Home
>
Journals & magazines
>
IEE Proceedings - Control Theory and Applications
>
Volume 144
Issue 3
IEE Proceedings - Control Theory and Applications
Volume 144, Issue 3, May 1997
Volumes & issues:
Volume 144, Issue 3
May 1997
-
- Author(s): S.K. Bag ; S.K. Spurgeon ; C. Edwards
- Source: IEE Proceedings - Control Theory and Applications, Volume 144, Issue 3, p. 209 –216
- DOI: 10.1049/ip-cta:19971122
- Type: Article
- + Show details - Hide details
-
p.
209
–216
(8)
The paper considers the development of output feedback sliding mode controllers for a class of uncertain linear systems. The presence of stable invariant zeros and matched uncertainty is incorporated in the design procedure. The sufficient conditions for developing static output feedback sliding mode controllers are first reviewed. If the so-called ‘Kimura–Davison’ conditions are not satisfied, it is shown that it may not be possible to determine a static output feedback sliding mode controller. In this case, dynamic output feedback sliding mode control is necessary. It is shown that both the switching surface design problem for the static case and the switching surface and compensator design for the dynamic case may be formulated as a static output feedback problem for particular system triples. A robust design procedure is used to solve this static output feedback problem to minimise the effects of any unmatched uncertainty which will affect the reduced order sliding motion in many practical systems. A controller is synthesised to tolerate matched model uncertainty. The measurements of robustness are described. A numerical example demonstrates the procedure. - Author(s): M.T. Söylemez and N. Munro
- Source: IEE Proceedings - Control Theory and Applications, Volume 144, Issue 3, p. 217 –224
- DOI: 10.1049/ip-cta:19970892
- Type: Article
- + Show details - Hide details
-
p.
217
–224
(8)
The concept of ‘pole colouring’ is introduced as a graphical aid to the problem of observing the closed-loop system pole variations as a function of uncertainty. Some possible cost functions that can be used to measure the performance robustness of the resulting systems are also considered. - Author(s): S.-K. Shen ; T.-T. Lee ; B.-C. Wang
- Source: IEE Proceedings - Control Theory and Applications, Volume 144, Issue 3, p. 225 –232
- DOI: 10.1049/ip-cta:19971001
- Type: Article
- + Show details - Hide details
-
p.
225
–232
(8)
A frequency domain design methodology for a linear, single-input–single-output (SISO), uncertain system with control input and states constraints under some unknown-but-bounded disturbances is developed. The maximum variation of the plant uncertainty is transferred to the values of equivalent external disturbance at the plant output with a fixed nominal plant. Similarly, the control input and states constraints, expressed in the time domain with bandwidth limitation and mapped into a set of target transfer functions, will also be treated as the equivalent external disturbances upon being transferred to the output. The maximum variation of the desired output tolerance will be determined as the fixed values at some specified frequencies. Thus, the uncertain system design problem coping with the control input and states constraints under some unknown-but-bounded disturbances will become an equivalent external disturbance rejection problem. The uncertain plant tracking problems and the problem of uncertain systems with control input and states constraints under some unknown-but-bounded disturbance are respectively provided as illustrative examples. - Author(s): D. Sankowski ; J. Kucharski ; W. Lobodzinski
- Source: IEE Proceedings - Control Theory and Applications, Volume 144, Issue 3, p. 233 –240
- DOI: 10.1049/ip-cta:19970959
- Type: Article
- + Show details - Hide details
-
p.
233
–240
(8)
The PID control algorithm applied in the paper is still one of the most popular in many branches of industry. Modern temperature controllers should be equipped with an identification algorithm which allows the determination of the controller settings automatically with high accuracy. This approach leads to the idea of autotuning controllers, which are often based on knowledge of the frequency response of the system. In the paper the multifrequency binary testing identification method in the frequency domain using multifrequency binary sequences (MBS) is applied. This method provides rich information about process dynamics in the form of a few points of the frequency response even though the process is only slightly disturbed. Additionally, the procedures discussed ensure proper choice of the controller structure and its parameters during a short duration identification experiment as well as correction of the errors due to the slow aspects of the furnace dynamics. This allows the MBS method to be applied in the closed loop structure just after the temperature set point has been reached. Start-up identification, which is also described, is a source of a priori knowledge, complementing the MBS method. The presented theory has been verified by experiments. A 20 kW industrial electric resistance furnace was used in the experimental work. - Author(s): A.L. Maitelli and T. Yoneyama
- Source: IEE Proceedings - Control Theory and Applications, Volume 144, Issue 3, p. 241 –248
- DOI: 10.1049/ip-cta:19971174
- Type: Article
- + Show details - Hide details
-
p.
241
–248
(8)
The paper presents an adaptive controller for discrete-time systems, with parameters modelled as a Gauss–Markov process with an unknown noise covariance matrix. The cost function adopted in the optimisation of the control performance is the sum of the output variances up to M steps ahead in time. Optimal predictors are used to estimate the future outputs y(k + i), i = 1, 2, ..., M, that are needed in the solution of the optimisation problem that yields the value of the control signal at a given time k. The estimates for the system parameters are obtained using a Kalman filter, together with an algorithm to tune the covariance matrix in real time. The adaptation mechanism reduces the risk of divergence of the Kalman filter, as shown by simulation results that illustrate the actual performance of the new controller under uncertainty in the noise covariance matrix. - Author(s): C. Kambhampati ; A. Delgado ; J.D. Mason ; K. Warwick
- Source: IEE Proceedings - Control Theory and Applications, Volume 144, Issue 3, p. 249 –254
- DOI: 10.1049/ip-cta:19970950
- Type: Article
- + Show details - Hide details
-
p.
249
–254
(6)
The last decade has seen the re-emergence of artificial neural networks as an alternative to traditional modelling techniques for the control of nonlinear systems. Numerous control schemes have been proposed and have been shown to work in simulations. However, very few analyses have been made of the working of these networks. The authors show that a receding horizon control strategy based on a class of recurrent networks can stabilise nonlinear systems. - Author(s): E. Ikonen and K. Najim
- Source: IEE Proceedings - Control Theory and Applications, Volume 144, Issue 3, p. 255 –262
- DOI: 10.1049/ip-cta:19970949
- Type: Article
- + Show details - Hide details
-
p.
255
–262
(8)
A new algorithm for training the parameters of a distributed logic processor is suggested, based on learning automata. A fuzzy inference system is implemented on a multilayer perceptron platform. Various possibilities for assigning learning automata are discussed and two assignment strategies are proposed. The behaviour of the resulting algorithm is illustrated using flue-gas NOx emission data measured from an industrial-size fluidised-bed combustor. - Author(s): C.H. Sing and B. Postlethwaite
- Source: IEE Proceedings - Control Theory and Applications, Volume 144, Issue 3, p. 263 –268
- DOI: 10.1049/ip-cta:19971139
- Type: Article
- + Show details - Hide details
-
p.
263
–268
(6)
The application of fuzzy logic to the design of nonlinear controllers has become increasingly popular in recent years. Most of the developments have been in controllers of the rule-based type. An alternative approach, and one which reflects trends in conventional control, is to use fuzzy logic to build a process model, and then to incorporate this into a standard model-based controller scheme. The paper proposes the application of fuzzy relational models (FRMs) for the non-linear control of a pH process. The pH in both a simulated and a laboratory continuously stirred tank reactor (CSTR) was controlled by a model predictive controller (MPC), incorporating a fuzzy model created using a recently developed method of FRM identification. The controller performance is compared with that of a fuzzy rule-based controller, that of a PID controller and that of a linear MPC. The comparison shows the superiority of fuzzy relational model-based control (FRMBC) for highly nonlinear processes. The suitability of the FRMBC for real-world applications is demonstrated by its control performance on a laboratory-scale plant. - Author(s): A. Moeini and D.P. Atherton
- Source: IEE Proceedings - Control Theory and Applications, Volume 144, Issue 3, p. 269 –275
- DOI: 10.1049/ip-cta:19971173
- Type: Article
- + Show details - Hide details
-
p.
269
–275
(7)
The A-function method is extended to analyse a relay feedback control system with additional nonlinearities in the feedback loops. The situation considered is where the output of the plant and/or its successive derivatives are fed back to the input through nonlinearities that are assumed to be of polynomial type. An example is also given to show that, in some cases, the method can be used in systems with saturation or a linear segmented nonlinearity in the feedback path. - Author(s): A.H.D. Markazi and N. Hori
- Source: IEE Proceedings - Control Theory and Applications, Volume 144, Issue 3, p. 277 –279
- DOI: 10.1049/ip-cta:19970867
- Type: Article
- + Show details - Hide details
-
p.
277
–279
(3)
- Author(s): M. O'Malley and A. de Paor
- Source: IEE Proceedings - Control Theory and Applications, Volume 144, Issue 3, page: 277 –277
- DOI: 10.1049/ip-cta:19970868
- Type: Article
- + Show details - Hide details
-
p.
277
(1)
Output feedback sliding mode design for linear uncertain systems
Robust pole assignment in uncertain systems
Frequency domain design method for uncertain systems under some unknown-but-bounded disturbances
Autotuning temperature control using identification by multifrequency binary sequences
Adaptive control scheme using real time tuning of the parameter estimator
Stable receding horizon control based on recurrent networks
Use of learning automata in distributed fuzzy logic processor training
pH control: handling nonlinearity and deadtime with fuzzy relational model-based control
Exact determination of the limit cycles in relay control systems with additional nonlinearities in the feedback loops
Reply: Comment on discretisation of continuous-time control systems with guaranteed stability
Comment: Discretisation of continuous-time control systems with guaranteed stability
Most viewed content for this Journal
Article
content/journals/ip-cta
Journal
5
Most cited content for this Journal
We currently have no most cited data available for this content.