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Feasible Suboptimal Model Predictive Control for Linear Plants with State Dependent Constraints


V. Nevistic

vol. AUT95-11

The problem of the model predictive control (MPC) subject to general state dependent constraints is considered. As an example, where this problem can arise, a hybrid approach for the control of nonlinear plants with input constraints, consisting of feedback linearization, embedded in model predictive control loop, is discussed. Besides some special cases, exact computation of MPC may become extremely complex due to the nonlinearity of MPC constraints. Therefore, a feasible suboptimal approach for the design of MPC for linear plants with nonlinear state dependent constraints, using their suitable time approximations, is proposed. Starting from the contraction mapping theorem, the sufficient conditions for the stability of the proposed MPC schemes are derived, and an appropriate procedure for stability checking is introduced as well. An example offers insight into the implementation of this approach, especially the trade-off between optimality and computational efficiency.


Type of Publication:

(04)Technical Report

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% Autogenerated BibTeX entry
@TechReport { Xxx:1995:IFA_1483,
    author={V. Nevistic},
    title={{Feasible Suboptimal Model Predictive Control for Linear
	  Plants with State Dependent Constraints}},
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