Titlebook: Assessment and Future Directions of Nonlinear Model Predictive Control; Rolf Findeisen,Frank Allg?wer,Lorenz T. Biegler Book 2007 Springer

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Model Predictive Control for Nonlinear Sampled-Data Systemser via an MPC strategy. Here our goal is to minimize the mismatch between the reference solution and the trajectory under control. We summarize the necessary theoretical results, discuss several aspects of the numerical implemenation and illustrate the algorithm by an example.

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Interior-Point Algorithms for Nonlinear Model Predictive ControlMPC may not be convex. In this case we propose sequential convex programming (SCP) as an alternative to sequential quadratic programming. The algorithms are appropriate for the convex program that arises at each iteration of such an SCP.

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Book 200730 years, model predictive control for linear systems has been widely applied, especially in the area of process control. However, today’s applications often require driving the process over a wide region and close to the boundaries of - erability, while satisfying constraints and achieving near-opt

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Conditions for MPC Based Stabilization of Sampled-Data Nonlinear Systems Via Discrete-Time Approxima also practically stabilizes the exact discrete-time model of the plant. The conditions are formulated in terms of the original continuoustime models and the design parameters so that they should be veri.able in advance.

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Hard Constraints for Prioritized Objective Nonlinear MPCethod for accomodation of discrete control objectives is equivalent to solution of the overall mixed-integer nonlinear programming problem. The performance of the algorithm is demonstrated on a simulated multivariable network of air pressure tanks.

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0170-8643 methods for NMPC, bring thepracticalapplicabilityofNMPCwithinreachevenforveryfastsystems.This has led to a series of new, exciting developments, along with new challenges in the area of NMPC.978-3-540-72698-2978-3-540-72699-9Series ISSN 0170-8643 Series E-ISSN 1610-7411

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