Nonlinear Model Predictive Control: Theory and Algorithms (Communications and Control Engineering)

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NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. NMPC schemes with and without stabilizing terminal constraints are detailed and intuitive examples illustrate the performance of different NMPC variants.

An introduction to nonlinear optimal control algorithms gives insight into how the nonlinear optimisation routine — the core of any NMPC controller — works. Nonlinear Model Predictive Control is primarily aimed at academic researchers and practitioners working in nonlinear control but the text is self-contained featuring background material on infinite-horizon optimal control and Lyapunov stability theory which makes the book accessible to graduate and advanced undergraduate students of control engineering and applied mathematics. Andrew Viterbi. Mathematics for Engineers.

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Nonlinear Model Predictive Control |

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Nonlinear Model Predictive Control

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Previous Article Analysis of multilevel Monte Carlo path simulation using the Milstein discretisation. Some regularity results for a double time-delayed 2D-Navier-Stokes model. Keywords: Model predictive control , cooperative control , feedback synthesis , nonlinear systems , multiobjective optimization.

Multiobjective model predictive control for stabilizing cost criteria. References: [1] A. Schematic illustration of a Pareto front for two objectives. Figure Options. Download full-size image Download as PowerPoint slide. The red parts indicate the nodominated values.