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Model Predictive Control: Techniques and Applications - Day 1 (Ref. No. 1999/095), IEE Two-Day Workshop on

Date 1999

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  • Model predictive control: the challenge of uncertainty

    Publication Year: 1999, Page(s):6/1 - 6/5
    Cited by:  Papers (4)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (352 KB)

    The importance of model predictive control derives primarily from its industrial success. Theory has contributed to the development of model predictive control mainly in its discovery of conditions that ensure closed-loop stability. Indeed, there is now a rich collection of of useful and interesting results on stability that we summarize because of their relevance to uncertainty, the main topic of... View full abstract»

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  • Predictive control using multiple model networks

    Publication Year: 1999, Page(s):5/1 - 5/7
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (328 KB)

    The aim of this paper is to describe a nonlinear modelling architecture, called the local model network (LMN), which introduces transparency while offering distinct advantages for nonlinear model-based control. Simulation results for a pH neutralisation process are used to illustrate the performance benefits of LMNs for two novel nonlinear dynamic matrix control schemes View full abstract»

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  • Synergy of predictive control and identification

    Publication Year: 1999, Page(s):4/1 - 4/3
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (192 KB)

    The paper briefly explains how synergy works for minimum variance (MV) and pole placement control (PPC). A general definition of synergy between control and identification is given. Then synergy is studied for model predictive control (MPC), both in simulations and in theory. The investigation for MPC is made more difficult by that the previous results from MV and PPC do not carry over as the cont... View full abstract»

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  • Model predictive control using dynamic integrated system optimisation and parameter estimation (DISOPE)

    Publication Year: 1999, Page(s):8/1 - 8/4
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (236 KB)

    DISOPE is a technique for solving optimal control problems where there are differences in structure and parameter values between reality and the model employed in the computations. The model reality differences can also allow for deliberate simplification of model characteristics and performance indices in order to facilitate the solution of the optimal control problem. The technique was developed... View full abstract»

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  • The benefits of prediction in learning control algorithms

    Publication Year: 1999, Page(s):3/1 - 3/3
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (164 KB)

    The emergence of intelligent control has seen a focus of attention on the ideas of learning control. This paper explores the relationship between the performance of learning algorithms and the structure of the system to be controlled. The importance of system's relative degree (pole-zero excess) and the system's zeros are described and the role of prediction in improving performance is demonstrate... View full abstract»

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  • Reducing the computational burden in predictive control

    Publication Year: 1999, Page(s):7/1 - 7/5
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (244 KB)

    Constraint handling in conventional predictive controllers requires the online solution of quadratic programs, this can be computationally very demanding. The optimisations required often limit achievable sample rates to slower than might be desired. This paper considers alternative optimisation strategies which can be used within predictive control and, at the cost of just a little suboptimality,... View full abstract»

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  • Analytic approach to generalised predictive control of nonlinear systems

    Publication Year: 1999, Page(s):9/1 - 9/3
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (148 KB)

    Various nonlinear predictive control methods for discrete-time systems have been developed. The main shortcoming of those methods is that online dynamic optimisation is required, which, in general, is non-convex, and its computational burden grows exponentially with the decision variables. Hence computational issue is an active subject in predictive control. Almost all engineering systems are desc... View full abstract»

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  • Model Predictive Control: Techniques and Applications - Day 1

    Publication Year: 1999, Page(s): 0_3
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (36 KB)

    First Page of the Article
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  • A brief overview of model predictive control

    Publication Year: 1999, Page(s):1/1 - 1/4
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (260 KB)

    The 3 basic ideas of model predictive control (MPC)-explicit use of a model, control sequence calculation to optimise a performance index, and a receding horizon strategy-are stated and illustrated. Advantages and disadvantages of MPC are discussed. Algorithms are compared View full abstract»

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  • Model predictive control: ideas for the next generation

    Publication Year: 1999, Page(s):2/1 - 2/2
    Cited by:  Papers (8)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (160 KB)

    Summary form only given. We point out several clearly discernible trends which point toward an extended need for new techniques to design process control and supervisory schemes. We show how this need can be met by a new generation of model predictive controllers. Rapid advances in computer and information technology are enabling the closer integration of the various decision and control tasks whi... View full abstract»

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