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Volume 141
Issue 4
IEE Proceedings - Control Theory and Applications
Volume 141, Issue 4, July 1994
Volumes & issues:
Volume 141, Issue 4
July 1994
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- Author(s): V. Etxebarria
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 4, p. 209 –215
- DOI: 10.1049/ip-cta:19941121
- Type: Article
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p.
209
–215
(7)
A neural network controller which is used for controlling unknown discrete-time DARMA systems is described. A two-layered neural network is used to estimate the unknown plant dynamics. The well known Widrow-Hoff delta rule is used as the learning algorithm for this network, to minimise the difference between the plant actual response and that predicted by the neural network. The control law is generated online using a second two-layered neural network, so that the plant output is brought to a desired reference signal. It is proved that the control objective is achieved by the closed-loop system and that the system remains closed-loop stable. Some simulation examples are also presented to evaluate the design. - Author(s): H. Wang ; M. Brown ; C.J. Harris
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 4, p. 216 –222
- DOI: 10.1049/ip-cta:19941153
- Type: Article
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p.
216
–222
(7)
A neural network scheme for modelling unknown nonlinear systems subject to immeasurable disturbances that satisfy stable, finite-order, recurrence relationships whose parameters are known is presented. The systems considered can be expressed as nonlinear ARMAX models and the disturbance is nonstochastic. Similar to robust servomechanism design, the nonlinear modes of the disturbances are assumed to be known and based on the knowledge of these modes; a new performance function for modelling the unknown nonlinear function is selected and a gradient descent algorithm which adjusts the weights in the neural network is derived. Convergence of this learning algorithm is proved when the disturbance satisfies a linear recurrence relationship, and the proposed approach is used to model nonlinear time series data which has been corrupted by immeasurable additive sinusoidal noise. - Author(s): S.M. Karbassi and D.J. Bell
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 4, p. 223 –226
- DOI: 10.1049/ip-cta:19941157
- Type: Article
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p.
223
–226
(4)
A new method is described for the assignment of eigenvalues of closed-loop plants in linear time-invariant multivariable systems. The parameterisation of controllers is based on the derivation of zero eigenvalue assignment by implementation of vector companion forms. This method is computationally very attractive and can be used for optimisation of the feedback matrix which assigns the closed-loop eigenvalues (from the set of real, complex conjugates or even those of the open-loop system) to the desired locations. A numerical example is presented to illustrate some advantages of this new explicit parameterisation of the controller gain matrix. - Author(s): C. Edwards and S.K. Spurgeon
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 4, p. 227 –234
- DOI: 10.1049/ip-cta:19941096
- Type: Article
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p.
227
–234
(8)
A tutorial description of results in the area of continuous nonlinear control and observation of uncertain systems is presented. The theoretical results are applied to the practical problem of temperature control of an industrial heating plant. The results obtained during plant trials are presented. A detailed comparison is made between the performance and robustness which may be achieved using a sophisticated commercial PID controller and that effected by the proposed methodology. - Author(s): M. Teshnehlab and K. Watanabe
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 4, p. 235 –242
- DOI: 10.1049/ip-cta:19941225
- Type: Article
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p.
235
–242
(8)
The paper principally describes the design of an artificial neural network using flexible sigmoid unit functions (FSUFs), referred to as flexible sigmoid function networks (FSFNs), to achieve both a high flexibility and a high learning ability in neural network structures from a given set of teaching patterns. An FSFN can generate an appropriate shape of the sigmoid function for each of the individual hidden- and output-layer units, in accordance with the specified inputs, desired output(s) and applied system. The paper proposes a learning method in which not only connection weights but also the sigmoid functions may be adjusted. The learning algorithm is derived by using the well known back-propagation algorithm. To demonstrate the validity of the proposed method, we apply the FSFN to the construction of a self-tuning computed torque controller for a two-link manipulator. It is then shown that the controller based on the FSFN gives a better control performance than that based on the traditional neural network. - Author(s): B.W. Choi ; D.-W. Gu ; I. Postlethwaite
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 4, p. 243 –248
- DOI: 10.1049/ip-cta:19941223
- Type: Article
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p.
243
–248
(6)
The paper presents a methodology for the problem of reducing the order of H∞ suboptimal controllers. It is shown how the order of H∞ suboptimal controllers may be reduced to n-p2 and how for some plants the order may be less than n-p2, where n is the size of the system matrix of the generalised plant and p2 is the number of process outputs. A set of H∞ suboptimal controllers of order n-p2 is characterised. A numerical example is given to illustrate the results. - Author(s): P.D. Olivier
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 4, p. 249 –254
- DOI: 10.1049/ip-cta:19941239
- Type: Article
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p.
249
–254
(6)
An online system identification scheme is proposed based on a Fourier-Laguerre series representation of the unknown impulse response. The unknown parameters are determined using a gradient estimator. Noise effects are considered. The proposed identification scheme is applied to a system with time delay. - Author(s): A.L. Maitelli and T. Yoneyama
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 4, p. 255 –260
- DOI: 10.1049/ip-cta:19941217
- Type: Article
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p.
255
–260
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A suboptimal dual controller is presented for a class of linear systems with unknown and randomly varying parameters. The loss function which is minimised consists of the sum of output variances up to two steps ahead in time, conditioned on past values of the control and output signals. Optimal predictors are used to replace the future outputs which are needed to compute the control signal at each step. The behaviour of the two-stage controller is illustrated by two examples. - Author(s): R. Danbury and M. Jenkinson
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 4, p. 261 –273
- DOI: 10.1049/ip-cta:19941222
- Type: Article
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p.
261
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(13)
There is a current trend in manufacturing industry towards the improvement of the flexibility and productivity of machinery. A popular way of achieving these improvements is to remove some or all of the mechanical couplings (gears and cams, for example) that are usually used to synchronise the various parts of the machine. Each part of the machine is instead driven by a servomotor, and the servomotors are controlled in such a way as to emulate the synchronisation functions of the mechanisms they replace. The control techniques that are usually used in these applications, however, only emulate real mechanisms in certain respects. The paper describes a control scheme which causes servomotors to behave in a coupled manner much more akin to that of a real mechanism. - Author(s): W.P. Heath ; J.A. Rossiter ; B. Kouvaritakis
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 4, p. 274 –276
- DOI: 10.1049/ip-cta:19941224
- Type: Article
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p.
274
–276
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The author shows that the mixed-weights least-squares (MWLS) algorithm of Rossiter and Kouvaritakis (ibid., vol. 140, p. 243-54, 1993) can be used for the general positive-definite quadratic programming problem under mild assumptions about the feasible set. This also allows one to use the MWLS algorithm for the general linear programming problem. Thus it is concluded that the algorithm deserves wider attention both in the control community (where an increasing range of problems require solutions to quadratic cost functions under inequality constraints) and in the numerical analysis community. The authors also provide a counter example to one of the proofs in the work of Rossiter and Kouvaritakis. Thus, although the convergence properties of the algorithm appear to be good from simulations, more attention to the numerical and convergence properties would be welcome.
Adaptive control of discrete systems using neural networks
Neural network modelling of unknown nonlinear systems subject to immeasurable disturbances
New method of parametric eigenvalue assignment in state feedback control
Robust nonlinear control of heating plant
Self tuning of computed torque gains by using neural networks with flexible structures
Low-order H∞ suboptimal controllers
Online system identification using Laguerre series
Two-stage suboptimal dual controller for systems with stochastic parameters using optimal predictors
Synchronised servomechanisms-the scalar-field approach
Constrained stable generalised predictive control
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