You are here

Advanced Model Predictive Control by T. Zheng

By T. Zheng

Show description

Read Online or Download Advanced Model Predictive Control PDF

Best system theory books

Manifolds, tensor analysis, and applications

The aim of this ebook is to supply middle fabric in nonlinear research for mathematicians, physicists, engineers, and mathematical biologists. the most objective is to supply a operating wisdom of manifolds, dynamical platforms, tensors, and differential varieties. a few functions to Hamiltonian mechanics, fluid mechanics, electromagnetism, plasma dynamics and keep an eye on conception are given utilizing either invariant and index notation.

Stability analysis of nonlinear systems

One carrier arithmetic has rendered the 'Et moi, "', si j'avait su remark en revenir, je n'y serais element all. "' human race. It has positioned good judgment again the place it belongs, at the topmost shelf subsequent Jules Verne to the dusty canister labelled 'discarded non sense'. The sequence is divergent; for this reason we are able to do anything with it.

Extra resources for Advanced Model Predictive Control

Sample text

1997). Primal-dual interior-point methods, SIAM. 2 Fast Nonlinear Model Predictive Control using Second Order Volterra Models Based Multi-agent Approach Bennasr Hichem and M’Sahli Faouzi Institut Supérieur Des Etudes Technologiques de SFAX Ecole Nationale d’ingénieur de Monastir Tunisia 1. Introduction Model predictive control (MPC) refers to a class of computer control algorithms that utilize a process model to predict the future response of a plant. During the past twenty years, a great progress has been made in the industrial MPC field.

These algorithms generally lead to the use of computationally intensive nonlinear techniques that make application almost impossible. In order to avoid this problem, the proposed concept algorithm utilizes a linear model extracted from the nonlinear model. A decentralized model and decentralized goals are then considered. A decentralized problem model consists of multiple smaller, independent subsystems in witch subsystem in an overall nonlinear system have his own independent goals and represented by a discrete model of the form: xl ( k + 1) = Al xl ( k ) + Blul ( k )   y l ( k ) = C l xl ( k ) (15) n Where xl ∈ ℜnx is the local state space; y l ∈ ℜ y is the measurement output of each subsystem; ul ∈ ℜnu is the local control input.

The control action based on (22) is transformed into new action with the following transformation [R. Fletcher, 1997]. ul ( k ) − f moy  ) ul ( k ) = f moy + f amp tanh( f amp   − f min f  f moy = max 2  f max + f min   f amp = 2   f max = min(ul max , ul ( k − 1) + Δul max ) f = max(ul min , ul ( k − 1) + Δul min )  min  (23) The optimum control law (22) for each agent does not guarantee the global optimum. Accordingly to that, nonlinear system requires coordination among the control agent’s action.

Download PDF sample

Rated 4.41 of 5 – based on 21 votes