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Volume 142
Issue 6
IEE Proceedings - Control Theory and Applications
Volume 142, Issue 6, November 1995
Volumes & issues:
Volume 142, Issue 6
November 1995
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- Author(s): G. Irwin ; M. Brown ; B. Hogg ; E. Swidenbank
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 529 –536
- DOI: 10.1049/ip-cta:19952293
- Type: Article
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p.
529
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A feedforward neural network is trained on noisy data from a validated computer simulation of a 200 MW oil fired, drum-type turbogenerator unit at Ballylumford power station in Northern Ireland. Local nonlinear models, based on a multilayer perceptron with one hidden layer, are shown to give comparable predictive results to those obtained from linear multivariable ARMAX models. Neural modelling issues like the dimension of the input vector, training with noisy data, training algorithms and model validation are highlighted and discussed. A global nonlinear neural network boiler model is developed and shown to produce significantly improved predictions of the plant outputs across the complete operating range. It is concluded that neural networks can constitute a powerful tool for nonlinear modelling and identification of industrial plant. - Author(s): H.-S. Hwang and K.-B. Woo
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 537 –544
- DOI: 10.1049/ip-cta:19952254
- Type: Article
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p.
537
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The paper presents an approach for identifying a fuzzy model composed of fuzzy-logic based linguistic rules for a multi-input/single-output system. The approach includes structure identification and parameter identification. We propose to utilise a fuzzy c-means clustering and genetic algorithm (GA) hybrid scheme to identify the structure and the parameters of a fuzzy model, respectively. To evaluate the advantages and the effectiveness of the suggested approach, we deal with numerical examples. Comparison shows that the proposed approach can produce the fuzzy model with higher accuracy and a smaller number of rules than previously achieved in other works. To show the global optimisation and local convergence of the GA hybrid scheme, we also consider an optimisation problem having a few local minima and maxima. - Author(s): A.L. Dexter
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 545 –550
- DOI: 10.1049/ip-cta:19952089
- Type: Article
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p.
545
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The paper describes a model based fault diagnosis scheme which uses explicit fuzzy reference models to describe the symptoms of both faulty and fault-free plant operation. The reference models are generated from training data which are produced by computer simulation of typical plant. A fuzzy matching scheme compares the parameters of a fuzzy partial model, identified online using normal operating data collected from the real plant, with the parameters of the reference models. The reference models are also compared to each other to take account of the ambiguity which arises at some operating points when the symptoms of correct and faulty operation are similar. Basic assignments, which indicate the strength of the evidence that the system is operating correctly or has a particular fault, are calculated from the fuzzy measures of the similarity. Results are presented which demonstrate the applicability of the scheme. - Author(s): J. Zhang and A.J. Morris
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 551 –561
- DOI: 10.1049/ip-cta:19952255
- Type: Article
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p.
551
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A technique for the modelling of nonlinear systems using a fuzzy neural network topology is described. The input space of a nonlinear system is initially divided into a number of fuzzy operating regions within which reduced order models are able to represent the system. The complete system model output, the global model, is obtained through the conjunction of the outputs of the local models. The fuzzy neural network approach to nonlinear process modelling provides a way of opening up the purely 'black box' approach normally seen in neural network applications. Process knowledge is used to identify appropriate local operating regions and as an aid to initialising the network structure. Fuzzy neural network models are also easier to interpret than conventional neural network models. The weights in a trained fuzzy network model can be interpreted in terms of process information. This technique has been applied to model the nonlinear dynamic behaviour of a pH reactor and two static nonlinear systems. Correlation based tests are used to assess the fuzzy network model validity for nonlinear dynamic systems. - Author(s): Q. Li ; A.N. Poo ; C.L. Teo ; C.M. Lim
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 562 –568
- DOI: 10.1049/ip-cta:19952220
- Type: Article
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p.
562
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A neural-network compensator is developed for the adaptive control of robot manipulators. The proposed compensator is implemented using the adaptive-linear-combiner algorithm with a special learning rule derived based on the Lyapunov method. Both the system stability and error convergence can be guaranteed. The resulting controller has an implementation advantage in that the adaptation part of the control structure is independent of the feedforward part of the same control algorithm and multirate sampling for the whole control system can therefore be applied. Simulation studies on a single-link manipulator show that the adaptive control system incorporated with the neurocompensator maintains a very good tracking performance even in the presence of large parameter uncertainties and external disturbance. The satisfactory control performance of this approach is also demonstrated by experimental results. - Author(s): B. Bona and M. Indri
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 569 –574
- DOI: 10.1049/ip-cta:19952123
- Type: Article
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p.
569
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When a manipulator interacts with the external environment, it is important to take into account the presence of friction between the end-effector and the contact surface, as this can cause coupling effects between force and position controlled degrees of freedom and compromise the system stability. In this paper, friction is described by a 'complete' model, and a fixed friction compensation function is proposed as an alternative strategy to compensation through high-gain controllers or by means of adaptive algorithms. A force/position controlled planar manipulator is considered to study the friction effects and the effectiveness of the proposed solutions. Simulation results are reported and discussed. - Author(s): J. Bigham and Z. Luo
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 575 –584
- DOI: 10.1049/ip-cta:19952165
- Type: Article
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p.
575
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A technique appropriate for performing diagnostic reasoning in models which contain both imprecise temporal information and uncertainty is described. Temporal delays along different causal paths can be modelled, including reasoning based on temporal precedence. Nominal-behaviour models and fault-mode models can be used. The single-fault assumption is not made, though it can be included as a degenerate case. A cost function is used to control the generation of explanations for observed events. The algorithm is incremental, with the cheapest explanations found first, when constraints on the cost function are satisfied. - Author(s): L.S. Shieh ; W.M. Wang ; J.S.H. Tsai
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 585 –594
- DOI: 10.1049/ip-cta:19952217
- Type: Article
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p.
585
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A new method for digital model conversion of a continuous-time uncertain system and a new digital redesign method for robust control of a sampled-data uncertain system are presented. The concept of the principle of equivalent areas together with interval arithmetic is utilised for finding the discrete-time uncertain model and the digital robust control law from the continuous-time uncertain state equation and the analogue robust control law, respectively. Using the newly digitally redesigned controllers, the resulting dynamic states of the digitally controlled sampled-data uncertain systems are able to closely match those of the original analogously controlled continuous-time uncertain systems for a relatively longer sampling period. - Author(s): M. Cotsaftis ; J. Robert ; M. Rouff ; C. Vibet
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 595 –602
- DOI: 10.1049/ip-cta:19952219
- Type: Article
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p.
595
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(8)
The role played in applied engineering sciences by the nonlinear decoupling method is investigated in the framework of Hamiltonian formalism. Some experiments and results in the fields of robotics, numerical analysis, hydrodynamics and medicine are summarised, and other applications of the method are suggested. - Author(s): K.C. Wan and V. Sreeram
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 603 –610
- DOI: 10.1049/ip-cta:19952110
- Type: Article
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p.
603
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A simple recursive method is proposed for the computation of the solution to the bilinear matrix equation. Algorithms are presented for both continuous and discrete time bilinear matrix equations. The method is based on an extension of the Astrom-Jury-Agniel algorithm. The proposed technique is illustrated by numerical examples and is compared in terms of efficiency against the Bartels-Stewart method. - Author(s): G.-R. Duan
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 611 –616
- DOI: 10.1049/ip-cta:19952167
- Type: Article
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p.
611
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Based on an improved result and a simple, complete parametric solution of the generalised Sylvester matrix equation AV+BW=EVF, a complete parametric approach to eigenstructure assignment in continuous-descriptor systems via output feedback is proposed; parametric forms of the gain matrix and the eigenvectors associated with the assigned finite closed-loop eigenvalues are presented. The approach requires no conditions on the assigned finite closed-loop poles, and guarantees the closed-loop regularity. - Author(s): L. Behera ; M. Gopal ; S. Chaudhury
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 617 –624
- DOI: 10.1049/ip-cta:19952023
- Type: Article
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p.
617
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The paper investigates the application of inversion of a radial basis function network (RBFN) to nonlinear control problems for which the structure of the nonlinearity is unknown. Initially, the RBF network is trained to learn the forward dynamics of the plant. Two different controller structures are then proposed based on this identified RBFN model. In one scheme, a feedback control law is derived based on the input prediction by inversion of the RBFN model so that the system is Lyapunov stable. The second kind of controller structure predicts the feedforward control action, while the fixed controller actuates the feedback stabilising signal. An extended Kalman filtering based algorithm is employed to carry out the network inversion during each sampling interval. Two examples are presented to verify the proposed scheme. Simulation results show that the performance of the controller based on the proposed network inversion scheme is efficient. - Author(s): M.O. Tokhi ; M.A. Hossain ; M.J. Baxter ; P.J. Fleming
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 625 –632
- DOI: 10.1049/ip-cta:19952256
- Type: Article
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p.
625
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The paper presents an investigation into parallel processing techniques for real-time adaptive control of a flexible beam structure. Three different algorithms, namely simulation, control and identification are involved in the adaptive control algorithm. These are implemented on a number of uniprocessor and multiprocessor, homogeneous and heterogeneous, computing platforms involving transputers, digital signal processing devices and several general purpose sequential processors. The partitioning and mapping of the algorithms on the homogeneous and heterogeneous architectures is also explored. The interprocessor communication speed is investigated to establish the real-time performance aspects of the processors on the basis of the nature of the algorithms involved. A close investigation into the performance of several compilers is made and discussed within the context of real-time implementations. Finally, a comparison of the results of the implementations is made. - Author(s): G.P. Reddy and M. Chidambaram
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 633 –637
- DOI: 10.1049/ip-cta:19952166
- Type: Article
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p.
633
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Near-optimal productivity control of a continuous bioreactor by a conventional proportional-integral controller poses stability problems, and certain nonlinear controllers yield excessive variation of the manipulated variable. A nonlinear controller based on the Hammerstein model is proposed to overcome such problems. The performance of the proposed nonlinear control is compared with that of the nonlinear controllers proposed by Henson and Seborg. - Author(s): J.-L. Wu and T.-T. Lee
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 638 –646
- DOI: 10.1049/ip-cta:19952253
- Type: Article
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p.
638
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The design of optimal linear, time-invariant systems with regional pole constraint is studied. The problem is to find the static state feedback controller to ensure that all closed-loop system poles lie inside the desired region H and, meanwhile, to minimise a multiobjective performance index. The desired region H can be represented by several inequalities. For some special cases, H may consist of several disjoint subregions. The performance index consists of two parts. One part is used to penalise the sustained error, and the other part is used to guarantee that the optimal solution will not occur on the boundary of the admissible controller set and to improve the robustness property of the closed-loop system. The necessary and sufficient condition for the existence of the admissible controller is found. The necessary condition that the optimal control law must be satisfied is derived. Furthermore, the robustness analysis of the regional pole restriction under unstructured perturbation is studied. Based on the Gersgorin's theorem a new method is presented, which calculates the allowable bounds of the unstructured perturbation, so that all the perturbed poles will still remain inside some regions. - Author(s): E. Mosca and C. Nava
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 647 –653
- DOI: 10.1049/ip-cta:19952069
- Type: Article
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p.
647
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The authors describe how to construct systematically a prefilter for the data entering the identifier, when the task of the latter is to provide a reduced-order plant model to be used for offset-free LQG control with integral action. The problem of choosing suitable dynamic weights for the control performance index is also addressed via a complementary identification procedure acting on the residual errors. - Author(s): L.S. Shieh ; I.C. Lin ; J.S.H. Tsai
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 654 –660
- DOI: 10.1049/ip-cta:19952124
- Type: Article
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p.
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Two issues are addressed: digital redesign of a continuous-time system with an input time delay, and translation of the pulse-amplitude modulated (PAM) controller obtained by the newly proposed digital redesign method into an equivalent pulse-width modulated (PWM) controller. A tuning parameter is introduced into the PAM controller so that the digitally controlled sampled-data states closely match the original continuous-time input time-delay states. Also, the principle of equivalent areas is applied to convert the newly developed PAM controller into an equivalent PWM controller so that the controlled states closely match a relatively longer sampling period. Two illustrative examples are provided to demonstrate the effectiveness of the proposed method. - Author(s): M. Yuan ; A.N. Poo ; G.S. Hong
- Source: IEE Proceedings - Control Theory and Applications, Volume 142, Issue 6, p. 661 –667
- DOI: 10.1049/ip-cta:19952122
- Type: Article
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p.
661
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The methodology of design of a conventional model-reference-adaptive-control is extended to design a direct neural for a class of nonlinear system with structural uncertainty. A structured feedforward neural network, a Sigmoid-linear network, is used as the controller, which can be interpreted as a nonlinear extension of the conventional adaptive control. Without a specific pretraining stage, the weights of the neural network are adjusted online to minimise the error between the plant output and the desired output signal, according to a learning law derived in light of gradient-descent method. The local stability can be achieved provided that proper conditions are satisfied for the system. Simulation studies are carried out for linear and nonlinear plants, respectively, and verify the applicability of the proposed control strategy.
Neural network modelling of a 200 MW boiler system
Linguistic fuzzy model identification
Fuzzy model based fault diagnosis
Fuzzy neural networks for nonlinear systems modelling
Developing a neurocompensator for the adaptive control of robots
Friction compensation and robustness issues in force/position controlled manipulators
Process for diagnostic reasoning integrating uncertain and temporal information
Digital modelling and digital redesign of sampled-data uncertain systems
Application of decoupling method to Hamiltonian systems
Solution of the bilinear matrix equation using Astrom-Jury-Agniel algorithm
Parametric approach for eigenstructure assignment in descriptor systems via output feedback
Inversion of RBF networks and applications to adaptive control of nonlinear systems
Heterogeneous and homogeneous parallel architectures for real-time active vibration control
Near-optimal productivity control of a continuous bioreactor
Optimal control with regional pole constraints via the mapping theory
Identification and dynamic weights for LQG control with integral action
Design of PWM controller for sampled-data system using digitally redesigned PAM controller
Direct neural control system: nonlinear extension of adaptive control
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