nature of Predatory Pricing in Large-Scale Market Economy with Genetic Algorithms

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Journal of systems engineering and Electronics 1997年第2期8卷 33-40,43-44页

作者： Chen Shuheng & Ni Chihchi(AIECON Research group, department of Economics,National Chengchi University, Taiwan 11623, P. R. China) AI-ECON Research Group Department of Economics National Chengchi University Taiwan 11623 P. R. China Department of Automatic Control Engg. Institute of Systems Engg. Huazhong University of Science and Technology Wuhan 430074 P. R. China

In this paper the nature of predatory pricing is analyzed with genetic algorithms. It is found that, even under the same payoff structure, the results of the coevolution of weak monopolists and entrants are sensitive to the representationof the decisionmaking process. Two representations are studied in this paper. One is the actionbased representation and the other the strategybased representation. The former is to represent a naive mind and the latter is to capture a sophisticated mind. For the actionbased representation, the convergence results are easily obtained and predatory pricing is only temporary in all simulations. However, for the strategybased representation, predatory pricing is not a rare phenomenon and its appearance is cyclical but not regular. Therefore, the snowball effect of a little craziness observed in the experimental game theory wins its support from this representation. Furthermore, the nature of predatory pricing has something to do with the evolution of the sophisticated rather than the naive minds.

关键词： Chainstore game Predatory pricing Evolutionary game Genetic algorithms Coevolutionary stability

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A nonlinear GPC algorithm (NLGPC) for muscle relaxant anaesthesia 4

A nonlinear GPC algorithm (NLGPC) for muscle relaxant anaest...

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4th European control Conference, ECC 1997

作者： Mahfouf, M. Linkens, D.A. Dynamics and Control Group Manchester School of Engineering University of Manchester Oxford Road ManchesterM13 9PT United Kingdom Intelligent Systems Group Department of Automatic Control and Systems Engineering University of Sheffield Mappin Street SheffieldSI 3JD United Kingdom

ISBN: (纸本)9783952426906

The popular Generalised Predictive control (GPC) algorithm is used to derive a discrete-time nonlinear predictive controller for muscle relaxant anaesthesia, in a Wiener structure form, and which exhibits strong nonlinearities as well as varying dead-time and dynamics due to patient-topatient variability. The proposed controller minimises the GPC quadratic performance index taking into account the nonlinear output predictions obtained using linear predictions of the linear part inferred using the inverse of the nonlinear function. The minimisation problem is solved using numerical search methods such as Genetic Algorithms. Moreover, variations in the model dynamics are estimated using a Recursive Least-Squares estimation algorithm (RLS). The proposed scheme is shown to be superior to the linear GPC algorithm even in the case of a significant mismatch between the assumed model nonlinearity and that in the actual process.

关键词： Genetic algorithms

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