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Advanced Model Predictive Control by T. Zheng

By T. Zheng

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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.

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