By Basil Kouvaritakis, Mark Cannon

Version dependent predictive keep watch over has proved to be a fertile zone of analysis, yet particularly has won huge, immense luck with undefined, specifically within the context of approach keep watch over. Non-linear version dependent predictive keep watch over is of specific curiosity as this top represents the dynamics of such a lot actual vegetation, and this ebook collects jointly the real effects that have emerged during this box that are illustrated through simulations on business versions. particularly there are contributions on suggestions linearization, differential flatness, regulate Lyapunov services, output suggestions, and neural networks. The overseas participants to the publication are all revered leaders in the box, which makes for crucial studying for complex scholars, researchers and industrialists within the box of regulate of advanced systems.

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Multivariable keep an eye on for commercial functions - ISBN 0863411177

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**Extra resources for Non-Linear Predictive Control: Theory & Practice**

**Example text**

This model structure is useful for approximating process responses which do not depend `too strongly' on past input values [13]. This section will concentrate on modelling and controller synthesis for the class of systems which can be represented adequately by secondorder Volterra series (or related input-output) models. 2 Model identi®cation A second-order Volterra series model can be decomposed as: y k h0 d k o k l k l k M h1 iu k À i i1 d k M h2 i; ju2 k À i 1 i1 o k 2 M iÀ1 h2 i; ju k À iu k À j i1 j1 where the linear, second-order diagonal and off-diagonal component can be assumed symmetric.

One ®ltering technique involves a projection of the Volterra series model onto the Laguerre basis, resulting in a Volterra-Laguerre model [11]. By using the orthogonal basis functions, the high-order Volterra model can be signi®cantly reduced in order. The second-order Volterra-Laguerre model resulting from the projection of a Volterra series model is given (in state-space form) by [16]: l k 1 A al k B au k T T y k C l k l kDl k 2 3 Here the linear state equations are dependent only on the user-de®ned Laguerre pole, 0 < a 1.

Parker, Edward P. Gatzke, Radhakrishnan Mahadevan, Edward S. Meadows and Francis J. Doyle III Abstract The nonlinear model predictive control (NMPC) algorithm is a powerful control technique with many open issues for research. This chapter highlights a few of these issues through a series of process and biosystems case studies. Control using nonlinear models can be further complicated when working with distributed parameter systems. An emulsion polymerisation process examines these challenges. Ef®cient solution techniques for NMPC problems are necessary when solution time or constraints are important.