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Volume 143
Issue 4
IEE Proceedings - Control Theory and Applications
Volume 143, Issue 4, July 1996
Volumes & issues:
Volume 143, Issue 4
July 1996
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- Author(s): Ya.Z. Tsypkin ; J.D. Mason ; K. Warwick
- Source: IEE Proceedings - Control Theory and Applications, Volume 143, Issue 4, p. 305 –308
- DOI: 10.1049/ip-cta:19960422
- Type: Article
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p.
305
–308
(4)
The paper proposes a method of performing system identification of a linear system in the presence of bounded disturbances. The disturbances may be piecewise parabolic or periodic functions. The method is demonstrated effectively on two example systems with a range of disturbances. - Author(s): W. Wu and Y.-S. Chou
- Source: IEE Proceedings - Control Theory and Applications, Volume 143, Issue 4, p. 309 –318
- DOI: 10.1049/ip-cta:19960303
- Type: Article
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p.
309
–318
(10)
Control design based on input–outputput feedback linearisation for a class of uncertain nonlinear systems with an input time delay is presented. The proposed parameterised co-ordinate transformation can transform the system model into singularly perturbed dynamics. An adjustable parameter can be tuned to satisfy a particular specification. The underlying theoretical approach is the Lyapunov stability theory and Razumikhin's stability theory. The key point of the paper is that the parameterised state feedback can effectively attenuate the output tracking error when the lumped nonlinearity satisfies the specific growth bound. The effectiveness of the method is illustrated by a simulation example on the temperature control of a nonisothermal chemical system with an input delay. - Author(s): K.P. Lam
- Source: IEE Proceedings - Control Theory and Applications, Volume 143, Issue 4, p. 319 –324
- DOI: 10.1049/ip-cta:19960438
- Type: Article
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p.
319
–324
(6)
Two methods for studying the consistency problems of a class of binary relation inference networks are described. One method is derived using the mathematical concepts of energy function (Et) and delta energy function (ΔEt), where both functions have closely related geometrical interpretations. By properly formulating ΔEt as matrix quadratic form, network convergence is shown to be directly related to the matrix property of negative semidefiniteness. The other method, which can be applied in either a discrete-time or continuous-time framework, is based on studying the eigenvalue problem for an associated state-space model of the inference network. The merits and limitations of the proposed methods are discussed, with reference to several specific examples. - Author(s): C.-L. Hwang
- Source: IEE Proceedings - Control Theory and Applications, Volume 143, Issue 4, p. 325 –332
- DOI: 10.1049/ip-cta:19960377
- Type: Article
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p.
325
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(8)
It is well known that the sliding mode control possesses the following advantages: fast response, less sensitive to uncertainties, and easy implementation. However, traditional sliding mode control often results in a chattering control input because of its discontinuous switching control. The chattering control input has some drawbacks: easy damage of mechanism and excitation of unmodelled dynamics. Although the boundary layer method can attenuate the degree of high-frequency control input, its stability is guaranteed only outside the boundary layer, and its asymptotic tracking often cannot be achieved if the boundary layer is insufficiently small. Furthermore, a fixed switching gain often gives too much energy for the purpose of trajectory tracking. Owing to these disadvantages of traditional sliding mode control (i.e. fixed switching gain and fixed boundary layer), one sufficient condition for a time-varying switching gain and a time-varying boundary layer, which is the memoryless function of the tracking error, is achieved to reduce the control effort in magnitude and frequency, and to ensure asymptotic tracking. To verify the effectiveness of the proposed control, computer simulations for the combination of weighted electrohydraulic position and differential pressure control are demonstrated. - Author(s): P. Shi ; M. Fu ; C.E. de Souza
- Source: IEE Proceedings - Control Theory and Applications, Volume 143, Issue 4, p. 333 –337
- DOI: 10.1049/ip-cta:19960332
- Type: Article
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p.
333
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(5)
The paper addresses the problem of loop transfer recovery (LTR) of continuous-time systems with sampled output measurements, that is, given an ideal (desired) continuous-time linear state feedback controller, the authors seek for a dynamic output feedback controller based on sampled measurements, such that the state feedback control is best approximated in a certain sense for robustness reasons. They first point out a simple fact that the so-called exact or asymptotic LTR is not possible for such sampled-data systems when the intersampling response is taken into account, regardless of the relative degree and mininium-phase properties and the sampling rate of the system. Based on this observation, the authors proceed to formulate a generalised loop transfer recovery problem which searches for an optimal dynamic output feedback controller which minimises the difference between the target loop transfer function and the output feedback based one in some H∞ sense. The main result then is to show that this generalised LTR problem is equivalent to a known filtering problem for sampled-data systems, which is solved in terms of a pair of differential and difference Riccati equations. - Author(s): F.N. Koumboulis and M.G. Skarpetis
- Source: IEE Proceedings - Control Theory and Applications, Volume 143, Issue 4, p. 338 –348
- DOI: 10.1049/ip-cta:19960301
- Type: Article
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p.
338
–348
(11)
For a test aircraft in a wind tunnel with magnetic suspension and balance systems, a decoupling control configuration is proposed. Using the input–output decoupling technique the flight variables are controlled independently at all frequencies and for all aerodynamic and electromagnetic conditions. The decoupling controllers are static. The performance of the resulting closed-loop system is quite satisfactory. - Author(s): T. Tsujimura ; T. Yabuta ; T. Morimitsu
- Source: IEE Proceedings - Control Theory and Applications, Volume 143, Issue 4, p. 349 –357
- DOI: 10.1049/ip-cta:19960362
- Type: Article
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p.
349
–357
(9)
The basic structure of a wire suspended robot which ‘walks’ along an aerial wire is proposed. The design and mechanism of robot legs to walk on a wire using linkage mechanisms is described. A slider-crank mechanism is analysed kinematically and applied to the robot legs. The robot has a gait achieved with the optimum design of the linkage mechanism which enables it simultaneously to avoid obstacles and travel stably. A walking robot was originally designed and constructed according to the evaluation of mobile stability. Experiments were carried out with the designed robot to clarify that the proposed method actually produces a stable walking motion. - Author(s): C.-L. Chen and C.-T. Hsieh
- Source: IEE Proceedings - Control Theory and Applications, Volume 143, Issue 4, p. 358 –366
- DOI: 10.1049/ip-cta:19960393
- Type: Article
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p.
358
–366
(9)
A new means for designating membership functions in a fuzzy logic controller (FLC) is presented. The method allows a novice to construct a set of membership functions, called shrinking-span membership functions (SSMFs), for a specific linguistic variable systematically by using only two parameters: number of elements of the term set and the shrinking factor for that linguistic variable. The SSMFs have different spans for various term set elements in the universe of discourse and this gives the FLC more power to deal with the nonlinearity of the control problems encountered in the real applications. When there is not enough domain knowledge about the process, the SSMFs make it possible for a designer to set up a reasonable and practical rule base for the FLC. According to the computational simulations presented, the satisfactory performance of such an FLC for several test problems can be acquired without laborious optimisation of the tuning parameters. Therefore the proposed approach narrows the gap between a theoretical FLC and a practical one and makes the FLC more down-to-earth. - Author(s): D.A. Linkens and H.O. Nyongesa
- Source: IEE Proceedings - Control Theory and Applications, Volume 143, Issue 4, p. 367 –386
- DOI: 10.1049/ip-cta:19960392
- Type: Article
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p.
367
–386
(20)
In designing controllers for complex dynamical systems there are needs that are not sufficiently addressed by conventional control theory. These relate mainly to the problem of environmental uncertainty and often call for human-like decision making requiring the use of heuristic reasoning and learning from past experience. Learning is required when the complexity of a problem or the uncertainty thereof prevents a priori specification of a satisfactory solution. Such solutious are then only possible through accumulating information about the problem and using this information to dynamically generate an acceptable solution. Such systems can be referred to as intelligent control systems. In recent years, ‘intelligent control’ has come to embrace diverse methodologies combining conventional control theory and emergent techniques based on physiological metaphors, such as neural networks, fuzzy logic, artificial intelligence, genetic algorithms and a wide variety of search and optimisation techniques. The paper reviews aspects of these emergent techniques, in particular, fuzzy logic, neural networks and genetic algorithms that pertain to realisation of intelligent control systems. The fundamental concepts and design techniques of each paradigm are dicussed, providing a compact reference for their application. - Author(s): J.R. Raol and H. Madhuranath
- Source: IEE Proceedings - Control Theory and Applications, Volume 143, Issue 4, p. 387 –394
- DOI: 10.1049/ip-cta:19960338
- Type: Article
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p.
387
–394
(8)
Various recurrent neural network architectures for solving the problems of parameter estimation in dynamical systems are presented. The architectures based on precomputation of weight/bias information (Hopfield neural network), direct gradient computation with and without normalisation and output error method are developed. A typical computer simulation result is given. - Author(s): M.A. Al-Akhras ; G.M. Aly ; R.J. Green
- Source: IEE Proceedings - Control Theory and Applications, Volume 143, Issue 4, p. 395 –400
- DOI: 10.1049/ip-cta:19960391
- Type: Article
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p.
395
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(6)
The authors present a novel intelligent control scheme based on an artificial neural network. The proposed controller is based on the multimodel approach to improve the system performance of a complex control system of linear or nonlinear characteristics when it operates at various operating conditions. The multimodel control scheme depends on the multiple representation of a process using different models that generate the control signal needed to make the system follow a prescribed desired trajectory. The proposed controller is implemented by a multilayer neural network to locate the model that best represents the process and generate the desired control signal to drive the process along the desired path. The proposed controller is robust as it can accommodate high and sudden deviation from the prescribed trajectory. Simulation results are included to illustrate the potential of the controller developed.
Identification of linear systems in the presence of piecewise polynomial disturbances
Output tracking control of uncertain nonlinear systems with an input time delay
Convergence analysis of binary relation inference networks
Sliding mode control using time-varying switching gain and boundary layer for electrohydraulic position and differential pressure control
Loop transfer recovery for systems under sampled measurements
Static controllers for magnetic suspension and balance systems
Design of a wire-suspended mobile robot capable of avoiding path obstacles
User-friendly design method for fuzzy logic controller
Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications
Neural network architectures for parameter estimation of dynamical systems
Neural network learning approach of intelligent multimodel controller
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