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Volume 141
Issue 1
IEE Proceedings - Control Theory and Applications
Volume 141, Issue 1, January 1994
Volumes & issues:
Volume 141, Issue 1
January 1994
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- Author(s): P.P. Groumpos
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 1, p. 1 –11
- DOI: 10.1049/ip-cta:19949441
- Type: Article
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As engineering systems become more complex, the need for 'optimal' designs becomes apparent and very important. A centralised approach can be economically unfeasible, owing to computer limitations, or because it is too complex and cumbersome to be implemented. In this paper, an approach using a suboptimal controller is proposed leading to a structural optimisation problem of large scale systems (LSS). The mathematical formulation of this problem is provided. A new structural controller is developed, and a step-by-step algorithm to design it is provided. An overall cost is defined and can be computed from the proposed algorithm. Two simple examples illustrating the applicability and usefulness of the new structural optimisation approach are included. - Author(s): S.G. Goodhart ; K.J. Burnham ; D.J.G. James
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 1, p. 12 –18
- DOI: 10.1049/ip-cta:19949748
- Type: Article
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When dealing with industrial systems it is pertinent to consider extending the linear self-tuning framework to include a knowledge of inherent plant nonlinearities. In heat transfer processes, the nonlinearities that arise may be represented by bilinear characteristics. Indeed, when systems exhibit bilinear characteristics it is not unreasonable to consider accommodating the bilinearity into the model structure within an extended self-tuning control framework and, in this respect, novel bilinear self-tuning control strategies are proposed. This paper describes the development of novel bilinear self-tuning pole-placement and generalised predictive control strategies and reports on their effectiveness when applied to an industrial heat treatment furnace. To provide for a realistic comparison, the performances achieved using the bilinear schemes are compared to that achieved when use is made of an existing autotuned industrial PID controller; their respective performances are quantified in terms of a devised measure of effectiveness. The results are very encouraging and indicate that, by adopting the bilinear self-tuning approach, significant potential improvements over existing state of the art controllers are possible. - Author(s): H. Sira-Ramirez ; M. Zribi ; S. Ahmad
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 1, p. 19 –24
- DOI: 10.1049/ip-cta:19949624
- Type: Article
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A dynamical multivariable discontinuous feedback control strategy of the sliding mode type is proposed for the altitude stabilisation of a nonlinear helicopter model in vertical flight. While retaining the basic robustness features associated with sliding mode control policies, the proposed approach also results in smoothed out (i.e. nonchattering) input trajectories and controlled state variable responses. - Author(s): M.H. Perng and J.S. Ju
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 1, p. 25 –32
- DOI: 10.1049/ip-cta:19949750
- Type: Article
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A two-stage procedural design method is proposed for optimally decoupled, robust multivariable control of a (stable or unstable, minimum phase or nonminimum phase) plant with multiple delays. At the first stage, an optimal decoupler is designed for minimising the cross coupling effect. A modified characteristic loci method is next applied during the second stage to complete the design. The present approach distinguishes itself from the prevailing approaches such as LQG/LTR and H∞ design by its unique merits that: (i) the cross coupling effect is minimised; (ii) this approach could be applied to those plants with multiple delays; (iii) a straightforward robustness criterion is integrated into the design methodology which would consequently lead to robustly stable design; and (iv) this approach gives a necessary condition for the resultant controller to be both stable and of minimum phase. In a wide class of problems this condition is also sufficient for a stable, minimum phase controller. - Author(s): L. Xu ; J.-P. Jiang ; J. Zhu
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 1, p. 33 –38
- DOI: 10.1049/ip-cta:19949749
- Type: Article
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The CMAC neural network is an adaptive system by which complex nonlinear functions can be represented by referring to a lookup table. In this paper, this network is applied to the state estimation and learning control of the continuous-stirred tank reactor (CSTR), which is a widely used polymerisation reactor system. The study involves the estimation of the online unmeasurable state and the realtime setpoint tracking of the two-input/two-output CSTR system. Simulation results show that the CMAC-based method is strong in self-learning and easy to realise, and is helpful for improving the nonlinear control performance. - Author(s): G.P. Chen ; O.P. Malik ; G.S. Hope
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 1, p. 39 –47
- DOI: 10.1049/ip-cta:19949779
- Type: Article
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Control limit constraints exist in nearly all practical systems. Using a concept of short-term behaviour index, their influence on the closed-loop system performance is investigated. With the proposed dynamic control limits method, control limit constraints can be adequately considered in the discrete controller design process. A general discrete model with time delay and coloured noise are of most interest and are discussed in detail. Combining the advantages of both minimum variance and pole assignment control strategies, a pole-shifting control algorithm is proposed. Successful application of the proposed method to several examples demonstrates its valuable features. - Author(s): A.Y. Zomaya
- Source: IEE Proceedings - Control Theory and Applications, Volume 141, Issue 1, p. 48 –56
- DOI: 10.1049/ip-cta:19949870
- Type: Article
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The accuracy of robot dynamic models plays an important role in the design of stable and robust robot controllers. The accuracy of these dynamic models is largely dependent on the precise measurement of robot dynamic parameters (e.g first moments, radius of gyration matrix, etc.). Towards this end, algorithms for the identification of these parameters need to be developed to obtain dynamic models with high fidelity, which will ultimately improve the future designs of robot controllers. This work aims to provide a viable solution to this problem. A simplified model of the dynamics is used to extract and group the dynamic parameters of a robot manipulator. The resulting equations are mapped onto a network of parallel processors to speed up the rate of computations and enable real-time implementations. The efficiency of the algorithm is demonstrated by a case study.
Structural modelling and optimisation of large scale systems
Bilinear self-tuning control of a high temperature heat treatment plant
Dynamical sliding mode control approach for vertical flight regulation in helicopters
Optimally decoupled robust control of MIMO plants with multiple delays
Supervised learning control of a nonlinear polymerisation reactor using the CMAC neural network for knowledge storage
Generalised discrete control system design method with control limit considerations
Extraction and computation of identifiable parameters in robot dynamic models: theory and application
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